EP4107750A1

EP4107750A1 - Neurodevelopmental/cognitive assessment and cognitive training on a digital device and identification and measurement of digital cognitive biomarkers

Info

Publication number: EP4107750A1
Application number: EP21707919.3A
Authority: EP
Inventors: Shivani LAMBA
Original assignee: Brightlobe Ltd
Current assignee: Brightlobe Ltd
Priority date: 2020-02-21
Filing date: 2021-02-19
Publication date: 2022-12-28
Also published as: WO2021165498A1

Abstract

Embodiments of methods and devices for evaluating and treating one or more neurodevelopmental delays within neurodevelopmental domains of pediatric subjects are presented. Embodiments of methods and systems for identifying and/or measuring cognitive biomarkers indicative of or predictive of neurodevelopmental disorders or a cognitive deficit in a subject are also presented.

Description

NEURODEVELOPMENTAL /COGNITIVE ASSESSMENT AND COGNITIVE TRAINING ON A DIGITAL DEVICE AND IDENTIFICATION AND MEASUREMENT OF DIGITAL COGNITIVE

BIOMARKERS

TECHNICAL FIELD

[0001] This disclosure relates generally to evaluating and treating one or more neurodevelopmental disorders or cognitive deficits within the neurodevelopmental or cognitive domains of pediatric or adult subjects, respectively, and more specifically, to methods and systems switching a device between a first assessment mode and a second training mode, based on the performance metric data received whilst the device is in the first mode.501

[0002] This disclosure also relates generally to identifying and/or measuring a cognitive biomarker indicative of a neurodevelopmental disorder or a cognitive or neurological deficit, and more specifically, to methods and systems for generating such a cognitive biomarker based upon statistical analysis performed on performance metric data in combination with contextual data.

BACKGROUND

[0003] The following discussion sets forth the inventors’ own knowledge of certain technologies and/or problems associated therewith. Accordingly, this discussion is not an admission of prior art, and it is not an admission of the knowledge available to a person of ordinary skill in the art.

[0004] Cognitive impairment, otherwise known as a cognitive deficit, is recognized as a major issue across diverse populations. From children through the elderly, abnormal neurodevelopment in childhood and cognitive impairment in adulthood can significantly impact quality of life. A number of pharmacological, medical device, and behavioral therapy approaches have been developed to target neurodevelopment and cognition both in abnormal and healthy populations. A cognitive impairment may relate to various types of neurodevelopmental delay. [0005] However, it is important to first accurately identify the neurodevelopmental or cognitive domains - in pediatric and adult populations, respectively - affected thereby enabling targeted interventions to ameliorate the deficit. A cognitive impairment may include various types of neurodevelopmental delay.

[0006] In the pediatric population, this cognitive impairment may be as a result of a neurodevelopmental delay. In the adult population, this cognitive impairment may either be developmental in origin or, more commonly, acquired, and thus, may be referred to more broadly as a cognitive deficit.

[0007] For assessing developmental milestones in the pediatric population, there are known neurodevelopmental domains (also known simply as developmental domains) for assessing progress in development. There are four categories of neurodevelopmental domains (as defined by the CDC and the American Academy of Pediatrics) : motor (divided into gross motor and fine motor), cognitive, language/communication and social/emotional.

[0008] For assessing cognitive decline in the adult population, cognitive domains have been classified in the American Psychiatric Association’s Diagnostic and Statistical Manual (DSM) V into six groups: complex attention, executive function, learning and memory, language, perceptual-motor/visuospatial function and social cognition.

[0009] Neurodevelopmental (or as it is more commonly described, developmental) delay is defined as failure to meet one or more developmental milestones within the expected timeframe. These delays may reflect the presence of an underlying neurodevelopmental disorder. Neurodevelopmental disorders arise during the developmental period (up to 18 years of age) and refer to abnormalities in the acquisition and execution of functions within the key neurodevelopmental domains. The etiology of neurodevelopmental delays and disorders is varied, encompassing environmental factors such as exposure to teratogens, toxins, abuse or neglect, as well as genetic, metabolic, endocrine, nutritional and infectious conditions. One or more of these factors may co-exist in an individual. [0010] A neurodevelopmental disorder may refer to a cluster of symptoms a person may experience with a known or unknown cause. As defined in the American Psychiatric Association’s Diagnostic and Statistical Manual (DSM) V, the neurodevelopmental disorders are a group of conditions with onset in the developmental period. The disorders typically manifest early in development, often before the child enters grade school, and are characterized by developmental deficits that produce impairments of personal, social, academic, or occupational functioning. The range of developmental deficits varies from very specific limitations of learning or control of executive functions to global impairments of social skills or intelligence.

[0011] Types of neurodevelopmental disorders include, but are not limited to, Intellectual Disabilities, Intellectual Disability (Intellectual Developmental Disorder),

Global Developmental Delay, Unspecified Intellectual Disability (Intellectual Developmental Disorder), Communication Disorders, Language Disorder, Speech Sound Disorder, Childhood-Onset Fluency Disorder (Stuttering), Social (Pragmatic) Communication Disorder, Unspecified Communication Disorder, Autism Spectrum Disorder, Autism Spectrum Disorder, Attention-Deficit/Hyperactivity Disorder, Attention- Deficit/Hyperactivity Disorder, Other Specified Attention-Deficit/Hyperactivity Disorder, Unspecified Attention-Deficit/ Hyperactivity Disorder, Specific Learning Disorder, Specific Learning Disorder, Motor Disorders, Developmental Coordination Disorder, Stereotypic Movement Disorder, Tic Disorders, Other Specified Tic Disorder, Unspecified Tic Disorder, Other Neurodevelopmental Disorders, Other Specified Neurodevelopmental Disorder, Unspecified Neurodevelopmental Disorder. It may also include other developmental disorders such as Rett Syndrome, dyslexia, dyspraxia, dyscalculia, dysgraphia, auditory processing disorder, language processing disorders, non-verbal learning disability, visual perceptual and visual motor deficit, Tourette syndrome, Turner syndrome, Fragile X, Neurofibromatosis, Down Syndrome, Klinefelter syndrome, Phenylketonuria, Seizure disorders, Prader-Willi syndrome, Williams syndrome etc.

[0012] As described in DSM V, a neurocognitive disorder, which may be present in adulthood, is defined as a disorder in which impaired cognition has not been present since birth or very early life, and thus represents a decline from a previously attained level of functioning.

[0013] Types of neurocognitive disorders include, but are not limited to, Delirium, Other Specified Delirium, Unspecified Delirium, Major and Mild Neurocognitive Disorders, Major or Mild Neurocognitive Disorder Due to Alzheimer’s Disease, Major or Mild Frontotemporal Neurocognitive Disorder, Major or Mild Neurocognitive Disorder With Lewy Bodies, Major or Mild Vascular Neurocognitive Disorder, Major or Mild Neurocognitive Disorder Due to Traumatic Brain Injury, Substance/Medication-Induced Major or Mild Neurocognitive Disorder, Major or Mild Neurocognitive Disorder Due to HIV Infection, Major or Mild Neurocognitive Disorder Due to Prion Disease, Major or Mild Neurocognitive Disorder Due to Parkinson’s Disease, Major or Mild Neurocognitive Disorder Due to Huntington’s Disease, Major or Mild Neurocognitive Disorder Due to Another Medical Condition, Major or Mild Neurocognitive Disorder Due to Multiple Etiologies, Unspecified Neurocognitive Disorder.

[0014] An emerging method of identifying a neurodevelopmental disorder is to detect a digital cognitive biomarker(s) resulting from the aggregation and analysis of performance metrics. Biomarkers can indicate or predict specific conditions or diseases, as well as track their progression or response to treatment. Digital cognitive biomarkers arise from the non-invasive collection of behavioral performance data. Several such biomarkers may be combined to create a cognitive signature for the individual and/or specific conditions. Biomarkers in the medical field are increasingly beneficial for the detection of disease-related information. As is understood in the art, a biomarker provides a measurable indicator of a biological state or condition, and can be used to examine bodily functions and/or indicate onset of disease.

[0015] As defined by the National Institutes of Health (N1H), a biomarker is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to therapeutic intervention. [0016] A digital biomarker may be defined as objective, quantifiable, physiological, and behavioral data that are collected and measured by means of digital devices, such as embedded environmental sensors, portables, wearables, implantables, or digestibles. Digital biomarkers allow objective, ecologically valid, long-term follow-up with frequent or continuous assessment that can be minimally obtrusive or function in the background of everyday activity. Further, these frequent measures can capture intraindividual variability in performance that may be the earliest indicator of change and thus detect subtle health transitions (eg, healthy to MCI) [Piau, A, Wild K, Mattek N, et al. Current State of Digital Biomarker Technologies for Real-Life, Home-Based Monitoring of Cognitive Function for Mild Cognitive Impairment to Mild Alzheimer Disease and Implications for Clinical Care: Systematic Review. Journal of Medical Internet Research. 2019;21(8):el2785 )

It is understood that a digital cognitive biomarker also may be referred to herein as a neurological biomarker or a cognitive biomarker, and may include biomarkers that are indicative of neurological assessments more broadly, such as motor skill assessment.

SUMMARY

[0017] In a first aspect, the present invention provides a computer-implemented method for evaluating and treating one or more neurodevelopmental delays or cognitive deficits within a neurodevelopmental domain or cognitive domains of a pediatric or adult subject, respectively, the method comprising: generating, when a device is in a first mode, a neurodevelopmental assessment task associated with a neurodevelopmental domain on a user interface of the device and receiving performance metric data associated with the neurodevelopmental assessment or cognitive assessment task; generating, when the device is in a second mode, a training task associated with the neurodevelopmental domain or cognitive domain on the user interface; and instructing the first device to change from the first mode to the second mode, based on the performance metric data received whilst the device is in the first mode.

[0018] The present invention also provides a device comprising a processor and a user interface, wherein the processor is configured to generate, when the device is in a first mode, a neurodevelopmental assessment task associated with a neurodevelopmental domain or a cognitive assessment task associated with a cognitive domain on a user interface of the device and receiving performance metric data associated with the neurodevelopmental assessment task or cognitive assessment task; to generate, when the device is in a second mode, a training task associated with the neurodevelopmental domain or cognitive domain on the user interface; and to change the first device from the first mode to the second mode, based on the performance metric data received whilst the device is in the first mode.

[0019] It is an object of the first aspect of the present invention to adaptively provide training to a subject undergoing cognitive assessment only when necessary. It is a further object of the present invention to improve cognitive development outcomes by providing a hybrid approach to cognitive assessment and training of a patient.

[0020] In a second aspect, the present invention provides a computer-implemented method to identify and/or measure a cognitive biomarker indicative of a neurodevelopmental disorder or cognitive deficit in a subject, comprising: obtaining performance metric data from a neurodevelopmental assessment or a cognitive assessment task task performed by a subject on a first user interface; obtaining contextual data from an input provided by a third party on a second user interface; receiving, at a processor, the performance metric data and the contextual data; performing a statistical analysis on the performance metric data in combination with the contextual data; and identifying and/or measuring the cognitive biomarker based on the statistical analysis. [0021] The present invention also provides a system for identifying and/or measuring a cognitive biomarker indicative of a neurodevelopmental disorder or a cognitive deficit in a subject, the system comprising an assessment device comprising a first processor and a first user interface; a secondary device associated with the assessment device, the secondary device comprising a second processor and a second user interface; and an external memory associated with the first processor and the second processor, and configured to store an aggregated set of data received in relation to a user of the assessment device; wherein the first processor is configured to: cause the first user interface to display an assessment task; obtain performance metric data associated with the assessment task; and store the performance metric data in the external memory; and wherein the second processor is configured to: obtain contextual data input on the second user interface, and store the contextual data in the external memory.

[0022] It is one object of the second aspect of the present invention to improve the accuracy of diagnoses of developmental disorders. In one embodiment, this may be done through the statistical aggregation of received performance metric data with received contextual data.

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Reference will now be made to the accompanying drawings, wherein:

[0024] FIG. 1 is a schematic of a first device with a user interface and a processor.

[0025] FIG. 2 is an exemplary embodiment of a method for performing the invention.

[0026] FIG. 3 is an exemplary method for determining whether to switch between an assessment mode and a training mode..

[0027] FIG. 4 is a schematic of an external server, comprising a processor and a memory.

[0028] Fig. 5 is an exemplary graphical user interface of an assessment task.

[0029] Fig. 6 is another exemplary graphical user interface of an assessment task.

[0030] FIG. 7 is an exemplary graphical user interface of a training task.

[0031] FIG. 8 is another exemplary graphical user interface of an assessment task.

[0032] FIG. 9 is another exemplary graphical user interface of a training task.

[0033] FIG. 10. is a schematic of neurodevelopmental domain tasks in memory, each domain task having a list of assessment tasks and a corresponding list of training tasks.

[0034] FIG. 11 is a schematic of a first device with a user interface and a processor. [0035] FIG. 12 is a schematic of a first device and a second device, each connected to an external memory.

[0036] FIG. 13 is an exemplary embodiment of a method for performing the invention.

[0037] FIG. 14 is an exemplary graphical user interface of an assessment task shown on a first user interface.

[0038] FIG. 15 is another exemplary graphical user interface of an assessment task shown on a first user interface.

[0039] FIG. 16 is an exemplary graphical user interface shown on a second user interface.

[0040] FIG. 17 is a schematic of a first device and a second device, each device configured to communicate directly with the other device.

[0041] FIG. 18 is a schematic of an external server, comprising a processor and a memory.

DETAILED DESCRIPTION

[0042] In a first aspect, embodiments disclosed herein are directed generally to methods and devices for generating a neurodevelopmental assessment task associated with a neurodevelopmental domain, a cognitive training task associated with the neurodevelopmental domain, and switching a mode of a device from a first mode associated with the neurodevelopmental assessment task to a second mode associated with the cognitive training task.

