A Confounding Factors-Inhibition Adversarial Learning Framework for Multi-site fMRI Mental Disorder Identification
Journal:
arXiv
Published Date:
Apr 12, 2025
Abstract
In open data sets of functional magnetic resonance imaging (fMRI), the
heterogeneity of the data is typically attributed to a combination of factors,
including differences in scanning procedures, the presence of confounding
effects, and population diversities between multiple sites. These factors
contribute to the diminished effectiveness of representation learning, which in
turn affects the overall efficacy of subsequent classification procedures. To
address these limitations, we propose a novel multi-site adversarial learning
network (MSalNET) for fMRI-based mental disorder detection. Firstly, a
representation learning module is introduced with a node information assembly
(NIA) mechanism to better extract features from functional connectivity (FC).
This mechanism aggregates edge information from both horizontal and vertical
directions, effectively assembling node information. Secondly, to generalize
the feature across sites, we proposed a site-level feature extraction module
that can learn from individual FC data, which circumvents additional prior
information. Lastly, an adversarial learning network is proposed as a means of
balancing the trade-off between individual classification and site regression
tasks, with the introduction of a novel loss function. The proposed method was
evaluated on two multi-site fMRI datasets, i.e., Autism Brain Imaging Data
Exchange (ABIDE) and ADHD-200. The results indicate that the proposed method
achieves a better performance than other related algorithms with the accuracy
of 75.56 and 68.92 in ABIDE and ADHD-200 datasets, respectively. Furthermore,
the result of the site regression indicates that the proposed method reduces
site variability from a data-driven perspective. The most discriminative brain
regions revealed by NIA are consistent with statistical findings, uncovering
the "black box" of deep learning to a certain extent.
