Normalization and cross-entropy connectivity in brain disease classification

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 standardize...

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Published iniScience Vol. 28; no. 4; p. 112226
Main Authors Wu, Haifeng, Li, Shunliang, Zeng, Yu
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 18.04.2025
Elsevier
Subjects
Biocomputational method
Mathematical biosciences
Neuroscience
Mathematical biosciences
Neuroscience
Biocomputational method
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Abstract 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
AbstractList 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.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.
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 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.
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
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. • 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
ArticleNumber 112226
Author Zeng, Yu
Li, Shunliang
Wu, Haifeng
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Snippet In resting-state functional magnetic resonance imaging (rs-fMRI), Pearson correlation has traditionally been the dominant method for constructing brain...
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SubjectTerms Biocomputational method
Mathematical biosciences
Neuroscience
Title Normalization and cross-entropy connectivity in brain disease classification
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https://www.ncbi.nlm.nih.gov/pubmed/40235587
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