Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
  • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
  • A61B5/4082—Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/369—Electroencephalography [EEG]
  • A61B5/372—Analysis of electroencephalograms
  • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/369—Electroencephalography [EEG]
  • A61B5/377—Electroencephalography [EEG] using evoked responses
  • A61B5/378—Visual stimuli
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
  • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
  • A61B5/4088—Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
  • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
  • A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy

Definitions

• the invention relates to a method and a device for determining the involvement of brain networks in the implementation of processes. More particularly, the invention relates to a device and a method for determining a correlation between the implementation of a given process (or task) and activation and / or connection. of brain networks. Even more specifically, the invention quantifies the level of interaction between the brain networks (functional connectivity) during the implementation of a given task.
• Cognitive deficits in Parkinson's disease are thought to be related to impaired functional brain connectivity. To date, changes in the cognitive functions of Parkinson's disease have never been explored with a dense EEG in order to establish a relationship between the degree of cognitive impairment on the one hand and the alterations in functional connectivity of brain networks, on the other hand.
• the invention relates to a method for determining a datum representative of a cerebral marker, said datum being obtained from at least one cerebral network involved in carrying out a given task, the method being implemented by means of an electronic device comprising means for obtaining encephalographic activity data.
• this method comprises the succession of the following steps:
• a step of processing encephalographic activity data delivering at least one functional connectivity matrix representative of a connectivity between cortical sources derived from said encephalographic activity data, each coefficient of said matrix being representative of a connectivity between two sources cortical;
• a step of statistically analyzing said at least one functional connectivity matrix delivering a probabilistic matrix of presence of at least one brain network
• said step of obtaining a cerebral marker (EWCI) as a function of said at least one cerebral network matrix comprises the implementation of the following formula:
• N the number of edges of the cerebral network
• Wi represents the weight of the edge i in the brain network.
• said step of processing the encephalographic activity data comprises:
• a plurality of pairwise connectivity analysis steps which includes, for each pair of cortical sources, at least one step of determining connectivity between the two sources of said pair;
• said step of processing encephalographic activity data delivering a square matrix, called functional connectivity, comprising, for each cortical source a connectivity value with all other previously determined cortical sources.
• said step of statistically analyzing said at least one functional connectivity matrix comprises, for a current functional connectivity matrix, the implementation of a network-based statistical analysis method, called the NBS method. .
• said step of statistically analyzing said at least one functional connectivity matrix comprises, for a current functional connectivity matrix: a covariance analysis step of each coefficient of the current functional connectivity matrix, delivering a probabilistic matrix, in which each coefficient is representative of a probability p of rejection of the null hypothesis for an edge of a cerebral network associated with said coefficient of the current functional connectivity matrix;
• the threshold T of component formation is between 0.01 and 0.001.
• the component formation threshold T is equal to 0.005.
• the invention also relates to an electronic device for determining a datum representative of a cerebral marker, said datum being obtained from at least one cerebral network involved in carrying out a given task, the device comprising means for obtaining data of encephalographic activities.
• a device comprises: means for processing encephalographic activity data, delivering at least one functional connectivity matrix representative of connectivity between cortical sources resulting from said encephalographic activity data, each coefficient of said matrix being representative of a connectivity between two cortical sources;
• the various steps of the methods according to the invention are implemented by one or more software or computer programs, comprising software instructions intended to be executed by a data processor of a relay module according to the invention. invention and being designed to control the execution of the various process steps.
• the invention is also directed to a program that can be executed by a computer or a data processor, which program includes instructions for controlling the execution of the steps of a method as mentioned above.
• This program can use any programming language, and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other form desirable shape.
• the invention also provides a data carrier readable by a data processor, and including instructions of a program as mentioned above.
• the information carrier may be any entity or device capable of storing the program.
• the medium may comprise storage means, such as a ROM, for example a CD ROM or a microelectronic circuit ROM, or a magnetic recording medium, for example a floppy disk or a disk. hard.
• the information medium may be a transmissible medium such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means.
