Magnetoencephalography as a tool in psychiatric research: current status and perspective

The application of neuroimaging to provide mechanistic insights into circuit dysfunctions in major psychiatric conditions and the development of biomarkers are core challenges in current psychiatric research. In this review, we propose that recent technological and analytic advances in Magnetoencephalography (MEG), a technique which allows the measurement of neuronal events directly and non-invasively with millisecond resolution, provides novel opportunities to address these fundamental questions. Because of its potential in delineating normal and abnormal brain dynamics, we propose that MEG provides a crucial tool to advance our understanding of pathophysiological mechanisms of major neuropsychiatric conditions, such as Schizophrenia, Autism Spectrum Disorders, and the dementias. In our paper, we summarize the mechanisms underlying the generation of MEG signals and the tools available to reconstruct generators and underlying networks using advanced source-reconstruction techniques. We then survey recent studies that have utilized MEG to examine aberrant rhythmic activity in neuropsychiatric disorders. This is followed by links with preclinical research, which have highlighted possible neurobiological mechanisms, such as disturbances in excitation/inhibition parameters, which could account for measured changes in neural oscillations. In the final section of the paper, challenges as well as novel methodological developments are discussed which could pave the way for a widespread application of MEG in translational research with the aim of developing biomarkers for early detection and diagnosis

The association of psychotic disorders, dopaminergic agents and resting-state EEG/MEG functional connectivity

Psychotic disorders are complex and heterogeneous mental disorders with low recovery rates despite a great amount of research on the topic. Various hypotheses exist as to the etiology of psychotic disorders. Amongst these, the dopamine hypothesis and the dysconnectivity hypothesis have been the most enduring in the last six decades. Little is known on how the dopamine and the dysconnectivity hypothesis are associated. The overarching research question of this thesis is to investigate this knowledge gap. Resting-state magneto- and electroencephalography (MEG, EEG) were chosen as non-invasive measurement modalities of dysconnectivity at the source and sensor level of the brain in publication 1. Parameters of resting-state EEG microstate classes A-D were used as a global analysis method of functional connectivity at the sensor level of the brain in publications 2 and 3. The first research question focused on finding systematic evidence on the association of the two hypotheses and was addressed by means of a systematic review (publication 1) of 20 studies published since 2000. Based on the review, no definite conclusion on the association of antipsychotic medication (that mainly acts on the dopamine system) and source- and sensor-level EEG/MEG functional connectivity could be drawn. The second research question focused on whether differences in parameters of resting-state EEG microstate classes A-D are associated to antipsychotic medication. It was addressed by a study (publication 2) that compared 19-channel clinical EEG recordings of medicated (mFEP, n = 17) and medication-naïve (untreated; uFEP, n = 30) patients with first-episode psychotic disorders (FEP). The study results revealed significant decrease of microstate class A and significant increase of microstate class B to differentiate mFEP from uFEP. The third research question focused on whether differences in parameters of resting-state EEG microstate classes A-D are associated with psychosis illness progression and transition to psychosis in FEP and ultra-high-risk (UHR) patients. It was addressed by a study (publication 3) that found significantly increased microstate class A to differentiate a combined group of medication-naïve FEP (n = 29) and UHR patients (n = 54) together from healthy controls (HC, n = 25); significantly decreased microstate class B to differentiate FEP from all UHR patients combined; and significantly decreased microstate class D to differentiate UHR-T patients with (n = 20) from UHR-NT patients without (n = 34) later transition to psychotic disorders using 19-channel EEG recordings. In conclusion across all three publications, an association between the dopamine and the dysconnectivity hypothesis could be demonstrated by means of resting-state EEG microstates assessed in publication 2 and 3. No definite conclusion could be drawn by the systematic review (publication 1). More studies with longitudinal designs are needed to rule-out between-subject differences, track response trajectories, pre-post effects of antipsychotic medication and their association with dysconnectivity. With increased effort, resting-state EEG microstates could contribute to establishing a robust biomarker in a multi- domain approach in order to inform clinicians for the diagnosis, treatment and outcome prediction of psychotic disorders

The role of neuronavigation in TMS-EEG studies : Current applications and future perspectives

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) allows measuring noninvasively the electrical response of the human cerebral cortex to a direct perturbation. Complementing TMSEEG with a structural neuronavigation tool (nTMS-EEG) is key for accurately selecting cortical areas, targeting them, and adjusting the stimulation parameters based on some relevant anatomical priors. This step, together with the employment of visualization tools designed to perform a quality check of TMS-evoked potentials (TEPs) in real-time during TMS-EEG data acquisition, is pivotal for maximizing the impact of the TMS pulse on the cortex and in ensuring highly reproducible measurements within sessions and across subjects. Moreover, storing stimulation parameters in the neumnavigation system can help in replicating the stimulation parameters within and across experimental sessions and sharing them across research centers. Finally, the systematic employment of neumnavigation in TMS-EEG studies is also critical to standardize measurements in clinical populations in search for reliable diagnostic and prognostic TMS-EEG-based biomarkers for neurological and psychiatric disorders.Peer reviewe

