Disease-Specific Regions Outperform Whole-Brain Approaches in Identifying Progressive Supranuclear Palsy: A Multicentric MRI Study

Abstract

To identify progressive supranuclear palsy (PSP), we combined voxel-based morphometry (VBM) and support vector machine (SVM) classification using disease-specific features in multicentric magnetic resonance imaging (MRI) data. Structural brain differences were investigated at four centers between 20 patients with PSP and 20 age-matched healthy controls with T1-weighted MRI at 3T. To pave the way for future application in personalized medicine, we applied SVM classification to identify PSP on an individual level besides group analyses based on VBM. We found a major decline in gray matter density in the brainstem, insula, and striatum, and also in frontomedian regions, which is in line with current literature. Moreover, SVM classification yielded high accuracy rates above 80% for disease identification in imaging data. Focusing analyses on disease-specific regions-of-interest (ROI) led to higher accuracy rates compared to a whole-brain approach. Using a polynomial kernel (instead of a linear kernel) led to an increased sensitivity and a higher specificity of disease detection. Our study supports the application of MRI for individual diagnosis of PSP, if combined with SVM approaches. We demonstrate that SVM classification provides high accuracy rates in multicentric data—a prerequisite for potential application in diagnostic routine.

Disentangling brain functional network remodeling in corticobasal syndrome – A multimodal MRI study

“…Of note, guiding feature selection with meta-analytically defined disease-specific ROIs improved generalizability. This finding is in agreement with other studies investigating several other neurodegenerative diseases and showing that ROIs defined in independent cohorts by systematic and quantitative meta-analyses can improve classification accuracy for diagnosis and differential diagnosis in imaging data (Bisenius et al., 2017; Dukart et al., 2011; Meyer et al., 2017; Mueller et al., 2017). Concerning rs-fMRI, our multicentric study included, to our knowledge, the largest patient cohort so far.…”

“…The analysis was performed either using whole-brain images or restricting feature selection based on meta-analytically derived disease-specific regions of interest (ROIs). We hypothesize that using a priori informed ROIs from meta-analyses might improve generalizability of the classification as already shown for several other neurodegenerative diseases (Bisenius et al., 2017; Dukart et al., 2011; Meyer et al., 2017; Mueller et al., 2017). ROIs were created based on MNI coordinates from (Albrecht et al., 2017).…”

“…To investigate connectivity, we applied data-driven whole-brain analysis – Eigenvector connectivity mapping (ECM) – and seed-based analysis. Additionally, we implemented an established multivariate machine learning technique, support vector machine (SVM) classification, to differentiate patients from controls based only on multimodal neuroimaging data (Ballarini et al., 2019; Bisenius et al., 2017; Dukart et al., 2011; Meyer et al., 2017; Mueller et al., 2017; Woo et al., 2017). We hypothesized that CBS, compared to controls, is characterized by widespread cortical atrophy and reductions in brain functional connectivity that can accurately distinguish CBS patients from controls at the single-subject level.…”

Atrophy in midbrain & cerebral/cerebellar pedunculi is characteristic for progressive supranuclear palsy – A double-validation whole-brain meta-analysis

“…In an atlas-based MRI volumetry study, high classification accuracy for PSP versus other parkinsonian syndromes (86%) was indeed driven by atrophy in the midbrain and cerebellar pedunculi (Huppertz et al, 2016). Another MRI study suggests that disease-characteristic regions extracted from meta-analyses can outperform whole-brain approaches in identifying PSP by increasing classification accuracy from 80% to 85% (Mueller et al, 2017). Furthermore, combining structural MRI with DTI in support vector machine classification can increase classification results to 100% accurate (Cherubini et al, 2014).…”

Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes

“…The midbrain atrophy rate may serve as an effective outcome measure in PSP clinical trials ( 134 ). In addition, the support vector machine classification method yielded accuracy rates >80% for predicting PSP diagnosis using disease-specific regions-of-interest (pallidum, putamen, caudate nucleus, thalamus, midbrain and insula) compared to the whole-brain approach ( 135 ).…”