Montagnese Marcella, Ebneabbasi Amir, García-San-Martín Natalia, Pecci-Terroba Clara, Romero-García Rafael, Morgan Sarah E, Cole James H, Seidlitz Jakob, Rittman Timothy, Bethlehem Richard A I
Department of Psychology, University of Cambridge, Downing Pl, Cambridge, CB2 3EB, UK.
Department of Clinical Neurosciences, University of Cambridge, Herchel Smith Building, Robinson Way, Cambridge, CB2 0SZ, UK.
medRxiv. 2025 Jun 11:2025.06.10.25328978. doi: 10.1101/2025.06.10.25328978.
Alzheimer's disease (AD) is characterised by inter-individual heterogeneity in brain degeneration, limiting diagnostic and prognostic precision. We present a novel framework integrating Morphometric Inverse Divergence (MIND) networks with hierarchical Bayesian large-scale population modelling to identify individual-level neuroanatomical deviations.
MIND networks quantify similarity between brain regions using multivariate MRI features. A normative model of regional MIND values trained on UK Biobank (N=35,133) was applied to the National Alzheimer's Coordinating Center cohort (N=3,567). We examined brain deviations across clinical stages, APOE genotypes, mortality risk, and neuropathological burden.
Negative deviations (reduced MIND) stratified disease stages (p<0.01) and showed functional network enrichment in AD. Greater negative deviations characterised APOE ε4 homozygotes and correlated with postmortem neuropathological severity (p=0.032). Neurobiological decoding revealed associations with neurotransmitter receptor distributions and cortical organisation properties.
This population neuroimaging modelling enables individualised brain mapping with direct utility for diagnosis, prognosis, and understanding of biological mechanisms.
阿尔茨海默病（AD）的特征是脑退化存在个体间异质性，这限制了诊断和预后的准确性。我们提出了一个将形态计量逆散度（MIND）网络与分层贝叶斯大规模人群建模相结合的新框架，以识别个体水平的神经解剖学偏差。
MIND网络使用多变量MRI特征量化脑区之间的相似性。在英国生物银行（N = 35133）上训练的区域MIND值规范模型被应用于国家阿尔茨海默病协调中心队列（N = 3567）。我们研究了不同临床阶段、APOE基因型、死亡风险和神经病理学负担下的脑偏差。
负偏差（MIND降低）对疾病阶段进行了分层（p < 0.01），并在AD中显示出功能网络富集。更大的负偏差是APOE ε4纯合子的特征，并且与死后神经病理学严重程度相关（p = 0.032）。神经生物学解码揭示了与神经递质受体分布和皮质组织特性的关联。
这种人群神经影像学建模能够实现个体化脑图谱绘制，对诊断、预后以及生物机制的理解具有直接应用价值。
