EMHapp: a pipeline for the automatic detection, localization and visualization of epileptic magnetoencephalographic high-frequency oscillations.

Objective.High-frequency oscillations (HFOs) are promising biomarkers for localizing epileptogenic brain tissue. Previous studies have revealed that HFOs that present concurrence with interictal epileptic discharges (IEDs) better delineate epileptogenic brain tissue, particularly for epilepsy patients with multitype interictal discharges. However, the analysis of noninvasively recorded epileptic HFOs involves many complex procedures, such as data preprocessing, detection and source localization, impeding the translation of this approach to clinical practice.Approach.To address these problems, we developed a graphical user interface (GUI)-based pipeline called EMHapp, which can be used for the automatic detection, source localization and visualization of HFO events concurring with IEDs in magnetoencephalography (MEG) signals by using a beamformer-based virtual sensor (VS) technique. An improved VS reconstruction method was developed to enhance the amplitudes of both HFO and IED VS signals. To test the capability of our pipeline, we collected MEG data from 11 complex focal epilepsy patients with surgical resections or seizure onset zones (SOZs) that were identified by intracranial electroencephalography.Main results.Our results showed that the HFO sources of eight patients were concordant with their resection margins or SOZs. Our proposed VS signal reconstruction approach achieved an 83.2% improvement regarding the number of detected HFO events and a 17.3% improvement in terms of the spatial overlaps between the HFO sources and the resection margins or SOZs in comparison with conventional VS reconstruction approaches.Significance.EMHapp is the first GUI-based pipeline for the analysis of epileptic magnetoencephalographic HFOs, which conveniently obtains HFO source locations using clinical data and enables direct translation to clinical applications.

Enhanced Fast-VESTAL for Magnetoencephalography Source Imaging: From Theory to Clinical Application in Epilepsy.

A novel magnetoencephalography source imaging approach called Fast Vector-based Spatio-Temporal Analysis (Fast-VESTAL) has been successfully applied in creating source images from evoked and resting-state data from both healthy subjects and individuals with neurological and/or psychiatric disorders, but its reconstructed source images may show false-positive activations, especially under low signal-to-noise ratio conditions. Here, to effectively reduce false-positive artifacts, we introduced an enhanced Fast-VESTAL (eFast-VESTAL) approach that adopts generalized second-order cone programming. We compared the spatiotemporal characteristics of the eFast-VESTAL approach to those of the popular distributed source approaches (e.g., the minimum L2-norm/ mixed-norm methods) using computer simulations and auditory experiments. More importantly, we applied eFast-VESTAL to the presurgical evaluation of epilepsy. Our results demonstrated that eFast-VESTAL exhibited a lower dipole localization error and/or a higher correlation coefficient (CC) between the estimated source time series and ground truth under various conditions of source waveforms. Experimentally, eFast-VESTAL displayed more focal activation maps and a higher CC between the raw and predicted sensor data in response to auditory stimulation. Notably, eFast-VESTAL was the most accurate method for noninvasively detecting the epileptic zones determined using more invasive stereo-electroencephalography in the comparison.

The Cortical Maps of Hierarchical Linguistic Structures during Speech Perception.

The hierarchical nature of language requires human brain to internally parse connected-speech and incrementally construct abstract linguistic structures. Recent research revealed multiple neural processing timescales underlying grammar-based configuration of linguistic hierarchies. However, little is known about where in the whole cerebral cortex such temporally scaled neural processes occur. This study used novel magnetoencephalography source imaging techniques combined with a unique language stimulation paradigm to segregate cortical maps synchronized to 3 levels of linguistic units (i.e., words, phrases, and sentences). Notably, distinct ensembles of cortical loci were identified to feature structures at different levels. The superior temporal gyrus was found to be involved in processing all 3 linguistic levels while distinct ensembles of other brain regions were recruited to encode each linguistic level. Neural activities in the right motor cortex only followed the rhythm of monosyllabic words which have clear acoustic boundaries, whereas the left anterior temporal lobe and the left inferior frontal gyrus were selectively recruited in processing phrases or sentences. Our results ground a multi-timescale hierarchical neural processing of speech in neuroanatomical reality with specific sets of cortices responsible for different levels of linguistic units.