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Popescu M, Popescu EA, DeGraba TJ, Hughes JD. Altered long-range functional connectivity in PTSD: Role of the infraslow oscillations of cortical activity amplitude envelopes. Clin Neurophysiol 2024; 163:22-36. [PMID: 38669765 DOI: 10.1016/j.clinph.2024.03.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/27/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024]
Abstract
OBJECTIVE Coupling between the amplitude envelopes (AEs) of regional cortical activity reflects mechanisms that coordinate the excitability of large-scale cortical networks. We used resting-state MEG recordings to investigate the association between alterations in the coupling of cortical AEs and symptoms of post-traumatic stress disorder (PTSD). METHODS Participants (n = 96) were service members with combat exposure and various levels of post-traumatic stress severity (PTSS). We assessed the correlation between PTSS and (1) coupling of broadband cortical AEs of beta band activity, (2) coupling of the low- (<0.5 Hz) and high-frequency (>0.5 Hz) components of the AEs, and (3) their time-varying patterns. RESULTS PTSS was associated with widespread hypoconnectivity assessed from the broadband AE fluctuations, which correlated with subscores for the negative thoughts and feelings/emotional numbing (NTF/EN) and hyperarousal clusters of symptoms. Higher NTF/EN scores were also associated with smaller increases in resting-state functional connectivity (rsFC) with time during the recordings. The distinct patterns of rsFC in PTSD were primarily due to differences in the coupling of low-frequency (infraslow) fluctuations of the AEs of beta band activity. CONCLUSIONS Our findings implicate the mechanisms underlying the regulation/coupling of infraslow oscillations in the alterations of rsFC assessed from broadband AEs and in PTSD symptomatology. SIGNIFICANCE Altered coordination of infraslow amplitude fluctuations across large-scale cortical networks can contribute to network dysfunction and may provide a target for treatment in PTSD.
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Affiliation(s)
- Mihai Popescu
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Elena-Anda Popescu
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Thomas J DeGraba
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - John D Hughes
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA; Behavioral Biology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
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Yu L, Peng W, Lin W, Luo Y, Hu D, Zhao G, Xu H, Dou Z, Zhang Q, Hong X, Yu S. Electroencephalography connectome changes in chronic insomnia disorder are correlated with neurochemical signatures. Sleep 2024:zsae080. [PMID: 38520362 DOI: 10.1093/sleep/zsae080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Indexed: 03/25/2024] Open
Abstract
STUDY OBJECTIVES This study aimed to investigate the alterations in resting-state electroencephalography (EEG) global brain connectivity (GBC) in patients with chronic insomnia disorder (CID) and to explore the correlation between macroscale connectomic variances and microscale neurotransmitter distributions. METHODS We acquired 64-channel EEG from 35 female CID patients and 34 healthy females. EEG signals were source-localized using individual brain anatomy and orthogonalized to mitigate volume conduction. Correlation coefficients between band-limited source-space power envelopes of the DK 68 atlas were computed and averaged across regions to determine specific GBC values. A support vector machine (SVM) classifier utilizing GBC features was employed to differentiate CID patients from controls. We further used Neurosynth and a 3D atlas of neurotransmitter receptors/transporters to assess the cognitive functions and neurotransmitter landscape associated with CID cortical abnormality maps, respectively. RESULTS CID patients exhibited elevated GBC within the medial prefrontal cortex and limbic cortex, particularly at the gamma carrier frequency, compared to controls (pFDR<0.05). GBC patterns were found to effectively distinguish CID patients from controls with a precision of 90.8% in the SVM model. The cortical abnormality maps were significantly correlated with meta-analytic terms like "cognitive control" and "emotion regulation." Notably, GBC patterns were associated with neurotransmitter profiles (pspin<0.05), with neurotransmitter systems such as norepinephrine, dopamine, and serotonin making significant contributions. CONCLUSIONS This work characterizes the EEG connectomic profile of CID, facilitating the cost-effective clinical translation of EEG-derived markers. Additionally, the linkage between GBC patterns and neurotransmitter distribution offers promising avenues for developing targeted treatment strategies for CID.
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Affiliation(s)
- Liyong Yu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Wei Peng
- Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Wenting Lin
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yucai Luo
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Daijie Hu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guangli Zhao
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hao Xu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zeyang Dou
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Qi Zhang
- Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Xiaojuan Hong
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Siyi Yu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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Luo H, Huang X, Li Z, Tian W, Fang K, Liu T, Wang S, Tang B, Hu J, Yuan TF, Cao L. An Electroencephalography Profile of Paroxysmal Kinesigenic Dyskinesia. Adv Sci (Weinh) 2024; 11:e2306321. [PMID: 38227367 DOI: 10.1002/advs.202306321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/24/2023] [Indexed: 01/17/2024]
Abstract
Paroxysmal kinesigenic dyskinesia (PKD) is associated with a disturbance of neural circuit and network activities, while its neurophysiological characteristics have not been fully elucidated. This study utilized the high-density electroencephalogram (hd-EEG) signals to detect abnormal brain activity of PKD and provide a neural biomarker for its clinical diagnosis and PKD progression monitoring. The resting hd-EEGs are recorded from two independent datasets and then source-localized for measuring the oscillatory activities and function connectivity (FC) patterns of cortical and subcortical regions. The abnormal elevation of theta oscillation in wildly brain regions represents the most remarkable physiological feature for PKD and these changes returned to healthy control level in remission patients. Another remarkable feature of PKD is the decreased high-gamma FCs in non-remission patients. Subtype analyses report that increased theta oscillations may be related to the emotional factors of PKD, while the decreased high-gamma FCs are related to the motor symptoms. Finally, the authors established connectome-based predictive modelling and successfully identified the remission state in PKD patients in dataset 1 and dataset 2. The findings establish a clinically relevant electroencephalography profile of PKD and indicate that hd-EEG can provide robust neural biomarkers to evaluate the prognosis of PKD.
