LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population

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 thes...

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Vydáno v:Experimental gerontology Ročník 197; s. 112585
Hlavní autoři: Chatterjee, Indranath, Bansal, Videsha
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier 01.11.2024
Témata:
Aged
Aged, 80 and over
Algorithms
Brain - diagnostic imaging
Case-Control Studies
Female
Gerontological research
Humans
Localized region extraction
Magnetic Resonance Imaging - methods
Male
Middle Aged
Multi-modal fusion
Multimodal Imaging - methods
Neurodegeneration
Non-pharmacological treatment
Parkinson Disease - diagnostic imaging
Parkinson's disease
Parkinson's disease
Multi-modal fusion
Non-pharmacological treatment
sMRI
Gerontological research
Neurodegeneration
Localized region extraction
Rs-fMRI
ISSN:1873-6815, 1873-6815
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Popis
Shrnutí: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.
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ISSN:1873-6815
1873-6815
DOI:10.1016/j.exger.2024.112585