EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health

Leveraging big data for causal understanding in mental health: a research framework

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause

Coherence Potentials Encode Simple Human Sensorimotor Behavior

Recent work has shown that large amplitude negative periods in the local field potential (nLFPs) are able to spread in saltatory manner across large distances in the cortex without distortion in their temporal structure forming ‘coherence potentials’. Here we analysed subdural electrocorticographic (ECoG) signals recorded at 59 sites in the sensorimotor cortex in the left hemisphere of a human subject performing a simple visuomotor task (fist clenching and foot dorsiflexion) to understand how coherence potentials arising in the recordings relate to sensorimotor behavior. In all behaviors we found a particular coherence potential (i.e. a cascade of a particular nLFP wave pattern) arose consistently across all trials with temporal specificity. During contrateral fist clenching, but not the foot dorsiflexion or ipsilateral fist clenching, the coherence potential most frequently originated in the hand representation area in the somatosensory cortex during the anticipation and planning periods of the trial, moving to other regions during the actual motor behavior. While these ‘expert’ sites participated more consistently, other sites participated only a small fraction of the time. Furthermore, the timing of the coherence potential at the hand representation area after onset of the cue predicted the timing of motor behavior. We present the hypothesis that coherence potentials encode information relevant for behavior and are generated by the ‘expert’ sites that subsequently broadcast to other sites as a means of ‘sharing knowledge’

Coherence Potentials: Loss-Less, All-or-None Network Events in the Cortex

Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential (LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks

High Variability Periods in the EEG Distinguish Cognitive Brain States

Objective: To describe a novel measure of EEG signal variability that distinguishes cognitive brain states. Method: We describe a novel characterization of amplitude variability in the EEG signal termed “High Variability Periods” or “HVPs”, defined as segments when the standard deviation of a moving window is continuously higher than the quartile cutoff. We characterize the parameter space of the metric in terms of window size, overlap, and threshold to suggest ideal parameter choice and compare its performance as a discriminator of brain state to alternate single channel measures of variability such as entropy, complexity, harmonic regression fit, and spectral measures. Results: We show that the average HVP duration provides a substantially distinct view of the signal relative to alternate metrics of variability and, when used in combination with these metrics, significantly enhances the ability to predict whether an individual has their eyes open or closed and is performing a working memory and Raven’s pattern completion task. In addition, HVPs disappear under anesthesia and do not reappear in early periods of recovery. Conclusions: HVP metrics enhance the discrimination of various brain states and are fast to estimate. Significance: HVP metrics can provide an additional view of signal variability that has potential clinical application in the rapid discrimination of brain states

Assessment of Population Well-being With the Mental Health Quotient: Validation Study

BackgroundThe Mental Health Quotient (MHQ) is an anonymous web-based assessment of mental health and well-being that comprehensively covers symptoms across 10 major psychiatric disorders, as well as positive elements of mental function. It uses a novel life impact scale and provides a score to the individual that places them on a spectrum from Distressed to Thriving along with a personal report that offers self-care recommendations. Since April 2020, the MHQ has been freely deployed as part of the Mental Health Million Project. ObjectiveThis paper demonstrates the reliability and validity of the MHQ, including the construct validity of the life impact scale, sample and test-retest reliability of the assessment, and criterion validation of the MHQ with respect to clinical burden and productivity loss. MethodsData were taken from the Mental Health Million open-access database (N=179,238) and included responses from English-speaking adults (aged≥18 years) from the United States, Canada, the United Kingdom, Ireland, Australia, New Zealand, South Africa, Singapore, India, and Nigeria collected during 2021. To assess sample reliability, random demographically matched samples (each 11,033/179,238, 6.16%) were compared within the same 6-month period. Test-retest reliability was determined using the subset of individuals who had taken the assessment twice ≥3 days apart (1907/179,238, 1.06%). To assess the construct validity of the life impact scale, additional questions were asked about the frequency and severity of an example symptom (feelings of sadness, distress, or hopelessness; 4247/179,238, 2.37%). To assess criterion validity, elements rated as having a highly negative life impact by a respondent (equivalent to experiencing the symptom ≥5 days a week) were mapped to clinical diagnostic criteria to calculate the clinical burden (174,618/179,238, 97.42%). In addition, MHQ scores were compared with the number of workdays missed or with reduced productivity in the past month (7625/179,238, 4.25%). ResultsDistinct samples collected during the same period had indistinguishable MHQ distributions and MHQ scores were correlated with r=0.84 between retakes within an 8- to 120-day period. Life impact ratings were correlated with frequency and severity of symptoms, with a clear linear relationship (R2>0.99). Furthermore, the aggregate MHQ scores were systematically related to both clinical burden and productivity. At one end of the scale, 89.08% (8986/10,087) of those in the Distressed category mapped to one or more disorders and had an average productivity loss of 15.2 (SD 11.2; SEM [standard error of measurement] 0.5) days per month. In contrast, at the other end of the scale, 0% (1/24,365) of those in the Thriving category mapped to any of the 10 disorders and had an average productivity loss of 1.3 (SD 3.6; SEM 0.1) days per month. ConclusionsThe MHQ is a valid and reliable assessment of mental health and well-being when delivered anonymously on the web