• the program according to the invention can be downloaded in particular on an Internet type network.
• the information carrier may be an integrated circuit in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the method in question.
• the invention is implemented by means of software and / or hardware components.
• module may correspond in this document as well to a software component, a hardware component or a set of hardware and software components.
• a software component corresponds to one or more computer programs, one or more subroutines of a program, or more generally to any element of a program or software capable of implementing a function or a program. set of functions, as described below for the module concerned.
• Such a software component is executed by a data processor of a physical entity (terminal, server, gateway, router, etc.) and is capable of accessing the hardware resources of this physical entity (memories, recording media, bus communication cards, input / output electronic cards, user interfaces, etc.).
• a hardware component corresponds to any element of a hardware set (or hardware) able to implement a function or a set of functions, as described below for the module concerned. It may be a hardware component that is programmable or has an integrated processor for executing software, for example an integrated circuit, a smart card, a memory card, an electronic card for executing a firmware ( firmware), etc.
• FIG. 1 generally presents the implementation of the method in which the invention is inscribed
• Figure 2 presents the results of frequency and network-based analyzes
• Figure 5 is an association diagram between cognitive score and connectivity index for A) Gl, G2 and G3 and B) Gl and G2;
• Figure 6 describes a device for implementing the proposed technique
• Figure 7 generally illustrates the method of the invention.
• the invention relates to a method and device for identifying altered brain networks associated with cognitive phenotypes of Parkinson's disease (and other diseases) using dense EEG data recorded at rest, with eyes closed. . It is an object of the invention to construct at least one static marker that will likely be used by another method or device to identify the presence or absence of early signs of an onset of the disease.
• the inventors have sought a solution making it possible to synthesize, in a given index, the degree of functional connectivity of brain networks implemented during the execution of a given task, which may in the context of the present be a task requiring action. on the part of the individual or a task to remain motionless, without performing any action, that is to say to be in a state of rest.
• the invention relates to a method of determining a datum representative of a cerebral marker, the data being obtained from at least one brain network involved in performing a given task, the method comprising:
• a step of processing (10) encephalographic activity data, delivering at least one functional connectivity matrix representative of connectivity between cortical sources derived from said encephalographic activity data, each matrix coefficient being indicative of connectivity between two cortical sources;
• the encephalographic data processing step which is described later, comprises:
• a surface electronic device for measuring encephalographic signals as a function of at least one pretreatment parameter;
• a device is for example a high density electroencephalography device;
• a plurality of pairwise connectivity analysis steps (103) which comprises, for each pair of cortical sources, at least one step of determining connectivity between the two sources of the pair;
• the step of processing the encephalographic activity data delivers a square matrix, called functional connectivity, comprising, for each cortical source a connectivity value with all the other previously determined cortical sources.
• the statistical analysis step (20), implemented using functional connectivity matrices, comprises, for a current functional connectivity matrix:
• ANCOVA covariance
• brain networks are characterized by sets of nodes (brain regions) connected by edges. Once nodes and edges are defined from neuroimaging data, network (organization) topological properties can be studied by graph theory metrics and functional connectivity by network-based statistics.
• neuroimaging techniques functional magnetic resonance imaging -I Mf-, magneto / electroencephalography -MEG / EEG-
• these combined approaches are used to characterize the functional changes associated with conditions such as Alzheimer's, Parkinson's disease, Huntington's disease, epilepsy, schizophrenia, autism and others.
• Parkinson's disease is the second most common neurodegenerative disease after Alzheimer's disease and affects more than 1% of people over 60 years of age. In addition to typical motor symptoms, cognitive deficits are common in Parkinson's disease. However, they are heterogeneous in their clinical presentation and progression. Early detection and evaluation These quantitative cognitive deficits are a critical clinical problem, not only to characterize the disease but also its progression. Several studies have previously reported alterations in cerebral network organization and functional connectivity associated with cognitive deficits in Parkinson's disease using standard I Mf, MEG, and EEG.
