Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review

Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly based on subjective assessments and self-reported measures. However, objective criteria as Electroencephalography (EEG) features would be helpful in detecting depressive states at early stages to prevent the worsening of the symptoms. Scientific community has widely investigated the effectiveness of EEG-based measures to discriminate between depressed and healthy subjects, with the aim to better understand the mechanisms behind the disorder and find biomarkers useful for diagnosis. This work offers a comprehensive review of the extant literature concerning the EEG-based biomarkers for MDD and its subtypes, and identify possible future directions for this line of research. Scopus, PubMed and Web of Science databases were researched following PRISMA’s guidelines. The initial papers’ screening was based on titles and abstracts; then full texts of the identified articles were examined, and a synthesis of findings was developed using tables and thematic analysis. After screening 1871 articles, 76 studies were identified as relevant and included in the systematic review. Reviewed markers include EEG frequency bands power, EEG asymmetry, ERP components, non-linear and functional connectivity measures. Results were discussed in relations to the different EEG measures assessed in the studies. Findings confirmed the effectiveness of those measures in discriminating between healthy and depressed subjects. However, the review highlights that the causal link between EEG measures and depressive subtypes needs to be further investigated and points out that some methodological issues need to be solved to enhance future research in this field

Complexity analysis of spontaneous brain activity: effects of depression and antidepressant treatment

Magnetoencephalography (MEG) allows the real-time recording of neural activity and oscillatory activity in distributed neural networks. We applied a non-linear complexity analysis to resting-state neural activity as measured using whole-head MEG. Recordings were obtained from 20 unmedicated patients with major depressive disorder and 19 matched healthy controls. Subsequently, after 6 months of pharmacological treatment with the antidepressant mirtazapine 30 mg/day, patients received a second MEG scan. A measure of the complexity of neural signals, the Lempel–Ziv Complexity (LZC), was derived from the MEG time series. We found that depressed patients showed higher pre-treatment complexity values compared with controls, and that complexity values decreased after 6 months of effective pharmacological treatment, although this effect was statistically significant only in younger patients. The main treatment effect was to recover the tendency observed in controls of a positive correlation between age and complexity values. Importantly, the reduction of complexity with treatment correlated with the degree of clinical symptom remission. We suggest that LZC, a formal measure of neural activity complexity, is sensitive to the dynamic physiological changes observed in depression and may potentially offer an objective marker of depression and its remission after treatment

Temporal and spatial neural dynamics in the perception of basic emotions from complex scenes

The different temporal dynamics of emotions are critical to understand their evolutionary role in the regulation of interactions with the surrounding environment. Here, we investigated the temporal dynamics underlying the perception of four basic emotions from complex scenes varying in valence and arousal (fear, disgust, happiness and sadness) with the millisecond time resolution of Electroencephalography (EEG). Event-related potentials were computed and each emotion showed a specific temporal profile, as revealed by distinct time segments of significant differences from the neutral scenes. Fear perception elicited significant activity at the earliest time segments, followed by disgust, happiness and sadness. Moreover, fear, disgust and happiness were characterized by two time segments of significant activity, whereas sadness showed only one long-latency time segment of activity. Multidimensional scaling was used to assess the correspondence between neural temporal dynamics and the subjective experience elicited by the four emotions in a subsequent behavioral task. We found a high coherence between these two classes of data, indicating that psychological categories defining emotions have a close correspondence at the brain level in terms of neural temporal dynamics. Finally, we localized the brain regions of time-dependent activity for each emotion and time segment with the low-resolution brain electromagnetic tomography. Fear and disgust showed widely distributed activations, predominantly in the right hemisphere. Happiness activated a number of areas mostly in the left hemisphere, whereas sadness showed a limited number of active areas at late latency. The present findings indicate that the neural signature of basic emotions can emerge as the byproduct of dynamic spatiotemporal brain networks as investigated with millisecond-range resolution, rather than in time-independent areas involved uniquely in the processing one specific emotion. Keywords: basic emotions, EEG, LORETA, ERP, IAPS, time, rapid perceptio

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

<p>Abstract</p> <p>Background</p> <p>The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact.</p> <p>Methods</p> <p>Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls.</p> <p>Results</p> <p>Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (<it>P </it>< 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz).</p> <p>Conclusions</p> <p>Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.</p

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Deconstructing the “Resting” State: Exploring the Temporal Dynamics of Frontal Alpha Asymmetry as an Endophenotype for Depression

