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Berridge CW, Devilbiss DM, Martin AJ, Spencer RC, Jenison RL. Stress degrades working memory-related frontostriatal circuit function. Cereb Cortex 2023; 33:7857-7869. [PMID: 36935095 PMCID: PMC10267631 DOI: 10.1093/cercor/bhad084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/20/2023] Open
Abstract
Goal-directed behavior is dependent on neuronal activity in the prefrontal cortex (PFC) and extended frontostriatal circuitry. Stress and stress-related disorders are associated with impaired frontostriatal-dependent cognition. Our understanding of the neural mechanisms that underlie stress-related cognitive impairment is limited, with the majority of prior research focused on the PFC. To date, the actions of stress across cognition-related frontostriatal circuitry are unknown. To address this gap, the current studies examined the effects of acute noise-stress on the spiking activity of neurons and local field potential oscillatory activity within the dorsomedial PFC (dmPFC) and dorsomedial striatum (dmSTR) in rats engaged in a test of spatial working memory. Stress robustly suppressed responses of both dmPFC and dmSTR neurons strongly tuned to key task events (delay, reward). Additionally, stress strongly suppressed delay-related, but not reward-related, theta and alpha spectral power within, and synchrony between, the dmPFC and dmSTR. These observations provide the first demonstration that stress disrupts the neural coding and functional connectivity of key task events, particularly delay, within cognition-supporting dorsomedial frontostriatal circuitry. These results suggest that stress-related degradation of neural coding within both the PFC and striatum likely contributes to the cognition-impairing effects of stress.
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Affiliation(s)
- Craig W Berridge
- Department of Psychology, University of Wisconsin, Madison, WI 53706, United States
| | | | - Andrea J Martin
- Department of Psychology, University of Wisconsin, Madison, WI 53706, United States
| | - Robert C Spencer
- Department of Psychology, University of Wisconsin, Madison, WI 53706, United States
| | - Rick L Jenison
- Department of Psychology, University of Wisconsin, Madison, WI 53706, United States
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2
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Liu H, Huang Z, Deng B, Chang Z, Yang X, Guo X, Yuan F, Yang Q, Wang L, Zou H, Li M, Zhu Z, Jin K, Wang Q. QEEG Signatures are Associated with Nonmotor Dysfunctions in Parkinson's Disease and Atypical Parkinsonism: An Integrative Analysis. Aging Dis 2023; 14:204-218. [PMID: 36818554 PMCID: PMC9937709 DOI: 10.14336/ad.2022.0514] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 05/14/2022] [Indexed: 11/18/2022] Open
Abstract
Parkinson's disease (PD) and atypical parkinsonism (AP), including progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), share similar nonmotor symptoms. Quantitative electroencephalography (QEEG) can be used to examine the nonmotor symptoms. This study aimed to characterize the patterns of QEEG and functional connectivity (FC) that differentiate PD from PSP or MSA, and explore the correlation between the differential QEEG indices and nonmotor dysfunctions in PD and AP. We enrolled 52 patients with PD, 31 with MSA, 22 with PSP, and 50 age-matched health controls to compare QEEG indices among specific brain regions. One-way analysis of variance was applied to assess QEEG indices between groups; Spearman's correlations were used to examine the relationship between QEEG indices and nonmotor symptoms scale (NMSS) and mini-mental state examination (MMSE). FCs using weighted phase lag index were compared between patients with PD and those with MSA/PSP. Patients with PSP revealed higher scores on the NMSS and lower MMSE scores than those with PD and MSA, with similar disease duration. The delta and theta powers revealed a significant increase in PSP, followed by PD and MSA. Patients with PD presented a significantly lower slow-to-fast ratio than those with PSP in the frontal region, while patients with PD presented significantly higher EEG-slowing indices than patients with MSA. The frontal slow-to-fast ratio showed a negative correlation with MMSE scores in patients with PD and PSP, and a positive correlation with NMSS in the perception and mood domain in patients with PSP but not in those with PD. Compared to PD, MSA presented enhanced FC in theta and delta bands in the posterior region, while PSP revealed decreased FC in the delta band within the frontal-temporal cortex. These findings suggest that QEEG might be a useful tool for evaluating the nonmotor dysfunctions in PD and AP. Our QEEG results suggested that with similar disease duration, the cortical neurodegenerative process was likely exacerbated in patients with PSP, followed by those with PD, and lastly in patients with MSA.
