Thukral Rhea A, Maximo Jose O, Lahti Adrienne C, Rutherford Saige E, Larson Jordan S, Zhang Hui, Marquand Andre F, Kraguljac Nina V
medRxiv. 2024 Aug 23:2024.08.23.24312480. doi: 10.1101/2024.08.23.24312480.
While there is a general consensus that functional connectome pathology is a key mechanism underlying psychosis spectrum disorders, the literature is plagued with inconsistencies and translation into clinical practice is non-existent. This is perhaps because group-level findings may not be accurate reflections of pathology at the individual patient level.
To characterize inter-individual heterogeneity in functional networks and investigate if normative values can be leveraged to identify biologically less heterogeneous subgroups of patients.
We used data collected in a case-control study conducted at the University of Alabama at Birmingham (UAB). We recruited antipsychotic medication-naïve first-episode psychosis patients from UAB outpatient, inpatient, and emergency room settings.
Individual-level patterns of deviations from a normative reference range in resting-state functional networks using the Yeo-17 atlas for parcellations.
Statistical analyses included 108 medication-naïve first-episode psychosis patients. We found that there is a high level of inter-individual heterogeneity in resting-state network connectivity deviations from the normative reference range. Interestingly 48% of patients did not have any functional connectivity deviations, and no more than 11.1% of patients shared functional deviations between the same regions of interest. In a analysis, we grouped patients based on deviations into four theoretically possible groups. We discovered that all four groups do exist in our experimental data and showed that subgroups based on deviation profiles were significantly less heterogeneous compared to the overall group (positive deviation group: z= -2.88, p = 0.002; negative deviation group: z= -3.36, p<0.001).
Our findings experimentally demonstrate that there is a high level of inter-individual heterogeneity in resting-state network pathology in first-episode psychosis patients which support the idea that group-level findings are not accurate reflections of pathology at the individual level. We also demonstrated that normative functional connectivity deviations may have utility for identifying biologically less heterogeneous subgroups of patients, even though they are not distinguishable clinically. Our findings constitute a significant step towards making precision psychiatry a reality, where patients are selected for treatments based on their individual biological characteristics.
How heterogeneous is individual-level resting-state functional network pathology in patients suffering from a first psychotic episode? Can normative reference values in functional network connectivity be leveraged to identify biologically more homogenous subgroups of patients? We report that functional network pathology is highly heterogeneous, with no more than 11% of patients sharing functional deviations between the same regions of interest. Normative modeling is a tool that can map individual neurobiological differences and enables the classification of a clinically heterogenous patient group into subgroups that are neurobiologically less heterogenous.
虽然人们普遍认为功能连接组病理学是精神病谱系障碍的关键潜在机制,但文献中存在诸多不一致之处,且尚未转化为临床实践。这可能是因为群体水平的研究结果可能无法准确反映个体患者层面的病理学特征。
描述功能网络中的个体间异质性,并研究是否可以利用规范值来识别生物学上异质性较低的患者亚组。
设计、设置和参与者:我们使用了在阿拉巴马大学伯明翰分校(UAB)进行的一项病例对照研究中收集的数据。我们从UAB门诊、住院和急诊室环境中招募了未服用抗精神病药物的首发精神病患者。
使用Yeo-17图谱进行分区,以获取静息态功能网络中与规范参考范围的个体水平偏差模式。
统计分析纳入了108名未服用药物的首发精神病患者。我们发现,静息态网络连接性与规范参考范围的偏差存在高度个体间异质性。有趣的是,48%的患者没有任何功能连接偏差,且在相同感兴趣区域之间共享功能偏差的患者不超过11.1%。在一项分析中,我们根据偏差将患者分为四个理论上可能的组。我们发现所有四个组在我们的实验数据中都存在,并表明基于偏差概况的亚组与总体组相比异质性显著更低(正偏差组:z = -2.88,p = 0.002;负偏差组:z = -3.36,p < 0.001)。
我们的研究结果通过实验证明,首发精神病患者静息态网络病理学存在高度个体间异质性,这支持了群体水平的研究结果不能准确反映个体水平病理学特征的观点。我们还证明,规范功能连接偏差可能有助于识别生物学上异质性较低的患者亚组,尽管它们在临床上无法区分。我们的研究结果朝着实现精准精神病学迈出了重要一步,即根据患者的个体生物学特征选择治疗方案。
首次精神病发作患者的个体水平静息态功能网络病理学有多异质?功能网络连接性的规范参考值能否用于识别生物学上更同质的患者亚组?我们报告功能网络病理学高度异质,在相同感兴趣区域之间共享功能偏差的患者不超过11%。规范建模是一种可以映射个体神经生物学差异的工具,能够将临床异质性患者群体分类为神经生物学上异质性较低的亚组。