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Affiliation(s)
- Huichun Luo
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Xiaojun Huang
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Ziyi Li
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Wotu Tian
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Kan Fang
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
- Department of Neurology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Taotao Liu
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Shige Wang
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Hunan Province, 410008, China
| | - Ji Hu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu, 226019, China
- Institute of Mental Health and drug discovery, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325000, China
| | - Li Cao
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
- Shanghai Neurological Rare Disease Biobank and Precision Diagnostic Technical Service Platform, Shanghai, China
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Zhu L, Pei Z, Dang G, Shi X, Su X, Lan X, Lian C, Yan N, Guo Y. Predicting response to repetitive transcranial magnetic stimulation in patients with chronic insomnia disorder using electroencephalography: A pilot study. Brain Res Bull 2024; 206:110851. [PMID: 38141788 DOI: 10.1016/j.brainresbull.2023.110851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/30/2023] [Accepted: 12/18/2023] [Indexed: 12/25/2023]
Abstract
Predicting responsvienss to repetitive transcranial magnetic stimulation (rTMS) can facilitate personalized treatments with improved efficacy; however, predictive features related to this response are still lacking. We explored whether resting-state electroencephalography (rsEEG) functional connectivity measured at baseline or during treatment could predict the response to 10-day rTMS targeted to the right dorsolateral prefrontal cortex (DLPFC) in 36 patients with chronic insomnia disorder (CID). Pre- and post-treatment rsEEG scans and the Pittsburgh Sleep Quality Index (PSQI) were evaluated, with an additional rsEEG scan conducted after four rTMS sessions. Machine-learning approaches were employed to assess the ability of each connectivity measure to distinguish between responders (PSQI improvement > 25%) and non-responders (PSQI improvement ≤ 25%). Furthermore, we analyzed the connectivity trends of the two subgroups throughout the treatment. Our results revealed that the machine learning model based on baseline theta connectivity achieved the highest accuracy (AUC = 0.843) in predicting treatment response. Decreased baseline connectivity at the stimulated site was associated with higher responsiveness to TMS, emphasizing the significance of functional connectivity characteristics in rTMS treatment. These findings enhance the clinical application of EEG functional connectivity markers in predicting treatment outcomes.
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Affiliation(s)
- Lin Zhu
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong, China
| | - Zian Pei
- Shenzhen Bay Laboratory, Shenzhen 518020, Guangdong, China
| | - Ge Dang
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong, China
| | - Xue Shi
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong, China
| | - Xiaolin Su
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong, China
| | - Xiaoyong Lan
- Shenzhen Bay Laboratory, Shenzhen 518020, Guangdong, China
| | - Chongyuan Lian
- Shenzhen Bay Laboratory, Shenzhen 518020, Guangdong, China
| | - Nan Yan
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yi Guo
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong, China; Shenzhen Bay Laboratory, Shenzhen 518020, Guangdong, China.
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Kummar AS, Correia H, Tan J, Fujiyama H. An 8-week compassion and mindfulness-based exposure therapy program improves posttraumatic stress symptoms. Clin Psychol Psychother 2023. [PMID: 37947043 DOI: 10.1002/cpp.2929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 10/09/2023] [Accepted: 10/15/2023] [Indexed: 11/12/2023]
Abstract
The persistence of posttraumatic stress symptoms (PTSS) can be debilitating. However, many people experiencing such symptoms may not qualify for or may not seek treatment. Potentially contributing to ongoing residual symptoms of PTSS is emotion dysregulation. Meanwhile, the research area of mindfulness and compassion has grown to imply emotion regulation as one of its underlying mechanisms; yet, its influence on emotion regulation in PTSS cohort is unknown. Here, we explored the potential effectiveness of an 8-week Compassion-oriented and Mindfulness-based Exposure Therapy (CoMET) for individuals with PTSS using a waitlist control design. A total of 28 individuals (27 females, age range = 18-39 years) participated in the study (17 CoMET; 11 waitlist control). Following CoMET, participants reported significant decreases in PTSS severity (from clinical to non-clinical levels), emotion dysregulation and experiential avoidance, as well as significant increases in mindfulness, self-compassion and quality of life. Electroencephalogram-based brain network connectivity analysis revealed an increase in alpha-band connectivity following CoMET in a network that includes the amygdala, suggesting that CoMET successfully induced changes in functional connectivity between brain regions that play a crucial role in emotion regulation. In sum, the current study demonstrated promising intervention outcomes of CoMET in effectively alleviating the symptoms of PTSS via enhanced emotion regulation.
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Affiliation(s)
- Auretta Sonia Kummar
- School of Psychology, College of Health & Education, Murdoch University, Perth, Western Australia, Australia
| | - Helen Correia
- School of Psychology, College of Health & Education, Murdoch University, Perth, Western Australia, Australia
- Psychological Sciences, Australian College of Applied Professions, Perth, Western Australia, Australia
| | - Jane Tan
- School of Psychology, College of Health & Education, Murdoch University, Perth, Western Australia, Australia
| | - Hakuei Fujiyama
- School of Psychology, College of Health & Education, Murdoch University, Perth, Western Australia, Australia
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, Western Australia, Australia
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6
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Zhao D, Zeng N, Zhang HB, Zhang Y, Shan J, Luo H, Zangen A, Yuan TF. Deep magnetic stimulation targeting the medial prefrontal and anterior cingulate cortices for methamphetamine use disorder: a randomised, double-blind, sham-controlled study. Gen Psychiatr 2023; 36:e101149. [PMID: 37781340 PMCID: PMC10533780 DOI: 10.1136/gpsych-2023-101149] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/25/2023] [Indexed: 10/03/2023] Open
Affiliation(s)
- Di Zhao
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ningning Zeng
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hang-Bin Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiatong Shan
- Department of Arts and Sciences, New York University Shanghai, Shanghai, China
| | - Huichun Luo
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Abraham Zangen
- Department of Life Sciences, Ben-Gurion University, Beer Sheva, Israel
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Co-Innovation Center of Neuroregeneration, Nantong University, Nantong, China
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7
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Gil Ávila C, Bott FS, Tiemann L, Hohn VD, May ES, Nickel MM, Zebhauser PT, Gross J, Ploner M. DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience. Sci Data 2023; 10:613. [PMID: 37696851 PMCID: PMC10495446 DOI: 10.1038/s41597-023-02525-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/31/2023] [Indexed: 09/13/2023] Open
Abstract
Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.
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Affiliation(s)
- Cristina Gil Ávila
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, München, Germany
| | - Felix S Bott
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Laura Tiemann
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Vanessa D Hohn
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Elisabeth S May
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Moritz M Nickel
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Paul Theo Zebhauser
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
- Center for Interdisciplinary Pain Medicine, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Markus Ploner
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany.
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany.
- Center for Interdisciplinary Pain Medicine, TUM School of Medicine, Technical University of Munich, München, Germany.
