Topographic variation in neurotransmitter receptor densities explains differences in intracranial EEG spectra

Neurotransmitter receptor expression and neuronal population dynamics show regional variability across the human cortex. However, currently there is an explanatory gap regarding how cortical microarchitecture and mesoscopic electrophysiological signals are mechanistically related, limiting our ability to exploit these measures of brain (dys)function for improved treatments of brain disorder; e.g., epilepsy. To bridge this gap, we leveraged dynamic causal modelling (DCM) and fitted biophysically informed neural mass models to a normative set of intracranial EEG data. Subsequently, using a hierarchical Bayesian modelling approach, we evaluated whether model evidence improved when information about regional neurotransmitter receptor densities is provided. We then tested whether the inferred constraints - furnished by receptor density - generalise across different electrophysiological recording modalities. The neural mass models explained regionally specific intracranial EEG spectra accurately, when fitted independently. Incorporating prior information on receptor distributions, further improved model evidence, indicating that variability in receptor density explains some variance in cortical population dynamics. The output of this modelling provides a cortical atlas of neurobiologically informed intracortical synaptic connectivity parameters that can be used as empirical priors in future - e.g., patient specific - modelling, as demonstrated in a worked example (a single-subject mismatch negativity study). In summary, we show that molecular cortical characteristics (i.e., receptor densities) can be incorporated to improve generative, biophysically plausible models of coupled neuronal populations. This work can help to explain regional variations in human electrophysiology, may provide a methodological foundation to integrate multi-modal data, and might serve as a normative resource for future DCM studies of electrophysiology.