WO2025155656A1 - System and methods for connectomic targeting of circuit pathologies in the brain - Google Patents

Abstract

Disclosed embodiments may provide techniques for generating data-driven tractography templates for inserting one or more leads of a deep-brain stimulation (DBS) device. A computer-implemented method can include accessing a plurality of images associated with one or more subjects. A subject can be associated with a known outcome from a DBS procedure performed for a disease or condition. The method can also include processing, for each image of the plurality of images, the image using a tractography algorithm to identify a set of white-matter activation pathways of the brain. The method can also include generating a population-based white-matter activation pathway map by merging the sets of white-matter activation pathways of the plurality of images. The method can also include defining, from the population-based white-matter activation pathway map, a tractography template that identifies a target white-matter region within the brain for inserting the one or more leads of the DBS device.

Description

SYSTEM AND METHODS FOR CONNECTOMIC TARGETING OF CIRCUIT PATHOLOGIES IN THE BRAIN

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Application No. 63/621 ,982, filed January 17, 2024, the entire contents of which are incorporated herein by reference in their entirety and for all purposes.

FIELD

[0002] The present disclosure relates generally to fractography targeting method for inserting one or more leads of a deep-brain stimulation device. In one example, the systems and methods described herein may relate to generating data-driven fractography templates for inserting one or more leads of a deep-brain stimulation device.

BACKGROUND

[0003] Deep-brain stimulation (DBS) is an established and highly efficacious treatment for movement disorders. Promising results have shown therapeutic utility in psychiatric disorders, including depression, and obsessive-compulsive disorder (OCD). DBS lead-implantation techniques typically require subject-specific targeting of a predefined optimal brain location and precise placement of the lead into the predefined optimal brain location, in order to achieve successful treatment outcomes regardless of target or disease state. Other invasive and non-invasive neuromodulation therapies (e.g. transcranial magnetic stimulation, magnetic resonance guided focused ultrasound surgery) require a similar specific targeting strategy in order to achieve optimal clinical outcomes.

[0004] Conventional targeting methods can include identifying gross anatomical landmarks identified from high resolution structural magnetic resonance imaging (MRI) scans. Many DBS targets are located in white matter tracts. As a result, there are no clear anatomical landmarks to guide precise implants. Therefore, it is challenging to implement a subject-specific connectome-based diffusion fractography targeting method that allows the identification of an individualized target location without the need for/use of an anatomical landmark or boundary. SUMMARY OF INVENTION

[0005] Disclosed embodiments may provide techniques for generating data-driven fractography templates for inserting one or more leads of a deep-brain stimulation (DBS) device. A computer-implemented method can include accessing a plurality of images associated with one or more subjects. In some instances, the plurality of images depict at least part of a brain of the one or more subjects. The plurality of images can include at least one of a Diffusion weighted imaging (DWI) image and a Computed Tomography (CT) image. In some instances, the CT image is captured at a particular time point, in which the particular time point is different from another time point at which the DWI image was captured.

[0006] A subject of the one or more subjects can be associated with a known outcome from a DBS procedure performed for a disease or condition. In some instances, the disease or condition includes depression, Parkinson's disease, dystonia, dementia, tremors, epilepsy, and obsessive compulsive disorder.

[0007] The computer-implemented method can also include processing, for each image of the plurality of images, the image using a fractography algorithm to identify a set of white-matter activation pathways of the brain. In some instances, processing the image using the tractography algorithm includes, for each image of the plurality of images: (i) identifying an estimated volume of tissue activated (VTA) from the at least part of the brain depicted in the image; and (ii) applying the probabilistic tractography algorithm to the estimated VTA to identify the set of white-matter activation pathways of the image. Before the plurality of images are processed, an image-registration algorithm can be applied to the plurality of images to align the plurality of images to a single coordinate system.

[0008] The computer-implemented method can also include generating a population-based white-matter activation pathway map by merging the sets of whitematter activation pathways of the plurality of images. In some instances, generating the population-based white-matter activation pathway map includes, for each image of the plurality of images: (i) detecting one or more false positive white-matter activation pathways from the set of white-matter activation pathways; and (ii) removing the one or more false positive white-matter activation pathways from the set of white-matter activation pathways. [0009] The computer-implemented method can also include defining, from the population-based white-matter activation pathway map, a tractography template associated with the disease or condition. In some instances, the tractography template includes a set of image masks, in which each of the set of image masks identifies a target white-matter region within the brain for inserting the one or more leads of the DBS device. In some instances, the target white-matter region includes a region within Subcallosal Cingulate Cortex (SCC), Forceps Minor, Subcortical Junction, or Cingulum bundle of the brain. The computer-implemented method can also include outputting the tractography template.

[0010] Disclosed embodiments may also provide techniques for identifying target white-matter regions of a brain. A computer-implemented method can include accessing one or more images associated with a particular subject. In some instances, the one or more images depict at least part of a brain of the particular subject. The one or more images can include a DWI image. The particular subject can be associated with a disease or condition. In some instances, the disease or condition includes depression, Parkinson's disease, dystonia, dementia, tremors, and epilepsy.

[0011] The computer-implemented method can also include extracting an image region from the one or more images, in which the extracted image region depicts a Subcallosal Cingulate Cortex (SCC) region of the brain. The computer-implemented method can also include accessing a tractography template associated with the disease or condition. In some instances, the tractography template includes a set of image masks, and the tractography template is generated based on at least one subject associated with a known outcome from a DBS procedure performed for the disease or condition.

[0012] The computer-implemented method can also include applying the tractography template to the extracted image region to determine a sub-region within the extract image region. In some instances, the sub-region includes a set of voxels, in which each voxel of the set of voxels includes a number of possible connections between the voxel and the set of image masks exceeding a predetermined threshold. The computer-implemented method can also include identifying, based on the sub-region, one or more target white-matter regions of the brain of the particular subject. In some instances, the computer-implemented method also includes administering the DBS procedure to the particular subject by inserting one or more leads of the DBS device into the one or more target white-matter regions of the brain.

[0013] In some instances, the one or more target white-matter regions of the brain of the particular subject are cross-validated to increase the effectiveness of the DBS procedure. As an illustrative example, the computer-implemented method can also include overlaying the tractography template on the extracted image region to identify a set of white-matter bundle regions. The computer-implemented method can also include determining, from the set of white-matter bundle regions, additional target white-matter regions of the brain of the particular subject. The computer- implemented method can also include comparing the one or more target whitematter regions with the additional target white-matter regions to identify one or more cross- validated target white-matter regions of the brain of the particular subject.

[0014] In an embodiment, a system comprises one or more processors and memory including instructions that, as a result of being executed by the one or more processors, cause the system to perform the processes described herein. In another embodiment, a non-transitory computer-readable storage medium stores thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to perform the processes described herein.

[0015] Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations can be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and, such references mean at least one of the embodiments.

[0016] Reference to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which can be exhibited by some embodiments and not by others.

[0017] The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms can be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various embodiments given in this specification.

[0018] Without intent to limit the scope of the disclosure, examples of instruments, apparatus, methods and their related results according to the embodiments of the present disclosure are given below. Note that titles or subtitles can be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control.

[0019] Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims, or can be learned by the practice of the principles set forth herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0021] Illustrative embodiments are described in detail below with reference to the following figures.

[0022] FIG. 1 illustrates an example schematic diagram for generating data-driven fractography templates, according to some embodiments.

[0023] FIG. 2 shows an example image that identifies subject-specific volume of tissue activated (VTA) for a disease or condition, according to some embodiments.

[0024] FIG. 3 shows an example image of a subject-specific white-matter activation pathways generated based on the estimated VTA, according to some embodiments.

[0025] FIG. 4 shows a set of example images that depict white-matter activation pathways of clinically weighted average map and common activation map of responders, according to some embodiments.

[0026] FIG. 5 shows a set of example images that depict population-based whitematter activation pathway map across Forceps Minor, Cingulum Bundle, and Subcortical junction regions, according to some embodiments.

[0027] FIG. 6 shows a set of example waypoint masks derived from the populationbased white-matter activation pathway map, according to some embodiments.

[0028] FIG. 7 shows an illustrative example of a process for generating data-driven fractography templates for inserting one or more leads of a deep-brain stimulation (DBS) device, in accordance with some embodiments.

[0029] FIG. 8 illustrates an example schematic diagram for identifying target whitematter regions of a brain using data-driven fractography templates, according to some embodiments

[0030] FIG. 9 shows examples images that identify target white-matter regions of a subject, according to some embodiments.

[0031] FIG. 10 shows a set of graphs showing a relationship between WMI and the time to stable response, according to some embodiments. [0032] FIG. 11 shows an illustrative example of a process for identifying target white-matter regions of a brain using data-driven tractography templates, in accordance with some embodiments.

[0033] FIG. 12 shows a computing system architecture including various components in electrical communication with each other using a connection in accordance with various embodiments.

[0034] In the appended figures, similar components and/or features can have the same reference label. Further, various components of the same type can be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

DETAILED DESCRIPTION

[0035] In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of certain inventive embodiments. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs.