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Cai J, Xu M, Cai H, Jiang Y, Zheng X, Sun H, Sun Y, Sun Y. Task Cortical Connectivity Reveals Different Network Reorganizations between Mild Stroke Patients with Cortical and Subcortical Lesions. Brain Sci 2023; 13:1143. [PMID: 37626499 PMCID: PMC10452233 DOI: 10.3390/brainsci13081143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/24/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
Accumulating efforts have been made to investigate cognitive impairment in stroke patients, but little has been focused on mild stroke. Research on the impact of mild stroke and different lesion locations on cognitive impairment is still limited. To investigate the underlying mechanisms of cognitive dysfunction in mild stroke at different lesion locations, electroencephalograms (EEGs) were recorded in three groups (40 patients with cortical stroke (CS), 40 patients with subcortical stroke (SS), and 40 healthy controls (HC)) during a visual oddball task. Power envelope connectivity (PEC) was constructed based on EEG source signals, followed by graph theory analysis to quantitatively assess functional brain network properties. A classification framework was further applied to explore the feasibility of PEC in the identification of mild stroke. The results showed worse behavioral performance in the patient groups, and PECs with significant differences among three groups showed complex distribution patterns in frequency bands and the cortex. In the delta band, the global efficiency was significantly higher in HC than in CS (p = 0.011), while local efficiency was significantly increased in SS than in CS (p = 0.038). In the beta band, the small-worldness was significantly increased in HC compared to CS (p = 0.004). Moreover, the satisfactory classification results (76.25% in HC vs. CS, and 80.00% in HC vs. SS) validate the potential of PECs as a biomarker in the detection of mild stroke. Our findings offer some new quantitative insights into the complex mechanisms of cognitive impairment in mild stroke at different lesion locations, which may facilitate post-stroke cognitive rehabilitation.
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Affiliation(s)
- Jiaye Cai
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
| | - Mengru Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Huaying Cai
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
| | - Yun Jiang
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
| | - Xu Zheng
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
| | - Hongru Sun
- Department of Electrocardiogram, Dongyang Traditional Chinese Medicine Hospital, Dongyang 322100, China;
| | - Yu Sun
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
- MOE Frontiers Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory for Brain-Computer Intelligence, Zhejiang University, Hangzhou 310016, China
| | - Yi Sun
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
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9
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Fu S, Liang S, Lin C, Wu Y, Xie S, Li M, Lei Q, Li J, Yu K, Yin Y, Hua K, Li W, Wu C, Ma X, Jiang G. Aberrant brain entropy in posttraumatic stress disorder comorbid with major depressive disorder during the coronavirus disease 2019 pandemic. Front Psychiatry 2023; 14:1143780. [PMID: 37333934 PMCID: PMC10272369 DOI: 10.3389/fpsyt.2023.1143780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/09/2023] [Indexed: 06/20/2023] Open
Abstract
Aim Previously, neuroimaging studies on comorbid Posttraumatic-Major depression disorder (PTSD-MDD) comorbidity found abnormalities in multiple brain regions among patients. Recent neuroimaging studies have revealed dynamic nature on human brain activity during resting state, and entropy as an indicator of dynamic regularity may provide a new perspective for studying abnormalities of brain function among PTSD-MDD patients. During the COVID-19 pandemic, there has been a significant increase in the number of patients with PTSD-MDD. We have decided to conduct research on resting-state brain functional activity of patients who developed PTSD-MDD during this period using entropy. Methods Thirty three patients with PTSD-MDD and 36 matched TCs were recruited. PTSD and depression symptoms were assessed using multiple clinical scales. All subjects underwent functional magnetic resonance imaging (fMRI) scans. And the brain entropy (BEN) maps were calculated using the BEN mapping toolbox. A two-sample t-test was used to compare the differences in the brain entropy between the PTSD-MDD comorbidity group and TC group. Furthermore, correlation analysis was conducted between the BEN changes in patients with PTSD-MDD and clinical scales. Results Compared to the TCs, PTSD-MDD patients had a reduced BEN in the right middle frontal orbital gyrus (R_MFOG), left putamen, and right inferior frontal gyrus, opercular part (R_IFOG). Furthermore, a higher BEN in the R_MFOG was related to higher CAPS and HAMD-24 scores in the patients with PTSD-MDD. Conclusion The results showed that the R_MFOG is a potential marker for showing the symptom severity of PTSD-MDD comorbidity. Consequently, PTSD-MDD may have reduced BEN in frontal and basal ganglia regions which are related to emotional dysregulation and cognitive deficits.
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Affiliation(s)
- Shishun Fu
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Sipei Liang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Chulan Lin
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Yunfan Wu
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Shuangcong Xie
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Meng Li
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Qiang Lei
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Jianneng Li
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Kanghui Yu
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Yi Yin
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Kelei Hua
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Wuming Li
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Caojun Wu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Xiaofen Ma
- The Department of Nuclear Medicine, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Guihua Jiang
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
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10
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Rountree-Harrison D, Berkovsky S, Kangas M. Heart and brain traumatic stress biomarker analysis with and without machine learning: A scoping review. Int J Psychophysiol 2023; 185:27-49. [PMID: 36720392 DOI: 10.1016/j.ijpsycho.2023.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 01/22/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
The enigma of post-traumatic stress disorder (PTSD) is embedded in a complex array of physiological responses to stressful situations that result in disruptions in arousal and cognitions that characterise the psychological disorder. Deciphering these physiological patterns is complex, which has seen the use of machine learning (ML) grow in popularity. However, it is unclear to what extent ML has been used with physiological data, specifically, the electroencephalogram (EEG) and electrocardiogram (ECG) to further understand the physiological responses associated with PTSD. To better understand the use of EEG and ECG biomarkers, with and without ML, a scoping review was undertaken. A total of 124 papers based on adult samples were identified comprising 19 ML studies involving EEG and ECG. A further 21 studies using EEG data, and 84 studies employing ECG meeting all other criteria but not employing ML were included for comparison. Identified studies indicate classical ML methodologies currently dominate EEG and ECG biomarkers research, with derived biomarkers holding clinically relevant diagnostic implications for PTSD. Discussion of the emerging trends, algorithms used and their success is provided, along with areas for future research.
