This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). It steps through an analysis in detail and provides a tutorial style explanation of the underlying theory and assumptions (i.e, priors). The analysis procedure involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. The preliminary first level (subject specific) DCM for fMRI analysis is covered in a companion paper. Here, we detail group-level analysis procedures that are suitable for use with data from any neuroimaging modality. This paper is accompanied by an example dataset, together with step-by-step instructions demonstrating how to reproduce the analyses.

Introduction & objectivesAdaptive deep brain stimulation (aDBS) uses feedback from brain signals to guide stimulation. A recent acute trial of unilateral aDBS showed that aDBS can lead to substantial improvements in contralateral hemibody Unified Parkinson’s Disease Rating Scale (UPDRS) motor scores and may be superior to conventional continuous DBS in Parkinson’s disease (PD). We test whether potential benefits are retained with bilateral aDBS and in the face of concurrent medication.MethodsWe applied bilateral aDBS in 4 patients with PD undergoing DBS of the subthalamic nucleus. aDBS was delivered bilaterally with independent triggering of stimulation according to the amplitude of β activity at the corresponding electrode. Mean stimulation voltage was 3.0±0.1 volts. Motor assessments consisted of double-blinded video-taped motor UPDRS scores that included both limb and axial features.ResultsUPDRS scores were 43% (p=0.04; Cohen’s d=1.62) better with aDBS than without stimulation. Motor improvement with aDBS occurred despite an average time on stimulation (ToS) of only 45%. Levodopa was well tolerated during aDBS and led to further reductions in ToS.ConclusionBilateral aDBS can improve both axial and limb symptoms and can track the need for stimulation across drug states.

See Moll and Engel (doi:) for a scientific commentary on this article.During pathologies like tremor, neural populations become locked into temporal configurations that reinforce neural synchrony to the point that motor function is compromised. Cagnan et al. present a novel stimulation approach to selectively control neural synchrony, and demonstrate that deep brain stimulation can be precisely timed to disrupt disease pathophysiology.