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<p>To investigate the topological architecture of white matter structural networks in patients with alopecia areata (AA), we conducted a cross-sectional study involving 186 participants, including 102 AA patients and 84 age- and sex-matched healthy controls (HCs). All participants underwent diffusion tensor imaging (DTI) using a 3.0-T MRI scanner. Graph theoretical analysis was employed to quantify key topological properties of structural networks, encompassing both global and nodal metrics. Compared with HCs, AA patients exhibited significantly shortened characteristic path length (1.353 ± 0.026 vs. 1.366 ± 0.028, <i>P</i> = 0.001) as well as increased clustering coefficient (0.705 ± 0.016 vs. 0.698 ± 0.015, <i>P</i> = 0.003), global efficiency (0.738 ± 0.015 vs. 0.733 ± 0.015, <i>P</i> = 0.008), and local efficiency (0.852 ± 0.008 vs. 0.849 ± 0.008, <i>P</i> = 0.032), indicating enhanced global integration and functional segregation suggestive of hyper-connectivity. Additionally, nodal analysis showed that alterations in AA patients were mainly located in regions involved in emotion-cognitive regulation. Such network-level perturbations may reflect neurobiological adaptive responses underlying the high prevalence of neuropsychiatric comorbidities in AA, and thus hold promise as potential imaging biomarkers for evaluating central nervous system (CNS) involvement in this disease. Collectively, these findings advance our understanding of the neuropathophysiological mechanisms underlying CNS perturbations in AA from a network neuroscience framework.</p>

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Alterations of white matter connectivity in alopecia areata patients

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Fangxue Yang,
  • Weihua Liao,
  • Yingxing Duan

摘要

To investigate the topological architecture of white matter structural networks in patients with alopecia areata (AA), we conducted a cross-sectional study involving 186 participants, including 102 AA patients and 84 age- and sex-matched healthy controls (HCs). All participants underwent diffusion tensor imaging (DTI) using a 3.0-T MRI scanner. Graph theoretical analysis was employed to quantify key topological properties of structural networks, encompassing both global and nodal metrics. Compared with HCs, AA patients exhibited significantly shortened characteristic path length (1.353 ± 0.026 vs. 1.366 ± 0.028, P = 0.001) as well as increased clustering coefficient (0.705 ± 0.016 vs. 0.698 ± 0.015, P = 0.003), global efficiency (0.738 ± 0.015 vs. 0.733 ± 0.015, P = 0.008), and local efficiency (0.852 ± 0.008 vs. 0.849 ± 0.008, P = 0.032), indicating enhanced global integration and functional segregation suggestive of hyper-connectivity. Additionally, nodal analysis showed that alterations in AA patients were mainly located in regions involved in emotion-cognitive regulation. Such network-level perturbations may reflect neurobiological adaptive responses underlying the high prevalence of neuropsychiatric comorbidities in AA, and thus hold promise as potential imaging biomarkers for evaluating central nervous system (CNS) involvement in this disease. Collectively, these findings advance our understanding of the neuropathophysiological mechanisms underlying CNS perturbations in AA from a network neuroscience framework.