使用皮质曲率来表征精神分裂症的认知亚型
Irina Papazova1, Stephan Wunderlich2, Boris Papazov3
1Psychiatry and Psychotherapy, Faculty of Medicine, University of Augsburg, Geschwister-Schönert-Straße 1, 86156, Augsburg, Germany; Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany; DZPG (German Center for Mental Health), partner site München, Augsburg, Germany.
Journal of psychiatric research
|March 26, 2024
概括
平均曲率 (MC) 有效地区分了精神分裂症患者的高和低认知表现. 这种神经解剖学参数在精神分裂症研究中有望识别不同的认知亚型.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 神经成像是一种神经成像.
背景情况:
- 认知缺陷是精神分裂症的主要症状.
- 缺陷严重程度的患者异质性使神经支研究复杂化.
- 识别认知特征的神经解剖学相关性至关重要.
研究的目的:
- 使用神经成像特征对具有高 (HighCog) 和低 (LowCog) 认知表现的患者进行分类.
- 为了确定哪个皮质特征 (灰质体积,皮质厚度或平均曲率) 最好区分认知形状.
- 识别与认知性能差异相关的关键大脑区域.
主要方法:
- 利用后勤回归和随机森林机器学习模型.
- 分析的皮质特征:灰质体积 (VOL),皮质厚度 (CT) 和平均曲率 (MC).
- 采用了两个独立的患者样本 (N=57发现,N=52验证).
主要成果:
- 基于平均曲率 (MC) 的分类模型实现了最高的性能 (AUC 76%和73%).
- 额头时和头大脑区域被确定为分类中最重要的.
- 在高Cog和低Cog组之间,在特定的大脑区域发现了MC的显著差异.
结论:
- 平均曲率 (MC) 是一个有希望的神经解剖学参数,用于表征精神分裂症的认知亚型.
- 基于MC的分析可以帮助区分精神分裂症的认知特征.
- 这种方法有助于理解精神分裂症认知缺陷的神经异质性.
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