[0043] Patients diagnosed with neurodevelopmental disorders or cognitive impairment can undergo cognitive therapies and/or cognitive treatment plans, with the aim of improving cognitive performance and improving the trajectory of cognitive development. Similar approaches for improving cognitive performance and/or preventing worsened performance can be also used for treating and managing patients with a cognitive decline, such as a cognitive impairment caused by dementia, Alzheimer’s, etc. [0044] It will be understood that where the specification refers to assessing and treating neurodevelopmental disorders by assessing neurodevelopmental domains in a pediatric population, similar approaches for the adult population are also considered. In other words, assessing/treating cognitive impairments and neurocognitive disorders by assessing cognitive domains is clearly envisaged by the specification. Similarly, whilst the application refers to neurodevelopmental assessments for targeting the neurodevelopmental domains, corresponding cognitive assessment can be performed on the adult population, to target the cognitive domains.

[0045] As shown in FIG. 1, a first device 100 according to an exemplary embodiment of the invention includes a first user interface 101 in communication with a first device processor 102. The first device processor 102 may be configured to communicate to an external memory 120, as would be understood by a skilled person. A separate communications module (not shown) may be implemented in the first device to communicate data from the first device processor to the external memory in a specific data format. The communications link maybe either wireless (Wi-fi, Ethernet, Zigbee, Bluetooth, etc.), or it may be a wired connection.

[0046] A neurodevelopmental disorder may arise during the developmental period and refers to abnormalities in the acquisition and execution of functions within key neurodevelopmental domains. Such a disorder may include neurodevelopmental delays. Such a delay may be detected through a repeated pattern of cognitive performance measured over time. Neurodevelopmental delay may be detected through cognitive biomarkers, which may be indicative of a neurodevelopmental disorder.

[0047] As defined by the National Institutes of Health (NIH), a biomarker is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to therapeutic intervention.

[0048] A neurodevelopmental assessment task is a task designed to assess proficiency in one or more of the following categories: motor (gross/fine), language/communication, cognitive, and social/emotional. These categories are also referred to as neurodevelopmental domains. In this way, a neurodevelopmental assessment task can be utilized to generate a cognitive biomarker which is indicative of a neurodevelopmental disorder, or to assess the performance of a patient who has been formally diagnosed with a neurodevelopmental disorder.

[0049] In the pediatric population, the neurodevelopmental assessment task allows the system to detect differences in performance within the four neuro-developmental domains. These aggregated performances (either in terms of absolute number or over a defined period of time) may then be translated into a biomarker for delay in motor development, for example. This can then be tracked longitudinally over time as a disease process evolves or a new medicine is introduced. Biomarkers can be obtained for each of the domains (i.e. motor, language, cognitive, and social/personal.

[0050] Bringing together data from patient performance in tasks from all the neurodevelopmental domains may subsequently allow the development of biomarkers that indicate a developmental disability or a specific neurodevelopmental disorder e.g. global developmental delay, attention-deficit/hyperactivity disorder.

[0051] In other words, the biomarker can be narrow and indicative of performance in one of the four neurodevelopmental domains. Alternatively, the biomarker can be more broad (taking into account performance in multiple neurodevelopmental domains) and indicative of a specific neurodevelopmental disorder.

[0052] A neurodevelopmental assessment task may be a single task presented to a subject, or it may be a sequence of tasks presented. Preferably, each individual neurodevelopmental assessment task is principally designed to assess a single facet of cognitive development, otherwise known as a neurodevelopmental domain. For example, a task may be designed to test for language skill in a subject.

[0053] Exemplary tasks may include, but are not limited to, an Eriksen Flanker Task, a Visual Puzzle task, various visual search tasks, digit symbol coding task, symbol search task, digit span task, picture span task, letter-number sequencing task, phonological awareness task, oral discourse comprehension task, numeracy tasks like the “more or less” task, matrix reasoning task, figure weights task, cancellation tests, picture concepts task, vocabulary task, reaction time task, spatial span task, paired associates learning task, delayed matching to sample task, and emotion recognition task..

[0054] The present invention seeks to improve the efficacy of cognitive treatments of a neurodevelopmental disorder, through providing a tailored approach to assessment and training, which may be uniquely presented for each individual subject.

[0055] It may be said that an assessment task corresponds to a task for assessing at least one facet of cognitive performance. The task is presented in a predictable and regulated manner. In other words, the assessment task is configured so as to isolate a specific metric of cognitive development, and does not include any hints, suggestions, guidance, or other interference which might bias the assessment. As detailed above, the neurodevelopmental assessment task may be any one of the tests known in the art for assessing cognitive ability.

[0056] In one example, a user of the device is presented with a fixed, specific number of tasks. The specific tasks which are generated are adjusted in difficulty based on the known or estimated age of the user.

[0057] It may also be said that a training task refers to any task which is designed to reinforce educational concepts for improving cognitive development outcomes. The training task may be associated with one or more neurodevelopmental assessment tasks, and thus, a training task associated with a specific neurodevelopmental assessment task may be presented to a subject under certain conditions.

[0058] A randomly selected number of training tasks may be generated and presented to the user. Again, the specific tasks which are generated may be adjusted in difficulty based on the known or estimated age of the user.

[0059] For example, upon completing a neurodevelopmental assessment task, the device processor may receive and analyze the performance metric data associated with the task. If one or more parameters in the performance metric data exceeds a threshold stored in the memory of the device, the processor can switch the assessment task into a training task. Whether or not the processor switches the tasks can also depend on how the performance of the task compares against a ‘floor’ of said task, i.e. the minimum allowable success rate in a task.

[0060] Alternatively, the processor might receive and analyze performance metric data during and throughout the neurodevelopmental assessment task. By processing the performance metric data in much the same way, the processor may be configured to make an early assessment of cognitive ability based upon the performance, end the assessment task (if, e.g., measuring performance significantly below the ‘floor’ performance of the task, or significantly above the ‘ceiling’ performance, i.e. the maximum allowable success rate for a task), and/or switch the task to a training task before allowing the assessment task to finish.

[0061] In other words, it may be said that receiving data “whilst the device is in the first mode” corresponds to receiving data either during the assessment task in the first mode or after the assessment task in the first mode is completed.

[0062] A method of switching a mode of the device is described in detail in FIG. 2. Specifically, in step 201, the method comprises generating, when the device is in a first mode, a neurodevelopmental assessment task associated with a neurodevelopmental domain on a user interface of the device. In steps 202 and 203, the method further comprises receiving performance metric data associated with the neurodevelopmental assessment task and generating, when the device is in a second mode, a training task associated with the neurodevelopmental domain on the user interface. Lastly, in step 204, the method comprises instructing the device to change from the first mode to the second mode based on the performance metric data received whilst the device is in the first mode.

[0063] As would be understood by the skilled person, the specific order of the steps in the method can be variable. For example, the instruction to the device, by the device’s processor, to switch modes can clearly happen before the generation of the training task.

[0064] The neurodevelopmental assessment task and training tasks are both generated on a user interface of a device. Herein, the “user” of said device is also referred to as a “subject” or a “patient”, interchangeably. [0065] In one embodiment of the present invention, the method for evaluating and treating one or more neurodevelopmental delays in a subject comprises: generating, when a device is in a first mode, a neurodevelopmental assessment task associated with a neurodevelopmental domain on a user interface of the device and receiving performance metric data associated with the neurodevelopmental assessment task; generating, when the device is in a second mode, a training task associated with the neurodevelopmental domain on the user interface; and instructing the first device to change from the first mode to the second mode, based on the performance metric data received whilst the device is in the first mode.

[0066] In another example, a method for evaluating and treating one or more cognitive deficits in a cognitive domain in an adult subject are considered. This method follows the same steps as the neurodevelopmental domain assessment and comprises the following: generating, when a device is in a first mode, a cognitive assessment task associated with a cognitive domain on a user interface of the device and receiving performance metric data associated with the cognitive assessment task; generating, when the device is in a second mode, a training task associated with the cognitive domain on the user interface; and instructing the first device to change from the first mode to the second mode, based on the performance metric data received whilst the device is in the first mode.

[0067] Again, as would be understood, where the below methods describe neurodevelopmental assessments and neurodevelopmental domains (for the assessment of pediatric development), the methods can be correspondingly implemented for the adult populations, in what is called cognitive assessment in a cognitive domain.

[0068] FIG. 3 describes one method for switching between an assessment mode and a training mode. First, in step 301, an assessment task can be presented to a user through a user interface of a device. This assessment task may be a known neurodevelopmental assessment, a series of questions, or, more broadly, any game which outputs performance metric data corresponding to the task. One of these metrics may be whether or not the test was successfully passed (trialSuccessful). In step 302, the processor can determine whether the test was passed by checking the trialSuccessful metric. If the task was performed successfully, another assessment can be presented to the user, and optionally, the processor can be configured to adjust the difficulty of the task (step 303). If the task was not performed successfully, a counter is incremented by the processor (step 304). The value of such a counter corresponds to how many times the specific assessment task has failed. In step 305, if the value of the failure counter exceeds an allowable number of failures (e.g. the processor allows 3 failures on a certain task), then the processor can be configured to present a training task corresponding to the assessment task. Otherwise, the processor can continue to present assessment tasks to the user. If at step 305, the number of failures exceeds a predetermined limit, the player has demonstrated that they cannot continue to engage with the task at a level of difficulty that should be typical for their age.

[0069] In step 306, this training task may be presented immediately to the user. In one embodiment, when the mode of the device has switched, the user will encounter the training version of the task the next time they try to use the assessment task. In another embodiment, the training task may have identical pass criteria as the associated assessment task, except that a training screen is presented, which guides a user through the task.

[0070] Exemplary guidance may include highlighting buttons, changing colors, providing instructions to the user (auditoiy/visual guidance), etc. These audio cues and visual feedback are part of a hint system designed to instruct and train the player. These hints do not exist during the standard assessment mode.

[0071] The device may be a smartphone, computer, smart watch, tablet, videogame console, or generally any device that has its own device processor and a user interface suitable for providing a neurodevelopmental assessment task to the subject. The user interface of the device may include, but is not limited to, a touch screen on a touch-sensitive device, a separate controller connected to a computer, such as a videogame controller, or a mouse and keyboard.

[0072] The device processor may be configured to process data received from the neurodevelopmental assessment task and the training task locally, and may be in communication with an electronic memory to store the received data and parameters associated with each task.

[0073] For example, the memory may store parameters related to success criteria for each task, along with a cognitive development category associated with each task.

[0074] Additionally, or alternatively, the device processor may be configured to communicate to an external processor and/or an external memory. In this way, the processing of the data can occur in either an external server or a different computer. Such a configuration may be beneficial to avoid the need for local processing on the device, which may be a computationally demanding process. The external processor may have the same computational ability as the device processor, and may include a corresponding memory to store the above parameters. Hereinafter, where a “processor” is described, it is understood that the processor may be either the device processor or an external processor.

[0075] Such external processing can be seen in FIG. 4. FIG. 4 depicts an external server 400, which comprises an external processor 401 communicably coupled to an external memory 120. Such features may be beneficial to improve data processing power and reduce the computational expense on the device processor.

[0076] The processor is configured to generate a neurodevelopmental assessment task on a user interface of a device. The processor may further be configured to select a neurodevelopmental assessment task from a list of neurodevelopmental assessment tasks. This list of neurodevelopmental assessment tasks may be in a memory connected to and in communication with the processor.

[0077] It is understood that “generating a neurodevelopmental assessment task” may refer to presenting a task on a user interface to a user of a device and monitoring the user interface to receive performance metric data associated with the task. It may also refer to the generation of a custom task. That is, a known task stored in memory may have a number of inputs, or adjustable parameters, which can be provided which modify the effect of the neurodevelopmental assessment task. [0078] In one embodiment, such inputs to a known neurodevelopmental assessment task may allow a task to be specifically customized for certain patients, either by adjusting one aspect of the difficulty of the task in a discrete way, or by presenting a task which is more effective to said certain patients. This may be beneficial to improve patient compliance.

[0079] FIGS. 5 to 7 show different user interfaces of an exemplary game which may be presented to a user for gathering performance metric data.

[0080] As shown in FIG. 5, in one embodiment, a Flanker task may be presented to a user on the first user interface 1000 as part of a neurodevelopmental assessment. It may be understood that user interface 1000 may be identical to user interface 101 or 110. In other words, the GUI shown in FIGS. 5 and 6 is simply one expression of a task on a display.

[0081] A Flanker task is beneficial for testing the ability for a user to inhibit irrelevant competing responses to a nonverbal stimulus. As a result, this task provides performance metric data which may be useful for assessing users who demonstrate a certain level of attention deficit (one marker for ADHD, attention deficit and hyperactivity disorder).

[0082] During gameplay of the Flanker task, a user must ignore the distractor flanks 1001 and select only the item in the middle of the row 1002, the potential selections shown in boxes 1003a and 1003b. FIG. 5 shows a flanker task where the shape of the object in the middle of the row is distinct from the surrounding objects, while in other embodiments, a flanker task can be presented where the color of the object in the middle of the row is distinct from the color of the surrounding objects (such as the GUI presented in FIG. 6).

[0083] It is understood that whilst the objects in FIGS. 5 and 6 are shown in a row, the application is not limited to a horizontal row of objects, as such. Moreover, the application also considers that the item in the middle may differ from the surrounding objects in color, orientation, shape, etc., so long as the object is recognizably different than the surround objects. In one embodiment, additional objects, either static or moving, may be presented on the screen to increase the difficulty of the task. [0084] If the task is failed a predetermined number of times, based upon the expected performance in the task for a user of a specific age group, then the device is configured to introduce hints. The processor may be configured to immediately stop the assessment task, switch into a training task, and seamlessly provide hints to the user. Such a process results in incomplete data from the assessment task, which may be disregarded in the overall neurodevelopmental assessment, or it may be recognized as a failure. Alternatively, the processor may be configured to allow the user to completely finish the Flanker task, but not provide another Flanker task in an assessment mode until the user has properly trained and demonstrated a sufficient proficiency at the task.

[0085] As shown in FIG. 6, in one embodiment, the training task GUI 1100 may include highlighting the relevant element 1101 in the center of the row. Thus, in FIG. 6, the distractions have been made more apparent, so as to highlight the distinctions in the colors. This visual level of guidance allows a user to more easily distinguish between the correct option 1102a and the incorrect option 1102b.

[0086] As shown in FIG. 7, in one embodiment, the training task GUI 1200 may include a visual cue which is a pointer 1201 directed towards the correct solution. Optionally, this pointer 1201 may be simultaneously presented with audio feedback, written instructions, or any other form of guidance.

