阿特拉斯分片对跨越六种精神疾病的功能连接分析的影响
Xiaoya Wu1,2, Chuang Liang1,2, Juan Bustillo3
1College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Human brain mapping
|April 2, 2025
概括
精神疾病中的大脑连接差异显示出低可重现性. 这项研究揭示了额头连接缺陷在疾病中很常见,但更细微的大脑分片可以提高分类准确性和可复制性.
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.
- 脑部成像 脑部成像
背景情况:
- 神经精神疾病表现出功能连接 (FC) 的改变,但由于大脑分片方法不一致,研究结果有所不同.
- FC研究的低可复制性和高变异性阻碍了可靠的诊断和治疗开发.
研究的目的:
- 系统地调查不同大脑分片方法如何影响六种神经精神疾病的功能连接 (FC) 网络分析.
- 识别可重复的FC缺陷,并评估图谱颗粒度对分类准确性的影响.
主要方法:
- 在关联,组差异和分类任务中比较了三种结构性大脑地图 (AAL,BNA,HCP_MMP_1.0) 和四种功能分片方法 (Yeo,Gordon,Schaefer).
- 分析了来自大量注意力缺陷多动障碍 (ADHD),自闭症谱系障碍 (ASD),精神分裂症 (SZ),精神分裂情绪障碍 (SAD),双相情感障碍 (BP) 和严重抑郁障碍 (MDD) 的休息状态功能性MRI数据.
主要成果:
- 与前额相关的FC缺陷在所有疾病中都是一致可重现的,无论使用的大脑图谱.
- 分类准确性和识别其他可复制的FC赤字受到所选择的分片方案的显著影响.
- 具有更细粒度的功能性地图集,特别是谢弗地图集,在分类任务中表现出更优异的性能,并产生了更可重复的FC缺陷模式.
结论:
- 面部相关的FC变化代表了多种精神疾病中潜在的常见和强大的神经生物学标志物.
- 大脑分片的选择显著影响了神经精神病学研究中FC发现的可复制性和诊断实用性.
- 建议使用具有更细粒度的功能模板来提高基于fMRI的静止状态FC分析的可靠性和准确性.
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