Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for displaying hubs in brain data. One of the methods includes receiving hub data, the hub data comprising anatomical data and connectivity data of at least part of a brain, the connectivity data comprising a first plurality of nodes and a second plurality of edges, each edge of the second plurality of edges connecting a pair of nodes among the first plurality of nodes; forwarding the hub data to a user computer for display; receiving a first user indication requesting a display of unexpected hub data; determining, in response to the first user indication, unexpected hub data comprising nodes that have connectivity outside of a specified threshold; and forwarding the unexpected hub data to the user computer for display.

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 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: 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: 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: 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: 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: 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 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: 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: 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: 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 medical imaging with distortion correction. One example includes obtaining distorted image data of a subject brain, the distorted image data comprising a time series of three-dimensional image tensors generated at least in part from an echo planar imaging session of the subject brain. A derived three-dimensional tensor is derived from the distorted image data. A non-rigid alignment function to non-rigidly align the derived three-dimensional tensor to a reference tensor is determined, producing a non-rigidly aligned derived 3D tensor. A rigid alignment function to rigidly align the non-rigidly aligned derived 3D tensor to the reference tensor is determined. Distortion-corrected image data is created by applying the rigid alignment function and the non-rigid alignment function to the time series of three-dimensional image tensors.

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.