[0087] In one embodiment, the amount of guidance presented to the user may correspond to the user’s previous performance in the assessment task. In other words, there may be levels of severity of guidance, where for continued failures, the system can be configured to continue introducing further guidance measures. In other words, the training task may first present a reduction in distractions to a first user, the training task may present guidance pointers for users for whom reductions in distractions are not adequate.

[0088] Another exemplary assessment task of one embodiment of the invention is shown in FIG 8. This task is a version of a 3, 6, and 9 box task, and it may be presented to the user on the first user interface 2000 as part of another neurodevelopmental assessment. It may be understood that user interface 2000 may be identical to user interface 101 or 110. Such a task is beneficial for determining whether children with autism spectrum disorders show deficits in spatial working memory.

[0089] During gameplay of the 3, 6, and 9 box task version, participants are asked to refuel planes 2001a, 2001b, 2001c flying across the screen by tapping them. Once the user has refueled a plane, e.g. 2001a, they must ensure that they do not revisit the same plane and tap it again. In other words, the user must hold in memory the location of the previous planes that have been refueled in order to complete each trial of the game successfully. It is understood that this embodiment is not exclusively limited to a certain shape of object, and the orientation of the objects is inconsequential to the operation of the game. Each plane may have a corresponding fuel gauge bar which shows how much the plane has been refueled whilst a user is interacting with the plane, in this case 2001a. In one embodiment, the number of taps required to refuel a plane may vary between the different planes on the screen, and any number of planes may appear on the screen, in order to vary the difficulty of the task. Alternatively, a single tap on a plane may fully refuel the plane.

[0090] FIG. 9 depicts a corresponding training task GUI 2100 for the 3, 6, and 9 box task version. In this example, the color of the plane 2101a, 2101b on the display which the user needs to interact with is changed, to demonstrate the correct option. Again, visual cues and audio feedback can be present. In one embodiment, these cues may involve a demonstration of an expected correct reaction, such as a pointer, similar to the one shown in FIG. 7.

[0091] In all of the above, the processor can be configured to measure how effective specific styles of guidance are to the individual user. That is, the processor is configured to measure the time between correctly solving a training task and when the guidance was presented to the user. The processor can also be configured to measure this time for each different style of guidance in the training task. By measuring this data, the processor can be utilized to determine which styles of hints/guidance are most effective for the specific user. For example, the processor could determine that on average, a device user takes 5 seconds to respond to auditory guidance, and 10 seconds to respond to written instructions. As a result of measuring the data (and optionally, storing such response times in memory), the processor can present auditory guidance to the user for subsequent tasks instead of written instructions (either as a first attempt of a hint, or in combination with other hints).

[0092] As would be understood by the skilled person, features on the graphical user interfaces 1000 and 2000 (as well as the corresponding training task GUIs, 1100, 1200, and 2100), such as the home button and the music on/off toggle, as well as the background art are not linked to the functionality of the neurodevelopmental assessment tasks, and may be variable based on the software parameters.

[0093] In one embodiment, the system could be configured to remove color or other sensory detail from a known neurodevelopmental assessment task for a specific patient, based upon a known neurodevelopmental disorder, such as an Autism Spectrum Disorder (ASD), or historical data related to said patient stored in memory. In this way, the system may be suitable for generating a neurodevelopmental assessment task not only based on a list of understood neurodevelopmental assessment tasks, but to adjust them accordingly based on the specific user of the device, if necessary.

[0094] It may be said that historical data may comprise the performance metric data, and optionally any contextual data, associated with the subject for a plurality of attempts at the neurodevelopmental assessment task. The plurality of attempts may be a specific number of attempts (e.g. 10 attempts at a task), or it may be the entire available history of attempts stored in a memory. In other words, the history of a single subject’s performance may be used to improve the efficacy of the treatment and improve the cognitive development trajectory.

[0095] Whilst presenting a neurodevelopmental assessment task and a training task on the same device are clearly considered, presenting the neurodevelopmental assessment task and the training task on separate devices is also considered by the present invention.

In other words, two or more devices could be used in a system, each of which could be associated with a single user account. In this way, a patient could, for example, receive a neurodevelopmental assessment task on their smart phone, and then receive a training task on a separate computer. [0096] It may be understood that a device configured to present neurodevelopmental assessment tasks and cognitive training tasks may operate in two modes. In other words, whilst the device is operating in a first mode, hereinafter referred to as an assessment mode, the device may be configured to only present neurodevelopmental assessment tasks.

[0097] It may also be understood that whilst the device is operating in a second mode, hereinafter referred to as a training mode, the device may be configured to only present cognitive training tasks, instead of neurodevelopmental assessment tasks.

[0098] Each neurodevelopmental assessment task may have one or more cognitive training tasks associated with it. In one embodiment, the neurodevelopmental assessment task is identical to the cognitive training task, except that certain task parameters are enabled for the cognitive training task and disabled for the neurodevelopmental assessment task, and vice versa.

[0099] For example, the neurodevelopmental assessment task may have a time limit associated with completing the task, whilst the time limit is removed in the training task. In another example, the neurodevelopmental assessment task may provide no auditory, visual, or tactile feedback of any kind during the task. On the other hand, a cognitive training task may provide these kinds of feedback in real-time, or may provide suggestions and/or guidance to a subject to complete the task.

[00100] The feedback may be positive or negative - that is, the feedback may be positive, i.e. suited to either provide rewards and guide the user towards a successful result, or negative, i.e. feedback designed to dissuade the user away from a certain, incorrect solution.

[00101] In one embodiment, the suggestions and/or guidance comprises an auditory or visual hint, or an explanation of how to perform the task. The suggestion may be adapted in response to specific parameters measured during the training task.

[00102] The neurodevelopmental assessment task may generate performance metric data, which can be utilized by the device processor to generate a neurodevelopmental assessment or determine a cognitive biomarker, or to indicate in which areas the subject needs additional training.

[00103] In one embodiment, the device may be configured, by the processor, to operate in a hybrid mode. Unlike the dedicated assessment and training modes, a hybrid mode refers to a mixed approach for delivering assessment and training tasks. In other words, the processor may recognize individual neurodevelopmental delays orcognitive deficits that correspond to performance in the assessment tasks, and for which a subject needs additional training. The processor may then present a training task instead of the corresponding neurodevelopmental assessment task when necessary.

[00104] Such a hybrid approach may improve patient compliance, as well as encourage immediate, specific guidance for patients with severe neurodevelopmental delays or cognitive deficits, where immediate training may be more beneficial for the retention of cognitive training, and thus, may improve patient outcomes.

[00105] With regard to FIG. 7 and FIG. 9, the training tasks may be presented in such a way that the guidance is seamless with the assessment task, once the assessment task has not been properly passed. In one embodiment, the processor is configured to switch into a training mode as soon as a certain amount of time has elapsed, which is indicative that a user of the device is struggling with completing the task.

[00106] In one embodiment, the same performance metric data measured in the neurodevelopmental assessment task is measured during, i.e. throughout, the associated training task. In this embodiment, the processor may receive and/or process the performance metric data in real time, instead of between each task.

[00107] The performance metric data may be a digital or analog result of a neurodevelopmental assessment task presented on a first user interface. It may also be said that performance metric data refers to any data measured during a neurodevelopmental assessment task, including whether or not the task was successfully passed, the time taken to accomplish the task, the number of attempts required to accomplish the task, whether or not any hints were required, the level of difficulty, the number of questions asked, the number of correct/incorrect responses, or any parameter specifically associated with the neurodevelopmental assessment task.

[00108] It may also be said that the performance metric data associated with the neurodevelopmental assessment task comprises at least one parameter. In one embodiment, at least one parameter comprises one or more of the following: the time taken for the subject to complete the neurodevelopmental assessment task; whether the neurodevelopmental assessment task was completed successfully; the number of times the neurodevelopmental assessment task was completed and was not completed successfully; and feedback from the user interface.

[00109] It may be understood that the “first mode” and “second mode” refer to specific configurations of the device, such that in the respective mode, the only task presented to the user is a neurodevelopmental assessment task or a training task, respectively.

[00110] The user interface on the device may not visually change between the neurodevelopmental assessment task and the training task. In other words, there may be no noticeable indicator that the device has been switched into a different mode. For example, there may be no change in the GUI/user interface of the device. In this way, a subject using the device can perform an assessment, and the device can switch into a training mode without alerting the user.

[00111] As a result, a complete or partial assessment of cognitive performance may be achieved whilst either interspersing training tasks with the assessment tasks, or highlighting and tagging failures in specific tasks which would necessitate training at a later stage.

[00112] One benefit of switching modes and/or interspersing neurodevelopmental assessment tasks with training tasks is the improvement in attention of the patient, which results in improved outcomes from a training routine.

[00113] Another benefit of the method of the present invention is that device resources are optimized and not wasted. In other words, because the training mode is only presented when necessary, there is an improvement in the general operation of a device suited for both neurodevelopmental assessment and cognitive training.

[00114] Another benefit of the method of the present invention is that training is more effective because it is more precisely tailored to the needs of the subject based on timely assessments of ability. Where training results in the improvement of the subject in respect of a neurodevelopmental disorder the training may be considered as a treatment of the neurodevelopmental disorder.

[00115] In one embodiment, the step of generating a neurodevelopmental assessment task comprises generating a plurality of neurodevelopmental assessment tasks sequentially when the device is in the first mode.

[00116] These neurodevelopmental assessment tasks may be presented to the subject as individual tasks, or may be presented sequentially in a fixed order or a random order. The sequence of neurodevelopmental assessment tasks each might be substantially similar tasks, which are each suited to measure a similar parameter of cognitive ability. [00117] Alternatively, the sequence could comprise different neurodevelopmental assessment tests from the initially presented test, suited to test the cognitive ability of the patient in a different area (e.g. a visual test was first presented, and the subsequent test presented will be a motor skill test).

[00118] The benefit of providing randomized and mixed styles of neurodevelopmental assessment tests are that assessments can be improved. Furthermore, training and assessment tasks can be interspersed, which allows a patient to continue developing in their global cognitive ability whilst simultaneously receiving training in specific, pinpointed cognitive areas.

[00119] In one embodiment, the step of generating a training task comprises generating a plurality of training tasks sequentially when the device is in the second mode.

[00120] Each of the plurality of training tasks may each be associated with a different neurodevelopmental assessment task.

[00121] Alternatively, the plurality of training tasks may each be associated with a single neurodevelopmental assessment task. Such a configuration is beneficial because where a patient requires additional training in a specific area, the processor can provide a series of different training tasks to the user to improve performance in that area.

[00122] As shown in FIG. 10, the device memory 103, 120 may include dedicated memory locations for each of the neurodevelopmental domains 501-505. Within each of the neurodevelopmental domain memory locations, there can be a list of neurodevelopmental assessment tasks associated with the neurodevelopmental domain (e.g. 501-la, 501-2a, etc.). There may also be a corresponding training task associated with the assessment task (e.g. 501-lb, 501-2b, etc.). In other words, assessment task 501-Na corresponds to training task 501-Nb. As described above, the training task may be identical to the assessment task, with different levels of guidance presented to the user. The amount of guidance provided in the training task may also be stored in the memory.

[00123] The device memory 103, 120 may also include a flag for each neurodevelopmental domain, or more specifically, for each neurodevelopmental assessment task. It may also include a history of the performance (e.g. success or failure data) in the neurodevelopmental domain/neurodevelopmental assessment task. As a result, the processor can be configured to check the history of the success in the task, check whether the task has been switched to a training mode or an assessment mode, and then pull the parameters of the task from the look-up table in the memory. In one example, the processor presents a randomly selected memory training task (e.g. 501-2b), because the previous memory assessment task was not performed at an acceptable level.

[00124] The plurality of neurodevelopmental assessment tasks and training tasks presented to a user can each have a variety of different, changeable features. That is, each of the plurality of cognitive training tasks can be configured differently during each presentation of the respective task (either with a difference in visual presentation, or a modification of the task itself) in order to prevent test-retest effects and improve learning outcomes. In other words, a single task can be presented to the patient multiple times, and may include one or more randomly generated parameter, such that the task does not identically resemble the previously presented task. [00125] Again, once the processor 102 has selected the appropriate assessment task or training task, it can be presented on user interface 101 for assessment and/or training.

[00126] In this case, the device is also configured to monitor performance metric data from each of the training tasks of the plurality of training tasks. Doing so is beneficial, because the device processor can store data related to which training tasks are most effective for cognitive development for a specific patient.

[00127] Because the generation of the training tasks by the processor allows for custom inputs in each task which control certain elements of the task and because the processor can monitor the performance metric data received from the training task, the processor can determine which task(s) are most effective for each patient.

[00128] This can be accomplished by storing performance metric data associated with each training task in a memory, and performing a statistical analysis across the historical data available for the patient. As a result, if a statistical analysis (e.g. a statistical regression or cross-correlation) is performed, the processor can determine whether any specific parameters of the task (e.g. type of hint, type of training task, auditoiy/visual/tactile feedback) are more indicative of improvements in cognitive development. As a result, such statistical analysis can be used to determine the overall effectiveness of the task, by evaluating which tasks (or more specifically, which parameters of those task) are most correlated with improved outcomes. If possible, the processor may be configured to present the subsequent training task having the most indicative parameter.

[00129] As an example, the processor may determine that a certain patient responds most effectively to visual, but not auditory, feedback. In response to this, the processor may present the subsequent training task with visual feedback.

[00130] This is beneficial because feedback can be tailored to specific patients, thus improving cognitive development trajectories and patient performance and maximizing the training effect. [00131] In other words, the present invention may improve detection of problematic areas in cognitive development, and target training for those areas specifically. It may further recognize which methods of training are most effective for said patient, and prioritize delivery of those specific styles of training in the problematic areas.

[00132] In one embodiment, the method further comprises identifying a category associated with each neurodevelopmental assessment task. It may be said that a “category” refers to any shared parameter associated with the neurodevelopmental assessment task, including, but not limited to, the type of task, the targeted cognitive skill, the presence of audio, etc. In other words, the categories associated with the neurodevelopmental assessment tasks correspond to the neurodevelopmental domains. Similarly, where the method is used for cognitive assessment in adults, the corresponding categories refer to the cognitive domains, as described above.