[0036] Disclosed embodiments may provide techniques for using tractography templates generated from white matter pathway activation analysis of subjects treated for a particular neurocircuit disorder or condition using a DBS procedure. The tractography templates can then be used to identify an optimal target location for inserting DBS leads into a brain of an individual patient having the particular neurocircuit disorder or condition. For example, a 4-bundle white-matter pattern in subcallosal cingulate cortex (SCC) across DBS responders for Treatment-Resistant Major Depression can be used to generate a corresponding tractography template for identifying optimal target location for inserting DBS leads to a given subject. [0037] In some instances, the tractography templates are generated based on population-based activation pathway map that represents brain-activation pathways of past subjects with known classification of disease outcome. The tractography templates can thus guide and identify the precise target location in any new subjects with similar diseases and conditions. In addition, the data-driven tractography templates can be refined and updated over time based on additional subjects, which can further improve the accuracy of identifying the target locations.

I. TECHNIQUES FOR GENERATING DATA-DRIVEN TRACTOGRAPHY TEMPLATES

[0038] A target location within a brain of a subject can be predicted using a diseasespecific tractography templates. The target location can then be used for an image- guided intervention of inserting leads of a DBS device. The tractography templates include a set of waypoint masks, which can be generated from a disease-specific white-matter pathway associated with an existing dataset of known disease- and treatment-specific responders. Calculating structural connectivity and extracting tentative activation pathways before the surgery based on the waypoint masks can predict a subject-specific optimal target location for a given subject. The predicted target location can then be translated to a treatment platform for defining the targeting strategy of the treatment (e.g. surgical trajectory of a DBS electrode) of the subject. For example, the tractography templates can be used for DBS implantation into the SCC region of the subject for the treatment of Major Depressive Disorder.

[0039] FIG. 1 illustrates an example schematic diagram 100 for generating data- driven tractography templates, according to some embodiments. As shown in FIG. 1 , a plurality of images associated with one or more subjects can be accessed. The plurality of images can include a Diffusion weighted imaging (DWI) image 102, a Computed Tomography (CT) image 104, and a T1 -weighted image 106 corresponding to each subject of the one or more subjects. In some instances, the CT image 104 is captured at a particular time point that is different from another time point at which the DWI image 102 and the T1 -weighted image 106 were captured. For example, for each of the subjects, structural T1 -weighted image 106 and DWI image 102 can be collected in a single session prior to deep brain stimulation surgery. By contrast, the postsurgical CT image 104 of the respective subject can be collected to identify the implanted deep brain stimulation lead contacts. [0040] The plurality of images can depict at least part of a brain of the one or more subjects, in which a subject of the one or more subjects is associated with a known outcome from a DBS procedure performed for a disease or condition. The disease or condition can include any neurocircuit conditions, including but not limited to depression, Parkinson’s disease, dystonia, dementia, tremors, epilepsy, and obsessive compulsive disorder. Other examples of the disease or condition can include: (i) spasmodic dysphonia; (ii) orthostatic tremor; (iii) Meige syndrome; (iv) cluster headache; (v) SUNCT; (vi) trigeminal neuropathy; (vii) trigeminal neuralgia; (viii) chronic paroxysmal hemicrania; (ix) chronic pain; (x) Tourette syndrome; (xi) aggressive behavior; (xii) camptocormia; (xiii) restless legs syndrome; (xiv) obesity/addictions; (xv) disorder of consciousness; and (xvi) Alzheimer disease.

[0041] In some instances, the known outcome can classify a subject being responsive or non-responsive to the DBS procedure after a predefined time period (e.g., 1 month, 3 months, 6 months, 9 months, 1 year, 2 years, 3 years, 4 years, 5 years, more than 5 years). For example, the plurality of images can correspond to: (i) a first group of subjects who were responsive or non-responsive to the DBS procedure after 6 months; and (ii) a second group of subjects who were responsive or non-responsive to the DBS procedure after 2 years.

A. Preprocessing image data

[0042] In some instances, the plurality images of subjects are preprocessed for generating the fractography templates. For example, T1 -weighted images 106 of the subjects can be preprocessed by segmenting the T1 -weighted images 106 into gray- and-white matter and cerebrospinal fluid (CSF) using a segmentation algorithm (e.g., FMRI B Automated Segmentation Tool). In some instances, the segmented CSF regions is used to generate a CSF mask, which can be used as a stop mask to reduce artificial connection errors caused by probabilistic fractography.

[0043] To accommodate for different sizes and shapes of the brains across the subjects, the segmented T1-weighted images 106 can be normalized to an MNI152 template (e.g., MNI152 standard-space T1 -weighted average structural temple image, FSL, FMRIB) using nonlinear transformation. The normalization can facilitate a particular voxel being measured in a given T1 -weighted image to correspond to the same anatomical landmark of other T1 -weighted images. Depending on characteristics of the subjects associated with the T1 -weighted images, different brain templates derived from various cohorts (e.g., Colin 27 Average Brain 2008) can be used. In some instances, T1-weighted images 106 are additionally preprocessed using skull stripping.

[0044] In addition to the T1 -weighted images 106, the DWI images 102 can be preprocessed using Gibbs ringing and bias correction followed by eddy current and motion correction. Once preprocessed, the DWI images 102 and the CT images 104 can be coregistered to the corresponding T1 -weighted images 106 by affine transformation. The DWI images 102 can additionally be preprocessed using rotation of b-vector and local tensor fitting. The coregistered DWI images 102 and CT images 104 can then be normalized to the MNI152 template by applying previously calculated transformation parameters associated with the T1 -weighted normalization.

B. Estimating subject-specific volume of tissue activated (VTA)

[0045] The preprocessed images (e.g., T1-weighted images, DWI images, CT images) associated with each subject can be processed to estimate a subjectspecific volume of tissue activated (VTA) 108 for the disease or condition. As a result, an estimated VTA 108 can be generated for each subject of the one or more subjects. To estimate the VTA 108, the DBS contact locations in the T1 -weighted image 106 can be initially determined by transferring the electrode and contact location identified from the CT image 104 to the corresponding T1 -weighted image 106. The subject-specific VTA 108 for the corresponding subject can then be created by using an electrical DBS field model, in which the electrical DBS field model was generated based on the identified contact location(s) in native T 1 space of the T1- weighted image 106.

[0046] In some instances, the VTA 108 for the subject is estimated by: (i) using a finite-element method with the known stimulation contact; and (ii) setting at a predefined time of stable response (6 months for this example) based on a superimposition of the subject’s postoperative CT image. FIG. 2 shows an example image 200 that identifies subject-specific VTA, according to some embodiments. As shown in FIG. 2, a red region 202 depicts the VTA estimated based on the parameters of stimulation contacts 204 and 206. The stimulation contact can include the following parameters: 130Hz; 90 microseconds; 6mA; and second contact from the bottom.

[0047] In some instances, machine-learning techniques are used to estimate VTA in the T1 -weighted images. For example, a trained machine-learning model such as artificial neural networks (ANNs) can be used to characterize the spatial extent of directly activated axons as a function of the stimulation parameter settings. The ANNs can be trained using a training dataset that includes a set of results corresponding to simulations that directly couple DBS electric field models with multicompartment cable models of axons. The ANNs can additionally be trained based on the training dataset that includes: (i) explicit representation of the DBS electrode design; (ii) use of current-controlled stimulation; and (iii) outputs from separate ANNs for representing DBS in gray matter or white matter. The gray matter can be represented in the DBS electric field model as an isotropic bulk tissue domain, while the white matter can be represented as an anisotropic bulk tissue domain with the axon models oriented parallel to the orientation of high electrical conductivity.

[0048] The machine-learning model may be any type of machine-learning model such as, but not limited to, a classifier (e.g., single-variate or multivariate that is based on k-nearest neighbors, Naive Bayes, Logistic regression, support vector machine, decision trees, an ensemble network of classifiers, and/or the like), regression model (e.g., such as, but not limited to, linear regressions, logarithmic regressions, Lasso regression, Ridge regression, and/or the like), clustering model (e.g., such as, but not limited to, models based on k-means, hierarchical clustering, DBSCAN, biclustering, expectation-maximization, random forest, and/or the like), deep learning model (e.g., such as, but not limited to, neural networks, convolutional neural networks, recurrent neural networks, long short-term memory (LSTM), multilayer perceptions, etc.), combinations thereof (e.g., disparate-type ensemble networks, etc.), or the like.

C. Determining subject-specific white-matter activation pathways

[0049] A fractography algorithm can then be applied to the estimated VTA 108 of each subject to identify a set of white-matter activation pathways 110 for the corresponding subject. The estimated VTA can thus be used as a “seed” for generating the set of white-matter activation pathways. In some instances, a set of white-matter activation pathways 110 of each subject is identified by generating a probability of connections (e.g., a connectivity map) between the estimated VTA 108 of the subject and a given voxel in the MRI image (e.g., the DWI image 102). The set of white-matter activation pathways 110 can include the one or more white-matter activation pathways of the subject that can matches at least part of the fractography template.

[0050] Various types of tractography algorithms can be used to identify the whitematter activation pathways. For example, the tractography algorithm can be a probabilistic tractography algorithm accessed from Functional Magnetic Resonance Imaging of the Brain (FMRIB) Linear Image Registration Tool (http://www.fmrib.ox.ac.uk/fsl). In some instances, tractography algorithms include deterministic and probabilistic methods. The deterministic methods an include four tensor-based deterministic tractography algorithms: (i) FACT algorithm as described in Mori et al. (1999), Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging, Ann. Neurol. 45 265-269; (ii) the second-order Runge-Kutta (RK2) method as described in Basser et al. (2000), In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44 625-632; (iii) the tensorline algorithm as described in Lazar et al. (2003), White matter tractography using diffusion tensor deflection. Hum. Brain Mapp. 18 306-321 ; and (iv) interpolated streamline (SL) methods as described in Conturo et al. (1999), Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. U.S.A. 96 10422- 10427. Two deterministic tractography algorithms based on fourth order spherical harmonic derived orientation distribution function (ODF)-FACT and RK2 — can also be used.

