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Exploring the white matter disruptions for Schizophrenia based on convolutional ensemble kernel randomized network.

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Title: Exploring the white matter disruptions for Schizophrenia based on convolutional ensemble kernel randomized network.
Authors: Varaprasad, S.A.1 (AUTHOR) sirigineedi21_rs@ece.nits.ac.in, Goel, Tripti1 (AUTHOR) triptigoel@ece.nits.ac.in, Tanveer, M.2 (AUTHOR) mtanveer@iiti.ac.in
Source: Neural Networks. Jan2026, Vol. 193, pN.PAG-N.PAG. 1p.
Subjects: Schizophrenia, White matter (Nerve tissue), Classification algorithms, Magnetic resonance imaging, Convolutional neural networks, Neurological disorders, Feature extraction, Ridge regression (Statistics)
Abstract: Schizophrenia (SZ) is characterized by cognitive impairments and widespread structural brain alterations. The potential adaptability of convolutional neural networks (CNN) to identify the complex and extensive brain alterations associated with SZ relies on its automatic feature learning capability. Structural magnetic resonance imaging (sMRI) is a non-invasive technique for investigating disruptions related to white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) of brain regions. We proposed an intrinsic CNN ensemble of kernel ridge regression-based random vector functional link (KRR-RVFL) architecture to explore the WM disruptions for SZ. In this approach, we have integrated an eight-layer CNN into five different KRR-RVFL classifiers for feature extraction and classification. The classifiers' outputs are averaged and fed to the final KRR-RVFL classifier for final classification. The KRR-RVFL classifier enhances stability and robustness by addressing non-linearity limitations in the standard RVFL network. The proposed CNN ensemble KRR-RVFL outperforms other classifiers with 97.33 % accuracy for the WM region, showing significant disruptions compared to GM and CSF. Furthermore, we calculated the correlation coefficient between tissue volumes and the scale of symptoms for GM and WM. According to the results, tissue volume for WM is reduced more than GM for SZ. The proposed model assists clinicians in exploring the role of WM disruptions for accurate diagnosis of SZ. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Schizophrenia (SZ) is characterized by cognitive impairments and widespread structural brain alterations. The potential adaptability of convolutional neural networks (CNN) to identify the complex and extensive brain alterations associated with SZ relies on its automatic feature learning capability. Structural magnetic resonance imaging (sMRI) is a non-invasive technique for investigating disruptions related to white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) of brain regions. We proposed an intrinsic CNN ensemble of kernel ridge regression-based random vector functional link (KRR-RVFL) architecture to explore the WM disruptions for SZ. In this approach, we have integrated an eight-layer CNN into five different KRR-RVFL classifiers for feature extraction and classification. The classifiers' outputs are averaged and fed to the final KRR-RVFL classifier for final classification. The KRR-RVFL classifier enhances stability and robustness by addressing non-linearity limitations in the standard RVFL network. The proposed CNN ensemble KRR-RVFL outperforms other classifiers with 97.33 % accuracy for the WM region, showing significant disruptions compared to GM and CSF. Furthermore, we calculated the correlation coefficient between tissue volumes and the scale of symptoms for GM and WM. According to the results, tissue volume for WM is reduced more than GM for SZ. The proposed model assists clinicians in exploring the role of WM disruptions for accurate diagnosis of SZ. [ABSTRACT FROM AUTHOR]
ISSN:08936080
DOI:10.1016/j.neunet.2025.108044