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Affiliation(s)
- Hailing Liu
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China.,Department of Neurology, Maoming People's Hospital, Maoming, Guangdong, China.
| | - Zifeng Huang
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China.
| | - Bin Deng
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China.
| | - Zihan Chang
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China.
| | - Xiaohua Yang
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China.
| | - Xingfang Guo
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China.
| | - Feilan Yuan
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China.
| | - Qin Yang
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China.
| | - Liming Wang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Haiqiang Zou
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangdong, China.
| | - Mengyan Li
- Department of Neurology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
| | - Zhaohua Zhu
- Clinical Research Centre, Orthopedic Centre, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China.
| | - Kunlin Jin
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX 76107, USA
| | - Qing Wang
- Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China.,Correspondence should be addressed to: Dr. Qing Wang, Department of Neurology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, China. .
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Li Q, Coulson Theodorsen M, Konvalinka I, Eskelund K, Karstoft KI, Bo Andersen S, Andersen TS. Resting-state EEG functional connectivity predicts post-traumatic stress disorder subtypes in veterans. J Neural Eng 2022; 19. [PMID: 36250685 DOI: 10.1088/1741-2552/ac9aaf] [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: 07/01/2022] [Accepted: 10/13/2022] [Indexed: 01/11/2023]
Abstract
Objective. Post-traumatic stress disorder (PTSD) is highly heterogeneous, and identification of quantifiable biomarkers that could pave the way for targeted treatment remains a challenge. Most previous electroencephalography (EEG) studies on PTSD have been limited to specific handpicked features, and their findings have been highly variable and inconsistent. Therefore, to disentangle the role of promising EEG biomarkers, we developed a machine learning framework to investigate a wide range of commonly used EEG biomarkers in order to identify which features or combinations of features are capable of characterizing PTSD and potential subtypes.Approach. We recorded 5 min of eyes-closed and 5 min of eyes-open resting-state EEG from 202 combat-exposed veterans (53% with probable PTSD and 47% combat-exposed controls). Multiple spectral, temporal, and connectivity features were computed and logistic regression, random forest, and support vector machines with feature selection methods were employed to classify PTSD. To obtain robust results, we performed repeated two-layer cross-validation to test on an entirely unseen test set.Main results. Our classifiers obtained a balanced test accuracy of up to 62.9% for predicting PTSD patients. In addition, we identified two subtypes within PTSD: one where EEG patterns were similar to those of the combat-exposed controls, and another that were characterized by increased global functional connectivity. Our classifier obtained a balanced test accuracy of 79.4% when classifying this PTSD subtype from controls, a clear improvement compared to predicting the whole PTSD group. Interestingly, alpha connectivity in the dorsal and ventral attention network was particularly important for the prediction, and these connections were positively correlated with arousal symptom scores, a central symptom cluster of PTSD.Significance. Taken together, the novel framework presented here demonstrates how unsupervised subtyping can delineate heterogeneity and improve machine learning prediction of PTSD, and may pave the way for better identification of quantifiable biomarkers.
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Affiliation(s)
- Qianliang Li
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Maya Coulson Theodorsen
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark.,Department of Military Psychology, Danish Veteran Centre, Danish Defence, Copenhagen, Denmark.,Research and Knowledge Centre, Danish Veteran Centre, Danish Defence, Ringsted, Denmark
| | - Ivana Konvalinka
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Kasper Eskelund
- Department of Military Psychology, Danish Veteran Centre, Danish Defence, Copenhagen, Denmark.,Research and Knowledge Centre, Danish Veteran Centre, Danish Defence, Ringsted, Denmark
| | - Karen-Inge Karstoft
- Research and Knowledge Centre, Danish Veteran Centre, Danish Defence, Ringsted, Denmark.,Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Søren Bo Andersen
- Research and Knowledge Centre, Danish Veteran Centre, Danish Defence, Ringsted, Denmark
| | - Tobias S Andersen
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark
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Yao S, Zhu J, Li S, Zhang R, Zhao J, Yang X, Wang Y. Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021. Front Psychiatry 2022; 13:830819. [PMID: 35677873 PMCID: PMC9167960 DOI: 10.3389/fpsyt.2022.830819] [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: 12/07/2021] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis. METHODS QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis. RESULTS A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends. CONCLUSION The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.
