Abstract: Electronic records are accessed from computer storage for a given subject, wherein the electronic records include natural language notes about the subject. Tokens are identified in the natural language notes. For each token, a corresponding intensity score is generated representing an intensity of match between the token and a particular dimension, wherein the intensity scores are each values on a first scale, wherein each dimension is one of a plurality of dimensions of a category out of a plurality of categories; generating rescaled-intensity scores from the intensity scores by rescaling the intensity scores from the first scale to a second scale different from the first scale. For each dimension of each category, a dimension-score is compiled based on the intensity scores; and categorizing the subject into at least one category based on the dimension scores. The subject is categorized into at least one category based on the dimension scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a mental health prediction for a patient. One of the methods includes obtaining brain data captured by one or more sensors characterizing a brain of a patient; determining a network graph from the brain data, wherein: a node of the network graph corresponds to a parcellation in the brain of the patient, and a subset of nodes of the network graph corresponds to a particular functional area of the brain; generating, for each of a plurality of nodes in the network graph, a measure of centrality of the node; generating, for the particular functional area of the brain and using the generated measures of centrality, a health score representing a measure of health of the functional area of the brain; generating a mental health prediction for the patient using the health score.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a mental health prediction for a patient using a centrality ranking of the brain of the patient. One of the methods includes obtaining brain data of a patient, wherein the brain data comprises, for each of a plurality of pairs of parcellations formed from a set of parcellations where each pair comprises a first parcellation and a second parcellation, data characterizing a number of tracts connecting the first parcellation and the second parcellation; determining a network graph from the brain data; generating, for each of a plurality of nodes in the network graph, a measure of centrality of the node; determining a centrality ranking of the plurality of nodes of the network graph according to the respective measures of centrality; generating a mental health prediction for the patient using the determined centrality ranking.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a subset of brain data of a patient. One of the methods comprises providing connectivity data for presentation to a user, the connectivity data characterizing, for each pair of parcellations comprising a first parcellation and a second parcellation from a plurality of parcellations, a degree of correlation between the brain activity of the first parcellation and the brain activity of the second parcellation in the brain of a patient; determining one or more elements of interest in the connectivity data; determining one or more parcellations associated with elements of interest in the connectivity data; obtaining brain atlas data; determining a subset of the brain atlas data associated with the determined parcellations; and providing the subset of the brain atlas data to a user device for rendering the subset to the user.

Abstract: The following provides an exemplary method. An image is generated. Scan data generated by the sensing of a subject brain is received and a sequence of volumetric-images are accessed. For at least some of the voxels in at least some of the volumetric images, a change value is determined for the voxel by comparing the current-intensity value of the voxel to the adjacent-intensity value of the comparison voxel. For at least some of the plurality volumetric images, a first-aggregate is determined using at least a mean value of change values of the volumetric image and a second-aggregate is determined using at least a median value of change values of the volumetric image. At least one, but not all, of the volumetric images is determined as invalid as a result of the aggregates.

Abstract: The following provides an exemplary method. An image is generated. Scan data generated by the sensing of a subject brain is received and a sequence of volumetric-images are accessed. For at least some of the voxels in at least some of the volumetric images, a change value is determined for the voxel by comparing the current-intensity value of the voxel to the adjacent-intensity value of the comparison voxel. For at least some of the plurality volumetric images, a first-aggregate is determined using at least a mean value of change values of the volumetric image and a second-aggregate is determined using at least a median value of change values of the volumetric image. At least one, but not all, of the volumetric images is determined as invalid as a result of the aggregates.

Abstract: A system and method of generating a graphical representation of a network of a subject human brain. The method comprises receiving, via a user interface, a selection of the network of the subject brain; determining, based on an MRI image of the subject brain and one or more identifiers associated with the selection, one or more parcellations of the subject brain (405); determining, using three-dimensional coordinates associated with each parcellation, corresponding tracts in a diffusion tensor image of the brain (425); and generating a graphical representation of the selected network (430), the graphical representation including at least one of (i) one or more surfaces representing the one or more parcellations, each surface generated using the coordinates, and (ii) the determined tracts.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining anomalous brain data. One of the methods includes obtaining brain data characterizing brain activity of a patient; for each of a plurality of pairs of parcellations comprising a first parcellation and a second parcellation, processing the brain data to generate a correlation between the brain activity of the first and second parcellations; obtaining second connectivity data that characterizes, for each of the plurality of pairs of parcellations, a normal range of correlations between the brain activity of the first and second parcellations; identifying one or more of the plurality of pairs of parcellations for which the correlation between brain activity of the first and second parcellations is outside of the corresponding normal range of correlations; and providing data characterizing the one or more identified pairs of parcellations for display to a user on a graphical interface.

Abstract: A cloud computing system is described that communicates with a virtual machine to reattach the face of a patient to brain imaging data before the brain imaging data is transmitted for display on a brain navigation system.

