Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia

Abstract

Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology.

LSD and psilocybin flatten the brain’s energy landscape: insights from receptor-informed network control theory

“…More broadly, these results demonstrate that the combination of network control theory and specific information about neurobiology (here exemplified by receptor distributions from PET) can offer powerful insights about brain function and how pharmacology may modulate it -opening the avenue for analogous studies on the effects of pharmacological interventions in clinical populations (e.g. depression, schizophrenia) 30,31 . While other recent computational approaches have successfully modeled the effects of serotonergic compounds on dynamic brain states 6,7 and the entropy of spontaneous neural activity 8 , the present approach is the first to do both while also quantitatively evaluating the energy landscape of the psychedelic state -thereby enabling us to provide empirical support for key theoretical predictions of the REBUS model.…”

“…Recent work utilized these tools to demonstrate that although the resting human brain has a spontaneous tendency to prefer certain brain-state transitions over others, cognitive demands can overcome this tendency in a way that is associated with age and cognitive performance. This work demonstrates that network control theory approaches can reveal neurobiologically and cognitively relevant brain activity dynamics [29][30][31] . 32 , we calculated the minimum energy required to transition between states (or maintain the same state) using each individual's brain-states derived from the psychedelic and placebo conditions separately.…”

“…We next sought to provide a direct test of the REBUS model's hypothesis about decreased energy requirements to transition between different states under psychedelics. To this end, we turned to network control theory 29,30,[40][41][42] , which offers a framework to quantify the ease of state transitions in a dynamical system. Specifically, we calculated the transition energy (TE), which is the minimum amount of energy that would need to be injected into a network (here, the structural connectome 32 ) to induce transitions between the possible states of its functional dynamics (note that the transition energy from a given state to itself is the energy required to remain in that state, sometimes referred to as "persistence energy").…”

The Development of the Mesoprefrontal Dopaminergic System in Health and Disease

“…The cause of the overall DA imbalance may be caused by deficits in local cortical or hippocampal networks that in turn lead to changes in the inputs to the VTA from these regions and ultimately to DA hypoactivity in VTA targets. Alternatively, or in addition, defects in the regulation of DA release in target regions (including the PFC) or in the developmental of the mesoprefrontal system could contribute to the DA hypoactivity ( Rice et al, 2016 ; Abi-Dargham, 2017 ; Chuhma et al, 2017 ; Grace, 2017 ; Walker et al, 2017 ; Sonnenschein et al, 2020 ; Braun et al, 2021 ). Whether the development of the mesoprefrontal DA system (or other parts of the DA system) is altered in patients with schizophrenia has not yet been studied in detail.…”

Age-associated network controllability changes in first episode drug-naïve schizophrenia

“…Average controllability as a network controllability metric quantifies capacity of brain regions or networks to steer the system to many easily reachable states, is a structural phenotype predicted to facilitate small changes in brain state [ 16 ]. The network control theory has been used in uncovering the underlying structural mechanism of macroscale brain dysfunction in multiple psychiatric and neurological disorders, such as bipolar disorder [ 19 ], schizophrenia [ 20 ], Parkinson’s disease [ 21 ] and epilepsy [ 22 ]. A recent study on network controllability of psychosis spectrum disorders further indicated that average controllability can predict positive psychosis spectrum symptoms better than all other network connectivity characteristics, considering the impact of both direct and indirect structural connectivity on the spread of activity [ 23 ].…”