[0051] In addition to the deterministic algorithms, probabilistic tractography algorithms can also be used: (i) “ball-and-stick model based probabilistic tracking” (Probtrackx) from the FSL toolbox as described in Behrens et al. (2007), Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34 144-155; (ii) the Hough voting method as described in Aganj et al. (2011 ), A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography. Med. Image Anal. 15414-425; and (iii) the probabilistic index of connectivity (PICo) method as described in Parker et al. (2003), A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements. J. Magn. Reson. Imaging 18242-254. The publications and descriptions relating to various tractography algorithms are incorporated by reference in their entirety for all purposes.

[0052] As an illustrative example, the proportional probability of connections between the VTA 108 and a given voxel in each patient’s brain can be calculated using a probabilistic tractography algorithm to generate the white-matter activation pathways 110. Right and left hemisphere white-matter activation pathways 110 can be generated using individually defined VTA 108 of the subject. As an illustrative example of the tractography algorithm, five thousand random samples per voxel can be sent out from each individual’s bilateral activation volumes to whole brain to generate a connectivity map. The whole-brain connectivity map can be divided by total number of streamlines sent out to compensate seed size difference. To remove false positive connections, each of the individual white-matter activation pathways 110 can be binarized using thresholding (e.g., 0.001 %) to generate binary activation pathways 112 associated with the subjects. The binary activation pathways 112 can be transformed to the MNI template for building group white-matter activation pathway maps. In some instances, various threshold values can be used to binarize each of the individual white-matter activation pathways 110, including but not limited to a threshold value selected from a range between 0.0001 % and 1 %.

[0053] FIG. 3 shows an example image 300 of a subject-specific white-matter activation pathways generated based on the estimated VTA, according to some embodiments. As shown in FIG. 3, a T1 -weighted image 302 of a given subject depicts the subject-specific white-matter activation pathways 110 corresponding to the left hemisphere of the brain. In addition, a T1 -weighted image 304 of the same subject depicts the subject-specific white-matter activation pathways 110 corresponding to the right hemisphere of the brain.

D. Constructing population-based white-matter activation pathway map

[0054] The sets of white-matter activation pathways 110 and/or binary activation pathways 112 corresponding to the individual subjects can be merged to generate a population-based white-matter activation pathway map 114. The population-based white-matter activation pathway map 114 can identify necessary white-matter activation pathways of the corresponding subjects associated with the plurality of images.

[0055] In some instances, the population-based white-matter activation pathway map 114 is generated by combining the clinically-weighted average and shared common map across the white-matter activation pathways 110 of the respective subjects. As an illustrative example, FIG. 4 shows a set of example images 400 that depict white-matter activation pathways of clinically weighted average map and common activation map of responders, according to some embodiments. As shown in FIG. 4, two white-matter activation pathways can be constructed from the plurality of images. For example, a set of images 402 can correspond to clinically-weighted (e.g., HDRS17 percentage improvement from baseline to six months) average map of all individual normalized population-based white-matter activation pathway maps. In another example, another set of images 404 can correspond to all responders (equal and more than 50% HDRS17 improvement at six months) that shared a population-based white-matter activation pathway map. Thus, the weighted average and shared common maps shown in FIG. 4 can be processed to generate the population-based white-matter activation pathway map 114.

[0056] FIG. 5 shows a set of example images 500 that depict population-based white-matter activation pathway map across Forceps Minor, Cingulum Bundle, and Subcortical junction regions, according to some embodiments. The population-based white-matter activation pathway map can include sub-pathways of three white-matter bundles: (i) forceps minor 502 corresponding to medial orbitofrontal cortex (OFC) to ventromedial prefrontal cortex (vmPFC), but lower than the tip of genu; (ii) cingulum bundle 504 corresponding to perigenual section of cingulum bundle; and (iii) subcortical junction 506 corresponding to midline white-matter pathway extending caudal from the SCC to the thalamus, the nucleus accumbens, and further extending laterally to the amygdala/hippocampus and brainstem.

E. Defining waypoint masks

[0057] Based on the population-based white-matter activation pathway map 114, a fractography template associated with the disease or condition can be generated. The tractography template can include a set of image masks, in which each mask identifies a target white-matter region within the brain for inserting the one or more leads of the DBS device. In some instances, the set of image masks include a set of waypoint masks 116, which specify one or more regions of the brain that all randomly sampled streamlines must pass through in order to be counted towards the probabilistic result. For example, three white-matter waypoint masks 116 in each hemisphere can be created using the population-based white-matter activation pathway map in the open source MNI152 template space (G. Grabner, et al. MICCCAI 2006).

[0058] The set of waypoint masks 116 can be generated by: (i) identifying, from the population-based white-matter activation pathway map 114, sub-pathways of whitematter bundles (e.g., forceps minor, cingulum bundle, subcortical junction) that are activated in the subjects of known classifications; and (ii) generating, based on the identified sub-pathways, the set of waypoint masks 116 for each hemisphere of the brain. The white-matter waypoint masks 116 can include prefrontal (medial OFC to vmPFC, z=-22 ~ 5) for forceps minor, midcingulate for cingulum bundle, and diagonal band of Broca for subcortical junction fibers. In some instances, in addition to waypoint masks, bilateral SCC target masks covering a broad SCC region can be generated.

[0059] In some instances, generating the set of waypoint masks 116 includes activating voxels based on one or more thresholds. For example, the populationbased white-matter activation pathway map 114 can be generated with various thresholds (i.e., 0.1 ~ 1 % probability of connection for CB and SJ, and 0.01 ~ 0.1 % for FM) being applied to the subject-specific white-matter activation pathways of each subject. Various threshold values for activating voxels of the white-matter bundles can be used, including not limited to a threshold value selected from a range between 0.0001 % and 10%.

[0060] For example, to minimize false positives, thresholds of 1 % for CB and SJ, and 0.1 % for FM were applied to each subject-specific white-matter activation pathways to generate the population-based white-matter activation pathway map 114. Within the shared white-matter bundles of all responders in the populationbased white-matter activation pathway map 114, the set of waypoint masks 116 can be selected to be as far as possible away from the SCC seed mask.

[0061] FIG. 6 shows a set of example waypoint masks 600 derived from the population-based white-matter activation pathway map, according to some embodiments. In particular, FIGS. 6 shows the example white-matter waypoint masks across various regions of the brain, in which a green region 602 depicts the necessary pathway atlas, a purple region 604 depicts the Subcallosal Cingulate seed, a red region 606 depicts the Forceps Minor waypoint, a yellow region 608 depicts the Cingulum bundle waypoint, and a blue region 610 depicts the Subcortical Junction waypoint.

F. Methods for generating data-driven tractography templates for inserting one or more leads of a DBS device

[0062] FIG. 7 shows an illustrative example of a process 700 for generating data- driven tractography templates for inserting one or more leads of a DBS device, in accordance with some embodiments. At step 702, a plurality of images associated with one or more subjects can be accessed. The plurality of images can depict at least part of a brain of the one or more subjects, in which a subject of the one or more subjects is associated with a known outcome from a DBS procedure performed for a disease or condition. The disease or condition can include any neurocircuit conditions, including but not limited to depression, Parkinson’s disease, dystonia, dementia, tremors, epilepsy, and obsessive compulsive disorder. Other examples of the disease or condition can include: (i) spasmodic dysphonia; (ii) orthostatic tremor; (iii) Meige syndrome; (iv) cluster headache; (v) SLINCT; (vi) trigeminal neuropathy; (vii) trigeminal neuralgia; (viii) chronic paroxysmal hemicrania; (ix) chronic pain; (x) Tourette syndrome; (xi) aggressive behavior; (xii) camptocormia; (xiii) restless legs syndrome; (xiv) obesity/addictions; (xv) disorder of consciousness; and (xvi) Alzheimer disease.

[0063] The plurality of images can include at least one of a Diffusion weighted imaging (DWI) image and a Computed Tomography (CT) image. In some instances, the CT image is captured at a particular time point that is different from another time point at which the DWI image was captured. For example, the CT image can be captured after the DBS procedure is performed, whereas the DWI image can be captured before the DBS procedure.

[0064] At step 704, each image of the plurality of images can be processed using a tractography algorithm to identify a set of white-matter activation pathways of the brain. To preprocess the plurality of images using the tractography algorithm, the following steps can be performed for each image: (i) an estimated volume of tissue activated (VTA) from the at least part of the brain depicted in the image can be identified; and (ii) the probabilistic tractography algorithm can be applied to the estimated VTA to identify the set of white-matter activation pathways of the image. In some instances, an image-registration algorithm is applied to the plurality of images to align the plurality of images to a single coordinate system, at which the tractography algorithm can be applied to each of the plurality of images.

[0065] At step 706, a population-based white-matter activation pathway map can be generated by merging the sets of white-matter activation pathways of the plurality of images. To generate the population-based white-matter activation pathway map, the following steps can be performed for each image: (i) one or more false positive white-matter activation pathways can be detected from the set of white-matter activation pathways; and (ii) the one or more false positive white-matter activation pathways can be removed from the set of white-matter activation pathways.

