Statistical learning of candidate network stratifications in schizophrenia

精神分裂症候选网络分层的统计学习

• 批准号: 283338900
• 负责人: Professor Dr. Danilo Bzdok
• 金额: --
• 依托单位: CEA Centre de Saclay
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/283338900?language=en
• 关键词: Statistical; learning; candidate; network; stratifications

Neuroimaging research is limited by 1) lacking consensus on a description system of mental operations, 2) inconsistent results across individual neuroimaging studies, and 3) scarcity of strong hypotheses to test in new experiments. These caveats challenge especially clinical neuroimaging in psychiatry but can probably be alleviated by combining the BrainMap neuroimaging database and pattern-recognition algorithms. An integrated methodological framework will derive hypotheses from multi-site schizophrenia samples (482 participants) and quantify their importance.The neurobiological knowledge stored in the BrainMap database will be condensed into a set of meta-analytic priors that capture the diversity of human cognition. The meta-analytic priors will enhance statistical properties and interpretability of exploratory analyses based on structural (i.e., voxel-based morphometry) and functional (i.e., task-unrelated resting-state correlations and task-related meta-analytic connectivity modeling) brain properties. Machine-learning methods (e.g., support vector machines, logistic regression, random forests) will automatically identify the most relevant meta-analytic priors for the research questions regarding neurobiological as well as demographic and clinical predictors. Pattern recognition procedures can thus test whether and how biologically meaningful priors relate to schizophrenia pathophysiology. The ensuing candidate priors can readily motivate and improve future hypothesis-driven investigations in schizophrenia.In sum, frequently diverging and hardly reconcilable research findings in schizophrenia call for new, strong hypotheses. The proposed approach can automatically formalize and predict complex relationships between the clinical exophenotype and neurobiological endophenotype of schizophrenia.

神经成像研究受到以下因素的限制：1)对心理操作的描述系统缺乏共识，2)各个神经成像研究的结果不一致，3)缺乏在新实验中检验的强有力的假设。这些警告对精神病学中的临床神经成像提出了挑战，但通过将BrainMap神经成像数据库和模式识别算法结合起来，可能会得到缓解。一个完整的方法论框架将从多个地点的精神分裂症样本(482名参与者)中得出假说，并量化它们的重要性。存储在BrainMap数据库中的神经生物学知识将被浓缩为一组元分析先验，以捕捉人类认知的多样性。元分析先验将增强基于结构(即，基于体素的形态计量学)和功能(即，与任务无关的静止状态关联和与任务相关的元分析连接性建模)大脑属性的探索性分析的统计特性和可解释性。机器学习方法(例如，支持向量机、逻辑回归、随机森林)将自动识别与神经生物学以及人口统计学和临床预测因素相关的研究问题的最相关的元分析先验。因此，模式识别程序可以测试在生物学上有意义的先例是否以及如何与精神分裂症病理生理学相关。随之而来的候选前科可以很容易地激励和改进未来对精神分裂症的假设驱动的研究。总而言之，精神分裂症的研究结果经常出现分歧和难以调和的情况，需要新的、强有力的假设。该方法可以自动形式化和预测精神分裂症的临床表型和神经生物学表型之间的复杂关系。