TL;DR: Recent advances in applying EEG to study pathophysiology, phenomenology, and treatment response in schizophrenia are discussed and potential EEG biomarkers of schizophrenia and its symptoms are reviewed.
Abstract: Clinical experience and research findings suggest that schizophrenia is a disorder comprised of multiple genetic and neurophysiological subtypes with differential response to treatment. Electroencephalography (EEG) is a non-invasive, inexpensive and useful tool for investigating the neurobiology of schizophrenia and its subtypes. EEG studies elucidate the neurophysiological mechanisms potentially underlying clinical symptomatology. In this review article recent advances in applying EEG to study pathophysiology, phenomenology, and treatment response in schizophrenia are discussed. Investigative strategies employed include: analyzing quantitative EEG (QEEG) spectral power during the resting state and cognitive tasks; applying machine learning methods to identify QEEG indicators of diagnosis and treatment response; and using the event-related brain potential (ERP) technique to characterize the neurocognitive processes underlying clinical symptoms. Studies attempting to validate potential EEG biomarkers of schizophrenia and its symptoms, which could be useful in assessing familial risk and treatment response, are also reviewed.
TL;DR: In this article, the gamma oscillatory activity was used as a potential EEG biomarker to evaluate the response of patients to antipsychotic treatment over 8 weeks, with no significant differences in gamma spectral power in any of the regions.
Abstract: Aims An abnormal activity in the electroencephalography (EEG) gamma band (>30 Hz) has been demonstrated in schizophrenia and this has been suggested to be reflecting a deficit in the development and maturation of the basic cognitive functions of attention, working memory and sensory processing. Hypothesizing gamma oscillatory activity as a potential EEG biomarker to antipsychotic response in schizophrenia, the present study aimed at measuring baseline spontaneous gamma activity in schizophrenia patients, and evaluating its response to antipsychotic treatment over 8 weeks. Methods Fifteen drug-free/naive patients were recruited, compared at baseline with 15 age-, sex- and education-matched healthy controls, and were followed up for 8 weeks' treatment on antipsychotics. Resting state EEG waves were recorded using high (192-channel) resolution EEG at admission, 4 weeks and 8 weeks. Spectral power was calculated using fast Fourier transformation, Hanning window. The power was averaged region-wise over nine regions in three frequency ranges (30–50 Hz, 50–70 Hz, 70–100 Hz). Results Patients and controls differed significantly at intake in terms of left temporal and parietal high (70–100 Hz) gamma power. Consequently, no significant differences were seen over the course of antipsychotic treatment on gamma spectral power in any of the regions. Conclusions Lack of significant effect of treatment on gamma power suggests that these gamma oscillations may be trait markers in schizophrenia.
TL;DR: A review of machine learning-based methods for schizophrenia classification using EEG data is presented in this paper, where the authors discuss their potentialities and limitations, as well as a starting point for future developments of effective EEG-based models that might predict the onset of schizophrenia.
Abstract: The complexity and heterogeneity of schizophrenia symptoms challenge an objective diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the boundaries of schizophrenia are not precisely demarcated from other nosologic categories, such as bipolar disorder. The early detection of schizophrenia can lead to a more effective treatment, improving patients’ quality of life. Over the last decades, hundreds of studies aimed at specifying the neurobiological mechanisms that underpin clinical manifestations of schizophrenia, using techniques such as electroencephalography (EEG). Changes in event-related potentials of the EEG have been associated with sensory and cognitive deficits and proposed as biomarkers of schizophrenia. Besides contributing to a more effective diagnosis, biomarkers can be crucial to schizophrenia onset prediction and prognosis. However, any proposed biomarker requires substantial clinical research to prove its validity and cost-effectiveness. Fueled by developments in computational neuroscience, automatic classification of schizophrenia at different stages (prodromal, first episode, chronic) has been attempted, using brain imaging pattern recognition methods to capture differences in functional brain activity. Advanced learning techniques have been studied for this purpose, with promising results. This review provides an overview of recent machine learning-based methods for schizophrenia classification using EEG data, discussing their potentialities and limitations. This review is intended to serve as a starting point for future developments of effective EEG-based models that might predict the onset of schizophrenia, identify subjects at high-risk of psychosis conversion or differentiate schizophrenia from other disorders, promoting more effective early interventions.
TL;DR: MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in major depressive disorder (MDD) and bipolar disorder (BD).
Abstract: Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared to those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically under-exploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e. oscillatory coupling). The reviewed literature highlights the relevance of probing local and inter-regional rhythmic synchronization in order to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations.
TL;DR: The contribution of animal p-EEG studies can further benefit by adherence to guidelines for methodological standardization, which are presently under construction by the International Pharmaco- EEG Society (IPEG).
Abstract: The contemporary value of animal pharmaco-electroencephalography (p-EEG)-based applications are strongly interlinked with progress in recording and neuroscience analysis methodology. While p-EEG in humans and animals has been shown to be closely related in terms of underlying neuronal substrates, both translational and back-translational approaches are being used to address extrapolation issues and optimize the translational validity of preclinical animal p-EEG paradigms and data. Present applications build further on animal p-EEG and pharmaco-sleep EEG findings, but also on stimulation protocols, more specifically pharmaco-event-related potentials. Pharmaceutical research into novel treatments for neurological and psychiatric diseases has employed an increasing number of pharmacological as well as transgenic models to assess the potential therapeutic involvement of different neurochemical systems and novel drug targets as well as underlying neuronal connectivity and synaptic function. Consequently, p-EEG studies, now also readily applied in modeled animals, continue to have an important role in drug discovery and development, with progressively more emphasis on its potential as a central readout for target engagement and as a (translational) functional marker of neuronal circuit processes underlying normal and pathological brain functioning. In a similar vein as was done for human p-EEG studies, the contribution of animal p-EEG studies can further benefit by adherence to guidelines for methodological standardization, which are presently under construction by the International Pharmaco-EEG Society (IPEG).
TL;DR: Spontaneous MEG data show that local and global neural organization is altered in SZ patients, suggesting MEG is a highly promising tool to fill in knowledge gaps about the neurophysiology of SZ to reach its fullest potential.
Abstract: Objective Neuroimaging studies provide evidence of disturbed resting-state brain networks in Schizophrenia (SZ). However, untangling the neuronal mechanisms that subserve these baseline alterations requires measurement of their electrophysiological underpinnings. This systematic review specifically investigates the contributions of resting-state Magnetoencephalography (MEG) in elucidating abnormal neural organization in SZ patients. Method A systematic literature review of resting-state MEG studies in SZ was conducted. This literature is discussed in relation to findings from resting-state fMRI and EEG, as well as to task-based MEG research in SZ population. Importantly, methodological limitations are considered and recommendations to overcome current limitations are proposed. Results Resting-state MEG literature in SZ points towards altered local and long-range oscillatory network dynamics in various frequency bands. Critical methodological challenges with respect to experiment design, and data collection and analysis need to be taken into consideration. Conclusion Spontaneous MEG data show that local and global neural organization is altered in SZ patients. MEG is a highly promising tool to fill in knowledge gaps about the neurophysiology of SZ. However, to reach its fullest potential, basic methodological challenges need to be overcome. Significance MEG-based resting-state power and connectivity findings could be great assets to clinical and translational research in psychiatry, and SZ in particular.