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Affiliation(s)
- Shun Yao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jieying Zhu
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Shuiyan Li
- Department of Rehabilitation Medicine, School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Ruibin Zhang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiubo Zhao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xueling Yang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - You Wang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning. Sci Rep 2020; 10:5937. [PMID: 32246035 PMCID: PMC7125168 DOI: 10.1038/s41598-020-62713-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 03/18/2020] [Indexed: 11/09/2022] Open
Abstract
Given the subjective nature of conventional diagnostic methods for post-traumatic stress disorder (PTSD), an objectively measurable biomarker is highly desirable; especially to clinicians and researchers. Macroscopic neural circuits measured using magnetoencephalography (MEG) has previously been shown to be indicative of the PTSD phenotype and severity. In the present study, we employed a machine learning-based classification framework using MEG neural synchrony to distinguish combat-related PTSD from trauma-exposed controls. Support vector machine (SVM) was used as the core classification algorithm. A recursive random forest feature selection step was directly incorporated in the nested SVM cross validation process (CV-SVM-rRF-FS) for identifying the most important features for PTSD classification. For the five frequency bands tested, the CV-SVM-rRF-FS analysis selected the minimum numbers of edges per frequency that could serve as a PTSD signature and be used as the basis for SVM modelling. Many of the selected edges have been reported previously to be core in PTSD pathophysiology, with frequency-specific patterns also observed. Furthermore, the independent partial least squares discriminant analysis suggested low bias in the machine learning process. The final SVM models built with selected features showed excellent PTSD classification performance (area-under-curve value up to 0.9). Testament to its robustness when distinguishing individuals from a heavily traumatised control group, these developments for a classification model for PTSD also provide a comprehensive machine learning-based computational framework for classifying other mental health challenges using MEG connectome profiles.
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Renaldi R, Kim M, Lee TH, Kwak YB, Tanra AJ, Kwon JS. Predicting Symptomatic and Functional Improvements over 1 Year in Patients with First-Episode Psychosis Using Resting-State Electroencephalography. Psychiatry Investig 2019; 16:695-703. [PMID: 31429218 PMCID: PMC6761798 DOI: 10.30773/pi.2019.06.20.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 06/20/2019] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Although early intervention from the beginning of a psychotic episode is essential for a better prognosis, biomarkers predictive of symptomatic and functional improvement in early psychotic disorders are lacking. This study aimed to investigate whether the spectral power of resting-state electroencephalography (EEG) can be used as a predictive marker of the 1-year prognosis in patients with first-episode psychosis (FEP). METHODS Twenty-four patients with FEP and matched healthy control (HC) subjects were examined with resting-state EEG at baseline. The symptomatic severity and functional status of FEP patients were assessed at baseline and reassessed after 1 year of usual treatment. Repeated measures analysis of variance was conducted to compare EEG spectral powers across the groups. Multiple regression analysis revealed EEG spectral powers predictive of symptomatic and functional improvement in FEP patients at the 1-year follow-up. RESULTS Delta band power in the frontal and posterior regions was significantly higher in patients with FEP than in HCs. Higher delta band power in the posterior region predicted later improvement of positive symptoms and general functional status. Lower delta band power in the frontal region predicted improvement of negative symptoms and general functioning after 1 year. CONCLUSION These results suggest that increased delta absolute power is observed from the beginning of psychotic disorders. Furthermore, decreased delta power in the frontal region and increased delta power in the posterior region might be used as a predictive marker of a better prognosis of FEP, which would aid early intervention in clinical practice.
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Affiliation(s)
- Rinvil Renaldi
- Department of Psychiatry, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Tak Hyung Lee
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Yoo Bin Kwak
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Andi J Tanra
- Department of Psychiatry, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia
| | - Jun Soo Kwon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.,Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
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