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Affiliation(s)
- Darius Rountree-Harrison
- Macquarie University, Balaclava Road, Macquarie Park, New South Wales 2109, Australia; New South Wales Service for the Rehabilitation and Treatment of Torture and Trauma Survivors (STARTTS), 152-168 The Horsley Drive Carramar, New South Wales 2163, Australia.
| | - Shlomo Berkovsky
- Macquarie University, Balaclava Road, Macquarie Park, New South Wales 2109, Australia
| | - Maria Kangas
- Macquarie University, Balaclava Road, Macquarie Park, New South Wales 2109, Australia
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11
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Xu M, Lin Z, Siegel CE, Laska EM, Abu-Amara D, Genfi A, Newman J, Jeffers MK, Blessing EM, Flanagan SR, Fossati S, Etkin A, Marmar CR. Screening for PTSD and TBI in Veterans using Routine Clinical Laboratory Blood Tests. Transl Psychiatry 2023; 13:64. [PMID: 36810280 DOI: 10.1038/s41398-022-02298-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 02/24/2023] Open
Abstract
Post-traumatic stress disorder (PTSD) is a mental disorder diagnosed by clinical interviews, self-report measures and neuropsychological testing. Traumatic brain injury (TBI) can have neuropsychiatric symptoms similar to PTSD. Diagnosing PTSD and TBI is challenging and more so for providers lacking specialized training facing time pressures in primary care and other general medical settings. Diagnosis relies heavily on patient self-report and patients frequently under-report or over-report their symptoms due to stigma or seeking compensation. We aimed to create objective diagnostic screening tests utilizing Clinical Laboratory Improvement Amendments (CLIA) blood tests available in most clinical settings. CLIA blood test results were ascertained in 475 male veterans with and without PTSD and TBI following warzone exposure in Iraq or Afghanistan. Using random forest (RF) methods, four classification models were derived to predict PTSD and TBI status. CLIA features were selected utilizing a stepwise forward variable selection RF procedure. The AUC, accuracy, sensitivity, and specificity were 0.730, 0.706, 0.659, and 0.715, respectively for differentiating PTSD and healthy controls (HC), 0.704, 0.677, 0.671, and 0.681 for TBI vs. HC, 0.739, 0.742, 0.635, and 0.766 for PTSD comorbid with TBI vs HC, and 0.726, 0.723, 0.636, and 0.747 for PTSD vs. TBI. Comorbid alcohol abuse, major depressive disorder, and BMI are not confounders in these RF models. Markers of glucose metabolism and inflammation are among the most significant CLIA features in our models. Routine CLIA blood tests have the potential for discriminating PTSD and TBI cases from healthy controls and from each other. These findings hold promise for the development of accessible and low-cost biomarker tests as screening measures for PTSD and TBI in primary care and specialty settings.
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12
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Punski-Hoogervorst JL, Engel-Yeger B, Avital A. Attention deficits as a key player in the symptomatology of posttraumatic stress disorder: A review. J Neurosci Res 2023; 101:1068-1085. [PMID: 36807926 DOI: 10.1002/jnr.25177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 01/05/2023] [Accepted: 01/28/2023] [Indexed: 02/22/2023]
Abstract
Posttraumatic stress disorder (PTSD) is a debilitating psychiatric disorder characterized by symptoms such as re-experiencing of the psychotrauma and hyperarousal. Although current literature mainly discusses the emotionally related aspects of these symptoms, studies also highlight the relation between re-experiencing, hyperarousability, and attention deficits, which are associated with poorer daily function and reduced quality of life. This review provides a comprehensive analysis of the existing research on attention deficits among adults with PTSD. A systematic search through five databases resulted in the inclusion of 48 peer-reviewed, English-language articles, describing 49 distinct studies. Using a total of 47 different attentional assessment tools, the majority of studies investigated sustained (n = 40), divided (n = 16), or selective (n = 14) attention. A total of 30 studies (61.2%) found significant correlations between PTSD symptoms and attention deficits, and 10 studies (20.4%) found that higher levels of attention deficits were predictive of worse PTSD symptoms. Moreover, neuroimaging results of six (f)MRI and three EEG studies identified various potential neurobiological pathways involved, including (pre)frontal attention networks. Together, the body of research shows that attention deficits in individuals with PTSD are common and occur in surroundings with emotionally neutral stimuli. Nonetheless, current treatment strategies do not target these attentional difficulties. We propose a novel perspective to PTSD diagnosis and treatment strategies based on attention deficits and their relation with top-down regulation of re-experiencing and subsequent other PTSD symptoms.
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Affiliation(s)
- Janne L Punski-Hoogervorst
- Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel
| | - Batya Engel-Yeger
- Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel
| | - Avi Avital
- Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel
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13
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Leviashvili S, Ezra Y, Droby A, Ding H, Groppa S, Mirelman A, Muthuraman M, Maidan I. EEG-Based Mapping of Resting-State Functional Brain Networks in Patients with Parkinson's Disease. Biomimetics (Basel) 2022; 7. [PMID: 36546931 DOI: 10.3390/biomimetics7040231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 11/30/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
(1) Background: Directed functional connectivity (DFC) alterations within brain networks are described using fMRI. EEG has been scarcely used. We aimed to explore changes in DFC in the sensory-motor network (SMN), ventral-attention network (VAN), dorsal-attention network (DAN), and central-executive network (CEN) using an EEG-based mapping between PD patients and healthy controls (HCs). (2) Methods: Four-minutes resting EEG was recorded from 29 PD patients and 28 HCs. Network’s hubs were defined using fMRI-based binary masks and their electrical activity was calculated using the LORETA. DFC between each network’s hub-pairs was calculated for theta, alpha and beta bands using temporal partial directed coherence (tPDC). (3) Results: tPDCs percent was lower in the CEN and DAN in PD patients compared to HCs, while no differences were observed in the SMN and VAN (group*network: F = 5.943, p < 0.001) in all bands (group*band: F = 0.914, p = 0.401). However, in the VAN, PD patients showed greater tPDCs strength compared to HCs (p < 0.001). (4) Conclusions: Our results demonstrated reduced connectivity in the CEN and DAN, and increased connectivity in the VAN in PD patients. These results indicate a complex pattern of DFC alteration within major brain networks, reflecting the co-occurrence of impairment and compensatory mechanisms processes taking place in PD.
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14
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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15
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Li Q, Weiland RF, Konvalinka I, Mansvelder HD, Andersen TS, Smit DJA, Begeer S, Linkenkaer-Hansen K. Intellectually able adults with autism spectrum disorder show typical resting-state EEG activity. Sci Rep 2022; 12:19016. [PMID: 36347938 DOI: 10.1038/s41598-022-22597-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/17/2022] [Indexed: 11/11/2022] Open
Abstract
There is broad interest in discovering quantifiable physiological biomarkers for psychiatric disorders to aid diagnostic assessment. However, finding biomarkers for autism spectrum disorder (ASD) has proven particularly difficult, partly due to high heterogeneity. Here, we recorded five minutes eyes-closed rest electroencephalography (EEG) from 186 adults (51% with ASD and 49% without ASD) and investigated the potential of EEG biomarkers to classify ASD using three conventional machine learning models with two-layer cross-validation. Comprehensive characterization of spectral, temporal and spatial dimensions of source-modelled EEG resulted in 3443 biomarkers per recording. We found no significant group-mean or group-variance differences for any of the EEG features. Interestingly, we obtained validation accuracies above 80%; however, the best machine learning model merely distinguished ASD from the non-autistic comparison group with a mean balanced test accuracy of 56% on the entirely unseen test set. The large drop in model performance between validation and testing, stress the importance of rigorous model evaluation, and further highlights the high heterogeneity in ASD. Overall, the lack of significant differences and weak classification indicates that, at the group level, intellectually able adults with ASD show remarkably typical resting-state EEG.
