Learning Interpretable and Robust Spatiotemporal Dynamics from fMRI for Precise Identification of Neurological Disorders

Resting-state functional magnetic resonance imaging (rs-fMRI) has significantly advanced the diagnosis of brain diseases. However, existing methods are generally limited to small, disease-specific datasets with less convincing outcomes or lack the interpretability needed to identify reliable disease...

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Vydáno v:Proceedings (IEEE International Conference on Bioinformatics and Biomedicine) s. 968 - 975
Hlavní autoři: Li, Youhao, Huang, Yongzhi, Gao, Qingchen, Chen, Pindong, Liu, Yong, Tu, Liyun
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 03.12.2024
Témata:
Autoencoders
Biological system modeling
Biomarkers
Brain disorders
Brain modeling
classification
Functional magnetic resonance imaging
Image reconstruction
Measurement
Neurological diseases
non-negative matrix factorization
Reliability
resting-state fMRI
Spatiotemporal phenomena
supervised variational autoencoders
ISSN:2156-1133
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Shrnutí:Resting-state functional magnetic resonance imaging (rs-fMRI) has significantly advanced the diagnosis of brain diseases. However, existing methods are generally limited to small, disease-specific datasets with less convincing outcomes or lack the interpretability needed to identify reliable disease-associated biomarkers. In this paper, we introduce a novel generative inference model that integrates a Variational Autoencoder (VAE) with Non-negative Matrix Factorization (NMF). Our model comprises three key components: an encoder for learning spatiotemporal dynamic feature embeddings within fMRI data, a decoder to reconstruct the input data from the encoded latent space, and a classifier to distinguish between neurological disorders and normal controls. The three components are simultaneously optimized to perform inference by estimating the posterior distribution of the latent variables from the input fMRI, yielding predictive and interpretable biomarkers for the diagnosis of neurological disorders. We extensively evaluated our method for identifying Autism Spectrum Disorder (ASD) and Alzheimer's Disease (AD) using two public datasets, ABIDE and ADNI. Experimental results show that our method achieves state-of-the-art performance across various metrics.
ISSN:2156-1133
DOI:10.1109/BIBM62325.2024.10822029