Genetic basis of dynamic brain states reveals cellular and disease associations
Ebneabbasi, A.; Whiteside, D. J.; Gu, Y.; Bethlehem, R. A. I.; Warrier, V.; Rittman, T.
Show abstract
Dynamic resting-state fMRI captures the time-varying patterns of brain activity that are obscured by static approaches. Hidden Markov Models (HMMs) characterise these dynamics as recurring whole-brain states and quantify their fractional occupancy (FO), the proportion of time spent in each state, yet the biological basis of inter-individual variation in FO remains unclear. Using data from 52,335 White UK Biobank participants, with replication in East and South Asian subsamples, this study examined the heritability, cellular and neurotransmitter basis of brain states, and their links with complex phenotypes. FO was significantly heritable and enriched for neuronal populations, particularly glutamatergic and GABAergic signalling. Analyses identified shared and state-specific loci and revealed genetic correlations, colocalisation, and potential causal relationships between FO and several phenotypes, including educational attainment, sleep duration, and disease risk. These findings establish dynamic brain states as biologically grounded intermediate phenotypes, linking genetic variation to neural dynamics, diseases and traits.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Neurophysiological signatures of cortical micro-architecture 94%
- Genetic correlates of evolutionary adaptations in cognitive functional brain networks and their relationship to human cognitive functioning and disease 94%
- Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis 94%
Similar papers in this journal
- Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases 94%
- Common variants contribute to intrinsic human brain functional networks 94%
- A map of transcriptional heterogeneity and regulatory variation in human microglia 94%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.