Abstract
We present a technical development in the dynamic causal modelling of electrophysiological responses that combines qualitatively different neural mass models within a single network. This affords the option to couple various cortical and subcortical nodes that differ in their form and dynamics. Moreover, it enables users to implement new neural mass models in a straightforward and standardized way. This generic framework hence supports flexibility and facilitates the exploration of increasingly plausible models. We illustrate this by coupling a basal ganglia-thalamus model to a (previously validated) cortical model developed specifically for motor cortex. The ensuing DCM is used to infer pathways that contribute to the suppression of beta oscillations induced by dopaminergic medication in patients with Parkinson's disease. Experimental recordings were obtained from deep brain stimulation electrodes (implanted in the subthalamic nucleus) and simultaneous magnetoencephalography. In line with previous studies, our results indicate a reduction of synaptic efficacy within the circuit between the subthalamic nucleus and external pallidum, as well as reduced efficacy in connections of the hyperdirect and indirect pathway leading to this circuit. This work forms the foundation for a range of modelling studies of the synaptic mechanisms (and pathophysiology) underlying event-related potentials and cross-spectral densities.
Highlights
One of the most challenging objectives in neuroscience is to translate experimental observations into neuronal mechanisms
We present a generalization in the implementation of Dynamic causal modelling (DCM) that accommodates a combination of different types of neural mass models within a single network
We have presented a generalization of DCM that affords greater latitude in its applications
Summary
One of the most challenging objectives in neuroscience is to translate experimental observations into neuronal mechanisms. We present a generalization in the implementation of DCM that accommodates a combination of different types of neural mass models within a single network (see Fig. 1) This is an important step towards the flexible use of DCM for studies in which individual regions require a distinct dynamical description – due to differences in microcircuitry or laminar organization. Data features in the form of event-related potentials (ERP) or crossspectral densities (CSD) are generated via spm_fy_erp.m and spm_fs_csd.m, respectively, where spectral responses are obtained via the system's transfer functions in spm_csd_mtf.m. Prior distributions for the parameters used in these observation functions are specified in spm_L_priors.m and spm_ssr_priors.m. In order invert a DCM, users first specify the model options – and network structure – in the graphical user interface (as a batch, or in a custom script) before calling one of the inversion routines spm_dcm_erp.m or spm_dcm_csd.m, depending on the data feature of interest.
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