Libedinsky Ilan, Helwegen Koen, Boonstra Jackson, Simón Laura Guerrero, Gruber Marius, Repple Jonathan, Kircher Tilo, Dannlowski Udo, van den Heuvel Martijn P
Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany.
Biol Psychiatry. 2025 Jun 1;97(11):1045-1058. doi: 10.1016/j.biopsych.2024.10.007. Epub 2024 Oct 16.
Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.
The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort.
PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [p] = [1.8 × 10, 4.5 × 10]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (p < 9.7 × 10), lower cognitive performance (p < 5.3 × 10), and lower general well-being (p < 9.7 × 10).
Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.
神经精神疾病和神经退行性疾病涉及脑功能连接的多种变化。作为寻找受影响连接和网络的特定镶嵌模式方法的替代方法,我们使用多连接组评分将与疾病相关的全脑连接特征量化为可解释的个性化分数。
多连接组评分(PCS)衡量个体功能连接反映某一特征全脑电路特征的程度。我们在22个数据集上计算了8种神经精神疾病(注意力缺陷多动障碍、焦虑相关障碍、自闭症谱系障碍、强迫症、双相情感障碍、重度抑郁症、分裂情感障碍和精神分裂症)和3种神经退行性疾病(阿尔茨海默病、额颞叶痴呆和帕金森病)的PCS,这些数据集来自10667名个体(5325名患者,5342名对照参与者)的静息态功能磁共振成像数据。我们还在基于人群的英国生物银行队列中的26673名个体中检查了PCS。
在8种神经精神疾病中的6种以及所有3种研究的神经退行性疾病的样本外患者中,PCS始终较高([最小值,最大值]:受试者工作特征曲线下面积 = [0.55, 0.73],错误发现率校正p值 [p] = [1.8 × 10, 4.5 × 10])。神经精神疾病PCS水平升高的个体表现出更高的神经质(p < 9.7 × 10)、更低的认知表现(p < 5.3 × 10)和更低的总体幸福感(p < 9.7 × 10)。
我们的研究结果揭示了脑部疾病中可推广的全脑连接改变。多连接组评分有效地将整个大脑中与疾病相关的特征聚集到一个可解释的、针对参与者的指标中。提供了一个用于PCS计算的工具箱。