Trial Associated Coherence Potentials.

<p>(<b>A</b>) Fraction of sites with identical waveform (correlation R≥0.8) arising simultaneously (Time-Matched) or within ±10 ms milliseconds (Time-Shifted) increased non-linearly with increasing nLFP amplitude. Time-matched and time-shifted correlations calculated at 300 randomly selected sites per electrode could be fit to a sigmoidal function with an R² of 0.99 and 0.95 respectively. (Light colors: values for each of the four behavioral tasks, dark lines: mean). Comparisons between randomly selected sites shown in red. (<b>B</b>) Large amplitude nLFPs (peak<-2SD from the mean; numbered 1–8 for 3 electrodes shown) were identified at all 59 electrodes across the entire recording spanning all 50 trials. Correlations between all pairs of identified nLFPs were calculated after aligning their peaks. The nLFPs were then grouped using a clustering algorithm based on their correlation values (8×8 correlation matrix shown here). (<b>C</b>) Three pairs of nLFPs with correlation values of 0.9, 0.7 and 0.5 show that increasing correlation reflects increasing similarity of temporal structure. (<b>D</b>) Cluster dispersion across trials for different correlation criteria for clustering in the right fist clenching task. At a low correlation criterion (light gray) all nLFPs collapse into a few clusters and thus span all trials (trial-spanning clusters). At higher correlation stringency, clusters splintered and only a few clusters persisted across all trials. R = 0.7 was chosen for further analysis. (<b>E</b>) Amplitude shuffled ECoG traces did not produce clusters that spanned all 50 trials. At high clustering stringencies (0.7) all clusters had only 1 nLFP demonstrating that these cascading nLFPs are a non-random phenomenon. (<b>F</b>) Splintering of trial-spanning clusters with increasing correlation criteria for clustering (right fist clenching task shown here, other tasks shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030514#pone.0030514.s001" target="_blank">Figure S1</a>). The lines connect the parent cluster from which smaller clusters have separated. Only trial-spanning clusters are shown in the figure. Two clusters persist across all trials even for high correlation criterion. Inset: Mean traces of the nLFPs in the two clusters at R = 0.7 are shown in the same order. (<b>G</b>) Cluster sizes (number of nLFPs) of the nine trial-spanning clusters from all four behavioral tasks. (Colors and the cluster names shown are used in subsequent figures).</p

Functional mapping and experimental setup.

<p>(<b>A</b>) Functional map created by stimulation of electrode pairs in a subdural electrode array (black dots) implanted in the left fronto-parietal cortical region in a human subject. The colors represent distinct sensorimotor response profiles of movement and/or sensation in the arm, hand and face as well as non-sensorimotor responses such as deficits in language comprehension. The numbers indicate the electrode #. Electrodes 49 and 50 produced seizures on stimulation. (<b>B</b>) Visualization of the electrode array (shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030514#pone-0030514-g001" target="_blank">Figure 1A</a>) transformed on to a symmetric 8×8 grid used in subsequent figures. (<b>C</b>) The subject was asked to mimic the visual cue (red line; either right or left fist clenching or right or left foot dorsiflexion) for the duration it was shown on a screen. Each visual cue was presented 50 times for 3 s followed by an interval varied randomly between3 and 5 ms. Motor responses were monitored simultaneously with surface EMG from both the wrist and foot muscles for all 4 tasks. ECoG traces (bottom) were simultaneously recorded from all 59 electrodes. Scale bar – 300 mV, 300 ms.</p