• the inventors recorded a dense, resting, closed-eyed EEG of individuals with Parkinson's disease whose cognitive profile was identified by cluster analysis of the results of a large battery of neuropsychological tests.
• the main objective of the inventors is to detect alterations in these functional networks according to the severity of the cognitive impairment.
• functional connectivity is examined using an "EEG source connectivity" method.
• EEG source connectivity Compared to fMRI studies of functional connectivity, a unique advantage of this method is that the networks can be directly identified at the level of the cerebral cortex from EEG recordings of the scalp, which consist of the direct measurement of the neuronal activity, contrary to the signals dependent on the level of oxygen in the blood (BOLD).
• BOLD level of oxygen in the blood
• the inventors assumed that brain network organization parameters would differ according to the cognitive state of individuals and that functional connectivity would be more impaired in individuals with cognitive deficits than cognitively intact or deficient individuals. less cognitive. From this assumption, the inventors have sought to construct an index (an index) that can be used to quantify this functional connectivity.
• an index an index
• the interest of the proposed and described methods is on the one hand the ability to identify characteristic networks on populations of individuals, and on the other hand, from these networks, to calculate an index, the index being a result to characterize the functional connectivity of networks.
• the proposed methods use the determination of functional networks, from data recorded from an individual, and methods of analyzing similarity and difference on these networks.
• the connectivity index that is calculated on these networks makes it possible to obtain a characteristic value from the weight of a large number of connections on the network pairs: the connectivity index is therefore considered to be the cerebral marker, d statistical origin, related to the implementation of the given task for an individual. Detailed explanations are given below, according to specific embodiments.
• Functional brain networks are identified using a method of determining dense EEG source connectivity. Paired functional connectivity is calculated for 68 brain regions in different EEG frequency bands. Brain network statistics are obtained both at the global level (network topology) and at the local level (inter-regional connections). The connectivity index (cerebral marker) is then calculated on the basis of a number of previously determined connectivity networks.
• dense EEGs are recorded with a cap having 128 channels including 122 scalp electrodes distributed according to the international system 10-05, two electrocardiogram electrodes and four bilateral electro-oculogram electrodes (EOG) for vertical and horizontal movements.
• EEG electro-oculogram electrodes
• the impedance of the electrodes is kept under 10 k ⁇ .
• the data is collected in this embodiment in a state of rest, eyes closed, for 10 minutes with the software BrainVision Recorder (Brain Products ® ).
• the subjects were ordered to do nothing and relax.
• the signals are sampled at 512 Hz and bandpass filtered between 1 and 45 Hz.
• the inventors selected the maximum number of four-second segments without artifact to perform the analyzes.
• An atlas-based approach was used to project EEG sensor signals onto an anatomical framework consisting of 68 cortical regions identified using the Desikan-Killiany atlas (Desikan et al., 2006) using Freesurfer software (http://freesurfer.net/).
• a model MRI and EEG data are co-recorded with the identification of the same anatomical landmarks (left and right pre-auricular points and nasion).
• a realistic head model is constructed by segmenting MRI using Freesurfer. The head field matrix is then calculated for a cortical mesh with 15,000 vertices using
• the method includes the use of a standard Fast Fourier Transform (FFT) for power spectrum analysis with the Welch technique and Hanning window (two second period and 50% overlap).
• FFT Fast Fourier Transform
• a relative power spectrum is calculated for each frequency band [delta (0.5-4 Hz); theta (4-8 Hz); alpha 1 (8-10 Hz); alpha 2 (10-13 Hz); beta (13-30 Hz); gamma (30-45 Hz)], with a frequency resolution of 0.5 Hz.
• POS is estimated at six frequency bands [delta (0.5-4 Hz); theta (4-8 Hz); alpha 1 (8-10 Hz); alpha 2 (10-13 Hz); beta (13-30 Hz); gamma (30-45 Hz)].
