Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease

Abstract

Patterns of neural activity underlie human cognition. Transitions between these patterns are orchestrated by the brain's network architecture. What are the mechanisms linking network structure to cognitively relevant activation patterns? Here we implement principles of network control to investigate how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic engine. We also systematically incorporate neurotransmitter receptor density maps (18 receptors and transporters) and disease-related cortical abnormality maps (11 neurodegenerative, psychiatric and neurodevelopmental diseases; N = 17 000 patients, N = 22 000 controls). Integrating large-scale multimodal neuroimaging data from functional MRI, diffusion tractography, cortical morphometry, and positron emission tomography, we simulate how anatomically-guided transitions between cognitive states can be reshaped by pharmacological or pathological perturbation. Our results provide a comprehensive look-up table charting how brain network organisation and chemoarchitecture interact to manifest different cognitive topographies. This computational framework establishes a principled foundation for systematically identifying novel ways to promote selective transitions between desired cognitive topographies.

The control costs of human brain dynamics

“…Network control theory is a powerful tool to investigate the emergence of dynamics from the structural scaffold of the brain [60]. Its application in neuroscience has yielded valuable insights, e.g., into individual differences in psychiatric conditions [48,62] or developmental changes in youth [17,80]. Notably, controllability is reported to increase asymptotically and plateau with adolescence [80], potentially explaining the absence of age effects observed in our results.…”

“…In analogy to this, it costs the brain more energy, called "control energy", to control its activity between particular states than others. This asymmetry has been the subject of study in previous literature [48,61] metabolism. Moreover, we extend our methodology to investigate organizational principles in brain dynamics through control costs.…”

“…Our analysis begins by delineating intrinsic networks as brain maps. When seeking to model task-related brain states, it is possible to rely on statistical maps of parameter estimates [8] or meta-analytic maps [48]. However, neither of these approaches is feasible for modelling brain activity at rest.…”

“…(b) Before employing a control model, we define what a brain state is, i.e., how maps of brain activity are represented. In previous literature, states have been defined as functional intrinsic networks [15,17,36], cytoarchitectonical hierarchy levels [61], or cognitive maps [48], to name a few. These states are represented as single points in an N -dimensional state space, with N being the number of brain regions that we control.…”

Neuro-Immune Communication at the Core of Craving-Associated Brain Structural Network Reconfiguration in Methamphetamine Users

“…Prior research has been limited by incomplete longitudinal data, small sample sizes, and short follow-up periods (Wang et al 2013;Salo et al 2009;Li et al 2015). Complex brain function depends on dynamic state transitions supported by the underlying structural scaffold (Luppi et al 2023). However, most previous studies have primarily focused on analyzing the difference in traditional average static or dynamic neuroimaging data (Owens et al 2019), neglecting the crucial causal relationship between structure and function, which restricts our comprehensive understanding.…”