Scholarlink

<p>Diagnostic assessments of mild cognitive impairment (MCI) are lengthy and burdensome, highlighting the need for new tools to detect MCI. Time-domain functional near-infrared spectroscopy (TD-fNIRS) can measure brain function in clinical settings and may address this need. In this study (NCT05996575), MCI patients (<i>n</i> = 50) and age-matched healthy controls (HC; <i>n</i> = 51) underwent TD-fNIRS recordings during cognitive tasks (Verbal Fluency, N-Back). Machine learning models were trained to distinguish MCI from HC using neural activity, cognitive task behavior, and self-reported impairment as input features. Significant group-level differences (MCI vs HC) were demonstrated in self-report, N-Back and Verbal Fluency behavior, and task-related brain activation. Classifier performance was similar when using self-report (AUC = 0.76) and self-report plus behavior (AUC = 0.79) as input features, but was strongest when neural metrics were included (AUC = 0.92). This study demonstrates the potential of TD-fNIRS to assess MCI with short brain scans in clinical settings. Clinical trial registration: NCT05996575.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A functional neuroimaging biomarker of mild cognitive impairment using TD-fNIRS

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月3日

查看全文
  • Julien Dubois,
  • John Gregory Duffy,
  • Ryan M. Field,
  • Erin M. Koch,
  • Zahra M. Aghajan,
  • Naomi Miller,
  • Katherine L. Perdue,
  • Gregory Sahagian,
  • Moriah Taylor

摘要

Diagnostic assessments of mild cognitive impairment (MCI) are lengthy and burdensome, highlighting the need for new tools to detect MCI. Time-domain functional near-infrared spectroscopy (TD-fNIRS) can measure brain function in clinical settings and may address this need. In this study (NCT05996575), MCI patients (n = 50) and age-matched healthy controls (HC; n = 51) underwent TD-fNIRS recordings during cognitive tasks (Verbal Fluency, N-Back). Machine learning models were trained to distinguish MCI from HC using neural activity, cognitive task behavior, and self-reported impairment as input features. Significant group-level differences (MCI vs HC) were demonstrated in self-report, N-Back and Verbal Fluency behavior, and task-related brain activation. Classifier performance was similar when using self-report (AUC = 0.76) and self-report plus behavior (AUC = 0.79) as input features, but was strongest when neural metrics were included (AUC = 0.92). This study demonstrates the potential of TD-fNIRS to assess MCI with short brain scans in clinical settings. Clinical trial registration: NCT05996575.