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Pakravan, Mansooreh. (1405). A Multi-Level Fuzzy Explainable Prototype Network for Sex Classification Across Brain Atlases. نشریه علمی دانشگاه سیستان و بلوچستان, 23(3), 165-191. doi: 10.22111/ijfs.2026.54363.9632
Mansooreh Pakravan. "A Multi-Level Fuzzy Explainable Prototype Network for Sex Classification Across Brain Atlases". نشریه علمی دانشگاه سیستان و بلوچستان, 23, 3, 1405, 165-191. doi: 10.22111/ijfs.2026.54363.9632
Pakravan, Mansooreh. (1405). 'A Multi-Level Fuzzy Explainable Prototype Network for Sex Classification Across Brain Atlases', نشریه علمی دانشگاه سیستان و بلوچستان, 23(3), pp. 165-191. doi: 10.22111/ijfs.2026.54363.9632
Pakravan, Mansooreh. A Multi-Level Fuzzy Explainable Prototype Network for Sex Classification Across Brain Atlases. نشریه علمی دانشگاه سیستان و بلوچستان, 1405; 23(3): 165-191. doi: 10.22111/ijfs.2026.54363.9632

A Multi-Level Fuzzy Explainable Prototype Network for Sex Classification Across Brain Atlases

Iranian Journal of Fuzzy Systems
دوره 23، شماره 3، مرداد و شهریور 2026، صفحه 165-191    اصل مقاله (7.01 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2026.54363.9632
نویسنده
Mansooreh Pakravan*
Electrical and Computer engineering department, Tarbiat Modares University
چکیده
Sex differences in resting-state functional brain organization have been widely reported, yet many deep learning models prioritize predictive performance over interpretability, limiting their ability to reveal underlying neurobiological mechanisms. We propose FXP-Net, a fuzzy explainable prototype-based neural network that combines temporal convolutional modeling with fuzzy prototype learning to enable intrinsically interpretable sex classification from resting-state fMRI. FXP-Net maps windowed ROI time series into a compact latent space in which class-specific prototypes are learned and activated through graded fuzzy membership functions, allowing each prediction to be explained via similarity to a small set of interpretable prototypes rather than opaque feature activations. To enforce interpretability, we introduce a prototype-regularized training objective that encourages intra-class clustering, inter-class separation, prototype diversity, and sparse activation. Across multiple brain atlases and repeated runs, FXP-Net achieves subject-level classification performance competitive with, and in several settings statistically superior to, a strong ConvNet baseline, with peak balanced accuracy exceeding 95 %. Importantly, a multi-level interpretability analysis, combining prototype-level and decision-level attributions reveals anatomically consistent patterns across Brainnetome, Glasser, and Gordon parcellations. Quantitative overlap analysis highlights regions within the dorsolateral prefrontal cortex (DLPFC), including lateral area 9/10 and area 46, as demonstrating robust cross-atlas agreement and consistently high importance. These findings are stable across runs and align with prior evidence linking DLPFC organization to sex-related differences in executive control and higher-order cognition. The Python implementation of the proposed FXP‑Net architecture is publicly available at: https://github.com/Mansooreh-Pakravan/FXPNet-Model.
کلیدواژه‌ها
Fuzzy prototype learning؛ Explainable deep learning؛ Resting-state fMRI؛ Sex differences؛ Multi-atlas brain analysis
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