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: 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 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 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: 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 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 a computer storage medium, for generating explainability data that explains a medical condition in a subject. In one aspect, a method comprises: obtaining data identifying a plurality of brain parcels that are predicted to be relevant to the medical condition; receiving fMRI data for a brain of a subject; processing the fMRI data for the brain of the subject to determine a respective activation score for each of the plurality of brain parcels that are predicted to be relevant to the medical condition; determining, for each of the plurality of brain parcels that are predicted to be relevant to the medical condition, a relative activation score for the brain parcel; and taking an action based on the relative activation scores.

Abstract: Disclosed herein are systems and methods for interactive graphical user interfaces (GUIs) that users (e.g., medical professionals) can use to interact with modelled versions of brains and easily and intuitively analyze deep and/or lateral structures in the brain. A user can, for example, selectively view structures and their connectivity data (e.g., nodes and edges) relative to other structures and connectivity data over a representation of a particular patient's brain. Emphasis can be minimized for certain foreground nodes and edges (e.g., lateral structures) to make it easier for the user to focus on and analyze deeper structures that otherwise can be challenging to visualize and understand. A method can include overlaying deep and non-deep nodes on a representation of a brain, displaying the representation of the brain in a GUI, receiving user input indicating interest in focusing on one or more deep nodes, and taking an action based on the input.

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: Methods and systems for measuring 3-dimensional distances on medical images are described. One example method includes receiving at least a first 2-dimensional image on a first plane and a second 2-dimensional image on a second plane, wherein the first plane and the second plane are different planes, selecting a first point on the first 2-dimensional image as a start point, selecting a second point on the second 2-dimensional image as an end point, determining a shortest path between the start point and the end point, performing a measurement of the shortest path between the start point and the end point, and, taking an action based on the measurement.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating visualization data for a selected neuro-network. In one aspect, a method comprises: receiving selection data selecting a network in a brain of a subject; processing magnetic resonance image data of the brain to identify a set of tracts that are predicted to be included in the selected network; processing the set of tracts to identify a proper subset of the set of tracts as being spurious tracts; generating a set of valid tracts by filtering the spurious tracts from the set of tracts that are predicted to be included in the selected network; providing visualization data for the selected network showing a three-dimensional spatial representation of both: (i) the valid tracts, and (ii) the spurious tracts, wherein the spurious tracts are visually distinguished from the valid tracts.

Abstract: fMRI data of a subject brain is accessed and may include a plurality of time-sequenced volumetric images of activity in a subject brain. A plurality of emotion vectors are accessed, each emotion vector tagged with a specified emotional state. From the fMRI data and using the emotion vectors, a plurality of fMRI state vectors are determined at various points in time, where each fMRI state vector is a combination of the emotion vectors and represents the state of the subject brain at a particular point in time. Flow data is determined to identify a trajectory, over time, of the subject brain as reflected by the fMRI data, through a state space defined by the emotion vectors, where the flow data is based at least in part on the fMRI vectors at various points in time. From the flow data, data is generated that shows changes, through time, in at least one emotional state of the brain.