Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs

Abstract

Deep brain stimulation is a treatment option for patients with drug-resistant epilepsy. The precise mechanism of neuromodulation in epilepsy is unknown and biomarkers are needed for optimizing treatment. The aim of this study was to describe the neural network associated with deep brain stimulation targets for epilepsy and to explore its potential application as a novel biomarker for neuromodulation. Using seed-to-voxel functional connectivity maps, weighted by seizure outcomes, brain areas associated with stimulation were identified in normative resting state functional scans of 1000 individuals. To pinpoint specific regions in the normative epilepsy deep brain stimulation network, we examined overlapping areas of functional connectivity between the anterior thalamic nucleus, centromedian thalamic nucleus, hippocampus, and less studied epilepsy deep brain stimulation targets. Graph network analysis was used to describe the relationship between regions in the identified network. Furthermore, we examined the associations of the epilepsy deep brain stimulation network with disease pathophysiology, canonical resting state networks and findings from a systematic review of resting state functional MRI studies in epilepsy deep brain stimulation patients. Cortical nodes identified in the normative epilepsy deep brain stimulation network were in the anterior and posterior cingulate, medial frontal and sensorimotor cortices, frontal operculum and bilateral insulae. Subcortical nodes of the network were in the basal ganglia, mesencephalon, basal forebrain, and cerebellum. anterior thalamic nucleus was identified as a central hub in the network with the highest betweenness and closeness values, while centromedian thalamic nucleus and hippocampus showed average centrality values. The caudate nucleus and mammillothalamic tract also displayed high centrality values. The anterior cingulate cortex was identified as an important cortical hub associated with the effect of deep brain stimulation in epilepsy. The neural network of deep brain stimulation targets shared hubs with known epileptic networks and brain regions involved in seizure propagation and generalization. Two cortical clusters identified in the epilepsy deep brain stimulation network included regions corresponding to resting state networks, mainly the default mode and salience networks. Our results were concordant with findings from a systematic review of resting state functional MRI studies in patients with deep brain stimulation for epilepsy. Our findings suggest that the various epilepsy deep brain stimulation targets share a common cortico-subcortical network, which might in part underpin the antiseizure effects of stimulation. Interindividual differences in this network functional connectivity could potentially be used as biomarkers in selection of patients, stimulation parameters, and neuromodulation targets.

Towards network-guided neuromodulation for epilepsy

“…Alternate targets include the central lateral thalamus, 157 , 158 pontine nucleus oralis, 158 hypothalamus 159 and caudate nucleus, 160 as well as others. 15 , 161 Further pre-clinical (including network analyses) and clinical evidence are required to investigate these potential seizure propagation points.…”

“…The availability of normative datasets—for example structural normative networks in the Human Connectome Project 189 or epilepsy-specific data such as stereo-EEG datasets—may allow for the identification of key propagation points in the individual. 190 A recent example of applying normative data is in the study by Vetkas et al ., 161 who used the normative functional (fMRI) dataset from 1000 adults to derive the nodes that are common to the networks of three clinically-used neurostimulation targets—the ANT, CMT and hippocampus. They used graph theory to show that the anterior cingulate and other regions of the default mode and salience networks were common nodes connected with these stimulation targets.…”

A Digital Realization of Neuroglial Interaction Model and Its Network Structure

“…Many neuropathological disorders such as epileptic seizures [9], Parkinson's disease [10], and sleep-related disturbances [11] are required to be carefully studied in order to discover possible pathways for diagnostic and treatment approaches. In recent years, bidirectional interaction between astrocytic network and neuronal activity have been determined in the area of computational neuroscience.…”

“…𝑦 ̂𝑖+1 = 𝑦 𝑖 + ℎ𝑓(𝑡 𝑖 , 𝑦 𝑖 ) (9) where h is the step size, △ 𝑡 is the time interval and by considering 𝑡 𝑖+1 = 𝑡 𝑖 + ℎ based on the Euler's approach; therefor, 𝑦 ̂𝑖+1 is the numerical simulation for function of y(t) at 𝑡 𝑖+1 , i.e., y(𝑡 𝑖+1 ) which is shown in equation ( 9) [39]. Moreover, the probability of a and b at time t are presented by 𝑝 𝑎 (𝑡) and 𝑝 𝑏 (𝑡); thus, the numerical simulation of ODE can be estimated as: where 𝑉 𝑡ℎ𝑟 is the threshold value for the membrane voltage and 𝛿 is the incremental adaptation with each spike.…”

Responsive Neurostimulation for Seizure Control: Current Status and Future Directions

“…Using graph theory metrics to target RNS lead placement based on the most highly connected foci, as opposed to using traditionally determined EFs and RNS in addition to resective surgery, RNS can serve to treat epilepsy in a fashion concordant with the network-based pathomechanism of the neurological disorder, in order to achieve improved seizure control [ 63 ].…”