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Zhang W, Zhao C, Tang F, Luo W. Automatic Positive and Negative Emotion Regulation in Adolescents with Major Depressive Disorder. Psychopathology 2023; 57:111-122. [PMID: 37647878 DOI: 10.1159/000533334] [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: 02/04/2023] [Accepted: 07/28/2023] [Indexed: 09/01/2023]
Abstract
INTRODUCTION Adolescents with major depressive disorder (MDD) exhibit hypoactivity to positive stimuli and hyperactivity to negative stimuli in terms of neural responses. Automatic emotion regulation (AER) activates triple networks (i.e., the central control network, default mode network, and salience network). Based on previous studies, we hypothesized that adolescents with MDD exhibit dissociable spatiotemporal deficits during positive and negative AER. METHODS We first collected EEG data from 32 adolescents with MDD and 35 healthy adolescents while they performed an implicit emotional Go/NoGo task. Then, we characterized the spatiotemporal dynamics of cortical activity during AER. RESULTS In Go trials, MDD adolescents exhibited reduced N2 amplitudes, enhanced theta power for positive pictures, and stronger bottom-up information flow from the left orbitofrontal cortex (OFC) to the right superior frontal gyrus compared to top-down information flow than the controls. In contrast, in NoGo trials, MDD adolescents exhibited elevated P3 amplitudes, enhanced theta power, and stronger top-down information flows from the right middle frontal gyrus to the right OFC and the left insula than the controls. CONCLUSION Overall, adolescents with MDD exhibited impaired automatic attention to positive emotions and impaired automatic response inhibition. These findings have potential implications for the clinical treatment of adolescents with MDD.
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Affiliation(s)
- Wenhai Zhang
- Mental Health Center, Yancheng Institute of Technology, Yancheng, China
- The Big Data Centre for Neuroscience and AI, Hengyang Normal University, Hengyang, China
| | - Cancan Zhao
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China
- School of Psychology, Shandong Normal University, Jinan, China
| | - Fanggui Tang
- The Big Data Centre for Neuroscience and AI, Hengyang Normal University, Hengyang, China
| | - Wenbo Luo
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China
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Fernandez-Del-Olmo M, Sánchez-Molina JA, Novo-Ponte S, Fogelson N. Directed connectivity in Parkinson's disease patients during over-ground and treadmill walking. Exp Gerontol 2023; 178:112220. [PMID: 37230335 DOI: 10.1016/j.exger.2023.112220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/17/2023] [Accepted: 05/22/2023] [Indexed: 05/27/2023]
Abstract
Treadmill walking is considered a useful therapeutic tool for improving gait in Parkinson's disease (PD) patients. The study investigated the role of top-down, frontal-parietal versus bottom-up parietal-frontal networks, during over-ground and treadmill walking in PD and control subjects, using functional connectivity. To this end, EEG was recorded simultaneously, during a ten-minute period of continuous walking either over-ground or on a treadmill, in thirteen PD patients and thirteen age-matched controls. We evaluated EEG directed connectivity, using phase transfer entropy in three frequency bands: theta, alpha and beta. PD patients showed increased top-down connectivity during over-ground compared with treadmill walking, in the beta frequency range. Control subjects showed no significant differences in connectivity between the two walking conditions. Our results suggest that in PD patients, OG walking was associated with increased allocation of attentional resources, compared with that on the TL. These functional connectivity modulations may shed further light on the mechanisms underlying treadmill versus overground walking in PD.
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Affiliation(s)
| | | | - Sabela Novo-Ponte
- Department of Neurology, Hospital Universitario Puerta de Hierro, Majadahonda, Madrid, Spain
| | - Noa Fogelson
- Department of Humanities, University Rey Juan Carlos, Madrid, Spain.
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Alterations in EEG functional connectivity in individuals with depression: A systematic review. J Affect Disord 2023; 328:287-302. [PMID: 36801418 DOI: 10.1016/j.jad.2023.01.126] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 01/22/2023] [Accepted: 01/30/2023] [Indexed: 02/19/2023]
Abstract
The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.
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Teng C, Wang M, Wang W, Ma J, Jia M, Wu M, Luo Y, Wang Y, Zhang Y, Xu J. Abnormal Properties of Cortical Functional Brain Network in Major Depressive Disorder: Graph Theory Analysis Based on Electroencephalography-Source Estimates. Neuroscience 2022; 506:80-90. [PMID: 36272697 DOI: 10.1016/j.neuroscience.2022.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/04/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022]
Abstract
Studies of scalp electroencephalography (EEG) had shown altered topological organization of functional brain networks in patients with major depressive disorder (MDD). However, most previous EEG-based network analyses were performed at sensor level, while the interpretation of obtained results was not straightforward due to volume conduction effect. To reduce the impact of this defect, the whole cortical functional brain networks of MDD patients were studied during resting state based on EEG-source estimates in this paper. First, scalp EEG signals were recorded from 19 patients with MDD and 20 normal controls under resting eyes-closed state, and cortical neural signals were estimated by using sLORETA method. Then, the correntropy coefficient of wavelet packet coefficients was performed to calculate functional connectivity (FC) matrices in four different frequency bands: δ, θ, α, β, respectively. Afterwards, topological properties of brain networks were analyzed by graph theory approaches. The results showed that the global FC strength of MDD patients was significantly higher than that of healthy subjects in α band. Also, it was found that MDD patients have abnormally increased clustering coefficient and local efficiency in both α and β bands compared to normal people. Furthermore, patients with MDD exhibited increased nodal clustering coefficients in the left lingual gryus and left precuneus in α band. In addition, β band global clustering coefficient was positively correlated with the scores of depression severity. Therefore, the findings indicated the cortical functional brain networks in MDD patients were disruptions, which suggested it would be one of potential causes of depression.