Abstract: DICOM data is automatically prepared for transit outside of the clinical-data infrastructure, by examining a plurality of metadata fields in the corresponding metadata in the DICOM data; identifying a first subset of the metadata fields as containing private information; identifying a second subset of the metadata fields as private-information free; accessing at least some of the plurality of layers of the DICOM data; and transforming the accessed layers into a single transmission-image, the transmission-image being in a format i) other than DICOM and ii) that stores the second subset of the metadata fields as transmission-metadata in a scheme that is non-redundant for a given transmission-image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying one or more invalid images characterizing the brain of a patient. One of the methods includes obtaining a data object comprising one or more images characterizing a brain of a patient; processing the data object to determine one or more particular images that are invalid; and providing, for display to a user on a graphical interface, data characterizing i) each particular image and ii) a respective reason that each particular image is invalid selected from a set of possible reasons that an image may be invalid.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a subset of brain data of a patient. One of the methods includes obtaining data characterizing a brain of a patient; determining a first prompt for presentation to a user; obtaining a first user input characterizing a first response to the first prompt; determining, using the first response to the first prompt, a second prompt for presentation to the user; obtaining a second user input characterizing a second response to the second prompt, wherein at least one of the first prompt or the second prompt seek a response based on a clinical observation of the patient; and determining a subset of the obtained data using the first response to the first prompt and the second response to the second prompt.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing brain data using autoencoder neural networks. One of the methods includes obtaining brain data captured by one or more sensors characterizing brain activity of a patient; processing the brain data to generate modified brain data that characterizes a predicted local effect of a future treatment on the brain of the patient; processing the modified brain data using an autoencoder neural network to generate reconstructed brain data; and determining, using the reconstructed brain data, a predicted global effect of the future treatment on the brain of the patient.

Abstract: A pre-event connectome of a subject brain is accessed, the pre-event connectome defining i) first functional nodes in the subject brain and ii) first edges that represent connections between the first functional nodes before the subject has undergone an event. A post-event connectome of the subject brain is accessed, the post-event connectome defining i) second functional nodes in the subject brain and ii) second edges that represent connections between the second functional nodes after the subject has undergone the event. A connectome-difference map data is generated that records the difference between the pre-event connectome and the post-event connectome. An action is taken based on the connectome-difference map data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicted brain data of a patient. One of the methods includes receiving montage configuration data for a specified montage; receiving raw EEG data captured using the specified montage from a brain of a particular subject; generating, using the montage configuration data and the raw EEG data, EEG connectivity data for the specified montage; using a generative neural network to map the EEG connectivity data to predicted fMRI connectivity data, the generative neural network having been trained using training EEG-fMRI connectivity data pairs, each pair comprising EEG connectivity data of a subject and fMRI connectivity data of the same subject; and taking an action based on the predicted fMRI connectivity data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing aphasia assessment. One of the methods includes receiving a recording, generating a text transcript of the recording, and generating speech quantifying and comprehension scores which can be used to determine an aphasia classification. Another method includes performing an aphasia assessment on a brain image to obtain an aphasia classification.

Abstract: A cloud computing system is described that communicates with a virtual machine to reattach the face of a patient to brain imaging data before the brain imaging data is transmitted for display on a brain navigation system.

Abstract: A system and method of generating a graphical representation of a network of a subject human brain. The method comprises receiving, via a user interface, a selection of the network of the subject brain; determining, based on an MRI image of the subject brain and one or more identifiers associated with the selection, one or more parcellations of the subject brain (405); determining, using three-dimensional coordinates associated with each parcellation, corresponding tracts in a diffusion tensor image of the brain (425); and generating a graphical representation of the selected network (430), the graphical representation including at least one of (i) one or more surfaces representing the one or more parcellations, each surface generated using the coordinates, and (ii) the determined tracts.

Abstract: A method (400) including: determining (702) a registration function [705, Niirf(T1)] for the particular brain in a coordinate space, determining (706) a registered atlas [708, Ard(T1)] from the registration function and an HCP-MMP1 Atlas (102) containing a standard parcellation scheme, performing (310, 619) diffusion tractography to determine a set [621, DTIp(DTI)] of brain tractography images of the particular brain, for a voxel in a particular parcellation in the registered atlas, determining (1105, 1120) voxel level tractography vectors [1123, Vje, Vjn] showing connectivity of the voxel with voxels in other parcellations, classifying (1124) the voxel based on the probability of the voxel being part of the particular parcellation, and repeating (413) the determining of the voxel level tractography vectors and the classifying of the voxels for parcellations of the HCP-MMP1 Atlas to form a personalised brain atlas [1131, PBs Atlas] containing an adjusted parcellation scheme reflecting the particular brain (Bb

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining anomalous brain data. One of the methods includes obtaining brain data characterizing brain activity of a patient; for each of a plurality of pairs of parcellations comprising a first parcellation and a second parcellation, processing the brain data to generate a correlation between the brain activity of the first and second parcellations; obtaining second connectivity data that characterizes, for each of the plurality of pairs of parcellations, a normal range of correlations between the brain activity of the first and second parcellations; identifying one or more of the plurality of pairs of parcellations for which the correlation between brain activity of the first and second parcellations is outside of the corresponding normal range of correlations; and providing data characterizing the one or more identified pairs of parcellations for display to a user on a graphical interface.