• Sadhana Kumari, Bharti Rana, Senthil KumaranSSDepartment of NMR, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India. Electronic address: senthil@aiims.edu., Shefali Chaudhary, Suman Jain, Achal Kumar Srivastava, and Roopa Rajan.
• Department of NMR, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India.
• Neuroscience. 2024 Jul 23; 551: 217228217-228.

IntroductionMagnetic resonance imaging (MRI) based brain morphometric changes in unilateral 6-hydroxydopamine (6-OHDA) induced Parkinson's disease (PD) model can be elucidated using voxel-based morphometry (VBM), study of alterations in gray matter volume and Machine Learning (ML) based analyses.MethodsWe investigated gray matter atrophy in 6-OHDA induced PD model as compared to sham control using statistical and ML based analysis. VBM and atlas-based volumetric analysis was carried out at regional level. Support vector machine (SVM)-based algorithms wherein features (volume) extracted from (a) each of the 150 brain regions (b) statistically significant features (only) and (c) volumes of each cluster identified after application of VBM (VBM_Vol) were used for training the decision model. The lesion of the 6-OHDA model was validated by estimating the net contralateral rotational behaviour by the injection of apomorphine drug and motor impairment was assessed by rotarod and open field test.Results And DiscussionIn PD, gray matter volume (GMV) atrophy was noted in bilateral cortical and subcortical brain regions, especially in the internal capsule, substantia nigra, midbrain, primary motor cortex and basal ganglia-thalamocortical circuits in comparison with sham control. Behavioural results revealed an impairment in motor performance. SVM analysis showed 100% classification accuracy, sensitivity and specificity at both 3 and 7 weeks using VBM_Vol.ConclusionUnilateral 6-OHDA induced GMV changes in both hemispheres at 7th week may be associated with progression of the disease in the PD model. SVM based approaches provide an increased classification accuracy to elucidate GMV atrophy.Copyright © 2024 IBRO. Published by Elsevier Inc. All rights reserved.

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