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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1474988
This article is part of the Research Topic Imaging in the Diagnosis and Treatment of Eye Diseases View all 6 articles

Impaired Interhemispheric Synchrony in Patients with Iridocyclitis and Classification Using Machine Learning: an fMRI Study

Provisionally accepted
Yan Tong Yan Tong 1Zhi Wen Zhi Wen 2Xin Huang Xin Huang 3*
  • 1 The Chinese University of Hong Kong, Shatin, China
  • 2 Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
  • 3 Jiangxi Provincial People's Hospital, Nanchang, Jiangxi Province, China

The final, formatted version of the article will be published soon.

    Background: This study examined the interhemispheric integration function pattern in patients with iridocyclitis utilizing the voxel-mirrored homotopic connectivity (VMHC) technique. Additionally, we investigated the ability of VMHC results to distinguish patients with iridocyclitis from healthy controls (HCs), which may contribute to the development of objective biomarkers for early diagnosis and cap 删除[T]: effectively 删除[T]: intervention in clinical set. Methods: Twenty-six patients with iridocyclitis and twenty-six matched HCs, in terms of sex, age, and education level, underwent resting-state functional magnetic resonance imaging (fMRI) examinations. The study employed the voxel-mirrored homotopic connectivity (VMHC) technique to evaluate interhemispheric integration functional connectivity indices at a voxel-wise level. The diagnostic efficacy of VMHC was evaluated using a support vector machine (SVM) classifier, with classifier performance assessed through permutation test analysis. Furthermore, correlation analyses was conducted to investigate the associations between mean VMHC values in various brain regions and clinical features.Results: Patients with iridocyclitis exhibited significantly reduced VMHC signal values in the bilateral inferior temporal gyrus, calcarine, middle temporal gyrus, and precuneus compared to HCs (voxel-level P < 0.01, Gaussian Random Field correction; cluster-level P < 0.05). Furthermore, the extracted resting-state zVMHC features effectively classified patients with iridocyclitis and HCs, achieving an area under the receiver operating characteristic curve (AUC) of 0.74 and an overall accuracy of 0.673 (P < 0.001, non-parametric permutation test).Our findings reveal disrupted interhemispheric functional organization in patients with iridocyclitis, offering insight into the pathophysiological mechanisms associated with vision loss and cognitive dysfunction in this patient population. This study also highlights the potential of machine learning in ophthalmology and the importance of establishing objective biomarkers to address diagnostic heterogeneity.

    Keywords: interhemispheric integration, Iridocyclitis, Support vector machine, machine learning, fMRI

    Received: 02 Aug 2024; Accepted: 28 Nov 2024.

    Copyright: © 2024 Tong, Wen and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Xin Huang, Jiangxi Provincial People's Hospital, Nanchang, 330006, Jiangxi Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.