[00133] Each neurodevelopmental assessment task may be assigned to one or more categories. That is, the neurodevelopmental assessment task and any further neurodevelopmental assessment task are tasks which are each designed to assess proficiency in one or more of the following categories: motor (gross/fine), language/communication, cognitive, and social/emotional. These categories may also be referred to as neurodevelopmental domains. Each of these neurodevelopmental domains may include any number of sub-domains, referring to a specific skill within the neurodevelopmental domain.

[00134] For each neurodevelopmental domain, there are a series of sub-tasks which may presented to a user. In other words, a sub-task of a neurodevelopmental domain may refer to a specific neurodevelopmental assessment task which falls under the same category of cognitive proficiency.

[00135] A list of sub-tasks designed to measure proficiency in language/communication skills includes, but is not limited to: an oral discourse comprehension task, and a phonological awareness task.

[00136] A list of sub-tasks designed to measure proficiency in motor skills includes, but is not limited to: a peg game task, a tracing task, and a colouring task. [00137] A list of sub-tasks designed to measure proficiency in cognition skills includes, but is not limited to: a 3, 6, and 9 task, a digit and picture span task, a visual puzzle task, a block design task, and a flanker task.

[00138] A list of sub-tasks designed to measure proficiency in social/emotional skills includes, but is not limited to: a first emotion recognition task (facial expression) and a second emotion recognition task (voice-only).

[00139] Additional skills which may be tested include, but are not limited to numeracy. Numeracy skills may be evaluated through a “more or less” style task.

[00140] The categorization of neurodevelopmental assessment tasks can be used to isolate neurodevelopmental assessment tasks for which a patient performs at a different level of competence. This is beneficial because typical neurodevelopmental assessment profiles comprise a wide variety of parameters which are tested. These parameters of cognitive performance includes one or more of, but not limited to, visual-spatial ability, verbal comprehension, processing speed, working memory and fluid reasoning. A patient with a neurodevelopmental disorder may require no further training in one or more of these areas, but may require more training in others.

[00141] In one embodiment, the category is selected from a plurality of categories, wherein the plurality of categories comprises one or more of the following categories: cognitive skills, motor skills, language skills, social skills and emotional regulation, including self-regulation.

[00142] In one embodiment, the step of generating the training task comprises generating a training task which is associated with the identified category of the neurodevelopmental assessment task.

[00143] In other words, by assigning neurodevelopmental assessment tasks and training tasks to a category, performance in one area can be tracked in an isolated fashion from the other categories. Accordingly, the processor can be configured to compare performance metric data from a neurodevelopmental assessment task or training task only against other performance metric data from similar tasks. [00144] This allows for a system which provides continued assessments and development in certain categories where no training is required, and a hybrid approach of assessments and training tasks when performance in a certain category of task is below a certain threshold.

[00145] In other words, where performance in an assessment task in a first category is below a threshold (e.g. below a typical score for the task) and performance in a second assessment task in a second category is acceptable, the processor is configured to only present training tasks in relation to the first category. In this case, doing so also switches the mode of the device only in relation to tasks which fall into first category.

[00146] In one embodiment, the method further comprises comparing the at least one parameter of the performance metric data against a first threshold parameter; and determining whether at least one parameter exceeds the first threshold parameter; and wherein instructing the first device to change from the first mode to the second mode comprises instructing the first device to change modes when at least one parameter exceeds the first threshold parameter.

[00147] The threshold parameter may be stored in memory. It may refer to an indicator of quality of performance in a specific task. It may also be said that each parameter of the performance metric data has an associated threshold parameter. The processor is configured to select whether to monitor one or more of the parameters against the associated threshold parameters, and instructing the device to switch modes if one or more of the monitored parameters exceed the threshold. Again, this may result in the device switching modes only in relation to certain neurodevelopmental assessment tasks, e.g. those tasks determined to be in the same category of tasks.

[00148] For example, the threshold parameter may comprise a predetermined number of allowable attempts, an allowable time to complete a task, an acceptable score in the task. The threshold parameter may also vary, depending on input from the user, which is stored in memory. Such input may include information including, but not limited to, the age, the gender, and the neurodevelopmental disorder of the subject using the device. [00149] It may be understood that when a “score” of a task is referred to, this can be understood to mean an average pass percentage (%) for a patient having certain criteria. Alternatively, the “score” of a task could refer to whether or not the performance in the task exceeded a ‘ceiling’ of the task (a maximum expected value for a user of a certain age group), or whether or not the performance in the task fell below a ‘floor’ of the task (a minimum expected value for a user of a certain age group). However, many different ways of scoring the task are considered, which would be entirely understood to a person skilled in the art of cognitive assessment. In this way, the criteria refers to one of the stored input values. In other words, a threshold parameter may be chosen from a predetermined value associated with the input value. For example, the threshold parameter may represent an acceptable score, which is associated with the typical result for a 5-year old. Therefore, for a 5-year old taking the neurodevelopmental assessment task, a score below the threshold parameter may trigger the device switching into a training mode.

[00150] Contextual data relating to the subject may be input by the subject, or by a third party, and stored in memory. Such data may be actively requested before, during, or after a neurodevelopmental assessment or training task on the device. Alternatively, such contextual data can be requested and input once and then stored in memory of the relevant device. For example, the age of the subject may be input only once and then stored in memory for improving the calculation of, or the determination of, the threshold parameter. Alternatively, the contextual data may be obtained at intermittent or regular intervals.

[00151] It may be said that intermittent or regular intervals refer to the request for contextual data. This data can be requested after single, known inputs (i.e. finishing a task) or the requests can be generated at random times, so as to provide more consistent streams of contextual data.

[00152] It may be said that contextual data refers to any data received in relation to the subject undergoing a cognitive assessment. Such data may include, but is not limited to, parameters, such as the age, sex, household income level, and geographical location of the subject. Compared with other parameters described elsewhere herein, these parameters vary slowly (or not at all) over time, and are therefore referred to as ‘fixed’ parameters for the purposes of this specification. The contextual data may also include health data of the subject, including but not limited to the amount of sleep, caffeine intake, sugar intake, diet of the subject, etc.

[00153] The threshold parameter may further include a variation around a typical value. That is, the threshold may be adjusted by the processor to correspond to an allowable variation from the expected value (e.g. to allow a 10% difference from the expected value). Such normal ranges can also be stored in the memory, and in some cases, can be adjusted by a third party.

[00154] In summary, if the patient is being administered a neurodevelopmental assessment task, preferably as part of a game shown on a device, one or more parameters related to the neurodevelopmental assessment task are monitored, such that if one or more of those parameters is an outlier, or above a known threshold, the processor can change the device from the assessment mode to the training mode in response to the monitored parameter.

[00155] The threshold parameter is adaptive, in that it can represent a typical value of similar subjects to the neurodevelopmental assessment task, including but not limited to expected success rates based on the average result from a set of historical data of the neurodevelopmental assessment task. This average result may be determined based, in part, upon the contextual data, by comparing the patient against other people who underwent the same neurodevelopmental assessment test with one or more data points of the contextual data (e.g. age) in common.

[00156] This historical data can be accessed from a variety of sources. The device processor can be configured to download historical data from the cloud or directly from separate devices for accessing broader population data. This data can also be downloaded and stored in memory, such that the processor can utilize this historical data for statistical analysis.

[00157] In one embodiment, the method may further comprise estimating a parameter of the subject based on the performance metric data measured in the neurodevelopmental assessment task, and calculating a difference between the estimated parameter and a predetermined parameter stored in a memory.

[00158] For example, the processor of a device may determine that a subject (in this case Max) performs at the level of an 8-year old. However, the predetermined parameter (input by Max’s parent) indicates that he is actually 11 years old. Thus, the processor can determine how much deviation from the baseline parameter Max’s development has. This deviation can be granular, and depend on one of the developmental domains.

[00159] In a further embodiment, the estimated parameter may be a demographic or physiological parameter. Such a physiological parameter includes, but is not limited to, age of the patient. It may further include any of the listed contextual data referred to above.

[00160] In this way, the method is suitable for making predictions about cognitive performance based upon the performance metric data of the neurodevelopmental assessment task. In a further embodiment, the method can further make predictions about cognitive performance based upon historical data of a single subject, stored in memory. In another embodiment, the predictions about cognitive performance are based on comparison to data stored in memory which corresponds to performance metrics of a wider population, e.g. of a group of subjects who have performed the same neurodevelopmental assessment tasks. That is, estimation of the parameter may be based upon, but is not limited to, a trajectory of cognitive development, performance in one or more metrics over time, or a comparison of the patient’s performance against another subject (by determining if and whether there is another similar subject).

[00161] The predetermined parameter may be input by the subject, or by a third party associated with the subject, on the device. The comparison of the estimated parameter and the predetermined parameter may be performed by the processor to calculate a difference.

[00162] Determining a difference between an estimated parameter and a predetermined parameter is beneficial because it highlights a developmental delay and can contribute to the creation of a cognitive biomarker indicative of a unifying neurodevelopmental disorder if present. Such an indicator can be fed back into the system to adjust difficulties of the neurodevelopmental assessment task or training task, adjust the amount of training presented to the subject, and/or be used as a predictive factor for trajectories of developmental performance.

[00163] In another embodiment, the method further comprises comparing the calculated difference to a second threshold parameter, and determining whether the calculated difference exceeds the second threshold parameter.

[00164] The method may further comprise instructing the first device to change from the first mode to the second mode by instructing the first device to change modes when the calculated difference exceeds the second threshold parameter.

[00165] For example, the processor may estimate an age of the subject, based upon an individual neurodevelopmental assessment task, a series of neurodevelopmental assessment tasks, or a history of data related to one or more neurodevelopmental assessment tasks. The processor is configured to compare this estimated age against the actual age of the subject. If the difference is above a threshold, which relates to an acceptable deviation from the predetermined parameter, then the processor can switch the neurodevelopmental assessment task to a training task.

[00166] That is, one way of switching between the assessment and training modes is to monitor a single patient in isolation and switch modes only after a trigger is met. Another way of switching involves estimating a parameter of the patient, comparing against a known value in memory, and only switching when the results are an outlier for a patient having said known value associated with them. Either of these methods of triggering switching of the modes may be used alone, or in combination with each other.

[00167] In one embodiment, the method further comprises if the neurodevelopmental assessment task is successfully completed, instructing the device to select a further neurodevelopmental assessment task from a list of further neurodevelopmental assessment tasks.

[00168] It may be understood that a further neurodevelopmental assessment task refers to a new task which is presented to the subject. This further neurodevelopmental assessment task may be substantially similar to the initially presented neurodevelopmental assessment task, except that the degree of difficulty has been adjusted (i.e. made harder or easier). A difficulty measurement of a known neurodevelopmental assessment test would be understood by the person skilled in the art, and corresponds to a typical performance rate of a subject having a known cognitive ability. This performance rate may be determined by a rate of success.

[00169] With regard to FIG. 10, the levels of difficulty of a task may be iteratively adjusted (for example, the parameters stored in memory location 501-la may be adjusted as the user of the device continues to use the program), or the difficulty levels may be stored separately in the memory 103, 120. In the latter, the processor 102 is configured to first select a task, then check the difficulty levels stored in memory (based upon previous performance), and then provide an appropriate assessment task on the user interface 101. [00170] In other words, one of the consequences of a successful, or unsuccessful, result in the neurodevelopmental assessment tasks may be to adjust the difficulty or progression of the neurodevelopmental assessment tasks presented to the subject.

[00171] Alternatively, the further neurodevelopmental assessment task may be an entirely different neurodevelopmental assessment test from the initially presented test, suited to test the cognitive ability of the patient in a different area (e.g. a visual test was first presented, and the subsequent test presented will be a motor skill test). Doing so may be beneficial to ensure that the result of the neurodevelopmental assessment is not biased based on the previously presented task to avoid test-retest effects.

[00172] In one embodiment, the further neurodevelopmental assessment task is selected based upon one of the following parameters: historical performance metric data associated with previous assessment tasks in the same category; the estimated parameter, the predetermined parameter, and the difference between the estimated demographic or physiological parameter and the predetermined demographic or physiological parameter.

[00173] In other words, the processor may analyze one or more of the above parameters to determine the difficulty level of the further neurodevelopmental assessment task, the type of neurodevelopmental assessment task, and requirements for a success in the neurodevelopmental assessment task, among other parameters. [00174] In one embodiment, the method may comprise, after a predetermined amount of time, generating the further neurodevelopmental assessment task on the user interface of the device and receiving performance metric data associated with the further neurodevelopmental assessment task. In much the same way as the first neurodevelopmental assessment tasks, the further task can be generated and monitored for performance metric data associated with the task.

[00175] In one embodiment, the further neurodevelopmental assessment task is a task which is associated with the initial neurodevelopmental assessment task but has a different level of difficulty than the initial neurodevelopmental assessment task. That is, the neurodevelopmental assessment task may be either an identical task to the earlier task, or it may be a task which is marked in the same category as the earlier task, but having a different level of difficulty.

[00176] A level of difficulty of a neurodevelopmental assessment task can be determined by comparing an assessment of user success rates against a typical, normal level for a user of a certain age. The level of difficulty may be adjusted in certain tasks, and for these cases, the processor is configured to use a look-up table to determine the correct difficulty parameters associated with the correct level of difficulty for a specific age. Alternatively, the processor may also select a different task, which has a more appropriate level of difficulty (e.g. in cases where an adjustment of the difficulty level in a specific task is not possible).

[00177] For example, Max (an 8-year old) may be performing at a performance level more closely aligned with that of a 6-year old with regard to his fine motor skills. By assessing this difference in age for a neurodevelopmental domain, the processor can adjust the difficulty to provide a more appropriate assessment and training routine.

[00178] In one embodiment, the method further comprises instructing, when the device is in the second mode, the first device to change from the second mode to the first mode only after an authorized override is received, and generating the further neurodevelopmental assessment task on the user interface of the device. [00179] That is, a processor in the device may be configured to prevent the device from presenting a further neurodevelopmental assessment task in the first, assessment mode once the processor has changed the device mode to the second, training mode until the processor receives an instruction to allow the further neurodevelopmental assessment task to be presented to a user and/or for the device to switch to the first, assessment mode. It may be said that switching modes only after an authorized override refers to re-enabling the assessment mode in the device, for either all neurodevelopmental assessment tasks or a subset of neurodevelopmental assessment tasks.

