Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques.

Conventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associated with functional impairment in "individual" patients. We investigated whether fMRI allows classification of individual motor impairment after stroke using support vector machines (SVMs). Forty acute stroke patients and 20 control subjects underwent resting-state fMRI. Half of the patients showed significant impairment in hand motor function. Resting-state connectivity was computed by means of whole-brain correlations of seed time-courses in ipsilesional primary motor cortex (M1). Lesion location was identified using diffusion-weighted images. These features were used for linear SVM classification of unseen patients with respect to motor impairment. SVM results were compared with conventional mass-univariate analyses. Resting-state connectivity classified patients with hand motor deficits compared with controls and nonimpaired patients with 82.6-87.6% accuracy. Classification was driven by reduced interhemispheric M1 connectivity and enhanced connectivity between ipsilesional M1 and premotor areas. In contrast, lesion location provided only 50% sensitivity to classify impaired patients. Hence, resting-state fMRI reflects behavioral deficits more accurately than structural MRI. In conclusion, multivariate fMRI analyses offer the potential to serve as markers for endophenotypes of functional impairment.

Quantitative spectroscopic imaging with in situ measurements of tissue water T1, T2, and density.

The use of tissue water as a concentration standard in proton magnetic resonance spectroscopy ((1)H-MRS) of the brain requires that the water proton signal be adjusted for relaxation and partial volume effects. While single voxel (1)H-MRS studies have often included measurements of water proton T(1), T(2), and density based on additional (1)H-MRS acquisitions (e.g., at multiple echo or repetition times), this approach is not practical for (1)H-MRS imaging ((1)H-MRSI). In this report we demonstrate a method for using in situ measurements of water T(1), T(2), and density to calculate metabolite concentrations from (1)H-MRSI data. The relaxation and density data are coregistered with the (1)H-MRSI data and provide detailed information on the water signal appropriate to the individual subject and tissue region. We present data from both healthy subjects and a subject with brain lesions, underscoring the importance of water parameter measurements on a subject-by-subject and voxel-by-voxel basis.