严重精神疾病风险的年轻人中的大脑连接体:纵向视角
Mohammed K Shakeel1,2,3, Paul D Metzak4, Mike Lasby5
1Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada. mohammed.kalathil@ucalgary.ca.
Brain imaging and behavior
|November 7, 2024
概括
在面临严重精神疾病 (SMI) 风险的年轻人中,使用先进的成像技术确定了大脑网络异常. 这些结构和功能连接的变化可能表明SMI发展的早期生物标志物.
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.
- 医疗成像医学成像
背景情况:
- 确定严重精神疾病 (SMI) 的生物标志物对于早期干预和预防策略至关重要.
- 处于跨诊断风险的年轻人代表了理解SMI发展轨迹的关键窗口.
研究的目的:
- 为了研究跨诊断性SMI风险的年轻人在一年内全脑结构和功能连接体的纵向变化.
- 为了确定潜在的神经成像生物标志物,区分SMI的风险阶段.
主要方法:
- 使用受约束的球形解卷用于全脑曲线图和计算的连接矩阵用于图形理论分析.
- 采用功能磁共振成像 (fMRI) 和图形理论来分析静止状态网络相关性.
- 应用线性混合模型和机器学习 (例如,中间中心性) 来分析结构和功能大脑数据.
主要成果:
- 在全球指标 (小世界兰巴达) 和静止状态网络 (前额头,默认模式,深灰质,视觉,背部注意力) 中揭示了结构和功能异常.
- 机器学习识别了角度环和双边时环之间的中心性变化,作为风险组之间的歧视性特征.
- 研究结果表明,大规模大脑网络中的异常是SMI的跨诊断风险的基础.
结论:
- 额头-体,体,默认模式和深灰质网络中的异常与跨诊断性SMI风险的神经生物学基础有关.
- 角度和时间旋转中的特定节点指标显示了作为SMI风险阶段的早期检测和差异化生物标志物的潜力.
- 纵向连接组分析为风险青少年严重精神疾病的发展途径提供了宝贵的见解.
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