Huang Yewen, Hussain Syed Ibrar, Labate Demetrio, Azencott Robert, Thompson Paul, Adhikari Bhim, Kochunov Peter
Department of Mathematics, University of Houston, Houston, TX 77203, USA,
Imaging Genetics Center, University of Southern California, Los Angeles, CA 90007, USA, USA,
Pac Symp Biocomput. 2025;30:647-663. doi: 10.1142/9789819807024_0046.
Illness related brain effects of neuropsychiatric disorders are not regionally uniform, with some regions showing large pathological effects while others are relatively spared. Presently, Big Data meta-analytic studies tabulate these effects using structural and/or functional brain atlases that are based on the anatomical boundaries, landmarks and connectivity patterns in healthy brains. These patterns are then translated to individual level predictors using approaches such as Regional Vulnerability Index (RVI), which quantifies the agreement between individual brain patterns and the canonical pattern found in the illness. However, the atlases from healthy brains are unlikely to align with deficit pattern expressed in specific disorders such as Major Depressive Disorder (MDD), thus reducing the statistical power for individualized predictions. Here, we evaluated a novel approach, where disorder specific templates are constructed using the Kullback-Leibler (KL) distance to balance granularity, signal-to-noise ratio and the contrast between regional effect sizes to maximize translatability of the population-wide illness pattern at the level of the individual. We used regional homogeneity (ReHo) maps extracted from resting state functional MRI for N = 2, 289 MDD sample (mean age ± s.d.: 63.2 ± 7.2 years) and N = 6104 control subjects (mean age ± s.d.: 62.9 ± 7.2 years) who were free of MDD and any other mental condition. The cortical effects of MDD were analyzed on the 3D spherical surfaces representing cerebral hemispheres. KL-distance was used to organize the cortical surface into 28 regions of interest based on effect sizes, connectivity and signal-to-noise ratio. The RVI values calculated using this novel approach showed significantly higher effect size of the illness than these calculated using standard Desikan brain atlas.
神经精神疾病与疾病相关的大脑影响在区域上并不均匀，一些区域显示出较大的病理影响，而其他区域则相对幸免。目前，大数据荟萃分析研究使用基于健康大脑的解剖边界、地标和连接模式的结构和/或功能脑图谱来将这些影响制成表格。然后，使用诸如区域易损性指数（RVI）等方法将这些模式转化为个体水平的预测指标，该指数量化个体脑模式与疾病中发现的典型模式之间的一致性。然而，来自健康大脑的图谱不太可能与诸如重度抑郁症（MDD）等特定疾病中表现出的缺陷模式对齐，从而降低了个体预测的统计效力。在此，我们评估了一种新方法，即使用库尔贝克-莱布勒（KL）距离构建疾病特异性模板，以平衡粒度、信噪比和区域效应大小之间的对比度，从而在个体水平上最大化全人群疾病模式的可转化性。我们使用从静息态功能磁共振成像中提取的区域同质性（ReHo）图谱，对N = 2289名MDD样本（平均年龄±标准差：63.2±7.2岁）和N = 6104名无MDD及任何其他精神疾病的对照受试者（平均年龄±标准差：62.9±7.2岁）进行研究。在代表大脑半球的三维球形表面上分析MDD的皮质效应。基于效应大小、连接性和信噪比，使用KL距离将皮质表面组织成28个感兴趣区域。使用这种新方法计算的RVI值显示出比使用标准德斯ikan脑图谱计算的疾病效应大小显著更高。
