Dopaminergic modulation of cortical motor network lateralization

Introduction Unilateral movements are primarily processed in contralateral cortical and subcortical areas and additionally in ipsilateral cerebellum, leading to an asymmetric pattern of neural activation. Decrease of lateralization is characteristic of aging (Naccarato et al., 2006; Wu et al., 2005), and disease, for example, in unilateral brain lesions or stroke (Carr et al., 1993; Rehme et al., 2011) and Parkinson's disease (PD; Wu et al., 2015). The explanation for imbalanced lateralization in drug-naive PD is an adaptive compensation, compatible with the finding that PD-associated deficient input from cortico-subcortical circuits is compensated by reduced cortical inhibition and increased cortical facilitation (Blesa et al., 2017). Here, we investigated the effect of dopamine depletion and substitution on cortical motor lateralization, with the hypothesis that lateralization decreases in advanced PD and that administration of levodopa, at least to a certain extent, reinstates lateralization. Methods We used fMRI to study motor activation in advanced PD patients and in healthy controls (HC) during unilateral upper and lower limb movements. Ten right-handed, left side symptom-dominant PD patients were tested in pseudo-randomized order after intake of their usual dopaminergic medication – 'ON' state – and after withdrawal of medication – 'OFF' state. Eighteen right-handed age-matched HC participated in a single session. We quantified activation lateralization using the average laterality index (AveLI; Matsuo et al., 2012) in three cortical motor regions of interest (ROIs): primary motor cortex (M1), supplementary motor area (SMA) and premotor cortex (PMC), during the four movement conditions. We compared AveLI between group pairs (PD OFF vs. HC, PD ON vs. HC, PD OFF vs. PD ON) within each ROI and movement condition. We estimated the effective connectivity between ROIs using bilinear dynamic causal modeling (DCM; Friston et al., 2003) and developed a measure to quantify the lateralization of the resulting connectivity networks to compare between groups. By constructing a group level parametric empirical Bayes (PEB) model (Friston et al., 2016) over all the subjects and conducting a search over nested models, we compared DCM parameter estimates between groups, thus providing the potential link between changes in motor lateralization and connectivity. Results In line with our predictions, motor activation lateralization as estimated with the AveLI showed a trend towards decrease in the PD OFF group compared to HC, in all three ROIs during left hand movement and in M1 during left foot movement (Fig. 1). Between-group differences were observed solely in conditions corresponding to movement of the more affected body side. Contrary to our hypothesis, dopamine substitution did not reinstate lateralization – in fact, AveLI in the PD ON group closely resembled that of the PD OFF group. Connectivity lateralization of input-specific modulation (DCM.B) networks was significantly lower in all conditions in the PD group as compared to HC. While on the body side more affected by PD, differences were found for both PD OFF and PD ON, input-specific modulation related to the less affected side was more altered in PD ON. PEB analysis revealed qualitatively more between-group differences in input-specific modulation on the more affected PD side and included many interhemispheric connections (Fig. 2). Conclusions Decreased lateralization is not only present in drug-naïve PD patients (Wu et al., 2015) but also in dopa-treated patients. Acute dopamine modulation does not alter lateralization. Decreased lateralization is evident in both fMRI activation amplitudes (as estimated with AveLI) and effective connectivity (as demonstrated through the DCM analysis)

Glutamate and Dysconnection in the Salience Network: Neurochemical, Effective Connectivity, and Computational Evidence in Schizophrenia

