Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state

Abstract

At the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity-in concert with network structureaffects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions' baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation PLOS COMPUTATIONAL BIOLOGY

Two classes of functional connectivity in dynamical processes in networks

“…In a nonlinear system the exact same perturbation can lead to different effects in different dynamical states and since the FC clearly depends on the dynamical state of the system, the effects of a local perturbation can be dependent on FC rather than on SC. Once again, computational simulations of virtual brain models informed by empirical SC information but augmented with nonlinear brain dynamics confirm the validity of our prediction [216]. Virtual brain models tuned to regimes maximizing the degeneracy of their 'dynome', sampled via a noise-driven exploration, also succeed in qualitatively reproducing the switching 'chronnectome' observed in resting state fMRI [217].…”

Models of communication and control for brain networks: distinctions, convergence, and future outlook

“…In contrast, the brain exhibits a rich set of dynamics encompassing many other types of behaviors. Numerical simulation studies have sought to understand how such rich dynamics, occurring atop a complex network, respond to perturbative signals such as stimulation Papadopoulos, Lynn, Battaglia, & Bassett, 2020). Yet, to more formally bring control-theoretic models closer to such dynamics and associated responses, the framework must be generalized…”

Band power modulation through intracranial EEG stimulation and its cross-session consistency

“…Variability in the baseline brain state may have impacted the consistency of the stimulation responses in our data set. Even the stimulation response within a session has been repeatedly found to depend on the underlying brain state [ 24 – 26 ]. This finding is corroborated here since consistency was found to be anti-correlated with both the difference in baseline mean band power and difference in memory task which can be understood as a difference in brain state ( figure 6(D) ).…”