[0066] At step 708, a tractography template associated with the disease or condition can be defined from the population-based white-matter activation pathway map. The tractography template can include a set of image masks, in which each of the set of image masks identifies a target white-matter region within the brain for inserting the one or more leads of the DBS device. The target white-matter region can include a region within Subcallosal Cingulate Cortex (SCC), Forceps Minor, Subcortical Junction, or Cingulum bundle of the brain.

[0067] At step 710, the tractography template can be outputted. In some instances, the tractography template is used to identify an optimal target location within whitematter tracts of a brain of another subject for inserting the one or more leads of the DBS device. The predicted target location can then be translated to a treatment platform for defining the targeting strategy of the treatment (e.g. surgical trajectory of a DBS electrode) of the subject. Process 700 terminates thereafter.

TECHNIQUES FOR IDENTIFYING TARGET WHITE-MATTER REGIONS OF A BRAIN USING DATA-DRIVEN TRACTOGRAPHY TEMPLATES

[0068] The present techniques can be further directed to using data-driven tractography templates to identify an optimal target location within white-matter tracts of a brain of a subject for inserting one or more leads of the DBS device. In some instances, the optimal target location is determined by applying the set of waypoint masks to the MRI image of the subject to extract white-matter bundles. One or more overlapping regions corresponding to the white-matter bundles can be determined as being the optimal target location for inserting the one or more leads of the DBS device.

[0069] In some instances, the optimal target location is determined by processing the MRI image data of the subject to generate a probability map for each waypoint mask of the set of waypoint masks. The probability map can include, for each voxel of a given waypoint mask, a probability of a corresponding white-matter bundle of the waypoint mask being connected to other white-matter bundles of the other waypoint masks of the template. The optimal target location can be determined based on the probability maps determined from the template.

[0070] FIG. 8 illustrates an example schematic diagram 800 for identifying target white-matter regions of a brain using data-driven fractography templates, according to some embodiments. In FIG. 8, one or more images associated with a particular subject can be accessed, in which the one or more images depict at least part of a brain of the particular subject. The one or more images can include a Diffusion weighted imaging (DWI) image 802 and a T1 -weighted image 804 corresponding to the particular subject. In some instances, the structural T1-weighted image 804 and DWI image 802 can be collected in a single session prior to deep brain stimulation surgery.

[0071] In some instances, the particular subject is associated with a disease or condition. The disease or condition can include any neurocircuit conditions, including but not limited to depression, Parkinson’s disease, dystonia, dementia, tremors, epilepsy, and obsessive compulsive disorder. Other examples of the disease or condition can include: (i) spasmodic dysphonia; (ii) orthostatic tremor; (iii) Meige syndrome; (iv) cluster headache; (v) SUNCT; (vi) trigeminal neuropathy; (vii) trigeminal neuralgia; (viii) chronic paroxysmal hemicrania; (ix) chronic pain; (x) Tourette syndrome; (xi) aggressive behavior; (xii) camptocormia; (xiii) restless legs syndrome; (xiv) obesity/addictions; (xv) disorder of consciousness; and (xvi) Alzheimer disease.

[0072] A fractography template associated with the disease or condition can be accessed. The fractography template can include a set of waypoint masks 806. The set of waypoint masks 806 can be generated based on analyzing DWI and CT images of at least one subject associated with a known outcome from a DBS procedure performed for the disease or condition. Additional implementation details for generating the tractography template are further described in Section I of the present disclosure. In some instances, the known outcome can classify a subject being responsive or non-responsive to the DBS procedure after a predefined time period (e.g., 1 month, 3 months, 6 months, 9 months, 1 year, 2 years, 3 years, 4 years, 5 years, more than 5 years).

G. Preprocessing image data

[0073] In some instances, the one or more images of the particular subject are preprocessed. For example, the T1 -weighted image 804 of the subject can be preprocessed by segmenting the T1 -weighted image 804 into gray-and-white matter and cerebrospinal fluid (CSF) using a segmentation algorithm (e.g., FMRIB Automated Segmentation Tool). In some instances, the segmented CSF regions is used to generate a CSF mask, which can be used as a stop mask to reduce artificial connection errors caused by probabilistic tractography.

[0074] In some instances, the segmented T1 -weighted image 804 can be normalized to an MNI152 template (e.g., MNI152 standard-space T1-weighted average structural temple image, FSL, FMRIB) using nonlinear transformation. The normalization would allow the T1 -weight image 804 to be aligned with the DWI image 802 and the waypoint masks 806 of the tractography template.

[0075] In addition to the T1-weighted image 804, the DWI image 802 can be preprocessed using Gibbs ringing and bias correction followed by eddy current and motion correction. Once preprocessed, the DWI image 802 and the waypoint masks 806 of the tractography template can be coregistered to the corresponding T1- weighted image 802 by affine transformation. The DWI images 102 can additionally be preprocessed using rotation of b-vector and local tensor fitting. In some instances, the DWI image 802 and the waypoint masks 806 of the tractography template can then be normalized to the MNI 152 template by applying previously calculated transformation parameters associated with the T1 -weighted normalization.

H. Tractography analysis between SCC seed mask and waypoint masks

[0076] An image region from the one or more images can then be extracted, in which the extracted image region depicts an SCC region of the brain. The extracted image region can correspond to a 3-mm radius spherical region of interest within the SCC region of the brain, which can be used to generate an SCC seed mask for identifying the pathways between the SCC region and the waypoint masks 806. For example, “tracks” from the SCC seed mask can be generated in each hemisphere depicted in the T1 -weighted image 804 of the particular subject.

[0077] A fractography algorithm can thus be applied to the SCC seed mask and the waypoint masks 806 subject to identify white-matter activation pathways 808 for the particular subject. As previously describe here, various types of fractography algorithms can be used to identify the white-matter activation pathways. For example, the fractography algorithm can be a probabilistic fractography algorithm accessed from Functional Magnetic Resonance Imaging of the Brain (FMRIB) Linear Image Registration Tool (http://www.fmrib.ox.ac.uk/fsl).

[0078] In some instances, fractography algorithms include deterministic and probabilistic methods. The deterministic methods an include four tensor-based deterministic tractography algorithms: (i) FACT algorithm; (ii) the RK2 method; (iii) the tensorline algorithm; and (iv) the interpolated SL methods. Two deterministic tractography algorithms based on fourth order spherical harmonic derived orientation distribution function (ODF)-FACT and RK2 — can also be used. In addition to the deterministic algorithms, probabilistic tractography algorithms can also be used: (i) the Probtrackx method; (ii) the Hough voting method; and (iii) the PICo method.

/. Predicting subject-specific optimal target location for successful DBS

[0079] Based on the white-matter activation pathways 808 for the particular subject, two computational methods can be used to predict an optimal target location in each hemisphere. The first method can include extracting three necessary white-matter bundles 810 from the white-matter activation pathways 808 of the particular subject. The three necessary white-matter bundles 810 can include anterior forceps minor (z = 5 ~ -22), cingulum bundle, and subcortical junction. The three necessary whitematter bundles 810 can be extracted using the waypoint masks 806 in the native anatomical space with individual subject-specific DWI data. The extracted three bundles 810 can be threshold and calculated to estimate overlapping regions within the SCC mask. The estimated overlapping regions within the SCC mask can be used to predict an optimal target location 812 for the particular subject. [0080] The second method can include determining voxel-wise probability of structural connections between the SCC region and six waypoint masks 806 based on the individual DWI data, respectively. In some instances, possible connections between the SCC region and the waypoint masks 806 are identified, and a subset of possible connections that exceed a predetermined threshold are identified as another optimal target location 816 for the particular subject. Probability maps 814, three in each hemisphere of the brain, can represent the voxel-wise probability of structural connections and be threshold to remove false positive connections to predict the optimal target location 816 that has the highest probability of connecting with waypoint masks. In some embodiments, the optimal target locations 812 and 816 can be cross-validated to identify a cross-validated target location for inserting the one or more leads of the DBS device.

[0081 ] FIG. 9 shows examples images 900 that identify target white-matter regions of a subject, according to some embodiments. In FIG. 9, a green region shows a predicted sweet spot, a red region shows forceps minor region, a yellow region shows cingulum bundle, a blue region shows subcortical junction pathway, a purple region shows SCC seed mask, and a white region shows burnmark of sweet spot in native T 1 space. An image 902 shows the target white-matter regions identified based on a three bundle extraction method. A highly overlapped region was selected as a target. In addition, an example image 904 that includes the target white-matter regions identified based on probability of connection from SCC seed mask.

J. Identifying trajectories for inserting leads using white-matter integrity

[0082] In some instances, parameters associated with white matter integrity (WMI) of the brain of the particular subject are used to further identify the optimal location for inserting the leads to the predicted target location. WMI is known to be associated with the time to stable response, and subjects with more damaged whitematter takes longer time to respond to the DBS procedure. FIG. 10 shows a set of graphs 1000 showing a relationship between WMI and the time to stable response, according to some embodiments. As shown in FIG. 10, a graph 1002 shows that WMI (Fractional Anisotropy: FA) is significantly associated with the time to stable well response (TSR2). [0083] Accordingly, to increase positive outcome of the DBS procedure (e.g., decrease the time to stable response after surgery) for a given subject, diffusion fractional anisotropy (FA) metrics can be determined for each white-matter bundle (FM/CB/SJ) associated with an initial target location (e.g., the optimal target location 816). The white-matter bundle having the highest diffusion FA metrics can then be identified as an optimal target location for inserting the leads of the DBS device to the particular subject.

