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Lechner S, Northoff G. Abnormal resting-state EEG phase dynamics distinguishes major depressive disorder and bipolar disorder. J Affect Disord 2024; 359:269-276. [PMID: 38795776 DOI: 10.1016/j.jad.2024.05.095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/22/2024] [Accepted: 05/18/2024] [Indexed: 05/28/2024]
Abstract
Changes in EEG have been reported in both major depressive disorder (MDD) and bipolar disorder (BD). Specifically, power changes in EEG alpha and theta frequency bands during rest and task are known in both disorders. This leaves open whether there are changes in yet another component of the electrophysiological EEG signal, namely phase-related processes that may allow for distinguishing MDD and BD. For that purpose, we investigate EEG-based spontaneous phase in the resting state of MDD, BD and healthy controls. Our main findings show: (i) decreased spontaneous phase variability in frontal theta of both MDD and BD compared to HC; (ii) decreased spontaneous phase variability in central-parietal alpha in MDD compared to both BD and HC; (iii) increased delays or lags of alpha phase cycles in MDD (but not in BD), which (iv) correlate with the decreased phase variability in MDD. Together, we show similar (decreased frontal theta variability) and distinct (decreased central-parietal alpha variability with increased lags or delays) findings in the spontaneous phase dynamics of MDD and BD. This suggests potential relevance of theta and alpha phase dynamics in distinguishing MDD and BD in clinical differential-diagnosis.
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Affiliation(s)
- Stephan Lechner
- The Royal's Institute of Mental Health Research, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1Z 7K4, Canada; Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, 1010 Vienna, Austria; Vienna Doctoral School Cognition, Behavior and Neuroscience, University of Vienna, 1030 Vienna, Austria.
| | - Georg Northoff
- The Royal's Institute of Mental Health Research, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1Z 7K4, Canada
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2
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Hu M, Zhang H, Ang KK. Brain Criticality EEG analysis for tracking neurodevelopment from Childhood to Adolescence. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082967 DOI: 10.1109/embc40787.2023.10340775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The brain criticality hypothesis suggests that neural networks and multiple aspects of brain activity self-organize into a critical state, and criticality marks the transition between ordered and disordered states. This hypothesis is appealing from computer science perspective because neural networks at criticality exhibit optimal processing and computing properties while having implications in clinical applications to neurological disorders. In this paper, we introduced brain criticality analysis to track neurodevelopment from childhood to adolescence using the electroencephalogram (EEG) data of 662 subjects aged 5 to 16 years from the Child Mind Institute. We computed brain criticality from long-range temporal correlation (LRTC) using detrended fluctuation analysis (DFA). We also compared the brain criticality analysis with standard EEG power analysis. The results showed a statistically significant increase in brain criticality from childhood to adolescence in the alpha band. A decreasing trend was observed in theta band from EEG power analysis, but a much higher variance was observed compared to the brain criticality analysis. However, the significant results were only observed in some EEG channels, and not observed if the analysis were performed separately with eyes-open and eyes-close condition. Nonetheless, the results suggest that brain criticality may serve as a biomarker of brain development and maturation, but further research is needed to improve brain criticality algorithms and EEG analysis methods.Clinical Relevance- The brain criticality analysis may be used to characterize and predict neurodevelopment in early childhood.
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3
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Perquin MN, van Vugt MK, Hedge C, Bompas A. Temporal Structure in Sensorimotor Variability: A Stable Trait, But What For? COMPUTATIONAL BRAIN & BEHAVIOR 2023; 6:1-38. [PMID: 36618326 PMCID: PMC9810256 DOI: 10.1007/s42113-022-00162-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/22/2022] [Indexed: 01/05/2023]
Abstract
Human performance shows substantial endogenous variability over time, and this variability is a robust marker of individual differences. Of growing interest to psychologists is the realisation that variability is not fully random, but often exhibits temporal dependencies. However, their measurement and interpretation come with several controversies. Furthermore, their potential benefit for studying individual differences in healthy and clinical populations remains unclear. Here, we gather new and archival datasets featuring 11 sensorimotor and cognitive tasks across 526 participants, to examine individual differences in temporal structures. We first investigate intra-individual repeatability of the most common measures of temporal structures - to test their potential for capturing stable individual differences. Secondly, we examine inter-individual differences in these measures using: (1) task performance assessed from the same data, (2) meta-cognitive ratings of on-taskness from thought probes occasionally presented throughout the task, and (3) self-assessed attention-deficit related traits. Across all datasets, autocorrelation at lag 1 and Power Spectra Density slope showed high intra-individual repeatability across sessions and correlated with task performance. The Detrended Fluctuation Analysis slope showed the same pattern, but less reliably. The long-term component (d) of the ARFIMA(1,d,1) model showed poor repeatability and no correlation to performance. Overall, these measures failed to show external validity when correlated with either mean subjective attentional state or self-assessed traits between participants. Thus, some measures of serial dependencies may be stable individual traits, but their usefulness in capturing individual differences in other constructs typically associated with variability in performance seems limited. We conclude with comprehensive recommendations for researchers. Supplementary Information The online version contains supplementary material available at 10.1007/s42113-022-00162-1.
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Affiliation(s)
- Marlou Nadine Perquin
- Biopsychology & Cognitive Neuroscience, Faculty of Psychology and Sports Science, Bielefeld University, Bielefeld, Germany
- Cognitive Neuroscience, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- CUBRIC, School of Psychology, Cardiff University, Cardiff, UK
| | - Marieke K. van Vugt
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
| | - Craig Hedge
- School of Psychology, College of Health & Life Sciences, Aston University, Aston, UK
| | - Aline Bompas
- CUBRIC, School of Psychology, Cardiff University, Cardiff, UK
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4
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Fosque LJ, Alipour A, Zare M, Williams-García RV, Beggs JM, Ortiz G. Quasicriticality explains variability of human neural dynamics across life span. Front Comput Neurosci 2022; 16:1037550. [PMID: 36532868 PMCID: PMC9747757 DOI: 10.3389/fncom.2022.1037550] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/27/2022] [Indexed: 08/26/2023] Open
Abstract
Aging impacts the brain's structural and functional organization and over time leads to various disorders, such as Alzheimer's disease and cognitive impairment. The process also impacts sensory function, bringing about a general slowing in various perceptual and cognitive functions. Here, we analyze the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state magnetoencephalography (MEG) dataset-the largest aging cohort available-in light of the quasicriticality framework, a novel organizing principle for brain functionality which relates information processing and scaling properties of brain activity to brain connectivity and stimulus. Examination of the data using this framework reveals interesting correlations with age and gender of test subjects. Using simulated data as verification, our results suggest a link between changes to brain connectivity due to aging and increased dynamical fluctuations of neuronal firing rates. Our findings suggest a platform to develop biomarkers of neurological health.
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Affiliation(s)
- Leandro J. Fosque
- Department of Physics, Indiana University, Bloomington, IN, United States
| | - Abolfazl Alipour
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | | | | | - John M. Beggs
- Department of Physics, Indiana University, Bloomington, IN, United States
| | - Gerardo Ortiz
- Department of Physics, Indiana University, Bloomington, IN, United States
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5
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O'Byrne J, Jerbi K. How critical is brain criticality? Trends Neurosci 2022; 45:820-837. [PMID: 36096888 DOI: 10.1016/j.tins.2022.08.007] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/27/2022] [Accepted: 08/10/2022] [Indexed: 10/31/2022]
Abstract
Criticality is the singular state of complex systems poised at the brink of a phase transition between order and randomness. Such systems display remarkable information-processing capabilities, evoking the compelling hypothesis that the brain may itself be critical. This foundational idea is now drawing renewed interest thanks to high-density data and converging cross-disciplinary knowledge. Together, these lines of inquiry have shed light on the intimate link between criticality, computation, and cognition. Here, we review these emerging trends in criticality neuroscience, highlighting new data pertaining to the edge of chaos and near-criticality, and making a case for the distance to criticality as a useful metric for probing cognitive states and mental illness. This unfolding progress in the field contributes to establishing criticality theory as a powerful mechanistic framework for studying emergent function and its efficiency in both biological and artificial neural networks.
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Affiliation(s)
- Jordan O'Byrne
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada; MILA (Quebec Artificial Intelligence Institute), Montreal, Quebec, Canada; UNIQUE Center (Quebec Neuro-AI Research Center), Montreal, Quebec, Canada.
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6
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Liu S, Liu X, Yan D, Chen S, Liu Y, Hao X, Ou W, Huang Z, Su F, He F, Ming D. Alterations in patients with first-episode depression in the eyes-open and eyes-closed conditions: A resting-state EEG study. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1019-1029. [PMID: 35412986 DOI: 10.1109/tnsre.2022.3166824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Altered resting-state EEG activity has been repeatedly reported in major depressive disorder (MDD), but no robust biomarkers have been identified until now. The poor consistency of EEG alterations may be due to inconsistent resting conditions; that is, the eyes-open (EO) and eyes-closed (EC) conditions. Here, we explored the effect of the EO and EC conditions on EEG biomarkers for discriminating MDD subjects and healthy control (HC) subjects. EEG data were recorded from 30 first-episode MDD and 26 HC subjects during an 8-min resting-state session. The features were extracted using spectral power, Lempel-Ziv complexity, and detrended fluctuation analysis. Significant features were further selected via the sequential backward feature selection algorithm. Support vector machine (SVM), logistic regression, and linear discriminate analysis were used to determine a better resting condition to provide more reliable estimates for identifying MDD. Compared with the HC group, we found that the MDD group exhibited widespread increased β and γ powers (p < 0.01) in both conditions. In the EO condition, the MDD group showed increased complexity and scaling exponents in the α band relative to HC subjects (p < 0.05). The best classification performance of the combined feature sets was found in the EO condition, with the leave-one-out classification accuracy of 89.29%, sensitivity of 90.00%, and specificity of 88.46% using SVM with the linear kernel classifier when the threshold was set to 0.7, followed by the β and γ spectral features with an average accuracy of 83.93%. Overall, EO and EC conditions indeed affected the between-group variance, and the EO condition is suggested as the more separable resting condition to identify depression. Specially, the β and γ powers are suggested as potential biomarkers for first-episode MDD.
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7
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Huang J, Ahlers E, Bogatsch H, Böhme P, Ethofer T, Fallgatter AJ, Gallinat J, Hegerl U, Heuser I, Hoffmann K, Kittel-Schneider S, Reif A, Schöttle D, Unterecker S, Gärtner M, Strauß M. The role of comorbid depressive symptoms on long-range temporal correlations in resting EEG in adults with ADHD. Eur Arch Psychiatry Clin Neurosci 2022; 272:1421-1435. [PMID: 35781841 PMCID: PMC9653316 DOI: 10.1007/s00406-022-01452-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/12/2022] [Indexed: 11/23/2022]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder, characterized by core symptoms of inattention, hyperactivity and impulsivity. Comorbid depression is commonly observed in ADHD-patients. Psychostimulants are recommended as first-line treatment for ADHD. Aberrant long-range temporal correlations (LRTCs) of neuronal activities in resting-state are known to be associated with disorganized thinking and concentrating difficulties (typical in ADHD) and with maladaptive thinking (typical in depression). It has yet to be examined whether (1) LRTC occur in ADHD-patients, and if so, (2) whether LRTC might be a competent biomarker in ADHD comorbid with current depression and (3) how depression affects psychostimulant therapy of ADHD symptoms. The present study registered and compared LRTCs in different EEG frequency bands in 85 adults with ADHD between groups with (n = 28) and without (n = 57) additional depressive symptoms at baseline. Treatment-related changes in ADHD, depressive symptoms and LRTC were investigated in the whole population and within each group. Our results revealed significant LRTCs existed in all investigated frequency bands. There were, however, no significant LRTC-differences between ADHD-patients with and without depressive symptoms at baseline and no LRTC-changes following treatment. However, depressed ADHD patients did seem to benefit more from the therapy with psychostimulant based on self-report.
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Affiliation(s)
- Jue Huang
- Department of Psychiatry and Psychotherapy, University of Leipzig, 04103, Leipzig, Germany.
| | - Eike Ahlers
- grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin, 10117 Berlin, Germany
| | - Holger Bogatsch
- grid.9647.c0000 0004 7669 9786Clinical Trial Centre Leipzig, Faculty of Medicine, University of Leipzig, 04107 Leipzig, Germany
| | - Pierre Böhme
- grid.411091.cDepartment of Psychiatry Psychotherapy and Preventive Medicine, University Hospital of Bochum, 44791 Bochum, Germany
| | - Thomas Ethofer
- grid.411544.10000 0001 0196 8249Department of Biomedical Magnetic Resonance, University Hospital of Tübingen, 72076 Tübingen, Germany ,grid.10392.390000 0001 2190 1447Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TüCMH), University of Tübingen, 72076 Tübingen, Germany
| | - Andreas J. Fallgatter
- grid.10392.390000 0001 2190 1447Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TüCMH), University of Tübingen, 72076 Tübingen, Germany
| | - Jürgen Gallinat
- grid.13648.380000 0001 2180 3484Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Ulrich Hegerl
- grid.411088.40000 0004 0578 8220Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital of Frankfurt – Goethe University, 60528 Frankfurt am Main, Germany
| | - Isabella Heuser
- grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin, 10117 Berlin, Germany
| | - Knut Hoffmann
- grid.411091.cDepartment of Psychiatry Psychotherapy and Preventive Medicine, University Hospital of Bochum, 44791 Bochum, Germany
| | - Sarah Kittel-Schneider
- grid.411088.40000 0004 0578 8220Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital of Frankfurt – Goethe University, 60528 Frankfurt am Main, Germany ,grid.411760.50000 0001 1378 7891Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Würzburg, 97080 Würzburg, Germany
| | - Andreas Reif
- grid.411088.40000 0004 0578 8220Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital of Frankfurt – Goethe University, 60528 Frankfurt am Main, Germany
| | - Daniel Schöttle
- grid.13648.380000 0001 2180 3484Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Stefan Unterecker
- grid.411760.50000 0001 1378 7891Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Würzburg, 97080 Würzburg, Germany
| | - Matti Gärtner
- grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin, 10117 Berlin, Germany ,grid.466457.20000 0004 1794 7698MSB Medical School Berlin, 14179 Berlin, Germany
| | - Maria Strauß
- grid.9647.c0000 0004 7669 9786Department of Psychiatry and Psychotherapy, University of Leipzig, 04103 Leipzig, Germany
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8
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Zimmern V. Why Brain Criticality Is Clinically Relevant: A Scoping Review. Front Neural Circuits 2020; 14:54. [PMID: 32982698 PMCID: PMC7479292 DOI: 10.3389/fncir.2020.00054] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 07/23/2020] [Indexed: 12/13/2022] Open
Abstract
The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience. The potential of criticality to explain various brain properties, including optimal information processing, has made it an increasingly exciting area of investigation for neuroscientists. Recent reviews on this topic, sometimes termed brain criticality, make brief mention of clinical applications of these findings to several neurological disorders such as epilepsy, neurodegenerative disease, and neonatal hypoxia. Other clinicallyrelevant domains - including anesthesia, sleep medicine, developmental-behavioral pediatrics, and psychiatry - are seldom discussed in review papers of brain criticality. Thorough assessments of these application areas and their relevance for clinicians have also yet to be published. In this scoping review, studies of brain criticality involving human data of all ages are evaluated for their current and future clinical relevance. To make the results of these studies understandable to a more clinical audience, a review of the key concepts behind criticality (e.g., phase transitions, long-range temporal correlation, self-organized criticality, power laws, branching processes) precedes the discussion of human clinical studies. Open questions and forthcoming areas of investigation are also considered.
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Affiliation(s)
- Vincent Zimmern
- Division of Child Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
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9
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Duncan NW, Hsu TY, Cheng PZ, Wang HY, Lee HC, Lane TJ. Intrinsic activity temporal structure reactivity to behavioural state change is correlated with depressive symptoms. Eur J Neurosci 2020; 52:4840-4850. [PMID: 32524682 DOI: 10.1111/ejn.14858] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 06/01/2020] [Accepted: 06/02/2020] [Indexed: 01/24/2023]
Abstract
The brain's intrinsic activity plays a fundamental role in its function. In normal conditions this activity is responsive to behavioural context, changing as an individual switches between directed tasks and task-free conditions. A key feature of such changes is the movement of the brain between corresponding critical and sub-critical states, with these dynamics supporting efficient cognitive processing. Breakdowns in processing efficiency can occur, however, in brain disorders such as depression. It was therefore hypothesised that depressive symptoms would be related to reduced intrinsic activity responsiveness to changes in behavioural state. This was tested in a mixed group of major depressive disorder patients (n = 26) and healthy participants (n = 37) by measuring intrinsic EEG activity temporal structure, quantified with detrended fluctuation analysis (DFA), in eyes-closed (EC) and eyes-open task-free states and contrasting between the conditions. The degree to which DFA values changed between the states was found to correlate negatively with depressive symptoms. DFA values did not differ between states in those with higher symptom levels, meaning that the brain remained in a less flexible sub-critical condition. This sub-critical condition in the EC state was further found to correlate with levels of maladaptive rumination. This may reflect a general cognitive inflexibility resulting from a lack in neural activity reactivity that may predispose people to overly engage in self-directed attention. These results provide an initial link between intrinsic activity reactivity and psychological features found in psychiatric disorders.
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Affiliation(s)
- Niall W Duncan
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Tzu-Yu Hsu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Paul Z Cheng
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Hsin-Yi Wang
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Department of Psychiatry, TMU Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Timothy J Lane
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City, Taiwan.,College of Humanities and Social Science, Taipei Medical University, Taipei, Taiwan
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10
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Wolff A, de la Salle S, Sorgini A, Lynn E, Blier P, Knott V, Northoff G. Atypical Temporal Dynamics of Resting State Shapes Stimulus-Evoked Activity in Depression-An EEG Study on Rest-Stimulus Interaction. Front Psychiatry 2019; 10:719. [PMID: 31681034 PMCID: PMC6803442 DOI: 10.3389/fpsyt.2019.00719] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 09/09/2019] [Indexed: 11/13/2022] Open
Abstract
Major depressive disorder (MDD) is a complex psychiatric disorder characterized by changes in both resting state and stimulus-evoked activity. Whether resting state changes are carried over to stimulus-evoked activity, however, is unclear. We conducted a combined rest (3 min) and task (three-stimulus auditory oddball paradigm) EEG study in n=28 acute depressed MDD patients, comparing them with n=25 healthy participants. Our focus was on the temporal dynamics of both resting state and stimulus-evoked activity for which reason we measured peak frequency (PF), coefficient of variation (CV), Lempel-Ziv complexity (LZC), and trial-to-trial variability (TTV). Our main findings are: i) atypical temporal dynamics in resting state, specifically in the alpha and theta bands as measured by peak frequency (PF), coefficient of variation (CV) and power; ii) decreased reactivity to external deviant stimuli as measured by decreased changes in stimulus-evoked variance and complexity-TTV, LZC, and power and frequency sliding (FS and PS); iii) correlation of stimulus related measures (TTV, LZC, PS, and FS) with resting state measures. Together, our findings show that resting state dynamics alone are atypical in MDD and, even more important, strongly shapes the dynamics of subsequent stimulus-evoked activity. We thus conclude that MDD can be characterized by an atypical temporal dynamic of its rest-stimulus interaction; that, in turn, makes it difficult for depressed patients to react to relevant stimuli such as the deviant tone in our paradigm.
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Affiliation(s)
- Annemnarie Wolff
- University of Ottawa Institute of Mental Health Research, Ottawa, ON, Canada
- Department of Cellular and Molecular Medicine and Neuroscience, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Sara de la Salle
- Department of Psychology, Faculty of Social Sciences, University of Ottawa, Ottawa, ON, Canada
| | - Alana Sorgini
- University of Ottawa Institute of Mental Health Research, Ottawa, ON, Canada
| | - Emma Lynn
- Department of Cellular and Molecular Medicine and Neuroscience, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Pierre Blier
- University of Ottawa Institute of Mental Health Research, Ottawa, ON, Canada
- Department of Cellular and Molecular Medicine and Neuroscience, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Verner Knott
- University of Ottawa Institute of Mental Health Research, Ottawa, ON, Canada
- Department of Cellular and Molecular Medicine and Neuroscience, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Department of Psychology, Faculty of Social Sciences, University of Ottawa, Ottawa, ON, Canada
| | - Georg Northoff
- University of Ottawa Institute of Mental Health Research, Ottawa, ON, Canada
- Department of Cellular and Molecular Medicine and Neuroscience, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
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11
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Moran JK, Michail G, Heinz A, Keil J, Senkowski D. Long-Range Temporal Correlations in Resting State Beta Oscillations are Reduced in Schizophrenia. Front Psychiatry 2019; 10:517. [PMID: 31379629 PMCID: PMC6659128 DOI: 10.3389/fpsyt.2019.00517] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 07/01/2019] [Indexed: 01/26/2023] Open
Abstract
Symptoms of schizophrenia (SCZ) are likely to be generated by genetically mediated synaptic dysfunction, which contribute to large-scale functional neural dysconnectivity. Recent electrophysiological studies suggest that this dysconnectivity is present not only at a spatial level but also at a temporal level, operationalized as long-range temporal correlations (LRTCs). Previous research suggests that alpha and beta frequency bands have weaker temporal stability in people with SCZ. This study sought to replicate these findings with high-density electroencephalography (EEG), enabling a spatially more accurate analysis of LRTC differences, and to test associations with characteristic SCZ symptoms and cognitive deficits. A 128-channel EEG was used to record eyes-open resting state brain activity of 23 people with SCZ and 24 matched healthy controls (HCs). LRTCs were derived for alpha (8-12 Hz) and beta (13-25 Hz) frequency bands. As an exploratory analysis, LRTC was source projected using sLoreta. People with SCZ showed an area of significantly reduced beta-band LRTC compared with HCs over bilateral posterior regions. There were no between-group differences in alpha-band activity. Individual symptoms of SCZ were not related to LRTC values nor were cognitive deficits. The study confirms that people with SCZ have reduced temporal stability in the beta frequency band. The absence of group differences in the alpha band may be attributed to the fact that people had, in contrast to previous studies, their eyes open in the current study. Taken together, our study confirms the utility of LRTC as a marker of network instability in people with SCZ and provides a novel empirical perspective for future examinations of network dysfunction salience in SCZ research.
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Affiliation(s)
- James K. Moran
- Department of Psychiatry and Psychotherapy, St. Hedwig Hospital, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Georgios Michail
- Department of Psychiatry and Psychotherapy, St. Hedwig Hospital, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, St. Hedwig Hospital, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Julian Keil
- Biological Psychology, Christian-Albrechts University Kiel, Kiel, Germany
| | - Daniel Senkowski
- Department of Psychiatry and Psychotherapy, St. Hedwig Hospital, Charité-Universitätsmedizin Berlin, Berlin, Germany
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12
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Vignaud P, Donde C, Sadki T, Poulet E, Brunelin J. Neural effects of mindfulness-based interventions on patients with major depressive disorder: A systematic review. Neurosci Biobehav Rev 2018; 88:98-105. [DOI: 10.1016/j.neubiorev.2018.03.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 01/19/2018] [Accepted: 03/06/2018] [Indexed: 12/16/2022]
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