Computing network-based features from intracranial EEG time series data: Application to seizure focus localization

Epileptic tissue localization using graph-based networks in the high frequency oscillation range of intracranial electroencephalography

PURPOSE

High frequency oscillations (HFOs) are an emerging biomarker of epilepsy. However, very few studies have investigated the functional connectivity of interictal iEEG signals in the frequency range of HFOs. Here, we study the corresponding functional networks using graph theory, and we assess their predictive value for automatic electrode classification in a cohort of 20 drug resistant patients.

METHODS

Coherence-based connectivity analysis was performed on the iEEG recordings, and six different local graph measures were computed in both sub-bands of the HFO frequency range (80-250 Hz and 250-500 Hz). Correlation analysis was implemented between the local graph measures and the ripple and fast ripple rates. Finally, the WEKA software was employed for training and testing different predictive models on the aforementioned local graph measures.

RESULTS

The ripple rate was significantly correlated with five out of six local graph measures in the functional network. For fast ripples, their rate was also significantly (but negatively) correlated with most of the local metrics. The results from WEKA showed that the Logistic Regression algorithm was able to classify highly HFO-contaminated electrodes with an accuracy of 82.5 % for ripples and 75.4 % for fast ripples.

CONCLUSION

Functional connectivity networks in the HFO band could represent an alternative to the direct use of distinct HFO events, while also providing important insights about hub epileptic areas that can represent possible surgical targets. Automatic electrode classification through FC-based classifiers can help bypass the burden of manual HFO annotation, providing at the same time similar amount of information about the epileptic tissue.

Pivotal Role of Subcortical Structures as a Network Hub in Focal Epilepsy: Evidence from Graph Theoretical Analysis Based on Diffusion-Tensor Imaging

Background and Purpose

There is accumulating evidence that epilepsy is caused by network dysfunction. We evaluated the hub reorganization of subcortical structures in patients with focal epilepsy using graph theoretical analysis based on diffusion-tensor imaging (DTI). In addition, we investigated differences in the values of diffusion tensors and scalars, fractional anisotropy (FA), and mean diffusivity (MD) of subcortical structures between patients with focal epilepsy and healthy subjects.

Methods

One hundred patients with focal epilepsy and normal magnetic resonance imaging (MRI) findings and 80 age- and sex-matched healthy subjects were recruited prospectively. All subjects underwent DTI to obtain data suitable for graph theoretical analysis. We investigated the differences in the node strength, cluster coefficient, eigenvector centrality, page-rank centrality measures, FA, and MD of subcortical structures between patients with epilepsy and healthy subjects.

Results

After performing multiple corrections, the cluster coefficient and the eigenvector centrality of the globus pallidus were higher in patients with epilepsy than in healthy subjects (p=0.006 and p=0.008, respectively). In addition, the strength and the page-rank centrality of the globus pallidus tended to be higher in patients with epilepsy than in healthy subjects (p=0.092 and p=0.032, respectively). The cluster coefficient of the putamen was lower in patients with epilepsy than in healthy subjects (p=0.004). The FA values of the caudate nucleus and thalamus were significantly lower in patients with epilepsy than in healthy subjects (p=0.009 and p=0.007, respectively), whereas the MD value of the thalamus was higher than that in healthy subjects (p=0.005).

Conclusions

We discovered the presence of hub reorganization of subcortical structures in focal epilepsy patients with normal MRI findings, suggesting that subcortical structures play a pivotal role as a hub in the epilepsy network. These findings further reinforce the idea that epilepsy is a network disease.

Decision Support System for Seizure Onset Zone Localization Based on Channel Ranking and High-Frequency EEG Activity

Interictal high-frequency oscillations (HFO) are a promising biomarker that can help define the seizure onset zone (SOZ) and predict the surgical outcome after the epilepsy surgery. The utility of HFO in planning the surgery, though, is unclear. Reasons include the variability of the HFO across patients and brain regions and the influence of the sleep-wake cycle, which causes large fluctuations in the ratio between the HFO observed in SOZ and non-SOZ regions. To cope with these limitations, a rank-based solution is proposed to identify the SOZ by using the HFO in multichannel intracranial EEG. A time-varying index of the epileptic susceptibility of the different brain areas is derived from the HFO rate and a support vector machine is applied on this index to identify the SOZ. The solution is trained and tested on separate groups of patients to avoid the use of patient-specific information and provides optimal SOZ prediction using as little as 30 min of recordings per channel (window). Tested on 14 patients with various combinations of seizure type, epilepsy etiology, and SOZ arrangement (172.7 ± 90.1 h/channel per patient and 75.6 ± 23.5 channels/patient, mean ± S.D.), our solution identified the SOZ with 0.92 ± 0.03 accuracy and 0.91 ± 0.03 area under the ROC curve (mean ± S.D.) across patients. For each patient, the window onset time was varied over 72 continuous hours and the prediction of the SOZ remained insensitive to the onset time, thus showing potential for surgery planning.

Toward a Mechanistic Understanding of Epileptic Networks

Focal epileptic seizures have long been considered to arise from a small susceptible brain area and spread through uninvolved regions. In the past decade, the idea that focal seizures instead arise from coordinated activity across large-scale epileptic networks has become widely accepted. Understanding the network model's applicability is critical, due to its increasing influence on clinical research and surgical treatment paradigms. In this review, we examine the origins of the concept of epileptic networks as the nidus for recurring seizures. We summarize analytical and methodological elements of epileptic network studies and discuss findings from recent detailed electrophysiological investigations. Our review highlights the strengths and limitations of the epileptic network theory as a metaphor for the complex interactions that occur during seizures. We present lines of investigation that may usefully probe these interactions and thus serve to advance our understanding of the long-range effects of epileptiform activity.

Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.