K. Methods for identifying target white-matter regions of a brain using data-driven tractography templates

[0084] FIG. 11 shows an illustrative example of a process 1100 for identifying target white-matter regions of a brain using data-driven tractography templates, in accordance with some embodiments. At step 1102, one or more images associated with a particular subject can be accessed. The one or more images can depict at least part of a brain of the particular subject, the particular subject can be associated with a disease or condition. The one or more images include a DWI image. The disease or condition can include any neurocircuit conditions, including but not limited to depression, Parkinson’s disease, dystonia, dementia, tremors, epilepsy, and obsessive compulsive disorder. Other examples of the disease or condition can include: (i) spasmodic dysphonia; (ii) orthostatic tremor; (iii) Meige syndrome; (iv) cluster headache; (v) SUNCT; (vi) trigeminal neuropathy; (vii) trigeminal neuralgia; (viii) chronic paroxysmal hemicrania; (ix) chronic pain; (x) Tourette syndrome; (xi) aggressive behavior; (xii) camptocormia; (xiii) restless legs syndrome; (xiv) obesity/addictions; (xv) disorder of consciousness; and (xvi) Alzheimer disease.

[0085] At step 1104, an image region can be extracted from the one or more images. The extracted image region can depict a Subcallosal Cingulate Cortex (SCC) region of the brain. The extracted image region can correspond to a region of interest within the SCC region of the brain, which can be used to generate a seed mask for identifying activation pathways between the SCC region and the image masks associated with the tractography template.

[0086] At step 1106, a tractography template associated with the disease or condition can be accessed. The tractography template can include a set of image masks. The tractography template can be generated based on at least one subject associated with a known outcome from a DBS procedure performed for the disease or condition. Additional implementation details for generating the fractography template are further described in Section I of the present disclosure.

[0087] At step 1108, the tractography template can be applied to the extracted image region to determine a sub-region within the extract image region. The subregion can include a set of voxels, in which each voxel of the set of voxels includes a number of possible connections between the voxel and the set of image masks exceeding a predetermined threshold.

[0088] At step 1110, one or more target white-matter regions of the brain of the particular subject can be identified based on the sub-region. The one or more target white-matter regions can be cross-validated by comparing the one or more target white-matter regions with additional target white-matter regions of the brain of the particular subject. In some instances, the additional target white-matter regions are identified by: (i) overlaying the tractography template on the extracted image region to identify a set of white-matter bundle regions; and (ii) determining, from the set of white-matter bundle regions, the additional target white-matter regions of the brain of the particular subject.

[0089] After the one or more target white-matter regions are identified, the DBS procedure can be administered to the particular subject by inserting the one or more leads of the DBS device into the one or more target white-matter regions of the brain. Process 1100 terminates thereafter.

II. EXAMPLE SYSTEMS

[0090] FIG. 12 illustrates a computing system architecture 1200, including various components in electrical communication with each other, in accordance with some embodiments. The example computing system architecture 1200 illustrated in FIG. 12 includes a computing device 1202, which has various components in electrical communication with each other using a connection 1206, such as a bus, in accordance with some implementations. The example computing system architecture 1200 includes a processing unit 1204 that is in electrical communication with various system components, using the connection 1206, and including the system memory 1214. In some embodiments, the system memory 1214 includes read-only memory (ROM), random-access memory (RAM), and other such memory technologies including, but not limited to, those described herein. In some embodiments, the example computing system architecture 1200 includes a cache 1208 of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 1204. The system architecture 1200 can copy data from the memory 1214 and/or the storage device 1210 to the cache 1208 for quick access by the processor 1204. In this way, the cache 1208 can provide a performance boost that decreases or eliminates processor delays in the processor 1204 due to waiting for data. Using modules, methods and services such as those described herein, the processor 1204 can be configured to perform various actions. In some embodiments, the cache 1208 may include multiple types of cache including, for example, level one (L1 ) and level two (L2) cache. The memory 1214 may be referred to herein as system memory or computer system memory. The memory 1214 may include, at various times, elements of an operating system, one or more applications, data associated with the operating system or the one or more applications, or other such data associated with the computing device 1202. [0091] Other system memory 1214 can be available for use as well. The memory 1214 can include multiple different types of memory with different performance characteristics. The processor 1204 can include any general purpose processor and one or more hardware or software services, such as service 1212 stored in storage device 1210, configured to control the processor 1204 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 1204 can be a completely self-contained computing system, containing multiple cores or processors, connectors (e.g., buses), memory, memory controllers, caches, etc. In some embodiments, such a self-contained computing system with multiple cores is symmetric. In some embodiments, such a self- contained computing system with multiple cores is asymmetric. In some embodiments, the processor 1204 can be a microprocessor, a microcontroller, a digital signal processor (“DSP”), or a combination of these and/or other types of processors. In some embodiments, the processor 1204 can include multiple elements such as a core, one or more registers, and one or more processing units such as an arithmetic logic unit (ALU), a floating point unit (FPU), a graphics processing unit (GPU), a physics processing unit (PPU), a digital system processing (DSP) unit, or combinations of these and/or other such processing units. [0092] To enable user interaction with the computing system architecture 1200, an input device 1216 can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, pen, and other such input devices. An output device 1218 can also be one or more of a number of output mechanisms known to those of skill in the art including, but not limited to, monitors, speakers, printers, haptic devices, and other such output devices. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the computing system architecture 1200. In some embodiments, the input device 1216 and/or the output device 1218 can be coupled to the computing device 1202 using a remote connection device such as, for example, a communication interface such as the network interface 1220 described herein. In such embodiments, the communication interface can govern and manage the input and output received from the attached input device 1216 and/or output device 1218. As may be contemplated, there is no restriction on operating on any particular hardware arrangement and accordingly the basic features here may easily be substituted for other hardware, software, or firmware arrangements as they are developed.

[0093] In some embodiments, the storage device 1210 can be described as nonvolatile storage or non-volatile memory. Such non-volatile memory or non-volatile storage can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, RAM, ROM, and hybrids thereof.

[0094] As described above, the storage device 1210 can include hardware and/or software services such as service 1212 that can control or configure the processor 1204 to perform one or more functions including, but not limited to, the methods, processes, functions, systems, and services described herein in various embodiments. In some embodiments, the hardware or software services can be implemented as modules. As illustrated in example computing system architecture 1200, the storage device 1210 can be connected to other parts of the computing device 1202 using the system connection 1206. In some embodiments, a hardware service or hardware module such as service 1212, that performs a function can include a software component stored in a non-transitory computer-readable medium that, in connection with the necessary hardware components, such as the processor 1204, connection 1206, cache 1208, storage device 1210, memory 1214, input device 1216, output device 1218, and so forth, can carry out the functions such as those described herein.

[0095] The disclosed systems and service of a fractography targeting application for inserting one or more leads of a deep-brain stimulation device can be performed using a computing system such as the example computing system illustrated in FIG. 12, using one or more components of the example computing system architecture 1200. An example computing system can include a processor (e.g., a central processing unit), memory, non-volatile memory, and an interface device. The memory may store data and/or and one or more code sets, software, scripts, etc. The components of the computer system can be coupled together via a bus or through some other known or convenient device.

[0096] In some embodiments, the processor can be configured to carry out some or all of methods and systems for generating data-driven fractography templates for inserting one or more leads of a deep-brain stimulation device described herein by, for example, executing code using a processor such as processor 1204 wherein the code is stored in memory such as memory 1214 as described herein. One or more of a user device, a provider server or system, a database system, or other such devices, services, or systems may include some or all of the components of the computing system such as the example computing system illustrated in FIG. 12, using one or more components of the example computing system architecture 1200 illustrated herein. As may be contemplated, variations on such systems can be considered as within the scope of the present disclosure.

[0097] This disclosure contemplates the computer system taking any suitable physical form. As example and not by way of limitation, the computer system can be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on- module (SOM)), a desktop computer system, a laptop or notebook computer system, a tablet computer system, a wearable computer system or interface, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, or a combination of two or more of these. Where appropriate, the computer system may include one or more computer systems; be unitary or distributed; span multiple locations; span multiple machines; and/or reside in a cloud computing system which may include one or more cloud components in one or more networks as described herein in association with the computing resources provider 1228. Where appropriate, one or more computer systems may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example and not by way of limitation, one or more computer systems may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.

[0098] The processor 1204 can be a conventional microprocessor such as an Intel® microprocessor, an AMD® microprocessor, a Motorola® microprocessor, or other such microprocessors. One of skill in the relevant art will recognize that the terms “machine-readable (storage) medium” or “computer-readable (storage) medium” include any type of device that is accessible by the processor.

[0099] The memory 1214 can be coupled to the processor 1204 by, for example, a connector such as connector 1206, or a bus. As used herein, a connector or bus such as connector 1206 is a communications system that transfers data between components within the computing device 1202 and may, in some embodiments, be used to transfer data between computing devices. The connector 1206 can be a data bus, a memory bus, a system bus, or other such data transfer mechanism. Examples of such connectors include, but are not limited to, an industry standard architecture (ISA” bus, an extended ISA (EISA) bus, a parallel AT attachment (PATA” bus (e.g., an integrated drive electronics (IDE) or an extended IDE (EIDE) bus), or the various types of parallel component interconnect (PCI) buses (e.g., PCI, PCIe, PCI-104, etc.).