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16
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Abstract
There is a growing interest in using electroencephalography (EEG) and source modeling to investigate functional interactions among cortical processes, particularly when dealing with pediatric populations. This paper introduces two pipelines that have been recently used to conduct EEG FC analysis in the cortical source space. The analytic streams of these pipelines can be summarized into the following steps: 1) cortical source reconstruction of high-density EEG data using realistic magnetic resonance imaging (MRI) models created with age-appropriate MRI templates; 2) segmentation of reconstructed source activities into brain regions of interest; and 3) estimation of FC in age-related frequency bands using robust EEG FC measures, such as weighted phase lag index and orthogonalized power envelope correlation. In this paper we demonstrate the two pipelines with resting-state EEG data collected from children at 12 and 36 months of age. We also discuss the advantages and limitations of the methods/techniques integrated into the pipelines. Given there is a need in the research community for open-access analytic toolkits that can be used for pediatric EEG data, programs and codes used for the current analysis are made available to the public.
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Affiliation(s)
- Wanze Xie
- School of Psychological and Cognitive Sciences, Peking University, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, China; Beijing Key Laboratory of Behavior and Mental Health, Peking University, China.
| | - Russell T Toll
- Department of Psychiatry, University of Texas Southwestern Medical Centre at Dallas, USA
| | - Charles A Nelson
- Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard Graduate School of Education, Cambridge, MA, USA
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17
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Chen J, Min C, Wang C, Tang Z, Liu Y, Hu X. Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model. Front Neurosci 2022; 16:878146. [PMID: 35812226 PMCID: PMC9257260 DOI: 10.3389/fnins.2022.878146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
In electroencephalograph (EEG) emotion recognition research, obtaining high-level emotional features with more discriminative information has become the key to improving the classification performance. This study proposes a new end-to-end emotion recognition method based on brain connectivity (BC) features and domain adaptive residual convolutional network (short for BC-DA-RCNN), which could effectively extract the spatial connectivity information related to the emotional state of the human brain and introduce domain adaptation to achieve accurate emotion recognition within and across the subject’s EEG signals. The BC information is represented by the global brain network connectivity matrix. The DA-RCNN is used to extract high-level emotional features between different dimensions of EEG signals, reduce the domain offset between different subjects, and strengthen the common features between different subjects. The experimental results on the large public DEAP data set show that the accuracy of the subject-dependent and subject-independent binary emotion classification in valence reaches 95.15 and 88.28%, respectively, which outperforms all the benchmark methods. The proposed method is proven to have lower complexity, better generalization ability, and domain robustness that help to lay a solid foundation for the development of high-performance affective brain-computer interface applications.
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18
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Rolle CE, Narayan M, Wu W, Toll R, Johnson N, Caudle T, Yan M, El-Said D, Watts M, Eisenberg M, Etkin A. Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions. J Neurosci Methods 2022; 367:109424. [PMID: 34826504 DOI: 10.1016/j.jneumeth.2021.109424] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/26/2021] [Accepted: 11/18/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND Electrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric. NEW METHODS We undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity. RESULTS Power envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn't strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals. CONCLUSIONS The competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.
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19
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Choi KM, Kim JY, Kim YW, Han JW, Im CH, Lee SH. Comparative analysis of default mode networks in major psychiatric disorders using resting-state EEG. Sci Rep 2021; 11:22007. [PMID: 34759276 PMCID: PMC8580995 DOI: 10.1038/s41598-021-00975-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 10/15/2021] [Indexed: 11/09/2022] Open
Abstract
Default mode network (DMN) is a set of functional brain structures coherently activated when individuals are in resting-state. In this study, we constructed multi-frequency band resting-state EEG-based DMN functional network models for major psychiatric disorders to easily compare their pathophysiological characteristics. Phase-locking values (PLVs) were evaluated to quantify functional connectivity; global and nodal clustering coefficients (CCs) were evaluated to quantify global and local connectivity patterns of DMN nodes, respectively. DMNs of patients with post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD), panic disorder, major depressive disorder (MDD), bipolar disorder, schizophrenia (SZ), mild cognitive impairment (MCI), and Alzheimer's disease (AD) were constructed relative to their demographically-matched healthy control groups. Overall DMN patterns were then visualized and compared with each other. In global CCs, SZ and AD showed hyper-clustering in the theta band; OCD, MCI, and AD showed hypo-clustering in the low-alpha band; OCD and MDD showed hypo-clustering and hyper-clustering in low-beta, and high-beta bands, respectively. In local CCs, disease-specific patterns were observed. In the PLVs, lowered theta-band functional connectivity between the left lingual gyrus and the left hippocampus was frequently observed. Our comprehensive comparisons suggest EEG-based DMN as a useful vehicle for understanding altered brain networks of major psychiatric disorders.
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Affiliation(s)
- Kang-Min Choi
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea.,School of Electronic Engineering, Hanyang University, Seoul, Republic of Korea
| | - Jeong-Youn Kim
- Center for Bionics, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Yong-Wook Kim
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea.,Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Jung-Won Han
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea.,School of Psychology, Sogang University, Seoul, Republic of Korea
| | - Chang-Hwan Im
- School of Electronic Engineering, Hanyang University, Seoul, Republic of Korea. .,Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea. .,Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Juhwa-ro 170, Ilsanseo-Gu, Goyang, 10370, Republic of Korea. .,Bwave Inc, Juhwa-ro, Goyang, 10380, Republic of Korea.
