Rosch, R. | bioRxiv Science

bioRxiv2025

Entropy of the resting state cortex in epilepsy

BackgroundEpilepsy has long been conceptualised as a disorder in which aberrant brain dynamics extend beyond the epileptogenic zone. Evidence demonstrates that loss of entropy is a generic feature of pathological dynamics in the brain, including the ictal state. However, the impact of recurrent seizures on entropy in the interictal state remains unknown. MethodsResting state magnetoencephalography (MEG) scans and resection masks of 32 individuals with epilepsy who had Engel I outcome post-surgery were retrospectively retrieved. Using co-registered FreeSurfer parcellations, we reconstructed the source localised MEG time series and computed sample entropy for 114 regions of interest. We then tested the association of entropy with the resected volume of the brain, and additional clinical variables including the age of seizure onset, seizure frequency and duration of epilepsy. To further understand the temporal relationship between seizure onset and entropy in the interictal state, we collected and computed sample entropy for week-long EEG traces from leucine-rich glioma inactivated 1 monoclonal antibody (LGI1-mAb) rodent models of autoimmune encephalitis (n=5) and control rats (n=5). ResultsIn individuals with epilepsy, a lower age of seizure onset was associated with lower mean sample entropy of the whole cortex (Spearmans rho =0.60, p<0.001; partial correlation =0.41, p=0.021). Entropy did not differ between the resected and non-resected regions of the brain. Furthermore, LGI1-mAb treated rodents showed a persistent decrease in sample entropy as compared to control rats, after the onset of seizures, and this difference was greatest during periods of highest seizure frequency (p<0.001). ConclusionRecurrent seizures are associated with a persistent decrease in entropy, even in the interictal state, and this decrease was found to be most profound and affecting the whole cortex in patients who had a lower age of seizure onset.

neuroscience↗

bioRxiv2024

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. Significance StatementUnderstanding the link between measures of brain function and their underlying molecular and synaptic constraints is essential for developing and validating personalised, pharmacological interventions. But despite increasing availability of detailed normative datasets of human brain structure and function -- across modalities and spatial scales -- translating between these remains challenging. Using two large normative datasets -- intracranial EEG recordings and autoradiographic receptor density distributions -- we demonstrate that generative models of these data can link structure to function. Specifically, we show that regional oscillatory neuronal population activity is shaped by the distribution of neurotransmitter receptors. This modelling furnishes an atlas of normative parameter values, which can provide neurobiologically informed priors for in-silico (e.g., Digital Twin) characterisation of normal and disordered brain functioning.

neuroscience↗