Analysis of cognitive dysfunction in Parkinson’s disease using voxel based morphometry and radiomics

• Published in: Cognitive processing 2024-08, Vol.25 (3), p.521-532
• Main Authors: Sivaranjini, S., Sujatha, C. M.
• Format: Article
• Language: English
• Subjects: Aged; Artificial Intelligence; Atrophy - pathology; Behavioral Sciences; Biomedical and Life Sciences; Biomedicine; Brain - diagnostic imaging; Brain - pathology; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - etiology; Cognitive Dysfunction - pathology; Cognitive Dysfunction - physiopathology; Dementia - diagnostic imaging; Dementia - pathology; Female; Gray Matter - diagnostic imaging; Gray Matter - pathology; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Middle Aged; Neurosciences; Parkinson Disease - complications; Parkinson Disease - diagnostic imaging; Parkinson Disease - pathology; Radiomics; Research Article
• ISSN: 1612-4782; 1612-4790; 1612-4790
• DOI: 10.1007/s10339-024-01197-x

Cognitive impairment in Parkinson’s disease (PD) is associated with changes in the brain anatomical structures. The objective of this study, is to identify the atrophy patterns based on the severity of cognitive decline and evaluate the disease progression. In this study, gray matter alterations are analysed in 135 PD subjects under 3 cognitive domains (91 Cognitively normal PD (NC-PD), 25 PD with Mild Cognitive Impairment (PD-MCI) and 19 PD with Dementia (PD-D)) by comparing them with 58 Healthy Control (HC) subjects. Voxel Based Morphometry (VBM) is used to segment the gray matter regions in magnetic resonance images and analyse the atrophy patterns statistically. Significant patterns of gray matter variations observed in the middle temporal and medial frontal region differentiate between HC and PD subject groups based on the severity of cognitive decline. Abnormalities in gray matter is substantiated through radiomic features extracted from the significant gray matter clusters. Significant radiomic features of the clusters are able to differentiate between the HC and PD-D subjects with an accuracy of 81.82%. Higher atrophy levels identified in PD-D subjects compared to NC-PD and PD-MCI group enables early diagnosis and treatment procedures. The combined and comprehensive analysis of gray matter alterations through VBM and radiomic features gives better assessment of cognitive impairment in PD.