Abstract: Disclosed are techniques for identifying and displaying spatial relationships between a seed parcel and other parcels in a patient's brain. A method can include obtaining connectivity data characterizing, for each pair of parcels including a first parcel and a second parcel from a group of parcels, a connectivity metric measuring a relationship between the first and second parcels in the patient's brain, obtaining brain atlas data, identifying a seed parcel from the group of parcels, determining one or more other parcels in the group of parcels having a connectivity metric with the seed parcel that satisfies a threshold connectivity metric condition to provide at least one seed-connected parcel, and for the seed-connected parcel, providing the brain atlas data and an indication of a degree of the connectivity metric between the seed parcel and seed-connected parcel for display as an overlay on a visualization of the brain atlas data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for visualizing complex connectome interactions relevant to a medical condition. One of the methods includes receiving brain data for a brain of a patient, processing the brain data to determine multiple brain parcels that are predicted to be relevant to a medical condition, determining, for each of multiple brain parcels that are predicted to be relevant to the medical condition, a respective brain network affiliation, and providing, to a user device of a user, data for displaying a visualization that includes one or more respective brain network affiliations determined for each of multiple brain parcels that are predicted to be relevant to the medical condition.

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: 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 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 for brain tractography by shading fiber bundles. In one aspect, a method includes forwarding patient brain data for presentation to a user; receiving, from the user, a selection of a brain parcel in response to presentation of the patient brain data; forwarding, for display to the user, tract health data comprising i) tract data for tracts connected to the brain parcel and ii) at least one metric for the tracts, the metric reflecting the health of the tracts; and taking an action in response to forwarding the tract health data.

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 visualizing complex connectome interactions relevant to a medical condition. One of the methods includes receiving brain data for a brain of a patient, processing the brain data to determine multiple brain parcels that are predicted to be relevant to a medical condition, determining, for each of multiple brain parcels that are predicted to be relevant to the medical condition, a respective brain network affiliation, and providing, to a user device of a user, data for displaying a visualization that includes one or more respective brain network affiliations determined for each of multiple brain parcels that are predicted to be relevant to the medical condition.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for harmonizing diffusion tensor images. One of the methods includes obtaining a diffusion tensor image; determining a set of RISH features for the diffusion tensor image; processing a model input generated from the set of RISH features using a machine learning model to generate a model output identifying an image transformation from a set of image transformations, wherein each image transformation in the set of image transformations corresponds to a respective different first MRI scanner and represents a transformation that, when applied to first diffusion tensor images captured by the first MRI scanner, harmonizes the first diffusion tensor images with second diffusion tensor images captured by a reference MRI scanner; and processing the diffusion tensor image using the identified image transformation to generate a harmonized diffusion tensor image.

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: 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: 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: Disclosed are techniques for identifying and displaying spatial relationships between a seed parcel and other parcels in a patient's brain. A method can include obtaining connectivity data characterizing, for each pair of parcels including a first parcel and a second parcel from a group of parcels, a connectivity metric measuring a relationship between the first and second parcels in the patient's brain, obtaining brain atlas data, identifying a seed parcel from the group of parcels, determining one or more other parcels in the group of parcels having a connectivity metric with the seed parcel that satisfies a threshold connectivity metric condition to provide at least one seed-connected parcel, and for the seed-connected parcel, providing the brain atlas data and an indication of a degree of the connectivity metric between the seed parcel and seed-connected parcel for display as an overlay on a visualization of the brain atlas data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting automatically a machine learning model that locates region(s) of the brain of a subject that is/are associated with a clinically relevant outcome. One of the methods includes: receiving a brain image dataset of a subject; receiving, from a user, an indication of a patient outcome of interest; selecting, based on the indication of a patient outcome of interest, a model from a plurality of models to produce a selected model; determining brain data of interest for the patient outcome of interest; determining, using the selected model, subject specific brain data of interest based on the brain image dataset of the subject and on the brain data of interest; and taking an action based on the subject specific brain data of interest.

Abstract: Methods, systems, and computer programs encoded on computer storage media, for selecting a model out of a number of models based on subject characteristics. One of the methods includes obtaining present subject connectivity matrix data for a present subject, obtaining present subject data describing the present subject where the present subject data is different from the present subject connectivity matrix data, determining a specific model to apply to the present subject connectivity matrix data based at least in part on the present subject data, determining the specific model using a model trained with fMRI data for brains of a plurality of past subjects and past subject data describing the past subjects, applying the specific model to identify a potential present subject brain condition based at least in part on the present subject connectivity matrix data, and taking an action based on identification of a potential present subject brain condition.

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: Disclosed herein are systems and methods for providing interactive graphical user interfaces (GUIs) for users, such as medical professionals, to glean insight about connectivity data associated with a particular brain. A method can include overlaying nodes representing locations of parcels of a patient's brain on a representation of a brain and displaying the representation of the brain with the overlaid nodes in a GUI. Nodes having connectivity above a first threshold can be represented in a first indicia and nodes having connectivity below a second threshold can be represented in a second indicia. The method can include receiving user input and taking an action based on the user input. The user input can include selecting an area of the representation of the brain for excision. Taking an action based on the input can include calculating an impact of excising the area of the brain on the particular patient.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for grouping brain parcellation data. One of the methods includes receiving brain parcellation data for a subject; receiving an indication from a user of a brain function category; forwarding data for display, the data comprising a set of functions within the brain function category; receiving a selection from the user of a brain function from the set of functions within the brain function category; determining a subset of the brain parcellation data for parcellations that have an overlap with the selected brain function where the overlap exceeds a threshold; and taking an action based on the determined subset of the brain parcellation data for parcellations that have an overlap with the selected brain function where the overlap exceeds a threshold.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing EEG source localization. One of the methods includes obtaining brain data comprising: EEG data comprising respective channel data corresponding to each of a plurality of electrodes of an EEG sensor, and fMRI data comprising respective voxel data corresponding to each of a plurality of voxels; identifying, in a three-dimensional coordinate system, a respective location for each electrode; generating, using the respective identified locations of each electrode, data representing a location in the three-dimensional coordinate system of each voxel; determining, for each electrode, a region of interest in the three-dimensional coordinate system; and identifying, for each electrode, one or more corresponding parcellations in the brain of the subject, wherein each parcellation that corresponds to an electrode at least partially overlaps with the region of interest of the electrode.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining and visualizing contribution values of different brain regions to a medical condition. One of the methods includes receiving brain data for a brain of a patient, processing the brain data to determine a partition of the data into a plurality of brain parcellation pairs, receiving an indication of a medical condition, determining a contribution value for at least some of the plurality of brain parcellation pairs, where the contribution value characterizes a contribution of the brain parcellation pair to the medical condition, and providing the contribution values for display on a user computing device.