Indexed by:
Abstract:
Temporal-frequency characteristics in fMRI data are key to distinguishing Autism Spectrum Disorder (ASD) from neurotypical individuals. However, the non-linearity and multidimensionality of fMRI data pose significant challenges. To address these, we introduce a Deep Non-linear Factorization method with a Wavelet Temporal-Frequency Attention module (Deep WTFAF) tailored for multidimensional fMRI analysis. By leveraging the wavelet domain, our approach applies temporal-frequency attention to assign weights to significant features, enhancing critical data while reconstructing incomplete fMRI data. This method enables deep non-linear factorization and effective feature representation for subsequent classification tasks. Validated on ASD-related fMRI datasets, Deep WTFAF outperforms traditional methods, maintaining essential information and ensuring robustness against high-dimensional and incomplete data. Stability theory proof further confirms the model's reliability, crucial for clinical applications like neurological disorder classification. © 2025 Elsevier Ltd
Keyword:
Reprint 's Address:
Email:
Source :
Pattern Recognition
ISSN: 0031-3203
Year: 2025
Volume: 165
7 . 5 0 0
JCR@2023
CAS Journal Grade:1
Cited Count:
SCOPUS Cited Count:
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 2
Affiliated Colleges: