Atypical Temporal Dynamics of Resting State Shapes Stimulus-Evoked Activity in Depression—An EEG Study on Rest–Stimulus Interaction

Wolff, Annemnarie; Sorgini, Alana; Lynn, Emma; Blier, Pierre; Knott, Verner; Northoff, Georg

doi:10.3389/fpsyt.2019.00719

Cited by 36 publications

(30 citation statements)

References 105 publications

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“…Greater resting-state values of LZC on the whole scalp in MDD patients were also observed by Bachmann et al [117]. Wolff et al [118] analyzed LZC values from EEG recorded during an auditory oddball paradigm, finding that LZC changes after stimulus onset were significantly lower in MDD group, but for deviant stimuli only. Cukic and colleagues [119] compared the HFD and SampEn values at resting-state in patients suffering from acute depressive episode, patients in remission and healthy participants.…”

Section: ) Eeg Complexity Metricsmentioning

confidence: 63%

Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review

et al. 2021

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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.

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Section: ) Eeg Complexity Metricsmentioning

confidence: 63%

Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review

et al. 2021

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“…The second component of ongoing activity can be found in pre-stimulus activity, that is, neural activity immediately preceding the onset of a specific stimulus or task, that is, 100–1000 ms before the onset of the stimulus [ 52 , 53 , 54 , 55 , 56 , 57 ]. This can be coined as the ‘temporo-spatial context’ as it precedes stimulus-induced activity but provides a temporo-spatial context for the latter.…”

Section: Neuronal Mechanisms—from Pre-stimulus To Stimulus-induced Ac...mentioning

confidence: 99%

From Shorter to Longer Timescales: Converging Integrated Information Theory (IIT) with the Temporo-Spatial Theory of Consciousness (TTC)

Northoff

Zilio

2022

Entropy

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Time is a key element of consciousness as it includes multiple timescales from shorter to longer ones. This is reflected in our experience of various short-term phenomenal contents at discrete points in time as part of an ongoing, more continuous, and long-term ‘stream of consciousness.’ Can Integrated Information Theory (IIT) account for this multitude of timescales of consciousness? According to the theory, the relevant spatiotemporal scale for consciousness is the one in which the system reaches the maximum cause-effect power; IIT currently predicts that experience occurs on the order of short timescales, namely, between 100 and 300 ms (theta and alpha frequency range). This can well account for the integration of single inputs into a particular phenomenal content. However, such short timescales leave open the temporal relation of specific phenomenal contents to others during the course of the ongoing time, that is, the stream of consciousness. For that purpose, we converge the IIT with the Temporo-spatial Theory of Consciousness (TTC), which, assuming a multitude of different timescales, can take into view the temporal integration of specific phenomenal contents with other phenomenal contents over time. On the neuronal side, this is detailed by considering those neuronal mechanisms driving the non-additive interaction of pre-stimulus activity with the input resulting in stimulus-related activity. Due to their non-additive interaction, the single input is not only integrated with others in the short-term timescales of 100–300 ms (alpha and theta frequencies) (as predicted by IIT) but, at the same time, also virtually expanded in its temporal (and spatial) features; this is related to the longer timescales (delta and slower frequencies) that are carried over from pre-stimulus to stimulus-related activity. Such a non-additive pre-stimulus-input interaction amounts to temporo-spatial expansion as a key mechanism of TTC for the constitution of phenomenal contents including their embedding or nesting within the ongoing temporal dynamic, i.e., the stream of consciousness. In conclusion, we propose converging the short-term integration of inputs postulated in IIT (100–300 ms as in the alpha and theta frequency range) with the longer timescales (in delta and slower frequencies) of temporo-spatial expansion in TTC.

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“…For the first choice, an EEGLAB based automatic artifact rejection pipeline ADJUST, developed by Mognon et al was used [54]. The first step in this method was to run independent component analysis (ICA) and extract different components of the signals [39,55]. To properly decompose the components, Onton et al suggested that the number of extracted components, n should be such that n 2 k = number of time points, k 20 [56].…”

Section: Eeg Preprocessingmentioning

confidence: 99%

“…However, this time, the filters were band-pass filters instead of a notch filter. Similar to the methods employed by Hussain et al and Wolff et al, the filtered signals of the four frequency bands were Hilbert's transformed to obtain phases (in degrees) from −750 ms to 0 ms [18,47,55]. The shorter time window of length 250 ms was not selected during phase estimation because a signal of length 250 ms will contain only 0.6 to 1.2 cycles of the theta frequency band potentially resulting in inaccurate phases.…”

Section: Frequency Band Phase Estimationmentioning

confidence: 99%

Differences in Power Spectral Densities and Phase Quantities Due to Processing of EEG Signals

Alam

Zhao

Goodwin

et al. 2020

Sensors

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There has been a growing interest in computational electroencephalogram (EEG) signal processing in a diverse set of domains, such as cortical excitability analysis, event-related synchronization, or desynchronization analysis. In recent years, several inconsistencies were found across different EEG studies, which authors often attributed to methodological differences. However, the assessment of such discrepancies is deeply underexplored. It is currently unknown if methodological differences can fully explain emerging differences and the nature of these differences. This study aims to contrast widely used methodological approaches in EEG processing and compare their effects on the outcome variables. To this end, two publicly available datasets were collected, each having unique traits so as to validate the results in two different EEG territories. The first dataset included signals with event-related potentials (visual stimulation) from 45 subjects. The second dataset included resting state EEG signals from 16 subjects. Five EEG processing steps, involved in the computation of power and phase quantities of EEG frequency bands, were explored in this study: artifact removal choices (with and without artifact removal), EEG signal transformation choices (raw EEG channels, Hjorth transformed channels, and averaged channels across primary motor cortex), filtering algorithms (Butterworth filter and Blackman–Harris window), EEG time window choices (−750 ms to 0 ms and −250 ms to 0 ms), and power spectral density (PSD) estimation algorithms (Welch’s method, Fast Fourier Transform, and Burg’s method). Powers and phases estimated by carrying out variations of these five methods were analyzed statistically for all subjects. The results indicated that the choices in EEG transformation and time-window can strongly affect the PSD quantities in a variety of ways. Additionally, EEG transformation and filter choices can influence phase quantities significantly. These results raise the need for a consistent and standard EEG processing pipeline for computational EEG studies. Consistency of signal processing methods cannot only help produce comparable results and reproducible research, but also pave the way for federated machine learning methods, e.g., where model parameters rather than data are shared.

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Atypical Temporal Dynamics of Resting State Shapes Stimulus-Evoked Activity in Depression—An EEG Study on Rest–Stimulus Interaction

Cited by 36 publications

References 105 publications

Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review

Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review

From Shorter to Longer Timescales: Converging Integrated Information Theory (IIT) with the Temporo-Spatial Theory of Consciousness (TTC)

Differences in Power Spectral Densities and Phase Quantities Due to Processing of EEG Signals

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