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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]
Copyright of Neural Networks is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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PubTypeId: academicJournal
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  Data: Exploring the white matter disruptions for Schizophrenia based on convolutional ensemble kernel randomized network.
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  Data: <searchLink fieldCode="DE" term="%22Schizophrenia%22">Schizophrenia</searchLink><br /><searchLink fieldCode="DE" term="%22White+matter+%28Nerve+tissue%29%22">White matter (Nerve tissue)</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Neurological+disorders%22">Neurological disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Ridge+regression+%28Statistics%29%22">Ridge regression (Statistics)</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Neural Networks is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.neunet.2025.108044
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      – Code: eng
        Text: English
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        StartPage: N.PAG
    Subjects:
      – SubjectFull: Schizophrenia
        Type: general
      – SubjectFull: White matter (Nerve tissue)
        Type: general
      – SubjectFull: Classification algorithms
        Type: general
      – SubjectFull: Magnetic resonance imaging
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Neurological disorders
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Ridge regression (Statistics)
        Type: general
    Titles:
      – TitleFull: Exploring the white matter disruptions for Schizophrenia based on convolutional ensemble kernel randomized network.
        Type: main
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            NameFull: Varaprasad, S.A.
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            NameFull: Goel, Tripti
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            NameFull: Tanveer, M.
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            – D: 01
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
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              Value: 193
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            – TitleFull: Neural Networks
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