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Authors: Shuning Han 1 ; 2 ; Zhe Sun 2 ; 3 ; Kanhao Zhao 4 ; Feng Duan 5 ; Cesar F. Caiafa 6 ; Yu Zhang 4 ; 7 and Jordi Solé-Casals 1 ; 8

Affiliations: 1 Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, 08500, Catalonia, Spain ; 2 Image Processing Research Group, RIKEN Center for Advanced Photonics, Riken, Wako-Shi, Saitama, Japan ; 3 Faculty of Health Data Science, Juntendo University, Urayasu, Chiba, Japan ; 4 Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, U.S.A. ; 5 Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, Nankai University, Tianjin, China ; 6 Instituto Argentino de Radioastronomía-CCT La Plata, CONICET/ CIC-PBA/ UNLP, V. Elisa 1894, Argentina ; 7 Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, U.S.A. ; 8 Department of Psychiatry, University of Cambridge, Cambridge CB20SZ, U.K.

Keyword(s): Alzheimer’s Disease, Mild Cognitive Impairment, Graph Convolutional Network, Functional Magnetic Resonance Imaging Analysis, Functional Connectivity.

Abstract: Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN framework obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse applications of GCNs in brain analysis and disease detection. (More)

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Paper citation in several formats:
Han, S.; Sun, Z.; Zhao, K.; Duan, F.; F. Caiafa, C.; Zhang, Y. and Solé-Casals, J. (2024). An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS; ISBN 978-989-758-688-0; ISSN 2184-4305, SciTePress, pages 656-666. DOI: 10.5220/0012414600003657

@conference{biosignals24,
author={Shuning Han. and Zhe Sun. and Kanhao Zhao. and Feng Duan. and Cesar {F. Caiafa}. and Yu Zhang. and Jordi Solé{-}Casals.},
title={An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS},
year={2024},
pages={656-666},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012414600003657},
isbn={978-989-758-688-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS
TI - An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment
SN - 978-989-758-688-0
IS - 2184-4305
AU - Han, S.
AU - Sun, Z.
AU - Zhao, K.
AU - Duan, F.
AU - F. Caiafa, C.
AU - Zhang, Y.
AU - Solé-Casals, J.
PY - 2024
SP - 656
EP - 666
DO - 10.5220/0012414600003657
PB - SciTePress