Mapping structural disconnection and transcriptomic signatures in Alzheimer's disease with MIND networks.

BACKGROUND

Alzheimer's disease (AD) is increasingly conceptualized as a disconnection syndrome involving widespread alterations in large-scale brain networks. Previous studies using morphometric similarity networks (MSNs) have revealed broad structural and transcriptomic changes, yet vertex-level structural disconnection and its molecular basis remain poorly understood. We applied morphometric inverse divergence (MIND), an innovative approach for fine-grained mapping of structural disconnection and its transcriptomic correlates in AD.

METHODS

Utilizing two independent datasets: [ADNI (219 AD, 219 cognitively normal, CN) and the Qilu dataset (100 AD, 137 CN)], we mapped robust MIND network alterations in AD patients and examined their associations with cognitive performance and biomarker quantifications. Additionally, we linked MIND connectome to spatial gene expression using partial least squares regression, followed by gene enrichment analysis to identify relevant biological pathways. Finally, to validate the clinical utility of MIND, a residual deep neural network (ResDNN) was developed to compare its diagnostic performance against MSNs in distinguishing AD from CN.

RESULTS

Significantly decreased MIND degree was identified in the bilateral frontal, lateral occipital, and posterior temporal lobes (P FDR < 0.05), positively correlating with MMSE score and FDG-PET SUVR (all P < 0.001). Conversely, increased MIND degree was observed in the bilateral cuneus, entorhinal, lingual, and parahippocampal regions (P FDR < 0.05), negatively correlating with cognition assessment, CSF Aβ-42 levels and FDG-PET SUVR (all P < 0.001). These AD-related MIND alterations were spatially correlated with gene expression profiles crucial for synaptic function, neurotransmission, and metabolic regulation. Importantly, MIND achieved superior diagnostic efficacy (AUC=0.90/0.88 in ADNI/Qilu) over MSNs.

CONCLUSIONS

We mapped a robust pattern of structural disconnection in Alzheimer's disease with MIND approach and associate it with particular transcriptomic signatures. These findings not only improve our mechanistic understanding of AD as a disconnection syndrome but also demonstrate MIND as a sensitive tool for identifying disease-specific alterations, holding promise for future mechanistic and clinical investigations into AD pathology.