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20
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Zhao D, Zhang M, Tian W, Cao X, Yin L, Liu Y, Xu TL, Luo W, Yuan TF. Neurophysiological correlate of incubation of craving in individuals with methamphetamine use disorder. Mol Psychiatry 2021; 26:6198-208. [PMID: 34385601 DOI: 10.1038/s41380-021-01252-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/24/2021] [Accepted: 07/28/2021] [Indexed: 01/01/2023]
Abstract
Previous studies both in laboratory animals and humans have reported that abstinence induces incubation of cue-induced drug craving for nicotine, alcohol, cocaine, and methamphetamine. However, current experimental procedures utilized to study incubation of methamphetamine craving do not incorporate the temporal dynamics of neuropsychological measures and electrophysiological activities associated with this incubation process. This study utilized the high-density electroencephalogram (EEG) signals as a rapid, inexpensive, and noninvasive measure of cue-induced craving potential. A total of 156 male individuals with methamphetamine use disorder (MUD) enrolled in this multisite, cross-sectional study. Structured clinical interview data, self-report questionnaires (cued craving, quality of sleep, impulsivity, anxiety, and depression) and resting-state, eye-closed 128 high-density channel EEG signals were collected at 5 abstinence duration time points (<1, 1-3, 3-6, 6-12, and 12-24 months) to track the neuropsychological and neurophysiological signatures. Cue-induced craving was higher after 1-3 months than after the other time points. This incubation effect was also observed for sleep quality but not for anxiety, depression, and impulsivity symptoms, along with exhibited decreased power spectrum for theta (5.5-8 Hz) and alpha (8-13 Hz), and increased in beta (16.5-26.5 Hz) frequency band. Source reconstructed resting-state EEG analysis showed increased synchronization of medial prefrontal cortex (MPFC) for the beta frequency band in 1-3 months abstinent MUD group, and associated with the incubation of craving. Remarkably, the robust incubation-related abnormalities may be driven by beta-band source space connectivity between MPFC and bilateral orbital gyrus (ORB). Our findings suggest the enhancement of beta activity in the incubation period most likely originates from a dysfunction involving frontal brain regions. This neurophysiological signature of incubation of craving can be used to identify individuals who might be most susceptible to relapse, providing a potential insight into future therapeutic interventions for MUD via neuromodulation of beta activity.
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21
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Salankar N, Koundal D, Mian Qaisar S. Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning. J Healthc Eng 2021; 2021:2146369. [PMID: 34484651 DOI: 10.1155/2021/2146369] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 07/22/2021] [Accepted: 08/12/2021] [Indexed: 12/02/2022]
Abstract
In this pandemic situation, importance and awareness about mental health are getting more attention. Stress recognition from multimodal sensor based physiological signals such as electroencephalogram (EEG) and electrocardiography (ECG) signals is a very cost-effective way due to its noninvasive nature. A dataset, recorded during the mental arithmetic task, consisting of EEG + ECG signals of 36 participants is used. It contains two categories of performance, namely, “Good” (nonstressed) and “Bad” (stressed) (Gupta et al. 2018 and Eraldeír et al. 2018). This paper presents an effective approach for the recognition of stress marker at frontal, temporal, central, and occipital lobes. It processes the multimodality physiological signals. The variational mode decomposition (VMD) strategy is used for data preprocessing and for the decomposition of signals into various oscillatory mode functions. Poincare plots (PP) are derived from the first eight variational modes and features from these plots have been extracted such as mean, area, and central tendency measure of the elliptical region. The statistical significance of the extracted features with p < 0.5 has been performed using the Wilcoxson test. The multilayer perceptron (MPLN) and Support Vector Machine (SVM) algorithms are used for the classification of stress and nonstress categories. MLPN has achieved the maximum accuracies of 100% for frontal and temporal lobes. The suggested method can be incorporated in noninvasive EEG signal processing based automated stress identification systems.
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22
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Cai M, Dang G, Su X, Zhu L, Shi X, Che S, Lan X, Luo X, Guo Y. Identifying Mild Cognitive Impairment in Parkinson's Disease With Electroencephalogram Functional Connectivity. Front Aging Neurosci 2021; 13:701499. [PMID: 34276350 PMCID: PMC8281812 DOI: 10.3389/fnagi.2021.701499] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 06/10/2021] [Indexed: 11/13/2022] Open
Abstract
Objective Cognitive impairment occurs frequently in Parkinson’s disease (PD) and negatively impacts the patient’s quality of life. However, its pathophysiological mechanism remains unclear, hindering the development of new therapies. Changes in brain connectivity are related to cognitive impairment in patients with PD, with the dorsolateral prefrontal cortex (DLPFC) being considered the essential region related to PD cognitive impairment. Nevertheless, few studies have focused on the global connectivity responsible for communication with the DLPFC node, the posterior division of the middle frontal gyrus (PMFG) in patients with PD; this was the focus of this study. Methods We applied resting-state electroencephalography (EEG) and calculated a reliable functional connectivity measurement, the debiased weighted phase lag index (dWPLI), to examine inter-regional functional connectivity in 68 patients with PD who were classified into two groups according to their cognitive condition. Results We observed that altered left and right PMFG-based functional connectivity associated with cognitive impairment in patients with PD in the theta frequency bands under the eyes closed condition (r = −0.426, p < 0.001 and r = −0.437, p < 0.001, respectively). Exploratory results based on the MoCA subdomains indicated that poorer visuospatial function was associated with higher right PMFG-based functional connectivity (r = −0.335, p = 0.005), and poorer attention function was associated with higher left and right PMFG-based functional connectivity (r = −0.380, p = 0.001 and r = −0.256, p = 0.035, respectively). Further analysis using logistic regression and receiver operating characteristic (ROC) curves found that this abnormal functional connectivity was an independent risk factor for cognitive impairment [odds ratio (OR): 2.949, 95% confidence interval (CI): 1.294–6.725, p = 0.01 for left PMFG; OR: 11.278, 95% CI: 2.578–49.335, p = 0.001 for right PMFG, per 0.1 U], and provided moderate classification power to discriminate between cognitive abilities in patients with PD [area under the ROC curve (AUC) = 0.770 for left PMFG; AUC = 0.809 for right PMFG]. Conclusion These preliminary findings indicate that abnormal PMFG-based functional connectivity patterns associated with cognitive impairment in the theta frequency bands under the eyes closed condition and altered functional connectivity patterns have the potential to act as reliable biomarkers for identifying cognitive impairment in patients with PD.
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Affiliation(s)
- Min Cai
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Ge Dang
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Xiaolin Su
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Lin Zhu
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Xue Shi
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Sixuan Che
- Department of Medical, The Fourth People's Hospital of Chengdu, Chengdu, China.,MOE Key Lab for Neuroinformation, Chengdu Mental Health Center, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoyong Lan
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Xiaoguang Luo
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Yi Guo
- Department of Neurology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China.,Shenzhen Bay Laboratory, Gladstone Institute of Neurological Disease, Shenzhen, Guangdong, China
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23
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Ressler KJ. Translating Across Circuits and Genetics Toward Progress in Fear- and Anxiety-Related Disorders. Focus (Am Psychiatr Publ) 2021; 19:247-255. [PMID: 34690590 PMCID: PMC8475910 DOI: 10.1176/appi.focus.19205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/15/2020] [Indexed: 06/13/2023]
Abstract
(Reprinted with permission from Am J Psychiatry 2020; 177:214-222).
