BACKGROUND

This specification relates to processing data related to the brain of a patient, e.g., functional magnetic resonance imaging (MRI) data and/or tractography data.

Brain functional connectivity data characterizes, for each of one or more pairs of locations within the brain of a patient, the degree to which brain activity in the pair of locations is correlated.

One can gather data related to the brain of the patient by obtaining and processing images of the brain of the patient, e.g., using magnetic resonance imaging (MM), diffusion tensor imaging (DTI), or functional MM imaging (fMRI). Diffusion tensor imaging uses magnetic resonance data to measure diffusion of water in a human brain. One can use the measured diffusion to generate tractography data, which can include images of neural tracts and corresponding white matter fibers of the subject brain.

Data related to the brain of a single patient can be highly complex and high-dimensional, and therefore difficult for a clinician to manually inspect and parse, e.g., to plan a surgery or diagnose the patient for a brain disease or mental disorder. For example, a correlation matrix, e.g., a correlation matrix of fMRI data, of the brain of a patient can be a matrix with hundreds of thousands or millions of elements.

SUMMARY

This specification relates to a system that can determine a subset of brain atlas data that is associated with a selected subset of brain data of a patient. The system can receive a user input identifying the selected subset of the brain data of the patient. The system can use the determined subset of the brain atlas data to generate target data for treatment of the patient. For example, the system can generate target data for high-precision targeted treatment of the brain, e.g., for transcranial magnetic stimulation (TMS). The system can also provide the subset of the brain atlas data for display to a user.

In this specification, brain data can be any data characterizing the brain of a patient. For example, brain data can include one or both of i) direct measurement data of the brain of the patient, e.g., images of the brain collected using brain imaging techniques, or ii) data that has been derived or generated from initial measurement data of the brain of the patient, e.g., correlation matrices.

In this specification, a parcellation is a predefined region of the brain. For example, a parcellation can be defined by boundaries on a three-dimensional volume of the brain. A parcellation can be defined such that the neurons in the parcellation are functionally similar according to one or more criteria. For example, a set of parcellations can be defined according to changes in cortical architecture, function, connectivity, and/or topography.

In this specification, a brain atlas is data that defines one or more parcellations of a brain of a patient, e.g., by defining the coordinates of the outline of the parcellation or the volume of the parcellation in a common three-dimensional coordinate system.

In this specification, target data can be any data that defines a particular region of the brain for targeted treatment. For example, target data can identify one or more particular parcellations of the brain of a patient, e.g., parcellations as defined by a brain atlas. In some cases, target data can be provided to one or more medical devices for executing the treatment of the patient, e.g., target data can be provided to an image guidance system for delivering treatment to the patient.

Particular embodiments of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages.

As discussed above, a set of brain data characterizing the brain of a single patient can often be incredibly large and complicated, and thus it can be difficult and time consuming for a user to extract useful information from the set of brain data. Using techniques described in this specification, a system can quickly determine the subset of a brain atlas that is clinically relevant to a user and provide the determined subset for display to the user, so that the user is not forced to search through and analyze a large amount of data that is not clinically relevant. Therefore, the amount of time that a user must spend to discover the portion of the brain atlas that is useful to the user can be drastically reduced, resulting in improved outcomes for patients, users and/or clinicians, especially when effective care requires time sensitive investigations.

As a particular example, using existing systems it might take a user multiple hours to extract the subset of brain data of a patient required for a particular treatment, and because of the extensive time and attention required, the extracted subset can be inaccurate and error-prone. Using systems described in this specification, the process of extracting useful brain data from a corpus of brain data can be fully automated and executed in a matter of seconds or minutes.

In this specification, data is “clinically relevant” if the data represents an answer to a question that a trained clinician might ask in clinical practice treating patients, e.g., a question asked by a clinician in order to treat a patient with a specific disease. For example, clinically relevant data can be brain data characterizing a portion of the brain that a clinician has determined behaves anomalously.

Using some existing techniques, a user might only be able to generate target data that identifies a large portion of the brain of a patient, e.g., the frontal lobe. Using techniques described in this specification, a system can generate precise target data that can be used to execute highly personalized treatment of a patient. For example, a user can identify brain data that is anomalous, and the system can determine from the identified brain data a precise region (e.g., a single parcellation) in the brain of the patient that is behaving anomalously. The system can then generate target data for treating the determined region. Therefore, treatment can be highly targeted and personalized for individual patients, resulting in improved health outcomes for patients.

In this specification, a set of brain data is “anomalous” if the values of the brain data are outside a normal range of values, e.g., as defined by a data set that includes brain data corresponding to multiple other patients, e.g., multiple other patients not known to have anomalous brain data.

The details of one or more embodiments of the subject matter of this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.