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Affiliation(s)
- Chaolin Teng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China; Department of Aerospace Medicine, The Air Force Medical University, Xi'an, Shaanxi 710068, PR China
| | - Mengwei Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Wei Wang
- Department of Psychiatry, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, PR China
| | - Jin Ma
- Department of Aerospace Medicine, The Air Force Medical University, Xi'an, Shaanxi 710068, PR China
| | - Min Jia
- Department of Psychiatry, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, PR China
| | - Min Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Yuanyuan Luo
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China; Department of Psychology, Xi'an Mental Health Center, Xi'an, Shaanxi 710061, PR China
| | - Yu Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Yiyang Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Jin Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China.
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Sun S, Liu L, Shao X, Yan C, Li X, Hu B. Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1705-1715. [PMID: 35759580 DOI: 10.1109/tnsre.2022.3181690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.
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Fogelson N, Diaz-Brage P. Altered directed connectivity during processing of predictive stimuli in psychiatric patient populations. Clin Neurophysiol 2021; 132:2739-2750. [PMID: 34571367 DOI: 10.1016/j.clinph.2021.07.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 07/06/2021] [Accepted: 07/20/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES The study investigated the role of top-down versus bottom-up connectivity, during the processing of predictive information, in three different psychiatric disorders. METHODS Electroencephalography (EEG) was recorded during the performance of a task, which evaluates the ability to use predictive information in order to facilitate predictable versus random target detection. We evaluated EEG event-related directed connectivity, in patients with schizophrenia (SZ), major depressive disorder (MDD), and autism spectrum disorder (ASD), compared with healthy age-matched controls. Directed connectivity was evaluated using phase transfer entropy. RESULTS We showed that top-down frontal-parietal connectivity was weaker in SZ (theta and beta bands) and ASD (alpha band) compared to control subjects, during the processing of stimuli consisting of the predictive sequence. In SZ patients, top-down connectivity was also attenuated, during the processing of predictive targets in the beta frequency band. In contrast, compared with controls, MDD patients displayed an increased top-down flow of information, during the processing of predicted targets (alpha band). CONCLUSIONS The findings suggest that top-down frontal-parietal connectivity is altered differentially across three major psychiatric disorders, specifically during the processing of predictive stimuli. SIGNIFICANCE Altered top-down connectivity may contribute to the specific prediction deficits observed in each of the patient populations.
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Affiliation(s)
- Noa Fogelson
- EEG and Cognition Laboratory, Department of Humanities, University Rey Juan Carlos, Madrid, Spain.
| | - Pablo Diaz-Brage
- EEG and Cognition Laboratory, Department of Humanities, University Rey Juan Carlos, Madrid, Spain
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Járdánházy A, Járdánházy T. The effect of photic stimulation alone and in combination with sleep deprivation after a seizure-like event - reappraisal by using linear and nonlinear EEG methods. Neurol Res 2021; 44:104-111. [PMID: 34334110 DOI: 10.1080/01616412.2021.1961186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
ObjectivesThe present study aimed to compare the effectiveness of different provocation tests used for the study of the 'susceptibility to seizure' by quantitative electroencephalography (EEG) analysis.MethodsEight subjects with a history of a seizure-like disturbed consciousness participated in this preliminary study. A routine EEG was carried out with photic stimulation (eyes closed and after eyes open) at the beginning of the investigation. Some days later, a sleep-deprived EEG was recorded with the same protocol. Selected epochs (in eyes closed condition) after the stimulations were analysed with Point(wise) Correlation Dimension (PD2i) and Synchronization Likelihood (SL) methods. The results were compared to those obtained by similar analysis of the resting state (control) epochs with Wilcoxon Signed Rank Test (p ≤ 0.05).ResultsIn our study, significantly lower grand mean PD2i and higher delta SL values were found in sleep-deprived state after stimulation with eyes closed compared to the control. Our results indicated a lower-dimensional, hypersynchronous state of the brain as a consequence of these combined provocations.DiscussionThis may correspond to a possible 'preictal' state of the brain. Accordingly, it is suggested that photic stimulation together with sleep deprivation seems to be more effective in provocation - especially when the stimulation was made with eyes closed.
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Altered directed connectivity during processing of implicit versus explicit predictive stimuli in Parkinson's disease patients. Brain Cogn 2021; 152:105773. [PMID: 34225173 DOI: 10.1016/j.bandc.2021.105773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/06/2021] [Accepted: 06/25/2021] [Indexed: 11/22/2022]
Abstract
The study investigated the role of top-down versus bottom-up connectivity, during the processing of implicit or explicit predictive information, in Parkinson's disease (PD). EEG was recorded during the performance of a task, which evaluated the ability to utilize either implicit or explicit predictive contextual information in order to facilitate the detection of predictable versus random targets. Thus, subjects performed an implicit and explicit session, where subjects were either unaware or made aware of a predictive sequence that signals the presentation of a subsequent target, respectively. We evaluated EEG event-related directed connectivity, in PD patients compared with healthy age-matched controls, using phase transfer entropy. PD patients showed increased top-down frontal-parietal connectivity, compared to control subjects, during the processing of the last (most informative) stimulus of the predictive sequence and of random standards, in the implicit and explicit session, respectively. These findings suggest that PD is associated with compensatory top-down connectivity, specifically during the processing of implicit predictive stimuli. During the explicit session, PD patients seem to allocate more attentional resources to non-informative standard stimuli, compared to controls. These connectivity changes shed further light on the cognitive deficits, associated with the processing of predictive contextual information, that are observed in PD patients.
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