[0100] The memory 1214 can include RAM including, but not limited to, dynamic RAM (DRAM), static RAM (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile random access memory (NVRAM), and other types of RAM. The DRAM may include error-correcting code (EEC). The memory can also include ROM including, but not limited to, programmable ROM (PROM), erasable and programmable ROM (EPROM), electronically erasable and programmable ROM (EEPROM), Flash Memory, masked ROM (MROM), and other types or ROM. The memory 1214 can also include magnetic or optical data storage media including read-only (e.g., CD ROM and DVD ROM) or otherwise (e.g., CD or DVD). The memory can be local, remote, or distributed.

[0101] As described above, the connector 1206 (or bus) can also couple the processor 1204 to the storage device 1210, which may include non-volatile memory or storage and which may also include a drive unit. In some embodiments, the nonvolatile memory or storage is a magnetic floppy or hard disk, a magnetic-optical disk, an optical disk, a ROM (e.g., a CD-ROM, DVD-ROM, EPROM, or EEPROM), a magnetic or optical card, or another form of storage for data. Some of this data is may be written, by a direct memory access process, into memory during execution of software in a computer system. The non-volatile memory or storage can be local, remote, or distributed. In some embodiments, the non-volatile memory or storage is optional. As may be contemplated, a computing system can be created with all applicable data available in memory. A typical computer system will usually include at least one processor, memory, and a device (e.g., a bus) coupling the memory to the processor.

[0102] Software and/or data associated with software can be stored in the nonvolatile memory and/or the drive unit. In some embodiments (e.g., for large programs) it may not be possible to store the entire program and/or data in the memory at any one time. In such embodiments, the program and/or data can be moved in and out of memory from, for example, an additional storage device such as storage device 1210. Nevertheless, it should be understood that for software to run, if necessary, it is moved to a computer readable location appropriate for processing, and for illustrative purposes, that location is referred to as the memory herein. Even when software is moved to the memory for execution, the processor can make use of hardware registers to store values associated with the software, and local cache that, ideally, serves to speed up execution. As used herein, a software program is assumed to be stored at any known or convenient location (from non-volatile storage to hardware registers), when the software program is referred to as “implemented in a computer-readable medium.” A processor is considered to be “configured to execute a program” when at least one value associated with the program is stored in a register readable by the processor. [0103] The connection 1206 can also couple the processor 1204 to a network interface device such as the network interface 1220. The interface can include one or more of a modem or other such network interfaces including, but not limited to those described herein. It will be appreciated that the network interface 1220 may be considered to be part of the computing device 1202 or may be separate from the computing device 1202. The network interface 1220 can include one or more of an analog modem, Integrated Services Digital Network (ISDN) modem, cable modem, token ring interface, satellite transmission interface, or other interfaces for coupling a computer system to other computer systems. In some embodiments, the network interface 1220 can include one or more input and/or output (I/O) devices. The I/O devices can include, by way of example but not limitation, input devices such as input device 1216 and/or output devices such as output device 1218. For example, the network interface 1220 may include a keyboard, a mouse, a printer, a scanner, a display device, and other such components. Other examples of input devices and output devices are described herein. In some embodiments, a communication interface device can be implemented as a complete and separate computing device. [0104] In operation, the computer system can be controlled by operating system software that includes a file management system, such as a disk operating system. One example of operating system software with associated file management system software is the family of Windows® operating systems and their associated file management systems. Another example of operating system software with its associated file management system software is the Linux™ operating system and its associated file management system including, but not limited to, the various types and implementations of the Linux® operating system and their associated file management systems. The file management system can be stored in the nonvolatile memory and/or drive unit and can cause the processor to execute the various acts required by the operating system to input and output data and to store data in the memory, including storing files on the non-volatile memory and/or drive unit. As may be contemplated, other types of operating systems such as, for example, MacOS®, other types of UNIX® operating systems (e.g., BSD™ and descendants, Xenix™, SunOS™, HP-UX®, etc.), mobile operating systems (e.g., iOS® and variants, Chrome®, Ubuntu Touch®, watchOS®, Windows 10 Mobile®, the Blackberry® OS, etc.), and real-time operating systems (e.g., VxWorks®, QNX®, eCos®, RTLinux®, etc.) may be considered as within the scope of the present disclosure. As may be contemplated, the names of operating systems, mobile operating systems, real-time operating systems, languages, and devices, listed herein may be registered trademarks, service marks, or designs of various associated entities.

[0105] In some embodiments, the computing device 1202 can be connected to one or more additional computing devices such as computing device 1224 via a network 1222 using a connection such as the network interface 1220. In such embodiments, the computing device 1224 may execute one or more services 1226 to perform one or more functions under the control of, or on behalf of, programs and/or services operating on computing device 1202. In some embodiments, a computing device such as computing device 1224 may include one or more of the types of components as described in connection with computing device 1202 including, but not limited to, a processor such as processor 1204, a connection such as connection 1206, a cache such as cache 1208, a storage device such as storage device 1210, memory such as memory 1214, an input device such as input device 1216, and an output device such as output device 1218. In such embodiments, the computing device 1224 can carry out the functions such as those described herein in connection with computing device 1202. In some embodiments, the computing device 1202 can be connected to a plurality of computing devices such as computing device 1224, each of which may also be connected to a plurality of computing devices such as computing device 1224. Such an embodiment may be referred to herein as a distributed computing environment.

[0106] The network 1222 can be any network including an internet, an intranet, an extranet, a cellular network, a Wi-Fi network, a local area network (LAN), a wide area network (WAN), a satellite network, a Bluetooth® network, a virtual private network (VPN), a public switched telephone network, an infrared (IR) network, an internet of things (loT network) or any other such network or combination of networks. Communications via the network 1222 can be wired connections, wireless connections, or combinations thereof. Communications via the network 1222 can be made via a variety of communications protocols including, but not limited to, Transmission Control Protocol/lnternet Protocol (TCP/IP), User Datagram Protocol (UDP), protocols in various layers of the Open System Interconnection (OSI) model, File Transfer Protocol (FTP), Universal Plug and Play (UPnP), Network File System (NFS), Server Message Block (SMB), Common Internet File System (CIFS), and other such communications protocols.

[0107] Communications over the network 1222, within the computing device 1202, within the computing device 1224, or within the computing resources provider 1228 can include information, which also may be referred to herein as content. The information may include text, graphics, audio, video, haptics, and/or any other information that can be provided to a user of the computing device such as the computing device 1202. In some embodiments, the information can be delivered using a transfer protocol such as Hypertext Markup Language (HTML), Extensible Markup Language (XML), JavaScript®, Cascading Style Sheets (CSS), JavaScript® Object Notation (JSON), and other such protocols and/or structured languages. The information may first be processed by the computing device 1202 and presented to a user of the computing device 1202 using forms that are perceptible via sight, sound, smell, taste, touch, or other such mechanisms. In some embodiments, communications over the network 1222 can be received and/or processed by a computing device configured as a server. Such communications can be sent and received using PHP: Hypertext Preprocessor (“PHP”), Python™, Ruby, Perl® and variants, Java®, HTML, XML, or another such server-side processing language.

[0108] In some embodiments, the computing device 1202 and/or the computing device 1224 can be connected to a computing resources provider 1228 via the network 1222 using a network interface such as those described herein (e.g. network interface 1220). In such embodiments, one or more systems (e.g., service 1230 and service 1232) hosted within the computing resources provider 1228 (also referred to herein as within “a computing resources provider environment”) may execute one or more services to perform one or more functions under the control of, or on behalf of, programs and/or services operating on computing device 1202 and/or computing device 1224. Systems such as service 1230 and service 1232 may include one or more computing devices such as those described herein to execute computer code to perform the one or more functions under the control of, or on behalf of, programs and/or services operating on computing device 1202 and/or computing device 1224. [0109] For example, the computing resources provider 1228 may provide a service, operating on service 1230 to store data for the computing device 1202 when, for example, the amount of data that the computing device 1202 exceeds the capacity of storage device 1210. In another example, the computing resources provider 1228 may provide a service to first instantiate a virtual machine (VM) on service 1232, use that VM to access the data stored on service 1232, perform one or more operations on that data, and provide a result of those one or more operations to the computing device 1202. Such operations (e.g., data storage and VM instantiation) may be referred to herein as operating “in the cloud,” “within a cloud computing environment,” or “within a hosted virtual machine environment,” and the computing resources provider 1228 may also be referred to herein as “the cloud.” Examples of such computing resources providers include, but are not limited to Amazon® Web Services (AWS®), Microsoft’s Azure®, IBM Cloud®, Google Cloud®, Oracle Cloud® etc.

[0110] Services provided by a computing resources provider 1228 include, but are not limited to, data analytics, data storage, archival storage, big data storage, virtual computing (including various scalable VM architectures), blockchain services, containers (e.g., application encapsulation), database services, development environments (including sandbox development environments), e-commerce solutions, game services, media and content management services, security services, server-less hosting, virtual reality (VR) systems, and augmented reality (AR) systems. Various techniques to facilitate such services include, but are not be limited to, virtual machines, virtual storage, database services, system schedulers (e.g., hypervisors), resource management systems, various types of short-term, midterm, long-term, and archival storage devices, etc.

[0111] As may be contemplated, the systems such as service 1230 and service 1232 may implement versions of various services (e.g., the service 1212 or the service 1226) on behalf of, or under the control of, computing device 1202 and/or computing device 1224. Such implemented versions of various services may involve one or more virtualization techniques so that, for example, it may appear to a user of computing device 1202 that the service 1212 is executing on the computing device 1202 when the service is executing on, for example, service 1230. As may also be contemplated, the various services operating within the computing resources provider 1228 environment may be distributed among various systems within the environment as well as partially distributed onto computing device 1224 and/or computing device 1202.

