Magnetoencephalography and Translational Neuroscience in Psychiatry

Abstract

The search for biomarkers and pathophysiological mechanisms in psychiatry is a major objective in current research. One important prerequisite for the success of this approach is noninvasive imaging techniques that yield insights into alterations of large-scale networks and establish relationships with preclinical work. Anatomical and functional magnetic resonance imaging (MRI/fMRI) have been extensively used for this purpose because of their ability to measure the layout of anatomical and functional networks with excellent spatial resolution. However, a limitation of this approach is that these techniques allow only indirect links to the physiology of underlying signal changes. Moreover, the temporal resolution of fMRI is in the range of seconds. In contrast, large-scale brain networks operate in the millisecond scale with frequencies of up to 200 Hz that are fundamental for cognitive processes, and these fast rhythmic fluctuations have been found to be impaired in schizophrenia and other psychiatric disorders. 1 Accordingly, these data highlight the importance of techniques that allow the measurement of brain signals with realistic temporal resolution and permit the accurate reconstruction of underlying networks.In this article, we would like to make the case that magnetoencephalography (MEG) is an imaging technique that is ideally suited for this goal. Although already first introduced in the 1980s, we believe that MEG is now at a point in which a comprehensive application of this approach toward discovering biomarkers and pathophysiological mechanisms in psychiatry is warranted because of significant advances in identifying the underlying generators through novel source reconstruction approaches as well as the wider availability of MEG systems (>200 MEG centers worldwide).Similarly to electroencephalography (EEG), MEG signals are generated primarily by currents based on postsynaptic potentials of pyramidal cells (Figure). However, there are important distinctions between both techniques that result from the different ways in which signals fluctuations in electrical fields of the brain are measured.Electroencephalography typically involves electrodes that are directly located on the scalp while MEG uses superconducting quantum interference devices that measure deviations within magnetic fields that are caused by electric activity. Superconducting quantum interference devices are used in 2 types: (1) magnetometers measure the magnetic field component along the direction perpendicular to the surface of the sensor and (2) gradiometers measure the spatial gradient rather than the magnitude of the field. Current state-of-the-art MEG systems use both sensors, the combination of which improves the detection of deeper sources. This is an important issue, as the strength of the magnetic signals rapidly decays with increasing distance and therefore measuring deeper sources, such as the hippocampus, thalamus, and amygdala, is

Magnetoencephalography in Cognitive Neuroscience: A Primer

“…The next years will also see new generations of OPM sensors combined into powerful multi-channel systems that will further expand the remit of MEG and might even allow simultaneous TMS stimulation. Together, these MEG-assisted approaches will likely help to identify spectral signatures of specific disorders to assist with early diagnosis and inform therapy (van Diessen et al, 2015;Schnitzler and Gross, 2005;Uhlhaas et al, 2018). This clinical approach can complement endeavors in cognitive neuroscience where MEG and EEG is used to identify individual spectral signatures of cognitive processes constrained by individual anatomy, shaped by phenotype and decoded with the help of cohort studies, machine learning, and computational models.…”

“…In addition, evidence exists that these brain oscillations reflect brain states, encode stimulus and task-relevant information, are expressed by individual brain areas in a characteristic manner, and cause rhythms in action and perception (Buzsaki, 2006;VanRullen, 2016). Furthermore, pathologically altered brain rhythms are associated with a variety of neurological and mental health disorders (Schnitzler and Gross, 2005;Uhlhaas et al, 2018). For more detailed information on the importance of brain rhythms, I refer readers to relevant reviews (Buzsá ki and Draguhn, 2004;Fries, 2015;Schnitzler and Gross, 2005;Siegel et al, 2012;Thut et al, 2012;Wang, 2010).…”

“…Resting-State Spectral Signatures Many MEG (and EEG) studies have investigated resting-state activity in healthy participants and patients (e.g., Cabral et al, 2017;Engels et al, 2017;Mandal et al, 2018;Uhlhaas et al, 2018). The spatiospectral structure of brain activity in this state is shaped and constrained by anatomical connectivity and by the areaspecific anatomical substrate (Mars et al, 2018), and it leads to functional correlations between brain areas that are reported as resting-state networks in fMRI studies (Park and Friston, 2013).…”

I. Bildgebung in den Neurowissenschaften: Medizinische Aspekte

“…Schnitzler and Gross 2005; Vgl. Uhlhaas et al 2018;Vgl. Butz et al 2013. gesunden Proband*innen und Patient*innen hat Netzwerkstruktu ren ergeben, die mit entsprechenden fMRI-Ruhezustandsnetzwerken übereinstimmen.…”

Hampered gamma oscillations induced by sad emotion underlying suicide attempt in major depressive disorder

“…Various depressive symptoms including suicide risk could be conceived as spatiotemporal disturbances of brain activities entailed “spatiotemporal psychopathology” 16 . Because of its excellent spatial and temporal resolution, Magnetoencephalography (MEG) has been applied to track the dysfunctions of brain spatiotemporal patterns of many psychiatric illnesses 17 . Analogously, we hypothesized that aberrant neuronal oscillations in the high‐frequency band may reflect the aberrant emotional regulation and contain valuable biomarkers to identify SA of patients with MDD.…”