Asymmetry in frontal electrocortical alpha-band (8–13 Hz) activity recorded during resting situations (i.e., in absence of a specific task) has been investigated in relation to emotion and depression for over 30 years. This asymmetry reflects an aspect of endogenous cortical dynamics that is stable over repeated measurements and that may serve as an endophenotype for mood or other psychiatric disorders. In nearly all of this research, EEG activity is averaged across several minutes, obscuring transient dynamics that unfold on the scale of milliseconds to seconds. Such dynamic states may ultimately have greater value in linking brain activity to surface EEG asymmetry, thus improving its status as an endophenotype for depression. Here we introduce novel metrics for characterizing frontal alpha asymmetry that provide a more in-depth neurodynamical understanding of recurrent endogenous cortical processes during the resting-state. The metrics are based on transient “bursts” of asymmetry that occur frequently during the resting-state. In a sample of 306 young adults, 143 with a lifetime diagnosis of major depressive disorder (62 currently symptomatic), three questions were addressed: (1) How do novel peri-burst metrics of dynamic asymmetry compare to conventional fast-Fourier transform-based metrics? (2) Do peri-burst metrics adequately differentiate depressed from non-depressed participants? and, (3) what EEG dynamics surround the asymmetry bursts? Peri-burst metrics correlated with traditional measures of asymmetry, and were sensitive to both current and past episodes of major depression. Moreover, asymmetry bursts were characterized by a transient lateralized alpha suppression that is highly consistent in phase across bursts, and a concurrent contralateral transient alpha enhancement that is less tightly phase-locked across bursts. This approach opens new possibilities for investigating rapid cortical dynamics during resting-state EEG

Der Einfluss von präfrontaler Gleichstromstimulation (tDCS) auf EEG- und fMRT-Ruhenetzwerke

Das Hauptziel der vorliegenden Dissertation war die Untersuchung des Einflusses der präfrontalen Gleichstromstimulation (tDCS) auf die mögliche Modulation kortikaler Netzwerke. Grundlage dieser kumulativen Dissertation sind die Publikationen: - Keeser D, Padberg F, Reisinger E, Pogarell O, Kirsch V, Palm U, Karch S, Möller HJ, Nitsche MA, Mulert C. Prefrontal direct current stimulation modulates resting EEG and event-related potentials in healthy subjects: a standardized low resolution tomography (sLORETA) study. Neuroimage. 2011 Mar 15;55(2):644-57. - Keeser D, Meindl T, Bor J, Palm U, Pogarell O, Mulert C, Brunelin J, Möller HJ, Reiser M, Padberg F. Prefrontal Transcranial Direct Current Stimulation Changes Connectivity of Resting-State Networks during fMRI. Journal of Neuroscience. 2011 Oct 26;31(43):15284-93. Beide Studien wurden doppelt-verblindet und plazebo-kontrolliert durchgeführt. In den Arbeiten wird mit zwei unterschiedlichen Verfahren, einem neurophysiolo-gischen Ruhe- und einem aktiven Gedächtnistestparadigma (EEG), sowie mit einer funktionellen Konnektivitäts-Magnetresonanztomographie (fcMRT) nachgewiesen, dass präfrontale tDCS kortikale Netzwerke moduliert. Diese Ergebnisse sollen hier wiedergegeben und diskutiert werden. Die Verteilung, Ausrichtung und das Aus-maß der auf tDCS beruhenden Effekte auf die Gehirnphysiologie sind bisher wenig erforscht. Die Erarbeitung weiterer spezifischer Hypothesen bezüglich der neuro-physiologischen Wirkung von präfrontaler tDCS ist entscheidend, um Hinweise auf künftige experimentelle und therapeutische tDCS-Anwendungen zu erhalten.The principal purpose of the present thesis was to investigate the influence of prefrontal direct current stimulation (tDCS) on the modulation of cortical networks. The bases of this cumulative thesis are the two publications: - Keeser D, Padberg F, Reisinger E, Pogarell O, Kirsch V, Palm U, Karch S, Möller HJ, Nitsche MA, Mulert C. Prefrontal direct current stimulation modulates resting EEG and event-related potentials in healthy subjects: a standardized low resolution tomography (sLORETA) study. Neuroimage. 2011 Mar 15;55(2):644-57. - Keeser D, Meindl T, Bor J, Palm U, Pogarell O, Mulert C, Brunelin J, Möller HJ, Reiser M, Padberg F. Prefrontal Transcranial Direct Current Stimulation Changes Connectivity of Resting-State Networks during fMRI. Journal of Neuroscience. 2011 Oct 26;31(43):15284-93. Both studies were carried out in a double-blinded, placebo-controlled manner. In the studies two different procedures, a neurophysiological electroencephalog-raphy (EEG) resting-state and an active EEG memory task paradigm, as well as a functional connectivity magnetic resonance imaging (fcMRI) procedure were used. Both studies proved that prefrontal tDCS modulates cortical networks. These results are presented and discussed. The distribution, direction, and extent of tDCS mediated effects on brain physiology are not well understood. The development of further hypotheses with regard to the neurophysiological effects of prefrontal tDCS is crucial to obtain informations for future experimental and therapeutic tDCS applica-tions