Mapping and mining interictal pathological gamma (30-100 Hz) oscillations with clinical intracranial EEG in patients with epilepsy

Diverse nature of interictal oscillations: EEG-based biomarkers in epilepsy

Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.

Human interictal epileptiform discharges are bidirectional traveling waves echoing ictal discharges

Interictal epileptiform discharges (IEDs), also known as interictal spikes, are large intermittent electrophysiological events observed between seizures in patients with epilepsy. Although they occur far more often than seizures, IEDs are less studied, and their relationship to seizures remains unclear. To better understand this relationship, we examined multi-day recordings of microelectrode arrays implanted in human epilepsy patients, allowing us to precisely observe the spatiotemporal propagation of IEDs, spontaneous seizures, and how they relate. These recordings showed that the majority of IEDs are traveling waves, traversing the same path as ictal discharges during seizures, and with a fixed direction relative to seizure propagation. Moreover, the majority of IEDs, like ictal discharges, were bidirectional, with one predominant and a second, less frequent antipodal direction. These results reveal a fundamental spatiotemporal similarity between IEDs and ictal discharges. These results also imply that most IEDs arise in brain tissue outside the site of seizure onset and propagate toward it, indicating that the propagation of IEDs provides useful information for localizing the seizure focus.

Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient.

Resolution revolution: epilepsy dynamics at the microscale

Our understanding of the neuronal mechanisms behind epilepsy dynamics has recently advanced due to the application of novel technologies, monitoring hundreds of neurons with single cell resolution. These developments have provided new theories on the relationship between physiological and pathological states, as well as common motifs for the propagation of paroxysmal activity. Although traditional electroencephalogram (EEG) recordings continue to describe normal network oscillations and abnormal epileptic events within and outside of the seizure focus, analysis of epilepsy dynamics at the microscale has found variability in the composition of macroscopically repetitive epileptiform events. These novel results point to heterogeneity in the underlying dynamics of the disorder, highlighting both the need and potential for more specific and targeted therapies.

Twenty-five years of progress: the view from NIMH and NINDS

As directors of two NIH institutes supporting neuroscience research, we explore the gap between 25 years of stunning progress in fundamental neuroscience and the persistent needs of those with brain disorders. We conclude that closing this gap will require a more detailed comprehension of brain function, a rethinking of how we approach translational science, a focus on human neurobiology, and a continuing commitment to build a diverse, innovative neuroscience workforce. In contrast to many other areas of medicine, we lack basic knowledge about our organ of interest. The next phase of progress on brain disorders will require a significantly deeper understanding of fundamental neurobiology.