R. Nathan Spreng

This study investigates the heritability of individualized cortical network topography using twin and family data, finding genetic influences on network size and spatial organization.

This study uses prediction correlation on resting-state fMRI data to analyze spatiotemporal functional interactions within and between large-scale brain networks, revealing distinct connectivity patterns.

The paper identifies hidden population-level subnetworks in social brain morphology using deep learning on UK Biobank data, showing they predict social traits and loneliness.

This study shows that basal forebrain volume predicts cortical degeneration in Alzheimer's disease, challenging the traditional view of pathogenesis.

The paper examines structure-function relationships in the neocortex, finding that structural and functional networks diverge in higher-order areas.

The paper examines age-related changes in brain structure and metabolism using multimodal imaging and connectome gradients, finding that higher-order transmodal regions show increased Aβ deposition and that hippocampal atrophy is more pronounced towards its posterior end.

This paper applies an author-topic model to ALE-type meta-analyses to explore heterogeneity, revealing functional sub-domains and task-dependent co-activation patterns.

The paper identifies two distinct subsystems within the frontoparietal control network (FPCN) that show different functional connectivity patterns with the default and dorsal attention networks across multiple conditions.

The paper uses resting-state fMRI to identify distinct functional networks in human basal forebrain nuclei, aligning with anatomical regions and showing unique projection patterns.

The paper examines structural covariance networks across age 6-94, identifying two age-dependent trends in neurocognitive networks using partial least squares.