[00180] In one example, the processor may request an acknowledgement from the subject or a third party to provide the authorized override. This may be beneficial to improve patient compliance. In this way, a parent or a medical professional can monitor the performance in a training task and only allow the device to be switched back to an assessment mode after accepting the acknowledgement or providing an input.

[00181] The processor may be configured to record all instances where the mode on the device has been switched from assessment to training mode and vice-versa. The processor can either store these instances in a memory, or each switch can trigger a notification to a third party. The notification can include, but is not limited to, a smartphone notification, an email, or a notification within a piece of software (either running locally on the device or remotely, such as a web based application).

[00182] In one example, the authorized override may be provided by the device itself. That is, the processor may be configured to provide an authorized override in instances where performance in the associated training task exceeds a certain level.

[00183] In these cases, the method may comprise receiving performance metric data associated with the training task, comparing a parameter of the performance metric data against a known threshold, and providing an authorized override if the performance metric parameter exceeds the threshold.

[00184] The performance metric data measured from the training task may be the same as the neurodevelopmental assessment task. Similarly, the same comparisons against thresholds with or without the use of historical data and population data can be utilized. The threshold can further be adjusted to ensure that the performance metric exceeds the necessary value by a certain margin. In one example, a plurality of successful training tasks (i.e. those which meet the criteria for a metric exceeding the threshold) must be received sequentially in order to generate the authorized override. This helps to ensure that the result of the training task is accurate and precise, to ensure that the patient is indeed at the appropriate level before returning the device to an assessment mode.

[00185] For example, a processor may be configured to determine when one or more of the measured parameters of the performance metric data meets or exceeds an expected value of success rate for a specific task associated with the age group of the patient.

[00186] In one alternative, the processor may generate a predetermined number of training tasks in a set before providing an authorized override. In a similar way, the processor may record the time at which the device was switched from the assessment mode to the training mode, and only permit the device to operate in the training for a certain period of time.

[00187] One benefit of the above method is that improved training outcomes can be realized, by improving access to large amounts of data in real-time and ensuring patient compliance with training routines. What’s more, by providing specific training tasks only when necessary (and optionally providing customized training tasks based on the patient), a more individualised approach to cognitive development can be realized, and specific interventions can be pinpointed based on patient outcomes and specific neurodevelopmental delays or cognitive deficits.

[00188] Another benefit of the method of the present invention is that it provides improved isolation of factors which are indicative of cognitive performance for a specific person, as well as providing accurate assessments of a patient to generate an improved predictive trajectory of cognitive development.

[00189] In one embodiment, after the device (or a configuration of the device having a subset of the neurodevelopmental assessment tasks) is switched back from the training mode to the assessment mode, the subsequent neurodevelopmental assessment task generated may be identical to the task which triggered the switch to the training mode. Thus, the processor of the device may be configured to store in memory the last task which was presented before the device was switched to the assessment mode.

[00190] Alternatively, the subsequently generated neurodevelopmental assessment task may be selected from a further list of neurodevelopmental assessment tasks. It may or may not have a different level of difficulty, which may be selected from the performance by the patient in the training tasks, or selected from an estimated parameter of the patient from the earlier neurodevelopmental assessment task.

[00191] In one embodiment, there is provided a device, the device comprising a processor and a user interface. The processor is configured to generate, when the device is in a first mode, a neurodevelopmental assessment task associated with a neurodevelopmental domain on a user interface of the device and receiving performance metric data associated with the neurodevelopmental assessment task; generate, when the device is in a second mode, a training task associated with the neurodevelopmental domain on the user interface; and instructing the first device to change from the first mode to the second mode, based on the performance metric data received whilst the device is in the first mode. The processor of the device is further configured to perform any of the method steps described above.

[00192] In another embodiment, the device may comprise a memory in communication with the processor, wherein the memory is configured to store the first and second threshold parameters, and the predetermined demographic or physiological parameter. The memory can include read-only memory (ROM), flash memory, one or more varieties of random access memory (RAM) (e.g. static RAM (SRAM), dynamic RAM (DRAM), or synchronous DRAM (SDRAM)), and/or a combination of memory technologies.

[00193] One further benefit of providing such a method and system is that access of assessment and training of a patient in remote areas can be improved. That is, such a system is suitable for delivering cognitive interventions without the presence of a medical professional, making such treatments better available to remote patients.

[00194] What’s more, by processing the performance metric data in a processor (either locally on a device, or in a server), there is an improvement in increased security and time for a cognitive treatment. That is, there is no long delay in waiting for health data to be analysed before appropriate treatment plans can be suggested. In other words, the processor is configured to automatically select, adjust, and generate treatment plans based on performance of a subject without necessitating a delay, which arises when a medical professional needs to personally review the data.

[00195] Furthermore, the security of a patient’s data is improved in such a method, because there is no need for health data to be shared. In such a method, the performance metric data, any contextual data, and data corresponding to the times at which the device was switched from an assessment mode to a training mode can all be encrypted. Such data can be associated with a single user account, and access to the data may require a password, or other cryptography key known in the art.

[00196] In a second aspect, embodiments disclosed herein are directed generally to methods and systems for identifying and/or measuring a cognitive biomarker based upon statistical analysis performed on performance metric data in combination with contextual data.

[00197] Reference will now be made in detail to embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. It will be paid attention that detailed description of known art will be omitted if it is determined that the arts can mislead the embodiments of the invention.

[00198] A digital cognitive biomarker can be used as an indicator of mild cognitive impairments that may herald more severe impairments (such as dementia or Alzheimer’s disease). Such digital cognitive biomarkers may also be referred to more specifically as digital cognitive biomarkers of a cognitive or neurological deficit.

[00199] A cognitive biomarker may be used to indicate or predict a neurodevelopmental disorder or cognitive impairment. It is also considered that the cognitive biomarker is identified for detecting a transient deficit, which may be indicative of mental fatigue or a temporary worsening in performance. [00200] Typically, such cognitive biomarkers are tested for by administering cognitive performance tasks to a subject, and monitoring the subject’s performance over time. Exemplary cognitive biomarkers may include biomarkers related to attention, memory, reasoning, comprehension, decision-making, problem-solving, language, and computation/processing speed.

[00201] A combination of these cognitive biomarkers to form cognitive signatures is useful in the early detection of neurodevelopmental disorders.

[00202] The term “cognitive biomarker” or “digital cognitive biomarker” as used herein, is intended to refer to any parameter which may result from non-invasive behavioral performance on objective cognitive tasks as an indicator or predictor of disease status.

[00203] It will be understood that where the specification refers to assessing and treating neurodevelopmental disorders by assessing neurodevelopmental domains in a pediatric population, similar approaches for the adult population are also considered. In other words, assessing/treating cognitive impairments by assessing cognitive domains is clearly envisaged by the specification.

[00204] Cognitive biomarkers for transient deficits may be useful to assess cognitive performance in relation to time-limited events/tests, such as admission tests e.g. to university, military, police, driving skills, surgical skills or any performance metric which may be affected by external/environmental factors (that can be captured through contextual data)

[00205] Cognitive biomarkers may include, but are not limited to, performance metric data obtained from a neurodevelopmental assessment task presented to a subject. Such a task may be suited for detecting performance in one or more, including but not limited to, of the following areas of cognitive performance: visual-spatial ability, verbal comprehension, processing speed, working memory, and fluid reasoning. The neurodevelopmental assessment task may also comprise a motor assessment component. [00206] In the pediatric population, the neurodevelopmental assessment task allows the system to detect differences in performance within the four neuro-developmental domains. These aggregated performances (either in terms of absolute number or over a defined period of time) may then be translated into a biomarker for delay in motor development, for example. This can then be tracked longitudinally over time as a disease process evolves or a new medicine is introduced. Biomarkers can be obtained for each of the domains (i.e. motor, language, cognitive, and social/personal).

[00207] Bringing together data from patient performance at tasks from all the neurodevelopmental domains may subsequently allow the development of biomarkers that indicate a developmental disability or a specific neurodevelopmental disorder e.g. global developmental delay, attention-deficit/hyperactivity disorder.

[00208] In other words, the biomarker can be narrow and indicative of performance in one of the four neurodevelopmental domains. Alternatively, the biomarker can be more broad (taking into account performance in multiple neurodevelopmental domains) and indicative of a specific neurodevelopmental disorder.

[00209] These neurodevelopmental assessment tasks may be presented to the subject as individual tasks, or may be presented sequentially in a fixed order or a random order. Where the application refers to presentation of tasks in a sequential manner, it may refer to presenting one task after another, in a chronological or sequential fashion.

[00210] Hereinafter, it may be understood that a neurodevelopmental assessment task refers to a task designed to assess proficiency in one or more of the following categories: motor (gross/fine), language, cognitive, and/or social/personal.

[00211] Exemplary tasks may include, but are not limited to, an Eriksen Flanker Task, a Visual Puzzle task, various visual search tasks, digit symbol coding task, symbol search task, digit span task, picture span task, letter-number sequencing task, phonological awareness task, oral discourse comprehension task, numeracy tasks like the “more or less” task, matrix reasoning task, figure weights task, cancellation tests, picture concepts task, vocabulary task, reaction time task, spatial span task, paired associates learning task, delayed matching to sample task, and emotion recognition task. [00212] The present invention seeks to assess the developmental status of the pediatric population on a hitherto unprecedented scale, through the aggregation and statistical analysis of contextual data in combination with data received from a neurodevelopmental assessment task. As a result of this additional contextual data, the present invention thereby improves the identification of neurodevelopmental disorders. Similarly, when applied to the aging adult population, the invention will also yield greater identification of cognitive impairment.

[00213] It may be said that contextual data refers to any data received in relation to the subject undergoing a neurodevelopmental or cognitive assessment. It may be ecological or environmental data, health data, or physiological data. Such data may include, but is not limited to, parameters, such as the age, sex, household income level, and geographical location of the subject. Compared with other parameters described elsewhere herein, these parameters may vary slowly (or not at all) over time, and are therefore referred to as ‘fixed’ parameters for the purposes of this specification. This is in contrast to the data which may vary day-to-day. The contextual data may also include more variable health data of the subject, including but not limited to the amount of sleep, caffeine intake, sugar intake, diet of the subject, exercise etc.

[00214] The contextual data may also include physiological data corresponding to the subject, e.g. a measured heart rate, blood pressure, respiratory rate, blood oxygen content, blood glucose levels, and so on, which may be received from an external device such as a wearable device.

[00215] Additionally, or alternatively, contextual data may refer to data received in relation to a third party, who has a connection to the subject (otherwise known as ancillary data). For example, if the third party is a parent or guardian of the subject, wherein the subject is undergoing a neurodevelopmental or cognitive assessment, the third party may provide contextual data regarding their own health or physical parameter. In this way, the third party can self-report and provide contextual data, which may be relevant to the performance of the subject in the neurodevelopmental assessment. Such data may include the same health data or fixed parameter data as above for the subject. [00216] The self-reported contextual data by a third party can also comprise further ancillary data. Ancillary data may refer to any data which is not directly exhibited by the subject, such as data relating to the environment, or data relating to a third party, such as a caretaker. This further data may include the emotion, stress level, happiness level of the third party. Such data may be relevant to identify whether, for example, high stress levels of a third party has any effect on the subject of the neurodevelopmental assessment. Such an understanding is important for allowing a more comprehensive treatment plan to be assigned to the subject, should the cognitive biomarker indicate a developmental disability or neurological deficit. In other words, the ancillary data may comprise an indication of the energy levels and/or the emotional level of the third party.

[00217] In other words, the third party can either self-report to provide contextual data about the third party, and/or they can report contextual data about the subject.

[00218] In this way, the present invention is capable of identifying and/or measuring new cognitive biomarkers not currently known in the art. Such novel, digital cognitive biomarkers may permit the identification of neurodevelopmental disorders in a low-cost, scalable and ecological manner as compared with traditional formal neuropsychological testing. An improvement on existing digital cognitive biomarkers may include greater sensitivity and/or specificity with regard to identification of neurodevelopmental disorders or cognitive impairment. The incorporation of contextual data can potentially provide insight into the etiology of the cognitive deficit identified, and shift attention beyond consideration of medical conditions alone.

[00219] Particularly for a child subject undergoing a neurodevelopmental assessment, receiving contextual data from a parent or guardian is beneficial to highlight relationships between contextual data of the parent or guardian and performance of the child. This allows for the creation of a more accurate and reliable biomarker, which can be used for improved diagnoses and improved decisions regarding specific treatments which may be appropriate for the child.

[00220] In one embodiment of the present invention, the method for identifying and/or measuring a cognitive biomarker comprises obtaining performance metric data from a neurodevelopmental assessment task performed by a subject on a first user interface and obtaining contextual data from an input provided by a third party on a second user interface.

[00221] The performance metric data may be a digital or analog result of a neurodevelopmental assessment task presented on a first user interface. It may also be said that performance metric data refers to any data measured during a neurodevelopmental assessment task, including whether or not the task was successfully passed, the time taken to accomplish the task, the number of attempts required to accomplish the task, whether or not any hints were required during the task, the level of difficulty of the task, the number of questions asked during the task, the number of correct/incorrect responses input during the task, or any parameter specifically associated with the neurodevelopmental assessment task.

[00222] This neurodevelopmental assessment task (which may be one, or include a plurality, of the many neurodevelopmental assessment tasks described above) is presented on the first user interface. In one embodiment, a first device comprises the first user interface. It may be understood that the first device is a videogame console, a smartphone, a computer, a tablet, or generally any device that has its own device processor and a user interface suitable for providing a neurodevelopmental assessment task to the subject.

[00223] As shown in FIG. 11, a first device 100 according to an exemplary embodiment of the invention includes a first user interface 101 in communication with a first device processor 102. The first device processor 102 may be configured to communicate to an external memory 120, as would be understood by a skilled person. A separate communications module (not shown) may be implemented in the first device to communicate data from the first device processor to the external memory in a specific data format. The communications link maybe either wireless (Wi-fi, Ethernet, Zigbee, Bluetooth, etc.), or it may be a wired connection.

