Normalization and cross-entropy connectivity in brain disease classification

• Published in: iScience Vol. 28; no. 4; p. 112226
• Main Authors: Wu, Haifeng, Li, Shunliang, Zeng, Yu
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 18.04.2025; Elsevier
• Subjects: Biocomputational method; Mathematical biosciences; Neuroscience; Mathematical biosciences; Neuroscience; Biocomputational method
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2025.112226

In resting-state functional magnetic resonance imaging (rs-fMRI), Pearson correlation has traditionally been the dominant method for constructing brain connectivity. This paper introduces an entropy-based connectivity approach utilizing subject-level Z score normalization, which not only standardizes signal amplitudes across subjects but also preserves interregional signal differences more effectively than Pearson correlation. Furthermore, the proposed method incorporates cross-entropy techniques, offering an advanced perspective on the temporal ordering of signals between brain regions rather than merely capturing their synchronization. Experimental results demonstrate that the proposed subject-normalized cross-joint entropy achieves superior classification accuracy in schizophrenia, mild cognitive impairment, and autism spectrum disorder, outperforming the conventional normalized correlation method by approximately 4%, 6%, and 7%, respectively. Additionally, the observed performance improvement may be attributed to changes in the symmetry of functional connectivity between brain regions—an aspect often overlooked in traditional functional connectivity analyses. [Display omitted] •Propose a brain connectivity method based on subject normalization•Limitations of cross-entropy supplementary correlation method•Assesses the potential of cross-entropy in brain disease classification tasks•Changes in the symmetry of brain entropy connectivity Neuroscience; Mathematical biosciences; Biocomputational method