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Kudriavtsev, N.

Publications and source records attributed to Kudriavtsev, N..

2 recordsLinked to original sources

Ageing reshapes the resting-state network landscape of the human brain

Ageing alters brain rhythms and large-scale functional organisation, but whether these changes reflect isolated effects or a coordinated reconfiguration remains unclear. We applied Frequency-resolved Network Estimation via Source Separation (FREQ-NESS) to source-reconstructed resting-state MEG from 164 healthy younger and older adults across two independent datasets. FREQ-NESS revealed that ageing reshaped the frequency-resolved organisation of whole-brain networks. Older adults showed a slower alpha-network peak and a flatter decay of network prominence from alpha into beta frequencies, together with changes in entropy across the spectrum. These spectral changes were accompanied by shifts in the spatial expression of dominant networks along the lateral, posterior-anterior, and inferior-superior spatial axes. Rather than following a uniform anatomical shift, the direction of age differences varied across frequencies. Together, these findings show that healthy ageing is associated with a reshaped configuration of endogenous brain networks, characterised not simply by a uniform loss of network organisation, but by selective reconfiguration of the prominence and spatiotemporal expression of whole-brain activity across frequencies.

neuroscience↗

Efficient ageing: Simulated lesion of the structural connectome reveals optimised decline in the healthy ageing brain

Healthy ageing is associated with widespread white-matter change and altered connectome organisation, yet the link between local microstructural decline and whole-brain network communication remains unclear. Here we combined tract-based spatial statistics (TBSS) with probabilistic tractography and graph analysis to quantify the connectome-level consequences of age-sensitive white-matter hotspots. In two independent diffusion MRI datasets of healthy young and older adults (n = 144 total; Dataset 1: 77 participants; Dataset 2: 67 participants), we first identified age-sensitive fractional anisotropy clusters and then used them as constraints in a cross-dataset simulated-lesion framework. This allowed us to estimate how strongly each structural connection depended on age-sensitive tissue and to test the resulting network effects against matched-mass null lesions. Across both datasets, ageing hotspot lesioning reduced global efficiency, but consistently less than expected under the null model, indicating a less-than-random disruption of network integration. Age-sensitive tissue was disproportionately embedded in the brains integrative backbone: proportional degree loss was strongest in high-degree nodes, rich-club connections showed the greatest hotspot dependence, and nodewise losses were concentrated in frontal, cingulate and subcortical association systems, whereas posterior sensory and temporo-limbic regions were relatively spared. These findings suggest that healthy ageing reflects a selective and constrained reconfiguration of structural connectivity rather than simply pointing to uniform decline.

neuroscience↗