High-frequency neural activity and human cognition: Past, present and possible future of intracranial EEG research

Electrophysiological foundations of the human default-mode network revealed by intracranial-EEG recordings during resting-state and cognition

Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume-conducted activity arising in multiple source areas. Independent component analysis (ICA) has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to unmix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC) projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20–80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity.

A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies 36±6 min of REM in one night of recorded sleep, while incorrectly labeling less than 10% of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near 80%), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night's worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data.

Over the past decades, studies of human brain networks have received growing attention as the assessment and modelling of connectivity in the brain is a topic of high impact with potential application in the understanding of human brain organization under both physiological as well as various pathological conditions. Under specific diagnostic settings, human neuronal signal can be obtained from intracranial EEG (iEEG) recording in epilepsy patients that allows gaining insight into the functional organisation of living human brain. There are two approaches to assess brain connectivity in the iEEG-based signal: evaluation of spontaneous neuronal oscillations during ongoing physiological and pathological brain activity, and analysis of the electrophysiological cortico-cortical neuronal responses, evoked by single pulse electrical stimulation (SPES). Both methods have their own advantages and limitations. The paper outlines available methodological approaches and provides an overview of current findings in studies of physiological and pathological human brain networks, based on intracranial EEG recordings.

Interictal high-frequency oscillation (HFO) is considered a promising biomarker of the epileptogenic zone. The pseudo-HFOs originating from artifacts and noise might escape HFO detectors and mislead the seizure onset zone (SOZ) localization. The purpose of this study is to propose a new sparse representation framework fused with a random forest classifier to detect the real HFOs and eliminate the pseudo-ones. In this scheme, each candidate event that passed a conventional amplitude threshold-based detector was represented locally in a sparse fashion. Specifically, the signal is divided into overlapping windows and using orthogonal matching pursuit, only a few oscillatory atoms selected from a predefined redundant Gabor dictionary were used to approximate the signal locally. Later, the approximations in overlapping segments are averaged to increase the smoothness. Finally, the ability to reconstruct an event is translated to informative features and fed into a random forest classifier. This technique was tested on 10 minutes of interictal intracranial EEG (iEEG) recordings recorded from 11 patients with epilepsy. In this framework, three experts visually inspected 4466 events captured by the amplitude threshold-based HFO detector in iEEG recordings and labeled them as real-HFO or Pseudo-HFO. We reached 89.77% classification accuracy in these labeled events. Furthermore, the success of the method assessed by calculating the spatial overlap between the detected HFOs and SOZ channels. Compared to conventional amplitude threshold-based HFO detector, our method resulted a significant 18.27% improvement in the localization of SOZ.

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

Electromagnetic source imaging using simultaneous scalp EEG and intracranial EEG: An emerging tool for interacting with pathological brain networks

Intracranial EEG evaluation of relationship within a resting state network

SPECIALTY GRAND CHALLENGE article Front. Psychiatry, 23 March 2012Sec. Child and Adolescent Psychiatry Volume 3 - 2012 | https://doi.org/10.3389/fpsyt.2012.00027

Intracranial video-EEG monitoring in presurgical evaluation of patients with refractory epilepsy

Much animal research into potential treatments for humans is wasted because it is poorly conducted and not evaluated through systematic reviews Clinicians and the public often consider it axiomatic that...

We argue that current animal research from comparative literature can assist students' understanding of basic developmental principles in courses that traditionally focus on human development. However, authors often exclude these studies from developmental textbooks. Our evaluation of 24 developmental texts published between 1995 and 2000 revealed 249 references of 154 different animal studies, which comprises less than 1.5% of references in all texts. Furthermore, the average publication date of the animal studies that did appear was 1976, reflecting an emphasis on older studies, rather than current research. We discuss how more recently published animal research articles can enhance class discussion and suggest that basic animal research is fundamental to the study of developmental processes.

Research in cardiomyopathy and myocarditis is currently proceeding along several lines in the United States. Cardiomyopathy is becoming much better recognized on a clinical basis, and the various forms of myocardial disease are probably much more common than was ever before realized. Likewise, a great deal of interest has been kindled in the topic of myocarditis. A relative clarification of nomenclature and gradual adoption of the international terminology and classification of the cardiomyopathies is also occurring in the USA. Research into the possible etiologies of cardiomyopathies is most intense in the theory of infectious-immune causation of dilated cardiomyopathy. But also of great interest are studies into the role of vasculopathy, toxins, and the autonomic nervous system in the development of myocardial disease. Investigations into the infectious-immune theory involve both animal and human studies. Animal studies include viral infections with different strains to try and obtain a more suitable model for the human disease. Basic research in animals has also advanced our understanding of immunologic mechanisms of tissue injury in postinfectious phases, including the discovery of possible myocardial neoantigens eliciting an immune response. Human research continues in immunologic reactions in dilated cardiomyopathy patients, genetic makeup, and familial predispostions. Suppressor and natural killer cell function both appear abnormal in dilated cardiomyopathy patients. Endomyocardial biopsy is perhaps now more used and accepted than ever. Its use in research is pivotal from the standpoint of the elucidation of possible progression from myocarditis to cardiomyopathy, the biochemical and enzymatic constitution of diseased myocardium, beta receptor density, and immunologic reactions.

Biological neural networks translate sensory information into neural code that is held in memory over long timescales. Theories for how this occurs often posit a functional role of neural oscillations. However, recent advances show that neural oscillations are often confounded with non-oscillatory, aperiodic neural activity. Here we analyze a dataset of intracranial human EEG recordings (N = 13; 10 female) to test the hypothesis that aperiodic activity plays a role in visual memory, independent and distinct from oscillations. By leveraging a new approach to time-resolved parameterization of neural spectral activity, we find event-related changes in both oscillations and aperiodic activity during memory encoding. During memory encoding, aperiodic-adjusted alpha oscillatory power significantly decreases while, simultaneously, aperiodic neural activity "flattens out". These results provide novel evidence for task-related dynamics of both aperiodic and oscillatory activity in human memory, paving the way for future investigations into the unique functional roles of these two neural processes in human cognition.

Attention Deficit Hyperactivity Disorder (ADHD) is a poorly understood neurodevelopmental disorder of multifactorial origin. Animal-based research has been used to investigate ADHD aetiology, pathogenesis and treatment, but the efficacy of this research for patients has not yet been systematically evaluated. However, such evaluation is important, given the resource consumption and ethical concerns incurred by animal use. Accordingly, we used the citation tracking facility within Web of Science to locate original research performed on animal models related to ADHD, prior to 2010. Human medical papers citing those animal studies were carefully analyzed by two independent raters to evaluate the contribution of the animal to the human studies. 211 publications describing relevant animal studies were located. Approximately half (3,342) of their 6,406 citations were by other animal studies. 446 human medical papers cited 121 of these 211 animal studies, a total of 500 times. 254 of these 446 papers were human studies of ADHD. However, only eight animal papers (cited 10 times) were relevant to the hypothesis of the human medical study in question. Three of these eight papers described results from both human and animal studies, but their citations solely referred to the human data. Five animal research papers were relevant to the hypotheses of the applicable human medical papers. Citation analysis indicates that animal research has contributed very little to contemporary understanding of ADHD. To ensure optimal allocation of Research & Development funds targeting this disease the contribution of other research methods should be similarly evaluated.