Frontiers in Aging Neuroscience (Sep 2022)

Potential of brain age in identifying early cognitive impairment in subcortical small-vessel disease patients

  • Yachen Shi,
  • Yachen Shi,
  • Yachen Shi,
  • Haixia Mao,
  • Qianqian Gao,
  • Guangjun Xi,
  • Guangjun Xi,
  • Siyuan Zeng,
  • Lin Ma,
  • Xiuping Zhang,
  • Lei Li,
  • Lei Li,
  • Zhuoyi Wang,
  • Zhuoyi Wang,
  • Wei Ji,
  • Wei Ji,
  • Ping He,
  • Yiping You,
  • Yiping You,
  • Kefei Chen,
  • Kefei Chen,
  • Junfei Shao,
  • Xuqiang Mao,
  • Xiangming Fang,
  • Feng Wang,
  • Feng Wang

DOI
https://doi.org/10.3389/fnagi.2022.973054
Journal volume & issue
Vol. 14

Abstract

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BackgroundReliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD.Materials and methodsA prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers.ResultsThe gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD (R2 = 0.535, p < 0.001). The GMV features were primarily distributed across the subcortical system (e.g., thalamus) and dorsal attention network. SSVD patients with age acceleration showed significantly poorer Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores. The classification model based on GMV features could accurately distinguish MCI patients from SCI patients (area under the curve = 0.883). The classification outputs of the classification model exhibited significant associations with MoCA scores, Trail Making Tests A and B scores, Stroop Color and Word Test C scores, information processing speed total scores, and plasma levels of total antioxidant capacity in SSVD patients.ConclusionBrain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.

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