Oregon Health & Science University

Washington University

Nous Imaging, Inc.

Damien A Fair

Nico Dosenbach

Abraham Snyder

Anders Perrone

Amy Mirro

Jonathan Koller

Oscar Miranda Dominguez

Eric Earl

Andrew Van

Rachel Klein

Kenneth Bruener

Timothy Laumann

Shannon K McWeeney

Chad Sylvester

Scott Marek

Deanna Greene

Jarod Roland

Evan Gordon

Scott Norris

Eric Feczko

Methods, computer-readable storage devices, and systems are described for reducing movement of a patient undergoing a magnetic resonance imaging (MRI) scan by aligning MRI data, the method implemented on a Framewise Integrated Real-time MRI Monitoring ('FIRMM') computing device including at least one processor in communication with at least one memory device. Aspects of the method comprise receiving a data frame from the MRI system, aligning the received data frame to a preceding data frame, calculating motion of a body part between the received data frame and the preceding data frame, calculating total frame displacement, and excluding data frames with a cutoff above a pre-identified threshold of the total frame displacement.

Real time monitoring and prediction of motion in MRI

A method of performing personalized neuromodulation on a subject is provided. The method includes acquiring functional magnetic resonance imaging (fMRI) data of a brain of the subject. The method also includes calculating functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain, based on the fMRI data. The method also includes identifying a target location in the brain to be targeted by neuromodulation based on the calculated functional connectivity.

Systems and method of precision functional mapping-guided interventional planning

An example method includes identifying training data indicating features of a sample population and clinical outcomes of the sample population. The clinical outcomes are associated with a heterogeneous condition. The method further includes generating decision trees in a Random Forest (RF) based on the training data, each one of the decision trees being configured to divide the sample population into multiple categories based on the features of the sample population. In response to generating the decision trees, a proximity matrix comprising multiple entries is generated using the RF. One of the entries indicates a proportion of the decision trees that categorize a first individual among the sample population and a second individual among the sample population into the same categories among the multiple categories. The method further includes identifying subgroups of the heterogeneous condition by detecting communities of the proximity matrix.

Subtyping heterogeneous disorders using functional random forest models

Growing community of inventors

Portland, OR, United States of America

Damien A Fair