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Dynamic Causal Brain Circuits During Working Memory and Their ARTICLE https://doi.org/10.1038/s41467-021-23509-x OPEN Dynamic causal brain circuits during working memory and their functional controllability ✉ ✉ Weidong Cai 1,2 , Srikanth Ryali 1, Ramkrishna Pasumarthy3, Viswanath Talasila4 & Vinod Menon 1,2,5 Control processes associated with working memory play a central role in human cognition, but their underlying dynamic brain circuit mechanisms are poorly understood. Here we use system identification, network science, stability analysis, and control theory to probe func- 1234567890():,; tional circuit dynamics during working memory task performance. Our results show that dynamic signaling between distributed brain areas encompassing the salience (SN), fronto- parietal (FPN), and default mode networks can distinguish between working memory load and predict performance. Network analysis of directed causal influences suggests the anterior insula node of the SN and dorsolateral prefrontal cortex node of the FPN are causal outflow and inflow hubs, respectively. Network controllability decreases with working memory load and SN nodes show the highest functional controllability. Our findings reveal dissociable roles of the SN and FPN in systems control and provide novel insights into dynamic circuit mechanisms by which cognitive control circuits operate asymmetrically during cognition. 1 Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA. 2 Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA. 3 Department of Electrical Engineering, Robert Bosch Center of Data Sciences and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, India. 4 Department of Electronics and Telecommunication Engineering, Center for Imaging Technologies, M. S. Ramaiah Institute of Technology, Bengaluru, India. 5 Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, ✉ CA, USA. email: [email protected]; [email protected] NATURE COMMUNICATIONS | (2021) 12:3314 | https://doi.org/10.1038/s41467-021-23509-x | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-23509-x lmost all cognitively demanding tasks depend on working (IPL)26–29. Meta-analyses of human neuroimaging studies have memory, the ability to maintain and manipulate infor- also highlighted consistent activation of these brain A 1–3 20,22,30,31 mation in the absence of sensory input . Working areas . Notably, these regions overlap prominently with memory is considered a “sketchpad of conscious thought” and a the SN and FPN19,32,33. In contrast, the posterior cingulate cortex foundational component of information processing and problem- (PCC) and ventromedial prefrontal cortex (VMPFC) encom- solving4. Cognitive control processes associated with working passing the default mode network (DMN) are deactivated during memory play an essential role in development5,6 and impair- cognitively demanding tasks21,34, but there have been suggestions ments in these processes are a key component of cognitive dys- that the degree of disengagement may have a direct impact on function in many psychiatric disorders, including schizophrenia working-memory performance35–37. Other related research has and attention deficit hyperactivity disorder7–11. Although con- suggested that the DMN may also encode task-relevant infor- siderable progress has been made in identifying brain regions mation during cognitively demanding tasks38,39. While these involved in working-memory-related cognitive processes, the studies point to a highly consistent pattern of working-memory- dynamic circuit mechanisms by which it operates in the human related neural activity in the SN, FPN, and DMN, there have been brain are poorly understood. In particular, little is known about no systematic investigations of their dynamic functional inter- dynamic causal and asymmetric interactions between distributed actions and network properties. brain regions during working memory task performance and Human neuroimaging studies of working memory have pri- their cognitive load-dependent network properties. Furthermore, marily focused on regional brain activation20,22,30,31 and undir- reliance on small sample sizes in previous studies has been a ected functional interactions37,40,41, and there is now growing serious limitation, leaving unclear the reliability of reported interest in examining how cognitive functions emerge as a result findings. of dynamic causal interactions between distributed brain Here we leverage data from the Human Connectome Project regions4. The need for such investigations has gathered particular (HCP)12 to investigate causal dynamic circuit mechanisms impetus in light of recent electrophysiological studies in monkeys involving cognitive control systems consistently implicated in and rodents demonstrating significant asymmetries in inter- working memory. The present study focuses on three major regional interactions during working memory4,42,43. Crucially, concepts: causal circuits and hubs, functional controllability of the focus on undirected functional connectivity in previous brain circuits, and reproducibility of findings. We first apply studies37,40,41 precludes inference of how brain networks or state-space models to determine dynamic causal circuits, probe regions might influence and control other regions. More recently, dynamic brain network properties including causal outflow and analysis of causal interactions among lateral prefrontal cortex inflow hubs of information flow13,14, and determine sources of regions has pointed to a key role for the dorsolateral prefrontal individual differences in working-memory task performance. cortex in integrating information for cognitive control Second, we develop and apply a control theory framework operations44,45. However, a comprehensive understanding of extending previous work15–18 to identify brain nodes that facil- dynamic functional interactions and causal hubs involving mul- itate distinct brain states associated with high and low cognitive tiple large-scale brain networks is still lacking. Specifically, the load. Third, we validate our findings using replicability and sta- relative order and potential hierarchies of causal signaling are bility analysis. Our computational tools drawn from state-space unknown. modeling, network science, and control theory, along with a ‘Big We investigated context-specific dynamic causal circuit Data’ approach allowed us to address fundamental gaps in our mechanisms involving core nodes of the SN–FPN–DMN cogni- understanding of causal networks underlying cognitive control tive control system using multivariate dynamic state-space sys- during working memory, their controllability, and tems identification (MDSI)46–48. MDSI uses a state-space reproducibility. model46–48 for estimating context-specific causal interactions in The first goal of our study is the identification of the directed latent neuronal signals after taking into account inter-regional neural circuits underlying cognition during working memory variations in hemodynamic response. A particular advantage of using fast temporal-resolution (subsecond) fMRI data and a large MDSI is that it does not require testing a large number of pre- sample of well-characterized healthy young adults. The theore- specified models, which is especially problematic as the number tical focus of the present work lies in a triple network model of of the models to be tested increases exponentially with the cognitive control systems which posits a key role for the salience number of nodes48. This approach not only enabled us to probe network (SN) in switching brain networks with the resultant large-scale causal networks associated with working memory, but engagement of the FPN and disengagement of the DMN during also, critically, to determine how directed network properties such cognitively demanding tasks13,19. Analysis of causal control cir- as causal hubs and network controllability change with working- cuits has the potential to inform how asymmetries and hier- memory load. archies in directed influence allow individual networks or specific We probed network properties and casual hubs associated with brain nodes to control others. context-specific interactions of the SN, FPN, and DMN. The past There is now extensive evidence demonstrating that the cog- decade has seen an explosion of research on the study of static nitive demanding n-back working-memory task engage or dis- brain network properties based on its structural and intrinsic engage core nodes of the salience (SN), frontoparietal networks functional connections. In contrast, little is known about causal (FPN), and default mode networks implicated in cognitive control hubs and how they change with working memory, or control20–22. Early research on the neural basis of cognitive more generally, cognitive load. Hubs are highly connected regions control processes engaged during working memory has primarily important for integration of activity across brain regions14,49,50. focused on the dorsolateral prefrontal cortex in the middle frontal The weighted directed graphs estimated by MDSI allowed us to gyrus (MFG) based on observations of neuronal activity during compute hubs from both the perspective of outflow from, and online maintenance of task-relevant information23–25. Since then, inflow into, each node of the SN, FPN, and DMN. This enabled non-human electrophysiological studies and human neuroima- us to test hypotheses about
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