[0112] Client devices, user devices, computer resources provider devices, network devices, and other devices can be computing systems that include one or more integrated circuits, input devices, output devices, data storage devices, and/or network interfaces, among other things. The integrated circuits can include, for example, one or more processors, volatile memory, and/or non-volatile memory, among other things such as those described herein. The input devices can include, for example, a keyboard, a mouse, a key pad, a touch interface, a microphone, a camera, and/or other types of input devices including, but not limited to, those described herein. The output devices can include, for example, a display screen, a speaker, a haptic feedback system, a printer, and/or other types of output devices including, but not limited to, those described herein. A data storage device, such as a hard drive or flash memory, can enable the computing device to temporarily or permanently store data. A network interface, such as a wireless or wired interface, can enable the computing device to communicate with a network. Examples of computing devices (e.g., the computing device 1202) include, but is not limited to, desktop computers, laptop computers, server computers, hand-held computers, tablets, smart phones, personal digital assistants, digital home assistants, wearable devices, smart devices, and combinations of these and/or other such computing devices as well as machines and apparatuses in which a computing device has been incorporated and/or virtually implemented.

[0113] The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general purposes computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, performs one or more of the methods described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may comprise memory or data storage media, such as that described herein. The techniques additionally, or alternatively, may be realized at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer, such as propagated signals or waves.

[0114] he program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, an application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general purpose processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor), a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated software modules or hardware modules configured for implementing a suspended database update system.

[0115] As used herein, the term “machine-readable media” and equivalent terms “machine-readable storage media,” “computer-readable media,” and “computer- readable storage media” refer to media that includes, but is not limited to, portable or non-portable storage devices, optical storage devices, removable or non-removable storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and/or data. A computer-readable medium may include a non- transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), solid state drives (SSD), flash memory, memory or memory devices.

[0116] A machine-readable medium or machine-readable storage medium may have stored thereon code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, or the like. Further examples of machine-readable storage media, machine-readable media, or computer-readable (storage) media include but are not limited to recordable type media such as volatile and non-volatile memory devices, floppy and other removable disks, hard disk drives, optical disks (e.g., CDs, DVDs, etc.), among others, and transmission type media such as digital and analog communication links.

[0117] As may be contemplated, while examples herein may illustrate or refer to a machine-readable medium or machine-readable storage medium as a single medium, the term “machine-readable medium” and “machine-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” and “machine-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the system and that cause the system to perform any one or more of the methodologies or modules of disclosed herein.

[0118] Some portions of the detailed description herein may be presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0119] It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or “generating” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within registers and memories of the computer system into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

[0120] It is also noted that individual implementations may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram (e.g., the example process 700 of FIG.7). Although a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process illustrated in a figure is terminated when its operations are completed, but could have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0121] In some embodiments, one or more implementations of an algorithm such as those described herein may be implemented using a machine learning or artificial intelligence algorithm. Such a machine learning or artificial intelligence algorithm may be trained using supervised, unsupervised, reinforcement, or other such training techniques. For example, a set of data may be analyzed using one of a variety of machine learning algorithms to identify correlations between different elements of the set of data without supervision and feedback (e.g., an unsupervised training technique). A machine learning data analysis algorithm may also be trained using sample or live data to identify potential correlations. Such algorithms may include k- means clustering algorithms, fuzzy c-means (FCM) algorithms, expectationmaximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms, and the like. Other examples of machine learning or artificial intelligence algorithms include, but are not limited to, genetic algorithms, backpropagation, reinforcement learning, decision trees, liner classification, artificial neural networks, anomaly detection, and such. More generally, machine learning or artificial intelligence methods may include regression analysis, dimensionality reduction, metalearning, reinforcement learning, deep learning, and other such algorithms and/or methods. As may be contemplated, the terms “machine learning” and “artificial intelligence” are frequently used interchangeably due to the degree of overlap between these fields and many of the disclosed techniques and algorithms have similar approaches.

[0122] As an example of a supervised training technique, a set of data can be selected fortraining of the machine learning model to facilitate identification of correlations between members of the set of data. The machine learning model may be evaluated to determine, based on the sample inputs supplied to the machine learning model, whether the machine learning model is producing accurate correlations between members of the set of data. Based on this evaluation, the machine learning model may be modified to increase the likelihood of the machine learning model identifying the desired correlations. The machine learning model may further be dynamically trained by soliciting feedback from users of a system as to the efficacy of correlations provided by the machine learning algorithm or artificial intelligence algorithm (i.e., the supervision). The machine learning algorithm or artificial intelligence may use this feedback to improve the algorithm for generating correlations (e.g., the feedback may be used to further train the machine learning algorithm or artificial intelligence to provide more accurate correlations).

[0123] The various examples of flowcharts, flow diagrams, data flow diagrams, structure diagrams, or block diagrams discussed herein may further be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a computer- readable or machine-readable storage medium (e.g., a medium for storing program code or code segments) such as those described herein. A processor(s), implemented in an integrated circuit, may perform the necessary tasks.

[0124] The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the implementations disclosed herein may be implemented as electronic hardware, computer software, firmware, or combinations thereof. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0125] It should be noted, however, that the algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the methods of some examples. The required structure for a variety of these systems will appear from the description below. In addition, the techniques are not described with reference to any particular programming language, and various examples may thus be implemented using a variety of programming languages. [0126] In various implementations, the system operates as a standalone device or may be connected (e.g., networked) to other systems. In a networked deployment, the system may operate in the capacity of a server or a client system in a clientserver network environment, or as a peer system in a peer-to-peer (or distributed) network environment.

[0127] The system may be a server computer, a client computer, a personal computer (PC), a tablet PC (e.g., an iPad®, a Microsoft Surface®, a Chromebook®, etc.), a laptop computer, a set-top box (STB), a personal digital assistants (PDA), a mobile device (e.g., a cellular telephone, an iPhone®, and Android® device, a Blackberry®, etc.), a wearable device, an embedded computer system, an electronic book reader, a processor, a telephone, a web appliance, a network router, switch or bridge, or any system capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that system. The system may also be a virtual system such as a virtual version of one of the aforementioned devices that may be hosted on another computer device such as the computer device 1202. [0128] In general, the routines executed to implement the implementations of the disclosure, may be implemented as part of an operating system or a specific application, component, program, object, module or sequence of instructions referred to as “computer programs.” The computer programs typically comprise one or more instructions set at various times in various memory and storage devices in a computer, and that, when read and executed by one or more processing units or processors in a computer, cause the computer to perform operations to execute elements involving the various aspects of the disclosure.

[0129] Moreover, while examples have been described in the context of fully functioning computers and computer systems, those skilled in the art will appreciate that the various examples are capable of being distributed as a program object in a variety of forms, and that the disclosure applies equally regardless of the particular type of machine or computer-readable media used to actually effect the distribution. [0130] In some circumstances, operation of a memory device, such as a change in state from a binary one to a binary zero or vice-versa, for example, may comprise a transformation, such as a physical transformation. With particular types of memory devices, such a physical transformation may comprise a physical transformation of an article to a different state or thing. For example, but without limitation, for some types of memory devices, a change in state may involve an accumulation and storage of charge or a release of stored charge. Likewise, in other memory devices, a change of state may comprise a physical change or transformation in magnetic orientation or a physical change or transformation in molecular structure, such as from crystalline to amorphous or vice versa. The foregoing is not intended to be an exhaustive list of all examples in which a change in state for a binary one to a binary zero or vice-versa in a memory device may comprise a transformation, such as a physical transformation. Rather, the foregoing is intended as illustrative examples. [0131] A storage medium typically may be non-transitory or comprise a non- transitory device. In this context, a non-transitory storage medium may include a device that is tangible, meaning that the device has a concrete physical form, although the device may change its physical state. Thus, for example, non-transitory refers to a device remaining tangible despite this change in state.

[0132] The above description and drawings are illustrative and are not to be construed as limiting or restricting the subject matter to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure and may be made thereto without departing from the broader scope of the embodiments as set forth herein. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description.

[0133] As used herein, the terms “connected,” “coupled,” or any variant thereof when applying to modules of a system, means any connection or coupling, either direct or indirect, between two or more elements; the coupling of connection between the elements can be physical, logical, or any combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import, when used in this application, shall refer to this application as a whole and not to any particular portions of this application. Where the context permits, words in the above Detailed Description using the singular or plural number may also include the plural or singular number respectively. The word “or,” in reference to a list of two or more items, covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, or any combination of the items in the list.

[0134] As used herein, the terms “a” and “an” and “the” and other such singular referents are to be construed to include both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0135] As used herein, the terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended (e.g., “including” is to be construed as “including, but not limited to”), unless otherwise indicated or clearly contradicted by context.

[0136] As used herein, the recitation of ranges of values is intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated or clearly contradicted by context. Accordingly, each separate value of the range is incorporated into the specification as if it were individually recited herein.

[0137] As used herein, use of the terms “set” (e.g., “a set of items”) and “subset” (e.g., “a subset of the set of items”) is to be construed as a nonempty collection including one or more members unless otherwise indicated or clearly contradicted by context. Furthermore, unless otherwise indicated or clearly contradicted by context, the term “subset” of a corresponding set does not necessarily denote a proper subset of the corresponding set but that the subset and the set may include the same elements (i.e. , the set and the subset may be the same).

