Interaction of synchronized dynamics in cortex and basal ganglia in Parkinson's disease

Abstract

Parkinson's disease pathophysiology is marked by increased oscillatory and synchronous activity in the beta frequency band in cortical and basal ganglia circuits. This study explores the functional connections between synchronized dynamics of cortical areas and synchronized dynamics of subcortical areas in Parkinson's disease. We simultaneously recorded neuronal units (spikes) and local field potentials (LFP) from subthalamic nucleus (STN) and electroencephalograms (EEGs) from the scalp in parkinsonian patients, and analysed the correlation between the time courses of the spike-LFP synchronization and inter-electrode EEG synchronization. We found the (non-invasively obtained) time course of the synchrony strength between EEG electrodes and the (invasively obtained) time course of the synchrony between spiking units and LFP in STN to be weakly, but significantly, correlated with each other. This correlation is largest for the bilateral motor EEG synchronization, followed by bilateral frontal EEG synchronization. Our observations suggest that there may be multiple functional modes by which the cortical and basal ganglia circuits interact with each other in Parkinson's disease: not only may synchronization be observed between some areas in cortex and the basal ganglia, but also synchronization within cortex and within basal ganglia may be related, suggesting potentially a more global functional interaction. More coherent dynamics in one brain region may modulate or activate the dynamics of another brain region in a more powerful way, causing correlations between changes in synchrony strength in the two regions.

Synchronized Beta-Band Oscillations in a Model of the Globus Pallidus-Subthalamic Nucleus Network under External Input

“…It just presents a signal with a broader and more biophysically realistic spectrum than the artificial signals considered above, which is satisfactory for illustrative purposes but limits extensive exploration. The EEG data used are the same data as were used in Ahn et al (2015). We used a signal recorded from C3 EEG electrode (left motor cortex) from a patient with strong hypokinetic symptoms, undergoing a surgery to implant DBS electrodes in the STN.…”

“…The applied current is then I STN ( t ) = A sin(φ( t )). The details of the data recordings and processing are available in Ahn et al (2015). …”

“…Both cortical and subcortical (basal ganglia) areas express these excessive synchronous oscillatory dynamics. These dynamics in cortex and basal ganglia are often synchronized or otherwise correlated with each other (e.g., Fogelson et al, 2006; Hirschmann et al, 2011; Litvak et al, 2011; Shimamoto et al, 2013; Ahn et al, 2015). The strength of this synchrony is affected by dopaminergic status (e.g., Sharott et al, 2005; Mallet et al, 2008a; Hirschmann et al, 2013) emphasizing its relevance to hypokinetic motor symptoms in Parkinson's disease.…”

Stabilizing Quorum-Sensing Networks via Noise

“…To this aim, Theorem 2 could be used to properly inject noise in order to somehow reset the network to a state where all nodes are stabilized at the origin. This is the case, for example, of important applications such as neural networks, where pathological synchronization among bursting neurons might be related to the tremors observed in patients affected by the Parkinson's disease [27]. Interestingly, the key idea behind Deep Brain Stimulation techniques is indeed that of perturbing the synchronization of neurons via noise, see e.g., [28].…”

Loss of coordination in complex directed networks: An incremental approach based on matrix measures

“…Notably, pathological synchronization among bursting neurons in the basal ganglia‐cortical loop has been hypothesized to play a key role in the tremors seen in patients with Parkinson disease. In such cases, deep brain stimulation techniques are used in an attempt to desynchronize neurons and hence to alleviate the effects of Parkinson (see, eg, previous works). A “classical” approach that can be used to study the onset of de‐synchronization is the master stability function, see, eg, Boccaletti et al and references therein.…”