Recurrent neural networks for reconstructing complex directed brain connectivity

Patient-clinician brain concordance underlies causal dynamics in nonverbal communication and negative affective expressivity

Patient-clinician concordance in behavior and brain activity has been proposed as a potential key mediator of mutual empathy and clinical rapport in the therapeutic encounter. However, the specific elements of patient-clinician communication that may support brain-to-brain concordance and therapeutic alliance are unknown. Here, we investigated how pain-related, directional facial communication between patients and clinicians is associated with brain-to-brain concordance. Patient-clinician dyads interacted in a pain-treatment context, during synchronous assessment of brain activity (fMRI hyperscanning) and online video transfer, enabling face-to-face social interaction. In-scanner videos were used for automated individual facial action unit (AU) time-series extraction. First, an interpretable machine-learning classifier of patients' facial expressions, from an independent fMRI experiment, significantly distinguished moderately painful leg pressure from innocuous pressure stimuli. Next, we estimated neural-network causality of patient-to-clinician directional information flow of facial expressions during clinician-initiated treatment of patients' evoked pain. We identified a leader-follower relationship in which patients predominantly led the facial communication while clinicians responded to patients' expressions. Finally, analyses of dynamic brain-to-brain concordance showed that patients' mid/posterior insular concordance with the clinicians' anterior insula cortex, a region identified in previously published data from this study¹, was associated with therapeutic alliance, and self-reported and objective (patient-to-clinician-directed causal influence) markers of negative-affect expressivity. These results suggest a role of patient-clinician concordance of the insula, a social-mirroring and salience-processing brain node, in mediating directional dynamics of pain-directed facial communication during therapeutic encounters.

Echo state network models for nonlinear Granger causality

While Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour, hence undermining the validity of MVAR-based GC (MVAR-GC). Most nonlinear GC estimators only cater for additive nonlinearities or, alternatively, are based on recurrent neural networks or long short-term memory networks, which present considerable training difficulties and tailoring needs. We reformulate the GC framework in terms of echo-state networks-based models for arbitrarily complex networks, and characterize its ability to capture nonlinear causal relations in a network of noisy Duffing oscillators, showing a net advantage of echo state GC (ES-GC) in detecting nonlinear, causal links. We then explore the structure of ES-GC networks in the human brain employing functional MRI data from 1003 healthy subjects drawn from the human connectome project, demonstrating the existence of previously unknown directed within-brain interactions. In addition, we examine joint brain-heart signals in 15 subjects where we explore directed interaction between brain networks and central vagal cardiac control in order to investigate the so-called central autonomic network in a causal manner. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.