Enhanced inter-network morphological coordination in high-order association networks: MIND derived cortical morphological similarity signatures for successful cognitive aging
Year: 2026
Type: preprint
Abstract: Background Cognitive aging exhibits substantial individual heterogeneity, and successful cognitive aging (SCA) describes older adults retaining relatively intact cognitive function. Conventional regional morphometric analyses cannot fully capture brain interactions across regions that underpin cognitive preservation. The Morphometric Inverse Divergence (MIND) supports construction of multivariate morphometric similarity networks at the vertex level to probe brain structural organization at the system level. Methods The present study included 63 participants, comprising 24 SCA individuals and 39 normal aging (NA) individuals. MIND networks were constructed from five morphological features at the vertex level using the Schaefer 400‑region atlas. Nodal metrics for graph theory were calculated, and values for the area under the curve (AUC) were adopted for statistical tests. Partial correlation analyses examined associations between imaging indices and episodic memory performance, with FDR correction applied for multiple comparisons. Results Compared with the NA group, SCA participants exhibited higher inter‑network cortical morphological similarity, which was concentrated in high‑order association networks (DMN, CON, DAN, VAN). Participants with SCA also showed elevated Module_Z (AUC) in the left VAN.PFC1.1. Inter‑network morphological similarity and nodal modularity of VAN.PFC1.1 were positively correlated with episodic memory scores. Conclusions Enhanced multidimensional morphological coordination across high‑order association networks and specialization of key prefrontal nodes characterize SCA. These structural connectome signatures may act as neural markers of cognitive resilience and yield potential targets for neuroprotective interventions related to aging.
Source: Research Square
Authors: Tianyuyi Feng, Jie Wang, Xiaohu Zhao
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225267956