Magnetoencephalography and Translational Neuroscience in Psychiatry

Intensity-dependent modulation of the early auditory gamma-band response in first-episode schizophrenia and its association with disease symptoms

BACKGROUND

Gamma-band activity has been the focus of considerable research in schizophrenia. Discrepancies exist regarding the integrity of the early auditory gamma-band response (EAGBR), a stimulus-evoked oscillation, and its relationship to symptoms in early disease. Variability in task design may play a role. This study examined sensitivity of the EAGBR to stimulus intensity and its relation to symptoms and functional impairments in the first-episode schizophrenia spectrum (FESz).

METHOD

Magnetoencephalography was recorded from 35 FESz and 40 matched healthy controls (HC) during presentation of 3 tone intensities (75 dB, 80 dB, 85 dB). MRIs were collected to localize auditory cortex activity. Wavelet-transformed single trial epochs and trial averages were used to assess EAGBR intertrial phase coherence (ITPC) and evoked power, respectively. Symptoms were assessed using the Positive and Negative Syndrome Scale.

RESULTS

Groups did not differ in overall EAGBR power or ITPC. While HC exhibited EAGBR enhancement to increasing intensity, FESz exhibited reduced power to the 80 dB tone and, relative to HC, increased power to the 75 dB tone. Larger power and ITPC were correlated with more severe negative, thought disorganization, and resistance symptoms. Stronger ITPC was associated with impaired social functioning.

DISCUSSION

EAGBR showed no overall deficit at disease onset. Rather, FESz exhibited a differential response across tone intensity relative to HC, emphasizing the importance of stimulus characteristics in EAGBR studies. Associations between larger EAGBR and more severe symptoms suggest aberrant synchronization driving overinclusive perceptual binding that may relate to deficits in executive inhibition of initial sensory activity.

Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder

BACKGROUND

Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response.

METHODS

Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions.

RESULTS

After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969).

LIMITATIONS

Relatively small sample size and not a double-blind design.

CONCLUSIONS

The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.

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

AIM

Major depressive disorder (MDD) is associated with high suicidality, especially for those with suicide attempt (SA). Although impaired oscillatory activity has been previously reported in patients with SA, little is known about precise temporal-spatial variability of its neural dynamics. To solve this, the current study probed the spectral power and network interactions underlying SA in MDD.

METHODS

The present study recruited 104 subjects including 56 subjects with MDD (30 with SA and 26 without SA) and 48 healthy controls, who performed sad expressions recognition task during magnetoencephalography (MEG) recording. By investigating source-reconstructed MEG-data, brain states representing different task stages were estimated from a Hidden Markov model. Spectrum power and network connectivity were compared via Gaussian Mixture Models, and fractional occupancy (FO) of states were compared via an independent F-test.

RESULTS

Brain states were corresponding to various frequencies (theta/beta/low gamma/ high gamma). In low gamma band (35-45 Hz), the early visual state exhibited increased activation and hyper inter-network connectivity between visual regions and the limbic system, while the middle fronto-parietal state exhibited attenuated activation and decreased intra-network connectivity within fronto-parietal regions in SA group. Crucially, FO values of these two states were significantly correlated with the suicide risks.

CONCLUSIONS

Suicide behavior of patients with MDD was significantly associated with aberrant oscillations in low gamma band. Elevated oscillations in occipital cortices and attenuated oscillations in fronto-parietal cortices were significantly associated with SA. Manifesting sadness indulging and reckless decision-making, the hampered temporal characteristics could help explain the neural-electric basis of SA.

The 40-Hz auditory steady-state response in bipolar disorder: A meta-analysis

OBJECTIVE

Bipolar disorder is characterized by aberrant neurophysiological responses as measured with electroencephalography (EEG) and magnetoencephalography (MEG), including the 40-Hz auditory steady-state response (ASSR). 40-Hz ASSR deficits are also found in patients with schizophrenia and may represent a transdiagnostic biomarker of neuronal circuit dysfunction. In this systematic review and meta-analysis, we summarize and evaluate the evidence for 40-Hz ASSR deficits in patients with bipolar disorder.

METHODS

We identified studies from PubMed, EMBASE, and SCOPUS. We assessed the risk of bias, calculated Hedges' g meta-level effect sizes, and investigated small-study effects using funnel plots and Egger regression.

RESULTS

Seven studies, comprising 396 patients with bipolar disorder and 404 healthy controls, were included in the meta-analysis. Studies displayed methodological heterogeneity and an overall high risk of bias. Patients with bipolar disorder showed consistent reductions in 40-Hz ASSR evoked power (Hedges' g = -0.49; 95% confidence intervals [-0.67, -0.31]) and inter-trial phase coherence (ITPC) (Hedges' g = -0.43; 95 %CI [-0.58, -0.29]) compared with healthy controls.

CONCLUSIONS

Our meta-analysis provides evidence that 40-Hz ASSRs are reduced in patients with bipolar disorder compared with healthy controls.

SIGNIFICANCE

Future large-scale studies are warranted to link 40-Hz ASSR deficits to clinical features and developmental trajectories.

Reliability and Individual Specificity of EEG Microstate Characteristics

Electroencephalography (EEG) microstates (MSs) are defined as quasi-stable topographies that represent global coherent activation. Alternations in EEG MSs have been reported in numerous neuropsychiatric disorders. Transferring the results of these studies into clinical practice requires not only high reliability but also sufficient individual specificity. Nevertheless, whether the amount of data used in microstate analysis influences reliability and how much individual information is provided by EEG MSs are unclear. In the current study, we aimed to assess the within-subject consistency and between-subject differences in the characteristics of EEG MSs. Two sets of eyes-closed resting-state EEG recordings were collected from 54 young, healthy participants on two consecutive days. The Raven Advanced Progressive Matrices test was conducted to assess general fluid intelligence (gF). We obtained four MSs (labeled A, B, C and D) through EEG microstate analysis. EEG MS characteristics including traditional features (the global explained variances, mean durations, coverages, occurrences and transition probabilities), the Hurst exponents and temporal dynamic features (the autocorrelation functions and the partial autocorrelation functions) were calculated and evaluated. The data with a duration greater than 2 min showed moderate to high reliability and individual specificity. The mean duration and coverage of MS C were significantly correlated with the gF score. The dynamic features showed a higher identification accuracy and were more significantly correlated with gF than the traditional MS features. These findings reveal that EEG microstate characteristics are reliably unique in single subjects and possess abundant inter-individual variability.

Magnetoencephalography in Cognitive Neuroscience: A Primer

Magnetoencephalography (MEG) is an invaluable tool to study the dynamics and connectivity of large-scale brain activity and their interactions with the body and the environment in functional and dysfunctional body and brain states. This primer introduces the basic concepts of MEG, discusses its strengths and limitations in comparison to other brain imaging techniques, showcases interesting applications, and projects exciting current trends into the near future, in a way that might more fully exploit the unique capabilities of MEG.