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24
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Zhang Y, Wu W, Toll RT, Naparstek S, Maron-Katz A, Watts M, Gordon J, Jeong J, Astolfi L, Shpigel E, Longwell P, Sarhadi K, El-Said D, Li Y, Cooper C, Chin-Fatt C, Arns M, Goodkind MS, Trivedi MH, Marmar CR, Etkin A. Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nat Biomed Eng 2021; 5:309-323. [PMID: 33077939 PMCID: PMC8053667 DOI: 10.1038/s41551-020-00614-8] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 08/24/2020] [Indexed: 12/21/2022]
Abstract
The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.
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Affiliation(s)
- Yu Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Wei Wu
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Russell T Toll
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sharon Naparstek
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Mallissa Watts
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Joseph Gordon
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Jisoo Jeong
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering "Antonio Ruberti", University of Rome Sapienza, Rome, Italy
- IRCCF Fondazione Santa Lucia, Rome, Italy
| | - Emmanuel Shpigel
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Parker Longwell
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Kamron Sarhadi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Dawlat El-Said
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Yuanqing Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Pazhou Lab, Guangzhou, China
| | - Crystal Cooper
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Cherise Chin-Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
- neuroCare Group, Munich, Germany
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Location AMC, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
- O'Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Charles R Marmar
- Steven and Alexandra Cohen Veterans Center for Post-traumatic Stress and Traumatic Brain Injury, New York University Langone School of Medicine, New York, NY, USA
- Center for Alcohol Use Disorder and PTSD, New York University Langone School of Medicine, New York, NY, USA
- Department of Psychiatry, New York University Langone School of Medicine, New York, NY, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Alto Neuroscience, Inc., Los Altos, CA, USA.
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25
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Trevizol AP, Downar J, Vila-Rodriguez F, Konstantinou G, Daskalakis ZJ, Blumberger DM. Effect of repetitive transcranial magnetic stimulation on anxiety symptoms in patients with major depression: An analysis from the THREE-D trial. Depress Anxiety 2021; 38:262-271. [PMID: 33305862 DOI: 10.1002/da.23125] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 10/23/2020] [Accepted: 11/14/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Despite the advances in the use of repetitive transcranial magnetic stimulation (rTMS) for the treatment of major depressive disorder (MDD), there is relatively little information about its effect on comorbid anxiety symptoms. METHODS Data from a large randomized noninferiority trial comparing intermittent theta-burst stimulation (iTBS) and high-frequency (10 Hz) rTMS delivered to the left dorsolateral prefrontal cortex (HFL) were analyzed. The primary aim was assessing changes in anxiety/somatization items from the 17-item Hamilton Depression Rating Scale (HAM-D) and the Brief Symptom Inventory (BSI-A), using baseline-adjusted change with an analysis of covariance (ANCOVA), with the final scores as the outcome and baseline scores as the adjustment covariates. RESULTS The analytical cohort comprised 388 participants (189 in HFL and 199 in iTBS groups). From baseline to the end of the rTMS course, the combined score from the anxiety items from the HAM-D dropped from 7.43 (SD = 2.15) to 4.24 (SD = 2.33) in the HFL group, and 7.33 (SD = 2.13) to 3.76 (SD = 2.23) in the iTBS group. The ANCOVA resulted in an effect from time (p < .0001), but not from group allocation (p = .793) or time × group interaction (p = .976). We observed mean changes in the BSI-A of -3.5 (SD = 5.4) and -3.2 (SD = 4.8), with significant effect of time (p < .0001) in the ANCOVA, but not group allocation (p = .793) or group × time interaction (.664). CONCLUSIONS Our findings suggest that both 10 Hz and iTBS may yield potential reductions in anxiety symptoms when used for the treatment of MDD. Our findings warrant future research into the effects of left-sided rTMS on depressed patients struggling with concurrent anxiety symptoms.
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Affiliation(s)
- Alisson P Trevizol
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Jonathan Downar
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.,MRI-Guided rTMS Clinic, Toronto Western Hospital, Toronto, Ontario, Canada.,Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Fidel Vila-Rodriguez
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.,Non-Invasive Neurostimulation Therapies Laboratory, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gerasimos Konstantinou
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Zafiris J Daskalakis
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Daniel M Blumberger
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
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26
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Jiao Y, Zhou T, Yao L, Zhou G, Wang X, Zhang Y. Multi-View Multi-Scale Optimization of Feature Representation for EEG Classification Improvement. IEEE Trans Neural Syst Rehabil Eng 2021; 28:2589-2597. [PMID: 33245696 DOI: 10.1109/tnsre.2020.3040984] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Effectively extracting common space pattern (CSP) features from motor imagery (MI) EEG signals is often highly dependent on the filter band selection. At the same time, optimizing the EEG channel combinations is another key issue that substantially affects the SMR feature representations. Although numerous algorithms have been developed to find channels that record important characteristics of MI, most of them select channels in a cumbersome way with low computational efficiency, thereby limiting the practicality of MI-based BCI systems. In this study, we propose the multi-scale optimization (MSO) of spatial patterns, optimizing filter bands over multiple channel sets within CSPs to further improve the performance of MI-based BCI. Specifically, several channel subsets are first heuristically predefined, and then raw EEG data specific to each of these subsets bandpass-filtered at the overlap between a set of filter bands. Further, instead of solving learning problems for each channel subset independently, we propose a multi-view learning based sparse optimization to jointly extract robust CSP features with L2,1 -norm regularization, aiming to capture the shared salient information across multiple related spatial patterns for enhanced classification performance. A support vector machine (SVM) classifier is then trained on these optimized EEG features for accurate recognition of MI tasks. Experimental results on three public EEG datasets validate the effectiveness of MSO compared to several other competing methods and their variants. These superior experimental results demonstrate that the proposed MSO method has promising potential in MI-based BCIs.