[0138] As used herein, use of conjunctive language such as “at least one of A, B, and C” is to be construed as indicating one or more of A, B, and C (e.g., any one of the following nonempty subsets of the set {A, B, C}, namely: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, or {A, B, C}) unless otherwise indicated or clearly contradicted by context. Accordingly, conjunctive language such as “as least one of A, B, and C” does not imply a requirement for at least one of A, at least one of B, and at least one of C.

[0139] As used herein, the use of examples or exemplary language (e.g., “such as” or “as an example”) is intended to more clearly illustrate embodiments and does not impose a limitation on the scope unless otherwise claimed. Such language in the specification should not be construed as indicating any non-claimed element is required for the practice of the embodiments described and claimed in the present disclosure.

[0140] As used herein, where components are described as being “configured to” perform certain operations, such configuration can be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof.

[0141] Those of skill in the art will appreciate that the disclosed subject matter may be embodied in other forms and manners not shown below. It is understood that the use of relational terms, if any, such as first, second, top and bottom, and the like are used solely for distinguishing one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. [0142] While processes or blocks are presented in a given order, alternative implementations may perform routines having steps, or employ systems having blocks, in a different order, and some processes or blocks may be deleted, moved, added, subdivided, substituted, combined, and/or modified to provide alternative or sub combinations. Each of these processes or blocks may be implemented in a variety of different ways. Also, while processes or blocks are at times shown as being performed in series, these processes or blocks may instead be performed in parallel, or may be performed at different times. Further any specific numbers noted herein are only examples: alternative implementations may employ differing values or ranges.

[0143] The teachings of the disclosure provided herein can be applied to other systems, not necessarily the system described above. The elements and acts of the various examples described above can be combined to provide further examples. [0144] Any patents and applications and other references noted above, including any that may be listed in accompanying filing papers, are incorporated herein by reference. Aspects of the disclosure can be modified, if necessary, to employ the systems, functions, and concepts of the various references described above to provide yet further examples of the disclosure.

[0145] These and other changes can be made to the disclosure in light of the above Detailed Description. While the above description describes certain examples, and describes the best mode contemplated, no matter how detailed the above appears in text, the teachings can be practiced in many ways. Details of the system may vary considerably in its implementation details, while still being encompassed by the subject matter disclosed herein. As noted above, particular terminology used when describing certain features or aspects of the disclosure should not be taken to imply that the terminology is being redefined herein to be restricted to any specific characteristics, features, or aspects of the disclosure with which that terminology is associated. In general, the terms used in the following claims should not be construed to limit the disclosure to the specific implementations disclosed in the specification, unless the above Detailed Description section explicitly defines such terms. Accordingly, the actual scope of the disclosure encompasses not only the disclosed implementations, but also all equivalent ways of practicing or implementing the disclosure under the claims. [0146] While certain aspects of the disclosure are presented below in certain claim forms, the inventors contemplate the various aspects of the disclosure in any number of claim forms. Any claims intended to be treated under 45 U.S.C. § 112(f) will begin with the words “means for”. Accordingly, the applicant reserves the right to add additional claims after filing the application to pursue such additional claim forms for other aspects of the disclosure.

[0147] The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Certain terms that are used to describe the disclosure are discussed above, or elsewhere in the specification, to provide additional guidance to the practitioner regarding the description of the disclosure. For convenience, certain terms may be highlighted, for example using capitalization, italics, and/or quotation marks. The use of highlighting has no influence on the scope and meaning of a term; the scope and meaning of a term is the same, in the same context, whether or not it is highlighted. It will be appreciated that same element can be described in more than one way.

[0148] Consequently, alternative language and synonyms may be used for any one or more of the terms discussed herein, nor is any special significance to be placed upon whether or not a term is elaborated or discussed herein. Synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and is not intended to further limit the scope and meaning of the disclosure or of any exemplified term. Likewise, the disclosure is not limited to various examples given in this specification. [0149] Without intent to further limit the scope of the disclosure, examples of instruments, apparatus, methods and their related results according to the examples of the present disclosure are given below. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control. [0150] Some portions of this description describe examples in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof.

[0151] Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In some examples, a software module is implemented with a computer program object comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. [0152] Examples may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.

[0153] Examples may also relate to an object that is produced by a computing process described herein. Such an object may comprise information resulting from a computing process, where the information is stored on a non-transitory, tangible computer readable storage medium and may include any implementation of a computer program object or other data combination described herein.

[0154] The language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the subject matter. It is therefore intended that the scope of this disclosure be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the examples is intended to be illustrative, but not limiting, of the scope of the subject matter, which is set forth in the following claims.

[0155] Specific details were given in the preceding description to provide a thorough understanding of various implementations of systems and components for a contextual connection system. It will be understood by one of ordinary skill in the art, however, that the implementations described above may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0156] The foregoing detailed description of the technology has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the technology to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. The described embodiments were chosen in order to best explain the principles of the technology, its practical application, and to enable others skilled in the art to utilize the technology in various embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the technology be defined by the claim.

Claims

1 . A computer-implemented method for generating data-driven fractography templates for inserting one or more leads of a deep-brain stimulation (DBS) device, the method comprising: accessing a plurality of images associated with one or more subjects, wherein the plurality of images depict at least part of a brain of the one or more subjects, and wherein a subject of the one or more subjects is associated with a known outcome from a DBS procedure performed for a disease or condition; processing, for each image of the plurality of images, the image using a tractography algorithm to identify a set of white-matter activation pathways of the brain; generating a population-based white-matter activation pathway map by merging the sets of white-matter activation pathways of the plurality of images; defining, from the population-based white-matter activation pathway map, a tractography template associated with the disease or condition, wherein the tractography template includes a set of image masks, and wherein each of the set of image masks identifies a target white-matter region within the brain for inserting the one or more leads of the DBS device; and outputting the tractography template.

2. The computer-implemented method of claim 1 , wherein the disease or condition includes depression, Parkinson’s disease, dystonia, dementia, tremors, epilepsy, and obsessive compulsive disorder.

3. The computer-implemented method of claim 1 or 2, wherein the plurality of images includes at least one of a Diffusion weighted imaging (DWI) image and a Computed Tomography (CT) image.

4. The computer-implemented method of claim 3, wherein the CT image is captured at a particular time point, wherein the particular time point is different from another time point at which the DWI image was captured.

5. The computer-implemented method of any one of claims 1 to 4, wherein the target white-matter region includes a region within Subcallosal Cingulate Cortex (SCC), Forceps Minor, Subcortical Junction, or Cingulum bundle of the brain.

6. The computer-implemented method of any one of claims 1 to 5, wherein processing the image using the tractography algorithm includes: for each image of the plurality of images: identifying an estimated volume of tissue activated (VTA) from the at least part of the brain depicted in the image; and applying the tractography algorithm to the estimated VTA to identify the set of white-matter activation pathways of the image.

7. The computer-implemented method of any one of claims 1 to 6, wherein generating the population-based white-matter activation pathway map includes: for each image of the plurality of images: detecting one or more false positive white-matter activation pathways from the set of white-matter activation pathways; and removing the one or more false positive white-matter activation pathways from the set of white-matter activation pathways.

8. The computer-implemented method of any one of claims 1 to 7, further comprising applying an image-registration algorithm to the plurality of images to align the plurality of images to a single coordinate system.

9. A computer-implemented method for identifying target whitematter regions of a brain, the method comprising: accessing one or more images associated with a particular subject, wherein the one or more images depict at least part of a brain of the particular subject, wherein the particular subject is associated with a disease or condition; extracting an image region from the one or more images, wherein the extracted image region depicts a Subcallosal Cingulate Cortex (SCC) region of the brain; accessing a tractography template associated with the disease or condition, wherein the tractography template includes a set of image masks, and wherein the tractography template is generated based on at least one subject associated with a known outcome from a DBS procedure performed for the disease or condition; applying the tractography template to the extracted image region to determine a sub-region within the extract image region, wherein the sub-region includes a set of voxels, and wherein each voxel of the set of voxels includes a number of possible connections between the voxel and the set of image masks exceeding a predetermined threshold; and identifying, based on the sub-region, one or more target white-matter regions of the brain of the particular subject

10. The computer-implemented method of claim 9, further comprising: overlaying the tractography template on the extracted image region to identify a set of white-matter bundle regions; and determining, from the set of white-matter bundle regions, additional target white-matter regions of the brain of the particular subject.

11 . The computer-implemented method of claim 10, further comprising: comparing the one or more target white-matter regions with the additional target white-matter regions to identify one or more cross-validated target white-matter regions of the brain of the particular subject.

12. The computer-implemented method of any one of claims 9 to

11 , wherein the disease or condition includes depression, Parkinson’s disease, dystonia, dementia, tremors, and epilepsy.

13. The computer-implemented method of any one of claims 9 to

12, further comprising administering the DBS procedure to the particular subject by inserting one or more leads of a DBS device into the one or more target white-matter regions of the brain.

14. The computer-implemented method of any one of claims 9 to 13, wherein the one or more images include a Diffusion weighted imaging (DWI) image.

15. A computer product comprising a non-transitory computer readable medium storing a plurality of instructions that when executed cause a computer system to perform the method of any one of the preceding claims.

16. A system comprising: the computer product of claim 15; and one or more processors for executing instructions stored on the computer readable medium.

17. A system comprising means for performing any of the above methods.

18. A system comprising one or more processors configured to perform any of the above methods.

19. A system comprising modules that respectively perform the steps of any of the above methods.