LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population
- Title
- LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population
- Creator
- Chatterjee I.; Bansal V.
- Description
- Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data. The LRE-MMF method utilizes the complementary strengths of sMRI and rs-fMRI: sMRI provides detailed anatomical information, while rs-fMRI captures functional connectivity patterns. We applied this approach to a dataset consisting of 20 PD patients and 20 healthy controls (HC), all scanned with a 3 T MRI. The primary objective was to determine whether the integration of sMRI and rs-fMRI through the LRE-MMF method improves the classification accuracy between PD and HC subjects. LRE-MMF involves the division of imaging data into localized regions, followed by feature extraction and dimensionality reduction using Principal Component Analysis (PCA). The resulting features were fused and processed through a neural network to learn high-level representations. The model achieved an accuracy of 75 %, with a precision of 0.8125, recall of 0.65, and an AUC of 0.8875. The validation accuracy curves indicated good generalization, with significant brain regions identified, including the caudate, putamen, thalamus, supplementary motor area, and precuneus, as per the AAL atlas. These results demonstrate the potential of the LRE-MMF method for improving early diagnosis and understanding of PD by effectively utilizing both sMRI and rs-fMRI data. This approach could contribute to the development of more accurate diagnostic tools. 2024 The Authors
- Source
- Experimental Gerontology, Vol-197
- Date
- 2024-01-01
- Publisher
- Elsevier Inc.
- Subject
- Gerontological research; Localized region extraction; Multi-modal fusion; Neurodegeneration; Non-pharmacological treatment; Parkinson's disease; Rs-fMRI; sMRI
- Coverage
- Chatterjee I., Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom, School of Technology, Woxsen University, Hyderabad, India, Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Bansal V., Department of Psychology, Christ University, Bangalore, 560029, India
- Rights
- All Open Access; Hybrid Gold Open Access
- Relation
- ISSN: 5315565; PubMed ID: 39306310; CODEN: EXGEA
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Chatterjee I.; Bansal V., “LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 25, 2025, https://archives.christuniversity.in/items/show/12732.