较弱的自上而下的认知控制和更强的自下而上的信号传输是精神分裂症的病原体
Xiaodan Lyu1, Tiantian Liu2, Yunxiao Ma2
1Cognitive Neuroscience Lab, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan.
Schizophrenia (Heidelberg, Germany)
|March 5, 2025
概括
精神分裂症涉及上下调节受损和增强的自下向上信号,影响认知和感知功能. 这项研究开发了一种使用静止状态fMRI的AI模型,以确定这些缺陷背后的神经机制.
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.
- 人工智能的人工智能
背景情况:
- 精神分裂症呈现异质症状,包括认知缺陷 (自上而下) 和感知障碍 (自下而上).
- 精确的神经机制,特别是自上而下的和自下而上的过程之间的相互作用,仍然不清楚.
- 了解这些相互作用对于诊断和潜在治疗精神分裂症至关重要.
研究的目的:
- 为了研究上下和下向上神经机制之间的异常相互作用是精神分裂症的基础的假设.
- 开发和验证一个人工智能模型用于诊断精神分裂症,使用静止状态功能磁共振成像 (fMRI).
- 识别特定的功能性大脑连接及其与临床症状的关系.
主要方法:
- 开发了一种改进的堆叠自编码器支持矢量机器 (ISAE-SVM) 模型,用于诊断精神分裂症.
- 利用静止状态fMRI数据和 permutation测试来识别有区别的功能连接.
- 采用光谱动态因果建模 (sDCM) 来分析大脑区域之间的定向相互作用.
主要成果:
- 根据ISAE-SVM模型,精神分裂症诊断的平均分类准确率为82%.
- 确定了五个关键的休息状态功能连接,与临床症状严重程度相关 (阳性和阴性综合征量表).
- sDCM分析显示,精神分裂症患者的自上向下调节减弱,自下向上信号增强.
结论:
- 这些发现支持这样一个假设:受损的上下调节和增强的自下向上信号是精神分裂症中核心神经机制.
- 开发的ISAE-SVM模型显示了使用神经成像数据对精神分裂症的客观诊断的希望.
- 这项研究为精神分裂症认知和感知症状的神经基础提供了新的见解.
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