Lewis Madison, Eack Shaun, Theis Nicholas, Keshavan Matcheri S, Prasad Konasale M
Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA 15213.
Department of Social Work, University of Pittsburgh School of Social Work, Pittsburgh, PA 15213.
bioRxiv. 2025 Jun 25:2025.06.19.660609. doi: 10.1101/2025.06.19.660609.
Traditional fMRI analyses often ignore regions that fail to reach statistical significance, assuming they are biologically unimportant. We tested the accuracy of this assumption using causal discovery based-analysis that go beyond associations/correlations to test the causality of one region's influence over the other. We hypothesized that the network of statistically significant (active network, AN) and non-significant regions (silent network, SN) interact and their features will causally influence psychopathology severity and working memory performance.
We examined AN and SN during N-BACK task on 25 FHR and 37 controls. Clusters with significantly different activations were juxtaposed to 360 Glasser atlas parcellations. The PC algorithm for causal discovery was implemented. Connectivity of regions with the highest alpha-centrality (HAC) were examined.
Seventy-seven Glasser regions were in the AN and the rest were silent nodes. Two regions showed HAC for FHR and HC. Among controls, one HAC region was silent (auditory association cortex) and the other one was active (insula). Among FHR, both were silent nodes (early auditory cortex). These HAC regions in both groups had bidirectional directed edges between each other forming a reciprocal circuit whose edge-weights causally "increased" magical ideation severity.
Causal connectivity between SN and AN suggests that the statistically non-significant and significant regions influence each other. Our findings question the merit of ignoring statistically non-significant regions and exclusively including statistically significant regions in the pathophysiological models. Our study suggests that causality analysis should receive greater attention.
传统的功能磁共振成像（fMRI）分析通常会忽略未达到统计学显著性的区域，认为它们在生物学上不重要。我们使用基于因果发现的分析方法来检验这一假设的准确性，这种方法超越了关联/相关性分析，以测试一个区域对另一个区域影响的因果关系。我们假设具有统计学显著性的区域网络（活跃网络，AN）和无显著性的区域网络（沉默网络，SN）相互作用，并且它们的特征会因果性地影响精神病理学严重程度和工作记忆表现。
我们在25名患有家族性高风险精神分裂症（FHR）个体和37名对照组个体进行N-回溯任务期间，对AN和SN进行了检查。将激活存在显著差异的簇与360个格拉斯哥图谱分区并列。实施了用于因果发现的PC算法。检查了具有最高α中心性（HAC）的区域的连通性。
77个格拉斯哥区域属于AN，其余为沉默节点。有两个区域在FHR个体和健康对照（HC）个体中显示出HAC。在对照组中，一个具有HAC的区域是沉默的（听觉联合皮层），另一个是活跃的（脑岛）。在FHR个体中，两个区域都是沉默节点（早期听觉皮层）。两组中的这些具有HAC的区域彼此之间具有双向有向边，形成了一个相互的回路，其边权重因果性地“增加”了奇幻思维的严重程度。
SN和AN之间的因果连通性表明，统计学上无显著性和有显著性的区域相互影响。我们的研究结果质疑了在病理生理模型中忽略统计学上无显著性区域而仅纳入统计学上有显著性区域的做法的价值。我们的研究表明，因果关系分析应受到更多关注。
