Radiomic deformation features to predict associations between affected brain regions and verbal fluency scores in...

Acute stroke is a systemic disease that leads to rapid cellular damage and functional impairments throughout brain regions, even those remote from the site of injury. It triggers widespread effects through various mechanisms, such as secondary degeneration of neurons, leading to diverse neurological deficits. Advances in machine learning approaches have shown promise towards predicting individualized outcomes at an early stage, through identifying imaging biomarkers of cognitive deficits. This ultimately allows for providing personalized treatment plans and early interventions to slow cognitive decline. In this study, we attempt to predict associations between the verbal fluency scores (VFS) and the brain regions with sub-clinical phenotypes of patients with acute stroke using a novel radiomic approach. Specifically, we extract radiomic features from T1-weighted (T1w) images that capture the structural alterations occurring in the brain regions due to stroke lesions. Our work explores the hypothesis that the degeneration occurring in brain regions, hence leading to cognitive impairment, can be quantified on routine imaging via radiomic features that capture the structural deformations resulting from stroke lesions. A retrospective analysis was conducted on the MRI scans (T1-weighted (T1w), FLAIR, Diffusion Weighted Imaging, and Apparent Diffusion Coefficient maps) of n = 31 subjects. Acute lesions ( < 7 days) were manually segmented, followed by registering the T1w scan of each subject to an MNI atlas via deformable registration. Structural deformations magnitudes were then obtained via the inverse-warp transformations of the diffeomorphic registration. These deformation magnitudes were used to extract descriptive statistics (mean, skewness, and kurtosis) for 117 brain regions parcellated using an MNI-based anatomical atlas. The statistics were correlated with the measured normalized VFS of each subject using Pearson's correlation coefficient. Further, since the cerebral regions of the left hemisphere are known to play a key role in speech, the group of subjects with only left hemispheric lesions (LHL) (n = 15) was analyzed separately. Our analysis demonstrated that key bilateral verbal regions in the brains of subjects with LHL are significantly correlated with the deformation features, such as Right Frontal Inferior Opercularis (skewness r = -0.67, p = 0.007), Right Temporal Inferior (mean r = 0.64, p = 0.009) and Superior (kurtosis r = -0.69, p = 0.004), and Cingulum Posterior Left and Right (mean r = 0.72, p = 0.003 and r = 0.73, p = 0.002). These results suggest that radiomic features capturing structural alterations occurring due to lesions can identify the brain regions affected at the early stages of stroke.