MAO-A and the EEG Recognition Memory Signal in Left Parietal Cortex

A key part of episodic memory, or memory for the events of our lives, is recognition memory. Recognition memory is the ability to remember previously encountered stimuli. Studies have linked recognition memory to the old/new effect, an EEG indicator of stimulus familiarity. Monoamine oxidase A (MAO-A) is an enzyme that catalyzes monoamines, leading to the depletion of norepinephrine, epinephrine, serotonin, and dopamine. MAO-A is more efficiently transcribed in individuals with a 4 repeating sequence variation (4R) of the MAO-A gene leading to less monoamine availability. As many of these monoamines have been linked to episodic memory, we hypothesized that individuals homozygous for the 4R MAO-A polymorphism would show differences in mean EEG signal amplitudes during recognition memory. EEG data was recorded as participants viewed both new words and words that had been previously presented. Our results show that mean peak amplitudes over the left parietal cortex 500-800 ms post-stimulus presentation for hits were greater than those for correct rejections, indicating the old/new effect. Critically, our results revealed an interaction between mean hit and correct rejection amplitude over the left parietal cortex and MAO-A group. Individuals homozygous for the 4R variation (the High MAO-A group) do not show an old/new effect due to increased correct rejection amplitudes. These results suggest that less monoamine availability leads to new stimuli being identified as old by the left parietal cortex

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

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.Comment: 29 pages,2 figures and 18 Table

EEG spectral coherence data distinguish chronic fatigue syndrome patients from healthy controls and depressed patients-A case control study

<p>Abstract</p> <p>Background</p> <p>Previous studies suggest central nervous system involvement in chronic fatigue syndrome (CFS), yet there are no established diagnostic criteria. CFS may be difficult to differentiate from clinical depression. The study's objective was to determine if spectral coherence, a computational derivative of spectral analysis of the electroencephalogram (EEG), could distinguish patients with CFS from healthy control subjects and not erroneously classify depressed patients as having CFS.</p> <p>Methods</p> <p>This is a study, conducted in an academic medical center electroencephalography laboratory, of 632 subjects: 390 healthy normal controls, 70 patients with carefully defined CFS, 24 with major depression, and 148 with general fatigue. Aside from fatigue, all patients were medically healthy by history and examination. EEGs were obtained and spectral coherences calculated after extensive artifact removal. Principal Components Analysis identified coherence factors and corresponding factor loading patterns. Discriminant analysis determined whether spectral coherence factors could reliably discriminate CFS patients from healthy control subjects without misclassifying depression as CFS.</p> <p>Results</p> <p>Analysis of EEG coherence data from a large sample (n = 632) of patients and healthy controls identified 40 factors explaining 55.6% total variance. Factors showed highly significant group differentiation (p < .0004) identifying 89.5% of unmedicated female CFS patients and 92.4% of healthy female controls. Recursive jackknifing showed predictions were stable. A conservative 10-factor discriminant function model was subsequently applied, and also showed highly significant group discrimination (p < .001), accurately classifying 88.9% unmedicated males with CFS, and 82.4% unmedicated male healthy controls. No patient with depression was classified as having CFS. The model was less accurate (73.9%) in identifying CFS patients taking psychoactive medications. Factors involving the temporal lobes were of primary importance.</p> <p>Conclusions</p> <p>EEG spectral coherence analysis identified unmedicated patients with CFS and healthy control subjects without misclassifying depressed patients as CFS, providing evidence that CFS patients demonstrate brain physiology that is not observed in healthy normals or patients with major depression. Studies of new CFS patients and comparison groups are required to determine the possible clinical utility of this test. The results concur with other studies finding neurological abnormalities in CFS, and implicate temporal lobe involvement in CFS pathophysiology.</p

Frontal activation asymmetry and social competence at four years of age: Left frontal hyper and hypo activation as correlates of social behavior in preschool children.

The pattern of frontal activation as measured by the ongoing electroencephalogram (EEG) may be a marker for individual differences in infant and adult disposition to respond with either positive or negative affect. We studied 48 4-year-old children who were first observed in same-sex quartets during free-play sessions, while making speeches, and during a ticket-sorting task. Social and interactive behaviors were coded from these sessions. Each child was subsequently seen 2 weeks later when EEG was recorded while the child attended to a visual stimulus. The pattern of EEG activation computed from the session was significantly related to the child's behavior in the quartet session. Children who displayed social competence (high degree of social initiations and positive affect) exhibited greater relative left frontal activation, while children who displayed social withdrawal (isolated, onlooking, and unoccupied behavior) during the play session exhibited greater relative right frontal activation. Differences among children in frontal asymmetry were a function of power in the left frontal region. These EEG/behavior findings suggest that resting frontal asymmetry may be a marker for certain temperamental dispositions

Brief training in mindfulness may normalize a blunted error-related negativity in chronically depressed patients

This is the final version of the article. Available from the publisher via the DOI in this record.The error-related negativity (ERN), an evoked-potential that arises in response to the commission of errors, is an important early indicator of self-regulatory capacities. In this study we investigated whether brief mindfulness training can reverse ERN deficits in chronically depressed patients. The ERN was assessed in a sustained attention task. Chronically depressed patients (n = 59) showed significantly blunted expression of the ERN in frontocentral and frontal regions, relative to healthy controls (n = 18). Following two weeks of training, the patients (n = 24) in the mindfulness condition showed a significantly increased ERN magnitude in the frontal region, but there were no significant changes in patients who had received a resting control (n = 22). The findings suggest that brief training in mindfulness may help normalize aberrations in the ERN in chronically depressed patients, providing preliminary evidence for the responsiveness of this parameter to mental training.This research was funded by German Research Foundation Grant No. BA2255 3-1, awarded to T.B. T.B. was also supported by a Heisenberg Fellowship from the German Research Foundation (BA2255 2-1

Investigating the EEG Error-Related Negativity in College Students with ADHD, Anxiety, and Depression

Error-related negativity (ERN) is an event-related potential elicited by the commission of errors. It appears as a negative deflection peaking between 50ms and 100ms after an erroneous response. Previous literature demonstrated that individuals who suffer from either anxiety or depression display a higher ERN amplitude compared to a control group. It has also been shown that people with ADHD display a lower ERN amplitude. Based on these findings, we investigated the relationships between these three disorders and their effects on the amplitude of the ERN. We recruited thirty-one students at East Tennessee State University and gathered data on their level of anxiety, depression, and ADHD through completion of three surveys: the Beck Anxiety Inventory, Beck Depression Inventory, and the ADHD self-report scale. Subsequently, participants were asked to perform a modified Flanker task while their EEG was collected using a 32-channel EEG cap. ERN amplitude for error responses was significantly higher than ERN amplitude for correct responses. In contrast with previous literature, no significant influence on the ERN was observed due to anxiety, depression, or ADHD. Additional research on the topic with larger sample size and different diagnostic procedures may be necessary to further investigate the phenomenon