[00224] As shown in FIG. 12, a system according to an exemplary embodiment of the invention may include a first device 100 (substantially the same as the device shown in FIG. 11) and a second device. The second device 110 includes a second user interface 111 in communication with a second device processor 112. The second device processor 112 may also be configured to communicate to the external memory 120, as would be understood by a skilled person. A separate communications module (not shown) may be implemented in the second device to communicate data from the second device processor to the external memory in a specific data format. The communications link may be either wireless (Wi-fi, Ethernet, Zigbee, Bluetooth, etc.), or it may be a wired connection.

[00225] The contextual data may be a digital or analog metric related to a specific parameter of the subject of the neurodevelopmental assessment task. The contextual data may be input by a third party on a second user interface. It may be understood that this “third party” refers to any person who is not the subject, such as a caretaker, medical professional, or parent. Additionally, or alternatively, the subject themselves may be capable of inputting self-reported contextual data. In other words, the subject can provide at least part of his or her own contextual data (such as inputting their own age, gender, etc.). A third party may also provide contextual data, such as in those cases where a subject (e.g. a child) is not capable of self-reporting. It is also considered that the processor may be configured to receive contextual data which is collected in the absence of any user input or feedback (e.g. monitoring of heartrate through a device or smartwatch).

[00226] In one embodiment, the first device also comprises the second user interface. For example, a single computer (i.e. the first device) can both provide the neurodevelopmental assessment task and also obtain the contextual data of the subject.

[00227] In another embodiment, a second device may comprise the second user interface. The second device may be a smartphone, a computer, a tablet, or generally any device that has its own device processor and a user interface suitable for requesting and/or inputting contextual data.

[00228] The first device and, if present, the second device each comprise a device processor, a clock in communication with the device processor, and a memory in communication with the device processor.

[00229] FIGS. 14 and 15 show two different user interfaces of games which may be presented to a user for gathering performance metric data. [00230] As shown in FIG 14., a Flanker task may be presented to a user on the first user interface 1000 as part of a neurodevelopmental assessment. It may be understood that user interface 1000 may be identical to user interface 101 or 110. Such a task is beneficial for testing the ability for a user to inhibit irrelevant competing responses to a nonverbal stimulus. As a result, this task provides performance metric data which may be useful for assessing users who demonstrate a certain level of attention deficit (one marker for ADHD, attention deficit and hyperactivity disorder).

[00231] During gameplay of the Flanker task, a user must ignore the distractor flanks 1001 and select only the item in the middle of the row 1002, the potential selections shown in boxes 1003a and 1003b. It is understood that whilst the objects in FIG. 14 are shown in a row, the application is not limited to a horizontal row of objects, as such. Moreover, the application also considers that the item in the middle may differ from the surrounding objects in color, orientation, shape, etc., so long as the object is recognizably different than the surround objects. In one embodiment, additional objects, either static or moving, may be presented on the screen to increase the difficulty of the task.

[00232] As shown in FIG 15., a version of a 3, 6, and 9 box task may be presented to the user on the first user interface 2000 as part of another neurodevelopmental assessment. It may be understood that user interface 2000 may be identical to user interface 101 or 110. Such a task is beneficial for determining whether children with autism spectrum disorders show deficits in spatial working memory.

[00233] During gameplay of the 3, 6, and 9 box task version, participants are asked to refuel planes 2001a, 2001b, 2001c flying across the screen by tapping them. Once the user has refueled a plane, e.g. 2001a, they must ensure that they do not revisit the same plane and tap it again. In other words, the user must hold in memory the location of the previous planes that have been refueled in order to complete each trial of the game successfully. It is understood that this embodiment is not exclusively limited to a certain shape of object, and the orientation of the objects is inconsequential to the operation of the game. Each plane may have a corresponding fuel gauge bar which shows how much the plane has been refueled whilst a user is interacting with the plane, in this case 2001a. In one embodiment, the number of taps required to refuel a plane may vary between the different planes on the screen, and any number of planes may appear on the screen, in order to vary the difficulty of the task. Alternatively, a single tap on a plane may fully refuel the plane.

[00234] As would be understood by the skilled person, features on the graphical user interfaces 1000 and 2000, such as the home button and the music on/off toggle, as well as the background art are not linked to the functionality of the neurodevelopmental assessment tasks, and may be variable based on the software parameters.

[00235] In one embodiment, contextual data may be requested or input once and then stored in memory of the relevant device. For example, the age of the subject may be input only once and stored in memory. Alternatively, the contextual data may be obtained at intermittent or regular intervals.

[00236] In one embodiment, the second user interface may prompt the third party to input the contextual data. Such prompting may be the result of turning on the device which comprises the second user interface (e.g. prompts the third party every time the second user interface is displayed), or may be carried out at regular intervals (e.g. every day at lpm), or in response to a trigger from the performance metric data (e.g. an unexpected performance on a neurodevelopmental assessment in relation to an expected result may result in a request for contextual data).

[00237] As shown in FIG. 16, a parent of a user (in this case, Max) may be presented with user interface 3000, either on the first device or the second device. In other words, Max’s parent may be considered the third party in context of this application. In one embodiment, this user interface is presented to the parent or guardian of the user through a separate application, although this is not limited to such an arrangement, as such.

Through this user interface, the parent or guardian can enter their observations about their child, i.e. the user of the first device. As shown in the figure, they can enter data on how Max slept, his mood that day, his caffeine and sugar intake, and the intensity of his physical exercise.

[00238] This contextual data is shown as sliding bars 3001-3004, each associated with a specific user parameter. Once the data has been input, the parent can confirm the data with a confirmation button 3005. Whilst sliding bars are shown on GUI 300, additionally or alternatively, specific questions (multiple choice, true/false, ranking from 1- 5, etc.) can be used to gather contextual data from Max’s parent. This data will be time- stamped based on when the parent inputs the data, and/or when the corresponding assessment task by Max was completed.

[00239] This information is considered against the metrics captured by the assessment to determine if, statistically, these readings may have had an impact on his performance compared to past performance and similar populations. For example, the system may be configured to determine whether Max’s lack of sleep has a consistently negative impact on his performance of a Flanker task, while caffeine and sugar intake may have a corresponding net positive effect.

[00240] Such data is beneficial to drive improvements in diagnosis and therapy selection, along with allowing for improved feedback into the assessment tasks. In one embodiment, the assessment task difficulty can be adjusted preemptively based upon known patterns of behavior. For example, if Max regularly exercises on Saturdays, which improves his cognitive performance, the system may learn this pattern over time, and present different tasks to Max accordingly on Saturdays.

[00241] In one embodiment, the method may further comprise prompting the third party, via the first or the second device, to input the contextual data at predetermined intervals.

[00242] Where multiple devices are present in a system, it is also considered that contextual data can be obtained from the both the first device and the second device at the processor, either simultaneously or at different times. In other words, a method for identifying and/or measuring a cognitive biomarker may comprise obtaining contextual data from an input provided on a second user interface on a second device and may further comprise obtaining contextual data from an input provided on a third user interface on the first device.

[00243] It is further understood that any number of auxiliary devices can be used to provide contextual data. Thus, the present invention is not limited to a first and second user interface or to first and second devices. Any number of biological sensors, devices, or medical instruments may also provide contextual data, which can be used to improve the identification and/or measurement of the cognitive biomarker.

[00244] The method further comprises receiving, at a processor, the performance metric data and the contextual data and performing a statistical analysis on the performance metric data in combination with the contextual data. It may be understood that the processor and the above device processors can each include any type of microprocessor or central processing unit (CPU), including programmable general-purpose or special-purpose microprocessors and/or any one of a variety of proprietary or commercially available single or multi-processor systems. Each processor may also be in communication with one or more memories configured to provide temporary storage for code to be executed by the processor or for data acquired from one or more users, storage devices, and/or databases. The memory can include read-only memory (ROM), flash memory, one or more varieties of random access memory (RAM) (e.g. static RAM (SRAM), dynamic RAM (DRAM), or synchronous DRAM (SDRAM)), and/or a combination of memory technologies.

[00245] It may be understood that the processor configured to receive the performance metric data and contextual data may be one of the device processors described above. That is, the method may comprise receiving at one of the device processors the performance metric data and the contextual data and performing the statistical analysis locally on one of the devices.

[00246] Alternatively, the processor may be an external processor in communication with the device processors in the first and second device processors. In other words, the processor may be a processor in an external server. Such a configuration may be beneficial to avoid the need for local processing on the first or second devices, which may be a computationally demanding process. It may be said that receiving data at the processor refers to obtaining data from a plurality of different sources directly, or indirectly, i.e. from a memory in communication with the processor which stores previously received data. [00247] It may be understood that a statistical analysis refers to the collection, organization, analysis, interpretation of data. Specifically, the performance metric data and the contextual data are the inputs to the analysis, which is performed on the processor, and the output of the analysis is an identification and/or measurement of the cognitive biomarker. As would be understood by the skilled person, a subset of the performance metric data may be compared to a corresponding subset of the contextual data. For example, the performance metric data may comprise data points, each with an associated time stamp. The contextual data may also have corresponding time stamps. In this way, the processor may perform an analysis on a certain time range of the performance metric data, or it may perform an analysis on the entirety of the available data in the performance metric data set.

[00248] The method also comprises identifying and/or measuring the cognitive biomarker based on the statistical analysis. As described above, the resulting, novel cognitive biomarker is improved over a generic cognitive biomarker which does not consider any contextual data.

[00249] Such a method as described above is presented in a flow-chart format in FIG. 13.

[00250] In one embodiment, performing the statistical analysis of the performance metric data based upon the contextual data comprises associating each data point of the contextual data with at least one data point of the performance metric data; comparing each data point of the contextual data to a threshold; and if a contextual data point exceeds the threshold, excluding the or each data point of the performance metric data associated with the contextual data point exceeding the threshold before performing the statistical analysis on the performance metric data.

[00251] It may be said that associating each data point of the contextual data with at least one data point of the performance metric data comprises matching each data point with at least one specific data point of the performance metric data. In one example, a data point of the contextual data may be associated with the subsequent data point of the performance metric data that is received at the processor. [00252] Alternatively, a data point of the contextual data may be associated with a data point of the performance metric data based on any one of the parameters of the data point, including but not limited to a time stamp. In other words, the data point of the contextual data may be matched to one (or more) data point of the performance metric data based on a time-stamp (e.g. all performance data received within an hour, a day, or a week of the contextual data will be associated). If multiple data points of contextual data are available, a data point of the performance metric data may be associated with the closest contextual data point (e.g. the point having the shortest time between the data received).

[00253] With regard to FIG 14., exemplary metrics which may be captured during the Flanker task include, but are not limited to, the following: targetObject (the sprite name/color/description of the target object in the middle), distractorObject (sprite/color/description of the distractor objects), numDistractors (the number of distractors which are “flanks”), targetDistractorCongruence (a measure of how similar the distractors are to the target), trialDifficulty (a level of modulated difficulty), and typeDifference (a description of the differences between target and flanks). This data may be supplemented with the trialNumber (the number of times the task has been attempted) and whether or not the trial was successful - trialSuccessful.

[00254] With regard to FIG. 15, exemplary metrics which may be captured during the 3, 6, and 9 box task version include, but are not limited to, the following: NumberOfTimesIncorrectlySearched (the number of incorrect taps on one plane), IncorrectLocationsSearched (an array of all locations tapped more than once). The duration of the trial (trialDuration), difficulty of the trial (trialDifficulty), the number of times the task has been attempted (trialNumber) and whether or not the trial was successful (trialSuccessful) are also monitored in this embodiment.

[00255] The above exemplary metrics may all be considered performance metric data, either taken alone or in combination with other data. An evaluation of these metrics against a baseline will generate a profile of how well a user is able to inhibit irrelevant, competing stimuli. [00256] Data received at the processor may be stored in a single memory location, or in two or more separate memory locations. In other words, the memory can store all data (including both performance metric data and contextual data) in sequential arrangement in a single location. Alternatively, the performance metric data can be stored in a first location in the memory, and the contextual data can be stored in a second location in the memory. Further techniques, such as load balancing and varying server structures can also be utilized such that data is stored and manipulated across different places on the server or servers.

[00257] In other words, the processor in this embodiment is configured to exclude any performance metric data associated with any contextual data which the processor determines to be unusual. For example, if the third party reports an unusual sleep pattern for the subject, the processor may disregard any performance metric data associated with the unusual sleep pattern. Doing so results in an improved, and more robust, diagnosis, because such a method can disregard an erroneous flag of a deficit. This results in an improved digital cognitive biomarker, in cases where the performance in the neurodevelopmental assessment tasks may otherwise be attributable to external factors.

[00258] Hereinafter, it may be understood that a parameter deemed to be an outlier refers to any parameter which exceeds a threshold stored in a memory. That is, the processor is configured to compare at least one data point of the contextual data against a threshold to determine whether said data point is an outlier. For example, for high measured levels of stress, a single data point which exceeds a known predetermined level may be sufficient to disregard all data within a certain number of hours from the reading, or disregard the subsequent performance metric data point before analyzing the performance metric data.

[00259] For certain parameters, such as sleep, it may be more beneficial to compare a plurality of data points of the contextual data. In this case, the threshold may be the number of consecutive days that the subject did not sleep six or more hours in a night. For example, if three or more nights of low sleep activity are detected by the processor, the processor can be configured to flag this information to either the subject or the third party, and then disregard performance metric data which can be associated with these contextual data points.

[00260] The threshold may be stored in a memory connected to the processor, and it may be a predetermined threshold. It may also be an adjustable threshold, which can be adjusted by a third party who has access to control the parameters used by the processor.

[00261] In this way, a third party, such as a parent/guardian of a user of the device, or a medical professional can adaptively control when/if contextual data will be deemed relevant for analysis. This control may be on a separate device, such that a medical professional, for example, can remotely control facets of the analysis.

[00262] In one embodiment, each data point of the contextual data comprises an input parameter associated with the input provided by the third party and a first time stamp, the first time stamp corresponding to a time at which the input was provided by the third party; and each data point of the performance metric data comprises a performance metric parameter and a second time stamp, the second time stamp corresponding to a time at which the neurodevelopmental assessment task was performed.