• the choice of wMNE / PLV is supported by two comparison analyzes performed and reported the superiority of wMNE / PLV over other reversal / connectivity combinations to accurately identify cortical brain networks from the leather EEG hairy during cognitive activity or epileptic activity.
• the inversion solutions are calculated using Brainstorm. Network measurements and network visualization are performed using BCT and EEGNET respectively.
• Networks can be illustrated by graphs, which are sets of nodes (brain regions) and edges (connectivity values) between these nodes.
• the method includes constructing 68-node graphs (i.e., the 68 previously identified cortical regions) and using all information from the functional connectivity matrix (phase lock value). This gives weighted, non-directed and fully connected networks in which the connection strength between each vertex pair (i.e., the weight) is defined as their connectivity value.
• Several metrics can be calculated to characterize weighted networks.
• the edge level reflects the functional connectivity by measuring each of the values correlation (weight) between the different brain regions. All of the above network measurements depend on the weights of the edges. As a result, they are standardized. They are expressed according to measurements calculated from random networks. Five hundred random substitution networks derived from the original networks are generated by the random reworking of the weights of the edges. Normalized values are calculated by dividing the original value by the average of the calculated values on the random graphs.
• Connectivity along network edges is characterized using network-based statistics.
• p values indicating the probability of rejection of the null hypothesis at each edge.
• a thresholded matrix is generated by applying to each p value a component formation threshold, T, and the size of each connected element in this thresholded matrix is obtained. This size of the components is then compared to that obtained for a zero distribution of maximum component sizes obtained using a permutation test to obtain p-values corrected for multiple comparisons.
• the NBS process finds subnets of connections considerably larger than would have been expected.
• Age and duration of formal education are entered as confounding factors in the ANCOVA for spectral and connectivity analyzes.
• Statistical analyzes are performed using SPSS Statistics 20.0 (IBM Corporation). A significance level of 0.01 (two-sided) is applied. Multiple test corrections are applied using the Bonferroni approach.
• the four metrics reflecting the global topology of the networks (P L , C c , Str and E G ) are calculated on the weighted non-directed graphs obtained for each subject of each group in all the frequency bands.
• the results show a decreasing trend when cognitive impairment worsened (from G1 to G3), in all frequency bands, with no significant difference.
• a typical example of the results obtained in the alpha 2 frequency band is shown in Figure 2.
• Figure 3 shows the results of the edge analysis performed using the NBS toolbox.
• the statistical tests (ANCOVA, corrected by permutation test) are applied to each connection in the networks calculated in all frequency bands (delta, theta, alpha 1, alpha 2 beta and gamma). Significant differences are found only between the networks calculated in the EEG alpha band (alpha 1 and alpha 2).
• a connected component comprising 229 edges and 57 regions, is statistically significant between G1 and G3 (p ⁇ 0.001, corrected using the permutation test, Figure 3C). Most of these reduced connections were parieto-frontal (14%), fronto-central (14%) and temporo-frontal (13%). Similar results are obtained on different threshold values (see Figure S2 and Figure S3 in the illustrative embodiment).
• EWCI edge connectivity index
• Figure 1 Structure of the examination. Individuals are classified by their cognitive performance as 1) cognitively intact individuals, 2) individuals with mild cognitive impairment and 3) individuals with severe cognitive impairment. Data: dense EEGs are recoded using 128 electrodes during the resting state (closed eyes). I M subjects were also available. The cortical sources are reconstructed by solving the inverse problem using the weighted minimum standard estimation (wMNE) method. An anatomical plotting was applied on the MRI model producing 68 regions of interest (Desikan-killany atlas) calculated using Freesurfer and then imported for another treatment in Brainstorm.
• wMNE weighted minimum standard estimation
• Functional connectivity was calculated between the 68 regional time series using the Phase Lock Value (PLV) method in six frequency bands: delta (0.5-4 Hz); theta (4-8 Hz); alpha 1 (8-10 Hz); alpha 2 (10-13 Hz); beta (13-30 Hz); gamma (30-45 Hz).
