Sacha J van Albada | Searcharxiv

arXiv2024

Unified neural field theory of brain dynamics underlying oscillations in Parkinson's disease and generalized epilepsies

The mechanisms underlying pathologically synchronized neural oscillations in Parkinson's disease (PD) and generalized epilepsies are jointly explored via a neural field model of the corticothalamic-basal ganglia (CTBG) system. The basal ganglia (BG) are approximated as a single effective population and their roles in modulating oscillatory corticothalamic (CT) dynamics and vice versa are analyzed. Besides normal EEG rhythms, enhanced activity around 4 Hz and 20 Hz exists in the model, consistent with characteristic frequencies in PD. These rhythms result from resonances in loops between the BG and CT populations, analogous to those underlying epileptic oscillations in a previous CT model. Dopamine depletion is argued to weaken the dampening of these resonances in PD, and network connections explain the significant coherence between BG, thalamic, and cortical activity around 4-8 Hz and 20 Hz. Parallels between the afferent and efferent connection sites of the thalamic reticular nucleus (TRN) and BG predict low dopamine to correspond to a reduced likelihood of tonic-clonic (grand mal) seizures, agreeing with experimental findings. Further, the model predicts an increased likelihood of absence (petit mal) seizure resulting from low dopamine levels matching experimental findings. Suppression of absence seizure activity is shown when afferent and efferent BG connections to the CT system are strengthened, consistent with other CTBG modeling studies. The BG are demonstrated to suppress activity of the CTBG system near tonic-clonic seizure states, providing insight into the reported efficacy of current treatments in BG circuits. Sleep states of the TRN are also found to suppress pathological PD activity matching observations. Overall, the findings demonstrate strong parallels between coherent oscillations in generalized epilepsies and PD, and provide insights into possible comorbidities.

q-bio.NC↗

arXiv2016

Hybrid scheme for modeling local field potentials from point-neuron networks

Due to rapid advances in multielectrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both basic research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining the efficiency of commonly used simplified point-neuron network models with the biophysical principles underlying LFP generation by real neurons. The scheme can be used with an arbitrary number of point-neuron network populations. The LFP predictions rely on populations of network-equivalent, anatomically reconstructed multicompartment neuron models with layer-specific synaptic connectivity. The present scheme allows for a full separation of the network dynamics simulation and LFP generation. For illustration, we apply the scheme to a full-scale cortical network model for a $\sim$1 mm$^2$ patch of primary visual cortex and predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate the role of synaptic input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its publicly available implementation in \texttt{hybridLFPy} form the basis for LFP predictions from other point-neuron network models, as well as extensions of the current application to larger circuitry and additional biological detail.

q-bio.NC↗