[00263] It may be said that the first and second time stamps are received from the clock in the first or second device, from where the respective contextual data and/or the performance metric data was obtained. Alternatively, the first and second time stamps may represent the time at which the performance metric and/or contextual data are received by the processor. In other words, the processor may be in communication with a clock, which provides the processor with a timestamp for use in processing and analyzing the data. This may be useful where data is sent automatically from data sources without clocks, and is received at the processor without an appropriate time stamp.

[00264] It may be understood that the contextual data and the performance metric data received by the processor may have a fixed data format, such that the data can be appropriately processed and stored in memory. Each data point may include a flag indicating whether the data is contextual data or performance metric data, a packet of data including any number of parameters, and a time stamp. [00265] Performing the statistical analysis of the performance metric data based upon the contextual data may comprise: sorting the performance metric data and the contextual data into a chronological order based upon the first and second time stamps; associating each data point of the contextual data with at least one data point of the performance metric data that is within a predetermined time of the data point of the contextual data.

[00266] Where data is received at the processor from a plurality of different devices, it is an aspect of the present invention to arrange each set of contextual data (i.e. each set of contextual data received from a single device) and the set of performance metric data in a chronological order to facilitate statistical analysis. As described above, the statistical analysis can be performed by matching a contextual data point to the closest, or to the subsequent, performance metric data point in time, for example by reference to a timestamp associated with each of the data.

[00267] In one embodiment, the method further comprises selecting a further neurodevelopmental assessment task from a list of neurodevelopmental assessment tasks in response to an output of the statistical analysis, and providing the further neurodevelopmental assessment task on the first user interface.

[00268] It may be understood that a further neurodevelopmental assessment task refers to a new task which is presented to the subject. This further neurodevelopmental assessment task may be substantially similar to the initially presented neurodevelopmental assessment task, except that the degree of difficulty has been adjusted (i.e. made harder or easier). A difficulty measurement of a known neurodevelopmental assessment test corresponds to the average success rate of a subject having a known cognitive ability. [00269] With regard to the Flanker test shown in FIG. 14, different assessments tests may be presented to adjust the difficulty of the task. In this way, difficulty measurement are known and could be implemented in different ways depending on particular tests, including adjusting the orientation, color, shape, location, etc. of the objects on the screen. Further, determining the difficulty level of a task may be determined from extracting data on previous games, and adjusted based upon the statistical analysis performed on this data. [00270] In other words, one of the consequences of a successful, or unsuccessful, result in the neurodevelopmental assessment tasks may be to adjust the difficulty or progression of the neurodevelopmental assessment tasks presented to the subject.

[00271] Alternatively, the further neurodevelopmental assessment task may be an entirely different neurodevelopmental assessment test from the initially presented test, suited to test the cognitive ability of the patient in a different area (e.g. a visual test was first presented, and the subsequent test presented will be a motor skill test). Doing so may be beneficial to ensure that the performance metric data from the subsequent neurodevelopmental assessment test is not biased by a previous performance.

[00272] Multiple further neurodevelopmental assessment tasks are also considered by the disclosure. That is, the method may comprise generating a neurodevelopmental assessment task (also known herein as a first neurodevelopmental assessment task), performing statistical analysis on performance metric data from the neurodevelopmental assessment task based upon, and together with reported contextual data, and then using the statistical analysis to inform the decision about which subsequent neurodevelopmental assessment task (also known as a second neurodevelopmental assessment task) to present. This second neurodevelopmental assessment task may be presented to the same subject on the first user interface. Analysis of the performance metric data of the second neurodevelopmental assessment task (along with corresponding contextual data) may be used to inform a decision about which task to present as a third neurodevelopmental assessment task, and so on.

[00273] In at least one embodiment, the neurodevelopmental assessment task and the further neurodevelopmental assessment task are tasks which are each designed to assess proficiency in one or more of the following categories: motor, cognitive, language/communication, and social/emotional. These categories may also be referred to as neurodevelopmental domains.

[00274] For each neurodevelopmental domain, there are a series of sub-tasks which may presented to a user. In other words, a sub-task of a neurodevelopmental domain may refer to a specific neurodevelopmental assessment task which falls under the same category of cognitive proficiency. [00275] A list of sub-tasks designed to measure proficiency in language/communication skills includes, but is not limited to: an oral discourse comprehension task, and a phonological awareness task.

[00276] A list of sub-tasks designed to measure proficiency in motor skills includes, but is not limited to: a peg game task, a tracing task, and a colouring task.

[00277] A list of sub-tasks designed to measure proficiency in cognition skills includes, but is not limited to: a 3, 6, and 9 task, a digit and picture span task, a visual puzzle task, a block design task, and a flanker task.

[00278] A list of sub-tasks designed to measure proficiency in social/emotional skills includes, but is not limited to: a first emotion recognition task (facial expression) and a second emotion recognition task (voice-only).

[00279] Additional skills which may be tested include, but are not limited to numeracy. Numeracy skills may be evaluated through a “more or less” style task.

[00280] In one embodiment, the method further comprises receiving, at the processor, a set of historical data, wherein the historical data comprises previously collected performance metric data and previously collected contextual data of the subject. In this way, historical data refers to a plurality of data points of performance metric data, either from a single subject or from a plurality of different subjects performing similar neurodevelopmental assessment tasks.

[00281] For example, the historical data may comprise population data, corresponding to performance metric data of one or more different subjects of the neurodevelopmental assessment task. In other words, the processor can receive data corresponding to not only the individual subject performing the neurodevelopmental assessment task, but also corresponding to a set of data from an entire population or subpopulation which performs corresponding neurodevelopmental assessment tasks. [00282] It is considered that the historical data, particularly the population data, can be imported from an external device. In one example, population data corresponding to all performance metric data available from children of the same age can be imported for comparison. This may include typical test results for a corresponding sub-population, against which the subject’s performance metric data can be compared. [00283] As a result, the processor may generate an assessment of cognitive ability or a cognitive biomarker for a specific individual, and then compare this assessment or cognitive biomarker against an assessment or a cognitive biomarker of a comparable person in the historical data.

[00284] Additionally or alternatively, the historical data may comprise the performance metric data, and optionally contextual data, associated with the subject for a plurality of attempts at the neurodevelopmental assessment task. The plurality of attempts may be a specific number of attempts (e.g. 10 attempts at a task), or it may be the entire available history of attempts stored in a memory. In other words, the history of a single subject’s performance may be used to improve the statistical analysis.

[00285] By knowing the trend of cognitive development based on past performances in the same task over time, any new performance metric data gathered which falls outside the projected trend (e.g. more than 1 standard deviation away from the expected result) may result in such data being disregarded, or a prompt for additional contextual data to be input.

[00286] In this way, even in an isolated environment, the processor is configured to determine trajectories of cognitive performance ability and detect unusual performance in a task, which can either be contextualized or flagged to a third party.

[00287] The immediate notification of such discrepancies is especially beneficial, as it helps improve patient compliance with a diagnosis/treatment plan, as contextual data can be added in real time as the data is collated. Indeed, because of the prospective collection of contextual data (either as a result of a prompt or a regular input), the value of the contextual data can be increased. Such contextual data is particularly accurate, thus allowing for significantly more relevant contextual data than what was previously possible. [00288] In one embodiment, the historical data can further be used to supplement the statistical analysis. That is, the statistical analysis may be performed on the historical data, the performance metric data of an individual neurodevelopmental assessment task, and contextual data (either the entire set, or an associated contextual data point) to provide an improved cognitive biomarker. [00289] Using historical data in the statistical analysis is beneficial because, as described above, it can be used to better improve the real-time assessment and analysis of unusual performance metric data.

[00290] Moreover, in the case where the historical data comprises data related to a greater population, the comparison between the performance in a neurodevelopmental assessment task or the associated biomarker against the population can inform decisions to provide better corrective treatments and/or therapies. Such a comparison provides a high level of granularity not presently achievable in the art. For example, by comparing the subject’s performance against an average of a similar patient, trajectories on performance of a subject and insight into developmental delay predictions can both be improved. Whilst a comparison against an average is described, it can be understood that the comparison may rely on any statistical parameter, including but not limited to a median or a mode of the sample.

[00291] Such comparisons can be guided by any of the contextual data above. In other words, the processor can compare a subject against a second subject in the historical data where the contextual data shows that the second subject has one or more similar data point in the contextual data, including but not limited to age, sex, geographical location, and health data. Further contextual data may include maternal health, birth order, and number of siblings. In other words, the processor can be configured to identify subjects who have a similar diagnosis and/or cognitive influencers.

[00292] Such a technique is beneficial in leveraging the data available for other patients, not only to improve the diagnosis of a new subject who presents similar cognitive performance having similar contextual data, but also to suggest improved, more granular training plans. For example, the processor can compare the performance metric data of all 10 year old male subjects against one another. One result of this comparison is that the processor can more quickly identify whether a patient’s biomarker is indicative of a developmental disability or neurological deficit. For example, if there is a close match to another subject detected, and the close match has been confirmed as having a specific developmental disability or neurological deficit, this is one indicator that a developmental disability or neurological deficit might be more likely. [00293] Another result of this comparison is that predictive analysis can be performed to demonstrate current performance levels of a subject against the population, as well as the rate of cognitive development. In other words, such a comparison can improve the detection of assessing cognitive performance at a more granular level. For example, such an output of the statistical analysis may be that a subject is performing 10% worse on visual tasks than an average subject of the same age and gender. However, it may be the case that for subjects having the same age, gender, and household income level, the patient is actually performing at an average level. Such analysis is beneficial because it avoids incorrect, or otherwise time consuming cognitive diagnoses, when the contextual data may, in fact, be the driving factor for the cognitive performance. It can also help qualify a patient for appropriate care or help engender a change in lifestyle to improve cognitive performance.

[00294] Based upon the historical data, the processor can also be configured to identify another subject in the historical data whose cognitive biomarker and/or contextual data matches most closely to the subject in question. An output of the analysis to determine which subject in the population data is the closest to the subject in question is that either improved prediction of a neurodevelopmental disorder or a cognitive deficit can be achieved or the two subjects can be monitored against each other and tracked. If one subject receives different treatment, or reacts more positively or more negatively to a certain contextual data point, tracking the two subjects may further help improve cognitive therapies and treatments for both subjects.

[00295] Such data may be fed back to the subject, or a third party, to provide suggestions on improving a therapy in real-time, based on the performance of the similar individual.

[00296] Correspondingly, for a subject who has been diagnosed, for example by a medical professional, with a developmental disability or a cognitive or neurological deficit, comparison against baseline performance metric data in the population may be beneficial to monitor treatment plans more efficiently.

[00297] In one embodiment, the method further comprises analyzing the historical data, and determining a developmental predictive trajectory based upon the data. It may be said that a developmental predictive trajectory refers to a prediction regarding the cognitive performance of a subject in any one of the developmental domains (motor (divided into gross motor and fine motor), cognitive, language/communication and social/emotional) in the future, along with the expected rate of increase in cognitive performance.

[00298] Such a predictive trajectory can be beneficial to estimate performance of an individual subject at a certain point of time in the future. It may also be beneficial to guide therapy adjustments for said subject, especially when the predictive trajectory estimates that the resultant performance is below a certain deviation from the average population or a similar subpopulation. In this way, the processor may determine a baseline performance metric data for the entire population or a subpopulation (determined in part based upon having one or more similar contextual data parameters to the subject) in relation to the specific neurodevelopmental assessment task. Should the subject’s predictive trajectory differ from the baseline parameter by more than a certain percentage, the processor can flag this deviation to the subject or a third party.

[00299] In one example, the processor is configured to present subsequent neurodevelopmental assessment tasks based on the deviation of the predictive cognitive trajectory, e.g. to present more training exercises in certain cognitive areas to improve the cognitive development in the subject.

[00300] The statistical analysis described above may comprise one or more of the following: a cross-correlation measurement between one or more parameters of the stored contextual data and one or more of the stored performance metrics or a statistical regression analysis between the input parameter and the performance metric parameter.

[00301] A cross-correlation measurement can be used to determine which of a plurality of contextual data points in the contextual data is most relevant to cognitive performance in an individual patient. Doing so, especially when taken across the entire history of data received in relation to a subject, is especially beneficially for isolating specific target factors which influence cognitive development or impairment. Such a technique allows the processor to identify whether, and which, of the reported factors are most relevant to the specific subject.

[00302] For example, for a child (e.g. Max) performing a neurodevelopmental assessment task, Max’s parent can supply both self-reported data (e.g. about their own emotion level) and data relating to the child (health data, nutrition data, sleep information, etc.). Thus, by analyzing the data over time, it might be determined that Max is most responsive to large variations in sleep. Such a finding helps isolate this parameter, which then allows the processor to normalize the performance metric data based on Max’s sleep. By determining which parameters of the contextual data are relevant, and which are not, the system can identify an improved cognitive biomarker by analyzing the performance metric data without the influence of the highly relevant contextual indicators.

[00303] The cross-correlation measurement provides an output which corresponds to an extent to which one or more parameters of the stored contextual data is correlated to the one or more stored performance metrics

[00304] In an exemplary embodiment, the processor in the present invention is configured to request that certain contextual data be input. The type of contextual data requested may be influenced in part by the output of the cross-correlation measurement(s) performed.

[00305] Should nutrition, for example, be found to be the most relevant factor for predicting a specific subject’s cognitive performance (or the level of improvement of cognitive performance of said subject from a previous measurement), the processor may request data to be input specifically in relation to the nutrition, either at regular intervals or in response to a cognitive performance metric that falls outside an acceptable range. The processor may further request more granular data about nutrition in such a case, including, e.g. number of calories, sugar intake, caffeine intake, etc.

[00306] A statistical regression analysis may further be useful for determining a functional relationship between an input parameter of the contextual data and a performance metric parameter. For example, the processor might compare a slope of the improvement in cognitive performance against input sleep data. This may be useful to determine how important variations in sleep are in detecting changes in the slope of a cognitive development trajectory. For example, such a calculation might help determine that losing a single hour of sleep per night has little to no effect on a specific subject, whilst 3-4 hours of lost sleep has a noticeable effect in cognitive performance.

[00307] By analyzing this data, improved developmental predictive trajectories can be made, based upon the estimated extent of impact that reported contextual data has on the performance metric data, and/or the level of improvement of cognitive ability measured by such a task.

