Kinematic Measures of Bimanual Performance are Associated With Callosum White Matter Change in People With Chronic Stroke.

OBJECTIVES: To investigate the relationship between bimanual performance deficits measured using kinematics and callosum (CC) white matter changes that occur in people with chronic stroke. DESIGN: Cross-sectional, observational study of participants with chronic stroke and age-matched controls. SETTING: Recruitment and assessments occurred at a stroke recovery research center. Behavioral assessments were performed in a controlled laboratory setting. Magnetic resonance imaging scans were performed at the Center for Biomedical Imaging. PARTICIPANTS: Individuals were enrolled and completed the study (N=39; 21 participants with chronic stroke; 18 age-matched controls with at least 2 stroke risk factors). MAIN OUTCOME MEASURES: Diffusion imaging metrics were obtained for each individual's CC and corticospinal tract (CST), including mean kurtosis (MK) and fractional anisotropy (FA). A battery of motor assessments, including bimanual kinematics, were collected from individuals while performing bimanual reaching. RESULTS: Participants with stroke had lower FA and MK in the CST of the lesioned hemisphere when compared with the non-lesioned hemisphere. The FA and MK values in the CST were correlated with measures of unimanual hand performance. In addition, participants with stroke had significantly lower FA and MK in the CC than matched controls. CC diffusion metrics positively correlated with hand asymmetry and trunk displacement during bimanual performance, even when correcting for age and lesion volume. CONCLUSIONS: These data confirm previous studies that linked CST integrity to unimanual performance and provide new data demonstrating a link between CC integrity and both bimanual motor deficits and compensatory movements.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.