Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies.
Andrea I LuppiS Parker SingletonJustine Y HansenKeith W JamisonDanilo BzdokAmy F KuceyeskiRichard F BetzelBratislav MišićPublished in: Nature biomedical engineering (2024)
The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.
Keyphrases
- mental health
- positron emission tomography
- magnetic resonance imaging
- computed tomography
- end stage renal disease
- white matter
- resting state
- chronic kidney disease
- optical coherence tomography
- newly diagnosed
- peritoneal dialysis
- multiple sclerosis
- emergency department
- functional connectivity
- mild cognitive impairment
- contrast enhanced
- cerebral ischemia
- brain injury
- chronic pain
- data analysis