Altered brain dynamics across bipolar disorder and schizophrenia revealed by overlapping brain states

Abstract

Aberrant brain dynamics putatively characterize bipolar disorder (BD) and schizophrenia (SCZ). Previous studies often adopted a state discretization approach when investigating how individuals recruited recurring brain states. Since multiple brain states are likely engaged simultaneously at any given moment, focusing on the dominant state can obscure changes in less prominent but critical brain states in clinical populations. To address this limitation, we introduced a novel framework to simultaneously assess brain state engagement for multiple rain states, and we examined how brain state engagement differs in patients with BD or SCZ compared to healthy controls (HC). Using task-based data from the Human Connectome Project, we applied nonlinear manifold learning and K-means clustering to identify four recurring brain states. We then examined how the engagement and transition variability of these four states differed between patients with BD, SCZ, and HC across two other international, open-source datasets. Comparing these measures across groups revealed significantly altered state transition variability, but not engagement, across all four states in individuals with BD and SCZ during both resting-state and task-based fMRI. In our post hoc and exploratory analysis, we also observed associations between state transition variability and age as well as avolition. Our results suggest that disrupted state transition variability affects multiple brain states in BD and SCZ. By studying several brain states simultaneously, our framework more comprehensively reveals how brain dynamics differ across individuals and in psychiatric disorders.

Investigating brain dynamics and their association with cognitive control in opioid use disorder using naturalistic and drug cue paradigms

“…There was no sex main effect (F(4,164)=1.323; p=0.264) or sex-by-group interaction (F(4,164)=0.327, p=0.860). Consistent with previous work(26), age showed a significant effect on variability (F(4,164)=6.329, p<0.001), but there was no age-by-group interaction (F(4,164)=2.295, p=0.061).Post-hoc ANOVAs were performed to explore group differences in each brain state separately (Supplementary Table4). Fixation (F(1,167)=12.343; p<0.001) and transition state engagement variability (F(1,167)=6.667; p=0.011) were significantly lower in individuals with OUD compared to HCs…”

“…In our prior work (5), we investigated how canonical brain networks contributed to each of these brain states. To this end, we identified the activated and deactivated brain regions (i.e., activation above or below 0, respectively; arbitrary unit) for each representative time point in a set of canonical brain networks.…”

“…Variability was calculated across the entire run for the naturalistic paradigm. As prior work has revealed significant 6 differences in variability during rest and task (26), we opted to compute variability within the rest and cue condition separately for the drug cue paradigm. As a validation analysis, we compared state engagement variability between conditions in the drug cue paradigm to test whether our brain dynamic measures were sensitive to changes induced by task demands.…”

Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences

“…Additionally, we only used PCA to define the manifold. However, many nonlinear methods exist 7,41,42 which may better identify task co-activation patterns. Further advances through nonlinear techniques may reveal complementary results.…”

Brain-body states embody complex temporal dynamics

“…By coupling dense, repeated measurement of the brain, body, and mind, we can begin to understand how individuals respond to and engage with different environmental challenges. This is critical as disordered brain-body interaction is a unifying tie connecting health-harming disorders such as cardiovascular disease to mental health and well-being [12]. While an unfortunate history of dualism has traditionally treated diseases of the mind and body as distinct, a dynamical perspective sees them united by aberrant pathways of interoceptive processing, such that maladaptive brain-body dynamics alter systemic factors such as inflammation, blood-brain barrier permeability, and gut microbiome expression, which in turn alter neural mechanisms governing reward, motivation, and action, ultimately fettering energy levels, exercise, and eating behaviour to begin the cycle anew.…”