[00308] In all of the above, the performance metric data may comprise a parameter measured in relation to the neurodevelopmental assessment task. This parameter measured corresponds to one or more of the following parameters: time taken to complete the neurodevelopmental assessment task and/or whether or not the assessment task was successfully completed. Any combination of this data can be received by the processor.

[00309] In one embodiment, the performance metric data is received from a neurodevelopmental assessment task presented on a first user interface of a first device. This first device may be an assessment device which comprises a videogame device, or any computer-implemented device configured to operate as a videogame device.

[00310] It may be said that the assessment device may comprise a smart phone, a smart watch, a tablet, a computer, a TV monitor connected to a processor and having an input device, or a separate specially-designed input system.

[00311] By implementing a neurodevelopmental and cognitive assessment system via an application (which can be run on a variety of different devices, some of which are portable), the rates of user attention can be greatly increased, and the exposure of the subject to more routine cognitive assessments can be greatly increased.

[00312] In one embodiment, the performance metric data and contextual data each comprise data associated with a user access account. Such data may be beneficial such as to allow the processor to store the data in a memory, if necessary, associated with the user access account. [00313] In one embodiment, there is also provided a system for improving an assessment of a medical condition, the system comprising: an assessment device comprising a first processor and a first user interface; a secondary device associated with the assessment device, the secondary device comprising a second processor and a second user interface; and an external memory associated with the first processor and the second processor, and configured to store an aggregated set of data received in relation to a user of the assessment device; wherein the first processor is configured to: cause the first user interface to display an assessment task; obtain performance metric data associated with the assessment task; and store the performance metric data in the external memory; and wherein the second processor is configured to: obtain contextual data input on the second user interface, and store the contextual data in the external memory.

[00314] In another embodiment, the secondary device is configured to pair to the first, assessment device over a wireless or wired link. The devices may be paired using Bluetooth, Wi-fi, RF, Zigbee, Ethernet, RFID/NFC, Infrared, BLE, Z-wave, cellular/mobile phone networks, and other internet connections. Alternatively, the two devices may each communicate to an external processor by any of the above communication means, and thus, are only paired to each other via an indirect link.

[00315] As shown in FIG. 17, such a system may comprise first device 300 and second device 310. The first device includes a first processor 308 communicably coupled to a first user interface 309 and a first communications module. The second device has a corresponding second processor 311, second user interface 312, and second communications module 313. Both of the processors are configured to communicate with an external memory. Whilst the second device 310 may be a device configured to prompt a user with questions for receiving contextual data, such as the questions shown in FIG. 16, it may also be a device suitable for merely reporting contextual data (such as a wearable device or a physiological sensor, with reporting capabilities).

[00316] Whilst not shown in FIG. 17, both of the first and second devices may include local memories, and they each may be configured to process the contextual data and the performance metric data received from the first device locally. [00317] As shown in FIG 18., data may be processed at an external server. When a device communicates directly to an external memory 120 in a server 400, a server processor 401 is configured to process the data stored in memory. This data may be processed in any of the ways described above, including, but not limited to, statistical regression analysis between contextual data and performance metric data based on timestamps. The output of this statistical analysis may be fed back into either the first or second devices for adjusting a parameter of one of these devices. Additionally, or alternatively, the output of the statistical regression may be utilized for improving diagnoses or treatment plans for patients, or with aiding in large-population sampling of cognitive behaviors.

[00318] Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. The term “coupled” is defined as “connected” and/or “in communication with,” although not necessarily directly, and not necessarily mechanically. The terms “a” and “an” are defined as one or more unless stated otherwise. The terms “comprise” (and any form of comprise, such as “comprises” and “comprising”), “have” (and any form of have, such as “has” and “having”), “include” (and any form of include, such as “includes” and “including”) and “contain” (and any form of contain, such as “contains” and “containing”) are open-ended linking verbs. As a result, a system, device, or apparatus that “comprises,” “has,” “includes” or “contains” one or more elements possesses those one or more elements but is not limited to possessing only those one or more elements. Similarly, a method or process that “comprises,” “has,” “includes” or “contains” one or more operations possesses those one or more operations but is not limited to possessing only those one or more operations.

Claims

1. A computer-implemented method for evaluating and treating one or more neurodevelopmental delays in a neurodevelopmental domain in a subject, the method comprising: generating, when a device is in a first mode, a neurodevelopmental assessment task associated with the neurodevelopmental domain on a user interface of the device and receiving performance metric data associated with the neurodevelopmental assessment task; generating, when the device is in a second mode, a training task associated with the neurodevelopmental domain on the user interface; and instructing the first device to change from the first mode to the second mode, based on the performance metric data received whilst the device is in the first mode.

2. The method of claim 1, wherein the step of generating a neurodevelopmental assessment task comprises generating a plurality of neurodevelopmental assessment tasks sequentially when the device is in the first mode, and wherein the step of generating a training task comprises generating a plurality of training tasks sequentially when the device is in the second mode.

3. The method of claim 1, wherein the neurodevelopmental domain comprises one or more of the following categories: motor skill, language/communication skill, cognition skill, and social/emotional skill.

4. The method of claim any preceding claim, wherein the performance metric data associated with the neurodevelopmental assessment task comprises at least one parameter, optionally wherein the at least one parameter comprises one or more of the following: the time taken for the subject to complete the neurodevelopmental assessment task; whether the neurodevelopmental assessment task was completed successfully; the number of times the neurodevelopmental assessment task was completed and was not completed successfully; and feedback from the user interface.

5. The method of claim 4, when dependent on claim 2, further comprising: receiving secondary performance metric data associated with each training task, and optionally, performing a statistical analysis on the secondary performance metric data to determine an indication of the relationship between a parameter of the secondary performance metric data and the performance metric data of the neurodevelopmental assessment task.

6. The method of claim 4 or 5, further comprising: comparing the at least one parameter against a first threshold parameter; and determining whether the at least one parameter exceeds the first threshold parameter; wherein instructing the first device to change from the first mode to the second mode comprises instructing the first device to change modes when the at least one parameter exceeds the first threshold parameter.

7. The method of any preceding claim, further comprising: estimating a parameter, optionally a physiological parameter, of the subject based on the performance metric data measured in the neurodevelopmental assessment task, calculating a difference between the estimated parameter and a predetermined parameter stored in memory.

8. The method of claim 7, further comprises: comparing the calculated difference to a second threshold parameter, and determining whether the calculated difference exceeds the second threshold parameter; wherein instructing the first device to change from the first mode to the second mode comprises instructing the first device to change modes when the calculated difference exceeds the second threshold parameter.

9. The method of claim 8 further comprising, if the neurodevelopmental assessment task is successfully completed, instructing the device to select a further neurodevelopmental assessment task from a list of further neurodevelopmental assessment tasks, optionally wherein the further neurodevelopmental assessment task is selected based upon one of the following parameters: historical performance metric data associated with previous assessment tasks in the same category; the estimated parameter, the predetermined parameter, and the difference between the estimated physiological parameter and the predetermined physiological parameter.

10. The method of claim 9, further comprising: after a predetermined amount of time, generating the further neurodevelopmental assessment task on the user interface of the device and receiving performance metric data associated with the further neurodevelopmental assessment task.

11. The method of claim 9 or 10, wherein the further neurodevelopmental assessment task is a task which is associated with the initial neurodevelopmental assessment task but has a different level of difficulty than the initial neurodevelopmental assessment task.

12. The method of claim 10 or claim 11, further comprising: instructing, when the device is in the second mode, the first device to change from the second mode to the first mode only after an authorized override is received, and generating the further neurodevelopmental assessment task on the user interface of the device.

13. A device comprising a processor and a user interface, wherein the processor is configured to perform the method of any preceding claim.

14. The device of claim 13, further comprising a memory in communication with the processor, wherein the memory is configured to store the first and second threshold parameters, and the predetermined physiological parameter.

15. A computer-readable medium, which when executed by a processor, is configured to perform the method of claims 1 to 12.

16. A substance or composition for use in treatment of a subject, wherein the treatment involves performing the method of any one of claims 1 to 12.

17. A computer-implemented method to identify and/or measure a cognitive biomarker indicative of a neurodevelopmental disorder or a cognitive deficit in a subject, comprising: obtaining performance metric data from a neurodevelopmental assessment task or a cognitive assessment task performed by a subject on a first user interface; obtaining contextual data from an input provided on a second user interface; receiving, at a processor, the performance metric data and the contextual data; performing a statistical analysis on the performance metric data in combination with the contextual data; and identifying and/or measuring the cognitive biomarker based on the statistical analysis.

18. The method of claim 17, wherein the first user interface is on a first device and wherein the second user interface is on a second device.

19. The method of claim 17, wherein the first user interface and the second user interface are both on a first device.

20. The method of any one of claims 17-19, wherein performing the statistical analysis of the performance metric data based upon the contextual data comprises: associating each data point of the contextual data with at least one data point of the performance metric data; comparing each data point of the contextual data to a threshold; and if a contextual data point exceeds the threshold, excluding the or each data point of the performance metric data associated with the contextual data point exceeding the threshold before performing the statistical analysis on the performance metric data.

21. The method of any one of claims 17-19, wherein each data point of the contextual data comprises an input parameter associated with the input provided by the third party and a first time stamp, the first time stamp corresponding to a time at which the input was provided by the third party; wherein each data point of the performance metric data comprises a performance metric parameter and a second time stamp, the second time stamp corresponding to a time at which the neurodevelopmental assessment task or the cognitive assessment task was performed.

22. The method of claim 21, wherein performing the statistical analysis of the performance metric data based upon the contextual data comprises: sorting the performance metric data and the contextual data into a chronological order based upon the first and second time stamps; associating each data point of the contextual data with at least one data point of the performance metric data that is within a predetermined time of the data point of the contextual data.

23. The method of any one of claims 17-22, further comprising: selecting a further neurodevelopmental assessment task or a further cognitive assessment task from a list of neurodevelopmental assessment tasks or cognitive assessment tasks in response to an output of the statistical analysis, and providing the further neurodevelopmental assessment task or the further cognitive assessment task on the first user interface.

24. The method of any one of claims 17-23, further comprising receiving, at the processor, a set of historical data, wherein the historical data comprises the performance metric data and the contextual data of the subject.

25. The method of claim 24, wherein the historical data may comprise the performance metric data associated with the subject for a plurality of attempts at the neurodevelopmental assessment task or the cognitive assessment task, and optionally, the corresponding contextual data for the plurality of attempts.

26. The method of claim 24, wherein the historical data comprises population data, corresponding to historical performance metric data of one or more different subjects of the neurodevelopmental assessment task or the cognitive assessment task.

27. The method of claim 25 or 26, when dependent on claim 24, further comprising selecting the further neurodevelopmental assessment task or the cognitive assessment task based upon the historical data.

28. The method of any one of claim 25 to 26, further comprising: analyzing the historical data, and determining a developmental predictive trajectory based upon the historical data.

29. The method of any one of claims 17 to 28, wherein performing a statistical analysis comprises performing a cross-correlation measurement between one or more parameters of the stored contextual data and one or more of the stored performance metrics.

30. The method of claim 29, wherein performing the cross-correlation measurement provides an output which corresponds to an extent to which the one or more parameters of the stored contextual data is correlated to the one or more stored performance metrics.

31. The method of any one of claims 17 to 30, wherein performing a statistical analysis comprises performing a statistical regression analysis between the input parameter and the performance metric parameter.

32. The method of any one of claims 17 to 31, wherein the performance metric data comprises a parameter measured in relation to the neurodevelopmental assessment task or the cognitive assessment task, optionally wherein the parameter measured corresponds to a time taken to complete the neurodevelopmental assessment task or the cognitive assessment task and/or whether or not the assessment task was successfully completed.

33. The method of any one of claims 17 to 32, wherein the neurodevelopmental assessment task is a task designed to assess proficiency in one or more of the following categories: motor, cognitive, language/communication, and social/emotional.

34. The method of any one of claims 17 to 32, wherein the cognitive assessment task is a task designed to assess proficiency in one or more of the following categories: complex attention, executive function, learning and memory, language, perceptual-motor/visuospatial function, and social cognition.

35. The method of any one of claims 17 to 34, wherein the first device is an assessment device and comprises a videogame device, or any computer- implemented device configured to operate as a videogame device.

36. The method of any one of claims 17 to 35, wherein the contextual data comprises physiological data corresponding to the subject, optionally wherein the physiological data comprises information corresponding to one or more of the following parameters: sugar intake, caffeine intake, exercise, or sleep.

37. The method of any one of claims 17 to 36, wherein the contextual data comprises ancillary data corresponding to a third party, optionally wherein the ancillary data comprises an indication of the energy levels and/or the emotional level of the third party.

38. The method of claim 18, wherein the second device is a secondary, networked device, such as a smartphone, tablet, or computer.

39. The method of any one of claims 17 to 38, wherein obtaining contextual data from an input provided on a second user interface comprises providing a prompt, via the second user interface, for a user to input the contextual data at predetermined intervals.

40. The method of any one of claims 17 to 39, wherein obtaining contextual data from an input provided on a second user interface comprises providing a prompt, via the second user interface, for a user to input the contextual data after a known trigger.

41. The method of any one of claims 17 to 40, wherein the performance metric data and contextual data each comprise data associated with a user access account.

42. A system for identifying and/or measuring a cognitive biomarker indicative of a neurodevelopmental disorder or a cognitive deficit in a subject, the system comprising: an assessment device comprising a first processor and a first user interface; a secondary device associated with the assessment device, the secondary device comprising a second processor and a second user interface; and an external memory associated with the first processor and the second processor, and configured to store an aggregated set of data received in relation to a user of the assessment device; wherein the first processor is configured to: cause the first user interface to display an assessment task; obtain performance metric data associated with the assessment task; and store the performance metric data in the external memory; and wherein the second processor is configured to: obtain contextual data input on the second user interface, and store the contextual data in the external memory.

43. The system of claim 42, wherein the secondary device is configured to pair to the assessment device over a wireless or wired link.

44. A computer-readable medium comprising instructions, which, when executed by a processor, are configured to perform any of the steps of claims 17 to 41.

45. A substance or composition for use in treatment of a subject, wherein the treatment involves performing the method of any one of claims 17 to 41, or operating the system of claims 42 or 43.