Background: Functional dysconnection in schizophrenia is underwritten by a pathophysiology of the glutamate neurotransmission that affects the excitation-inhibition balance in key nodes of the salience network. Physiologically, this manifests as aberrant effective connectivity in intrinsic connections involving inhibitory interneurons. In computational terms, this produces a pathology of evidence accumulation and ensuing inference in the brain. Finally, the pathophysiology and aberrant inference would partially account for the psychopathology of schizophrenia as measured in terms of symptoms and signs. We refer to this formulation as the 3-level hypothesis. Methods: We tested the hypothesis in core nodes of the salience network (the dorsal anterior cingulate cortex [dACC] and the anterior insula) of 20 patients with first-episode psychosis and 20 healthy control subjects. We established 3-way correlations between the magnetic resonance spectroscopy measures of glutamate, effective connectivity of resting-state functional magnetic resonance imaging, and correlations between measures of this connectivity and estimates of precision (inherent in evidence accumulation in the Stroop task) and psychopathology. Results: Glutamate concentration in the dACC was associated with higher and lower inhibitory connectivity in the dACC and in the anterior insula, respectively. Crucially, glutamate concentration correlated negatively with the inhibitory influence on the excitatory neuronal population in the dACC of subjects with first-episode psychosis. Furthermore, aberrant computational parameters of the Stroop task performance were associated with aberrant inhibitory connections. Finally, the strength of connections from the dACC to the anterior insula correlated negatively with severity of social withdrawal. Conclusions: These findings support a link between glutamate-mediated cortical disinhibition, effective-connectivity deficits, and computational performance in psychosis

A tutorial on group effective connectivity analysis, part 2: second level analysis with PEB

This tutorial 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). This 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. We walk through this approach in detail, using data from a published fMRI experiment that characterised individual differences in hemispheric lateralization in a semantic processing task. The preliminary subject specific DCM analysis is covered in detail in a companion paper. This tutorial is accompanied by the example dataset and step-by-step instructions to reproduce the analyses

Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems

Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy

BACKGROUND: Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depressive disorders. A recent multi-center study found no consistent changes in correlation-based (undirected) resting-state connectivity after ECT. Effective (directed) connectivity may provide more insight into the working mechanism of ECT. OBJECTIVE: We investigated whether there are consistent changes in effective resting-state connectivity. METHODS: This multi-center study included data from 189 patients suffering from severe unipolar depression and 59 healthy control participants. Longitudinal data were available for 81 patients and 24 healthy controls. We used dynamic causal modeling for resting-state functional magnetic resonance imaging to determine effective connectivity in the default mode, salience and central executive networks before and after a course of ECT. Bayesian general linear models were used to examine differences in baseline and longitudinal effective connectivity effects associated with ECT and its effectiveness. RESULTS: Compared to controls, depressed patients showed many differences in effective connectivity at baseline, which varied according to the presence of psychotic features and later treatment outcome. Additionally, effective connectivity changed after ECT, which was related to ECT effectiveness. Notably, treatment effectiveness was associated with decreasing and increasing effective connectivity from the posterior default mode network to the left and right insula, respectively. No effects were found using correlation-based (undirected) connectivity. CONCLUSIONS: A beneficial response to ECT may depend on how brain regions influence each other in networks important for emotion and cognition. These findings further elucidate the working mechanisms of ECT and may provide directions for future non-invasive brain stimulation research

Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy

Background Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depressive disorders. A recent multi-center study found no consistent changes in correlation-based (undirected) resting-state connectivity after ECT. Effective (directed) connectivity may provide more insight into the working mechanism of ECT. Objective We investigated whether there are consistent changes in effective resting-state connectivity. Methods This multi-center study included data from 189 patients suffering from severe unipolar depression and 59 healthy control participants. Longitudinal data were available for 81 patients and 24 healthy controls. We used dynamic causal modeling for resting-state functional magnetic resonance imaging to determine effective connectivity in the default mode, salience and central executive networks before and after a course of ECT. Bayesian general linear models were used to examine differences in baseline and longitudinal effective connectivity effects associated with ECT and its effectiveness. Results Compared to controls, depressed patients showed many differences in effective connectivity at baseline, which varied according to the presence of psychotic features and later treatment outcome. Additionally, effective connectivity changed after ECT, which was related to ECT effectiveness. Notably, treatment effectiveness was associated with decreasing and increasing effective connectivity from the posterior default mode network to the left and right insula, respectively. No effects were found using correlation-based (undirected) connectivity. Conclusions A beneficial response to ECT may depend on how brain regions influence each other in networks important for emotion and cognition. These findings further elucidate the working mechanisms of ECT and may provide directions for future non-invasive brain stimulation research.publishedVersio