Machine learning methods evaluation for identification of cognitive phenotypes in multiple sclerosis and their MRI correlates.
Authors
Affiliations (6)
Affiliations (6)
- Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland.
- Department of Neurology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland.
- Department of Neurology with the Stroke Subunit Prof. K. Gibinski University Clinical Center of Medical University of Silesia in Katowice, Katowice, Poland.
- Department of Psychology, Faculty of Health Sciences in Katowice, Medical University of Silesia, Katowice, Poland.
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland.
- Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education in Katowice, Katowice, Poland.
Abstract
Cognitive impairment (CI) is common in multiple sclerosis (MS) yet poorly captured by conventional disability scales. Although neuropsychological assessment and magnetic resonance imaging (MRI) are routinely used separately, there is no simple clinically applicable framework integrating cognitive performance with structural brain changes to identify patients at increased risk of cognitive decline. Integrating neuropsychological testing with MRI-based atrophy metrics may yield clinically useful cognitive phenotypes with differential patterns of brain atrophy measures. Data were collected from 79 patients with multiple sclerosis (PwMS) who underwent comprehensive neuropsychological assessment and brain MRI. Neuropsychological variables were subjected to a feature selection procedure based on variance and quartile coefficient of dispersion filtering, followed by Pearson correlation and mutual information (MI) analyses to generate reduced feature sets. These feature sets were used as input for unsupervised clustering with the Partitioning Around Medoids (PAM) algorithm to identify cognitive phenotypes. Differences between the resulting groups in the degree of brain atrophy measures were subsequently evaluated using appropriate statistical tests-one-way ANOVA or the Kruskal-Wallis test. <i>Post hoc</i> analysis was performed using a pairwise <i>t</i>-test, Welch's <i>t</i>-test, or Wilcoxon test with the Holm-Bonferroni correction, depending on the data distribution and variance. The feature selection procedure based on variance and mutual information identified neuropsychological features that were subsequently used for clustering. Based on these features, the PAM algorithm identified three distinct groups of PwMS that differed in their clinical characteristics, degree of brain atrophy measures, and cognitive phenotype, ranging from preserved cognition to global cognitive impairment. Three cognitive phenotypes with differential patterns of brain atrophy measures integrate neuropsychological testing with MRI measures into a clinically applicable framework that may help bridge the gap between structural imaging findings and everyday cognitive assessment in PwMS. This approach may improve screening, enable earlier detection of CI, improve monitoring, and provide valuable information for rehabilitation planning.