Diving Deep into Alzheimer’s: Unraveling the Brain’s Mysteries

Published on May 16, 2022

Imagine the brain is an intricate puzzle, with Alzheimer’s disease (AD) as its hidden enigma. Scientists faced the challenge of deciphering the clues that separate AD patients from healthy individuals. To crack the case, we developed a cutting-edge tool called the multi-modal LassoNet framework. This framework combines two imaging techniques, resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI), to unveil the hidden features linked to AD in the brain. By applying this powerful neural network-based approach, we were able to identify key brain regions like the Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, and Fusiform_R that are involved in AD. Our findings not only open new doors for AD research but also enhance our understanding of how this complex disorder develops. Explore our groundbreaking study to unravel the mysteries of Alzheimer’s and join us in untangling the web of this enigmatic condition!

Alzheimer’s disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.

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