Method and system for diagnosis of attention deficit hyperactivity disorder from magnetic resonance images

摘要

A method and system for automated diagnosis of attention deficit hyperactivity disorder (ADHD) from magnetic resonance images is disclosed. Anatomical features are extracted from a structural magnetic resonance image (MRI) of a patient. Functional features are extracted from a resting-state functional MRI (rsFMRI) series of the patient. An ADHD diagnosis for the patient is determined based on the anatomical features, the functional features, and phenotypic features of the patient using a trained classifier. An ADHD subtype may then be determined for patients diagnosed as ADHD positive using a second trained classifier.

主权项

1. A method for automated diagnosis of attention deficit hyperactivity disorder (ADHD), comprising:
extracting anatomical features from a structural magnetic resonance image (MRI) of a patient; extracting functional features from a resting-state functional MRI (rsFMRI) series of the patient, comprising:
extracting an rsFMRI time series for each of a plurality of brain regions by mapping voxels in each of a plurality of image volumes in the rsFMRI series to a plurality of brain regions and extracting an rsFMRI time series for each brain region based on the voxels mapped to that brain region in the plurality of image volumes in the rsFMRI series by calculating, for each of M brain regions, an average of voxels mapped to that brain region in each of N image volumes in the rsFMRI series, resulting in an M ×N matrix including the rsFMRI time series for each of the brain regions, andextracting the functional features based on the rsFMRI time series for each of the plurality of brain regions; and determining an ADHD diagnosis for the patient based on the anatomical features, the functional features, and phenotypic features of the patient using a trained machine learning classifier.