Noninvasive imaging of the high frequency brain activity in focal epilepsy patients

Abstract

High-frequency (HF) activity represents a potential biomarker of the epileptogenic zone in epilepsy patients, the removal of which is considered to be crucial for seizure-free surgical outcome. We proposed a high frequency source imaging (HFSI) approach to noninvasively image the brain sources of the scalp-recorded HF EEG activity. Both computer simulation and clinical patient data analysis were performed to investigate the feasibility of using the HFSI approach to image the sources of HF activity from noninvasive scalp EEG recordings. The HF activity was identified from high-density scalp recordings after high-pass filtering the EEG data and the EEG segments with HF activity were concatenated together to form repetitive HF activity. Independent component analysis was utilized to extract the components corresponding to the HF activity. Noninvasive EEG source imaging using realistic geometric boundary element head modeling was then applied to image the sources of the pathological HF brain activity. Five medically intractable focal epilepsy patients were studied and the estimated sources were found to be concordant with the surgical resection or intracranial recordings of the patients. The present study demonstrates, for the first time, that source imaging from the scalp HF activity could help to localize the seizure onset zone and provide a novel noninvasive way of studying the epileptic brain in humans. This study also indicates the potential application of studying HF activity in the presurgical planning of medically intractable epilepsy patients.

Fig. 1

Illustration diagram and study design of imaging high frequency (HF) activity. A: Experimental recording including non-REM sleep scalp EEG recording, pre-operative and post-operative MRI scans. B: Concatenated high frequency activity. C: Independent component according to the HF activity. D: Patient-specific boundary element head model. E: Source imaging of the HF activity. F: Surgical resection and intracranial recording of the patient.

Fig. 2

Computer simulation of HF activity in a standard head volume conduction model. (a) Location of one simulated dipole source without extent in frontal lobe. (b) Scalp EEG traces generated from the simulated source. (c) Estimated sources and independent component of the HF activity. (d) Localization error (LE) of computer simulation in 1000 trials. The simulated dipoles are categorized in four groups according to the dipole locations (Frontal, Occipital, Parietal and Temporal groups).

Fig. 3

Results in Patient 1. (a) Left: Raw EEG; Right: High-pass filtered EEG above 30 Hz. HF activity is marked with red circle. (b) Spatial map and temporal activation of one HF component. (c) Source imaging results of HF activity. (d) Surgical resection is highlighted in red.

Fig. 4

Results in Patient 2. (a) Left: Raw EEG; Right: High-pass filtered EEG above 30 Hz. HF activity is marked with red circle. (b) Spatial map and temporal activation of one HF component. (c) Source imaging results of HF activity. (d) Surgical resection is highlighted in red.

Fig. 5

Results in Patient 5. (a) Left: Raw EEG; Right: High-pass filtered EEG above 30 Hz. HF activity is marked with red circle. (b) Spatial map and temporal activation of one HF component. (c) Source imaging results of HF activity. (d) Seizure onset zone of intracranial recording is highlighted with red dots.

Fig. 6

Comparison between source imaging of HF activity and interictal spike in Patient 4. (a) Source imaging results of HF activity. (b) Source imaging results of interictal spike. (c) Intracranial recording and surgical resection.

Fig. 7

Localization error between source imaging of HF activity and spike (a) Localization error comparing with surgical resection. (b) Localization error comparing with SOZ of intracranial recording.