Emitted P3a and P3b in chronic schizophrenia and in first-episode schizophrenia

Neurophysiological biomarkers may be useful for identifying the presence of schizophrenia and the schizophrenia prodrome among at-risk individuals prior to the emergence of psychosis. This study examined the emitted P3 to absent stimuli on a tone counting task in patients with chronic schizophrenia and newly-diagnosed patients. The P3 is biphasic, with the earlier peak (P3a) reflecting automatic orienting and the later peak (P3b) reflecting cognitive processing. Twenty-four individuals with long-term schizophrenia (minimum 5 years diagnosis; SZ) were compared to 24 matched controls (HCSZ), and 23 individuals within 6 months of their first psychotic episode (FE) were compared to 22 matched controls (HCFE). Participants were presented with standard sets of four identical tones (1 kHz, 50 ms, 330 ms SOA, 750 ms ITI). For one in seven sets, the fourth tone was missing. Participants simply counted the number of tones within each set, with no instruction to detect missing tones. The P3a emitted by missing tones was significantly reduced in SZ compared to HCSZ (p=.044). The P3b emitted by missing tones was significantly reduced in both SZ and FE compared to their matched control groups (p=.049 and p=.036 respectively). SZ and FE showed impaired generation of the emitted P3b during selective attention to stimuli. The emitted P3b may be useful to understand cognitive neuropathophysiology early in psychosis, and shows promise as a biomarker to help detect the true schizophrenia prodrome among clinical high risk individuals prior to disease onset

Topological Biomarker of Alzheimer’s Disease

For years, it has been assumed that the cerebral accumulation of pathologic protein forms is the main trigger of Alzheimer’s disease (AD) pathology; however, recent studies revealed strong evidences that the alternations in synaptic activity precede and affect the homeostasis of amyloid-beta and tau, both of which aggregate during AD. Given that the neuropathological changes, characteristic for AD, start decades before the onset of the first symptoms, when alternations become irreversible, it is crucial to find a biomarker that can detect the preclinical signs of disease, presumably synaptic dysfunction of specific cerebral areas. Here is presented a novel, a high potential neuroimaging biomarker that can detect the postsynaptic dysfunction of specific neural substrate located in medial prefrontal cortex (mPFC) during sensory gating processing of a simple auditory stimulus. The magnetoencephalography-based localization of mPFC gating activation has the potential not only to detect symptomatic AD but also to become a predictor of cognitive decline related to the pathophysiological processes of AD, both at the individual level. The strengths of proposed biomarker lie in the simplicity of using a binary value, i.e., activated or not activated a neural generator along with its potential to follow the evolution of the pathophysiological process of disease from preclinical phase. The novel biomarker does not require estimation of uniform cutoff levels and standardization processes, the main problems of so far proposed biomarkers. Ability to individually detect AD pathology during putative preclinical and clinical stages, absolute noninvasiveness, and large effect size give this biomarker a high translation capacity and clinical potential

Guidelines for the recording and evaluation of pharmaco-EEG data in man: the International Pharmaco-EEG Society (IPEG)

The International Pharmaco-EEG Society (IPEG) presents updated guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-EEG data in man. Since the publication of the first pharmaco-EEG guidelines in 1982, technical and data processing methods have advanced steadily, thus enhancing data quality and expanding the palette of tools available to investigate the action of drugs on the central nervous system (CNS), determine the pharmacokinetic and pharmacodynamic properties of novel therapeutics and evaluate the CNS penetration or toxicity of compounds. However, a review of the literature reveals inconsistent operating procedures from one study to another. While this fact does not invalidate results per se, the lack of standardisation constitutes a regrettable shortcoming, especially in the context of drug development programmes. Moreover, this shortcoming hampers reliable comparisons between outcomes of studies from different laboratories and hence also prevents pooling of data which is a requirement for sufficiently powering the validation of novel analytical algorithms and EEG-based biomarkers. The present updated guidelines reflect the consensus of a global panel of EEG experts and are intended to assist investigators using pharmaco-EEG in clinical research, by providing clear and concise recommendations and thereby enabling standardisation of methodology and facilitating comparability of data across laboratories

Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

While diagnosing schizophrenia by physicians based on patients' history and their overall mental health is inaccurate, we report on promising results using a novel, fast and reliable machine learning approach based on electroencephalography (EEG) recordings. We show that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic (ICD-10 F20.0) and non-schizophrenic persons with a very good balanced accuracy of 96.77 percent. We evaluate our approach on EEG data from an open neurological and psychiatric repository containing 499 one-minute recordings of n=28 participants (14 paranoid schizophrenic and 14 healthy controls). Since the fact that neither diagnostic tests nor biomarkers are available yet to diagnose paranoid schizophrenia, our approach paves the way to a quick and reliable diagnosis with a high accuracy. Furthermore, interesting insights about the most predictive subbands were gained by analyzing the electroencephalographic spectrum up to 100 Hz