• the connectivity matrices are compared between the groups using two levels of network analysis i) high level topology where the inventors calculated four network metrics: the clustering coefficient, the strength, the characteristic path length and the overall efficiency; and ii) edge analysis where the inventors performed statistical analysis between the groups at each network connection using the network-based statistics (NBS) approach.
• NBS network-based statistics
• Figure 2 A. Frequency-Based Analysis: Standard deviation ⁇ mean values of the power spectral density for each group of individuals in six frequency bands: delta (0.5-4 Hz); theta (4-8 Hz); alpha 1 (8-10 Hz); alpha 2 (10-13 Hz); beta (13-30 Hz); gamma (30-45 Hz).
• Figure 3 edge analysis (alpha 2).
• Functional connection subnetworks showing a significant difference between the three groups in alpha 2.
• the top row presents representations based on a graph of these sub-networks, each region being represented by a red sphere drawn in according to the stereotactic coordinates of its centroid, and each edge of supra-threshold represented by a dark green line.
• the size of the node represents the number of significantly different connections from the node itself.
• connectivity was higher in G1> G2 (A), G1> G3 (B) and G2> G3 (C).
• the bottom row shows the proportion (%) of each type of connection in each subnet, as categorized by the lobes that each edge interconnects.
• F frontal
• T temporal
• P parietal
• C central
• O occipital.
• FIG. 4 Edge analysis (alpha 1). Functional connection subnetworks showing a significant difference between the three groups in alpha 1.
• the top row presents representations based on a graph of these subnetworks, each region being represented by a red sphere drawn in function of the stereotactic coordinates of its centroid, and each edge of supra-threshold represented by a dark green line.
• the size of the node represents the number of significantly different connections from the node itself.
• connectivity was higher in G2> G3 (A) and G1> G3 (B).
• the bottom row shows the proportion (%) of each type of connection in each subnet, as categorized by the lobes that each edge interconnects.
• F frontal
• T temporal
• P parietal
• C central
• O occipital.
• Figure 5 Dispersion diagram of the association between cognitive score and edge connectivity index for A) Gl, G2 and G3 and B) Gl and G2.
• the description also proposes a device for estimating networks and obtaining statistical markers.
• the device may be specifically designed to estimate networks and obtain statistical markers or any electronic device comprising a non-transitory computer readable medium and at least one processor configured by computer readable instructions stored in the non-transitory computer readable medium for setting implement any method of the description.
• the device for estimating the installation of a camera comprises a central processing unit (CPU) 62, a random access memory (RAM) 61, a read only memory (ROM) 63, a storage device which are connected via a bus in such a way that they can communicate with each other.
• the CPU controls the entire device by executing a loaded program in the RAM.
• the CPU also performs various functions by executing a program (s) (or application (s)) loaded into the RAM.
• RAM stores various kinds of data and / or program (s).
• the ROM also stores various kinds of data and / or program (s) (Pg).
• the storage device such as a hard disk drive, an SD card, a USB memory and so on, also stores various kinds of data and / or program (s).
• the device performs the method for estimating networks and obtaining statistical markers as a result of the CPU executing written instructions in a program (s) loaded into the RAM, the program (s) ( s) being read from the ROM or storage device and loaded into the RAM.
• the device may be a server, a computer, a tablet, a smartphone or a medical device therein.
• the device comprises at least one input adapted to receive data from a dense EEG, at least one other input parameter, the processor (s) for estimating networks and obtaining statistical markers, and at least one output adapted to output data associated with markers or networks.
• the description also relates to a computer program product comprising computer executable program code recorded on a computer-readable non-transitory storage medium, the computer executable program code, when executed, performing the method for estimating the installation of the computer program.
• a camera The computer program product may be recorded on a CD, hard drive, flash memory, or other suitable computer readable medium. It can also be downloaded from the Internet and installed in a device so as to estimate the pose of a camera as previously exposed.

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• 2017
  • 2017-02-27 FR FR1751585A patent/FR3063378A1/fr active Pending
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