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LaGoy AD, Kaskie R, Connaboy C, Germain A, Ferrarelli F. Overnight Sleep Parameter Increases in Frontoparietal Areas Predict Working Memory Improvements in Healthy Participants But Not in Individuals With Posttraumatic Stress Disorder. Biol Psychiatry Cogn Neurosci Neuroimaging 2021; 6:1110-1117. [PMID: 33757792 DOI: 10.1016/j.bpsc.2020.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 12/02/2020] [Accepted: 12/17/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND Preliminary evidence indicates that non-rapid eye movement (NREM) sleep is implicated in enhancing working memory (WM) performance across days in healthy individuals. While REM sleep has been implicated in other forms of memory, its role in WM remains unclear. Further, the relationship between sleep changes and WM improvement is largely unknown in posttraumatic stress disorder (PTSD). Examining the relationship between changes in sleep and WM improvement in healthy participants and participants with PTSD may inform cognitive enhancement strategies and intervention targets. METHODS Repeated assessments of WM and overnight measurement of NREM and REM sleep parameters were performed in 79 participants (participants with PTSD: n = 33) during a 48-hour laboratory stay. Relationships between sleep parameter changes, WM performance changes, and clinical characteristics were analyzed in PTSD and healthy groups. RESULTS A between-night enhancement in both NREM and REM sleep parameters in frontoparietal areas predicted across-day better WM performance in healthy participants, particularly in those with improved performance. In contrast, in participants with PTSD, an enhancement of these sleep parameters predicted a worse WM performance and was also associated with more PTSD-related sleep disturbances. CONCLUSIONS This study shows that higher sleep activity in frontoparietal areas leads to enhanced WM performance in healthy individuals, whereas in individuals with PTSD, it likely reflects the presence of sleep disturbances that interfere with WM improvement. Interventions focused on addressing sleep disturbances could therefore ameliorate cognitive impairments in individuals with PTSD.
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Affiliation(s)
- Alice D LaGoy
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Rachel Kaskie
- University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Christopher Connaboy
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Anne Germain
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Fabio Ferrarelli
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
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28
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Gaur P, Chowdhury A, McCreadie K, Pachori RB, Wang H. Logistic Regression with Tangent Space based Cross-Subject Learning for Enhancing Motor Imagery Classification. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3099988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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29
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Zhao J, Li K, Xi X, Wang S, Saravanan V, Samuel RDJ. Analysis of complex cognitive task and pattern recognition using distributed patterns of EEG signals with cognitive functions. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05439-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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30
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You Z, Zeng R, Lan X, Ren H, You Z, Shi X, Zhao S, Guo Y, Jiang X, Hu X. Alzheimer's Disease Classification With a Cascade Neural Network. Front Public Health 2020; 8:584387. [PMID: 33251178 PMCID: PMC7673399 DOI: 10.3389/fpubh.2020.584387] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 09/28/2020] [Indexed: 11/25/2022] Open
Abstract
Classification of Alzheimer's Disease (AD) has been becoming a hot issue along with the rapidly increasing number of patients. This task remains tremendously challenging due to the limited data and the difficulties in detecting mild cognitive impairment (MCI). Existing methods use gait [or EEG (electroencephalogram)] data only to tackle this task. Although the gait data acquisition procedure is cheap and simple, the methods relying on gait data often fail to detect the slight difference between MCI and AD. The methods that use EEG data can detect the difference more precisely, but collecting EEG data from both HC (health controls) and patients is very time-consuming. More critically, these methods often convert EEG records into the frequency domain and thus inevitably lose the spatial and temporal information, which is essential to capture the connectivity and synchronization among different brain regions. This paper proposes a cascade neural network with two steps to achieve a faster and more accurate AD classification by exploiting gait and EEG data simultaneously. In the first step, we propose attention-based spatial temporal graph convolutional networks to extract the features from the skeleton sequences (i.e., gait) captured by Kinect (a commonly used sensor) to distinguish between HC and patients. In the second step, we propose spatial temporal convolutional networks to fully exploit the spatial and temporal information of EEG data and classify the patients into MCI or AD eventually. We collect gait and EEG data from 35 cognitively health controls, 35 MCI, and 17 AD patients to evaluate our proposed method. Experimental results show that our method significantly outperforms other AD diagnosis methods (91.07 vs. 68.18%) in the three-way AD classification task (HC, MCI, and AD). Moreover, we empirically found that the lower body and right upper limb are more important for the early diagnosis of AD than other body parts. We believe this interesting finding can be helpful for clinical researches.
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Affiliation(s)
- Zeng You
- Department of Neurology, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen, China.,Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Runhao Zeng
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiaoyong Lan
- Department of Neurology, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen, China
| | - Huixia Ren
- Department of Neurology, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen, China.,The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Zhiyang You
- Department of Neurology, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen, China.,Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xue Shi
- Department of Neurology, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen, China
| | - Shipeng Zhao
- Department of Neurology, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen, China.,Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yi Guo
- Department of Neurology, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen, China
| | - Xin Jiang
- Department of Geriatrics, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen, China
| | - Xiping Hu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Etkin A. A Reckoning and Research Agenda for Neuroimaging in Psychiatry: Response to Henderson et al. Am J Psychiatry 2020; 177:638-639. [PMID: 32605448 DOI: 10.1176/appi.ajp.2020.19080801r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Amit Etkin
- Alto Neuroscience, Los Altos, Calif.; the Department of Psychiatry and Behavioral Sciences and the Wu Tsai Neurosciences Institute, Stanford University, Stanford, Calif
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32
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Rolle CE, Fonzo GA, Wu W, Toll R, Jha MK, Cooper C, Chin-Fatt C, Pizzagalli DA, Trombello JM, Deckersbach T, Fava M, Weissman MM, Trivedi MH, Etkin A. Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial. JAMA Psychiatry 2020; 77:397-408. [PMID: 31895437 PMCID: PMC6990859 DOI: 10.1001/jamapsychiatry.2019.3867] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
IMPORTANCE Despite the widespread awareness of functional magnetic resonance imaging findings suggesting a role for cortical connectivity networks in treatment selection for major depressive disorder, its clinical utility remains limited. Recent methodological advances have revealed functional magnetic resonance imaging-like connectivity networks using electroencephalography (EEG), a tool more easily implemented in clinical practice. OBJECTIVE To determine whether EEG connectivity could reveal neural moderators of antidepressant treatment. DESIGN, SETTING, AND PARTICIPANTS In this nonprespecified secondary analysis, data were analyzed from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care study, a placebo-controlled, double-blinded randomized clinical trial. Recruitment began July 29, 2011, and was completed December 15, 2015. A random sample of 221 outpatients with depression aged 18 to 65 years who were not taking medication for depression was recruited and assessed at 4 clinical sites. Analysis was performed on an intent-to-treat basis. Statistical analysis was performed from November 16, 2018, to May 23, 2019. INTERVENTIONS Patients received either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks. MAIN OUTCOMES AND MEASURES Electroencephalographic orthogonalized power envelope connectivity analyses were applied to resting-state EEG data. Intent-to-treat prediction linear mixed models were used to determine which pretreatment connectivity patterns were associated with response to sertraline vs placebo. The primary clinical outcome was the total score on the 17-item Hamilton Rating Scale for Depression, administered at each study visit. RESULTS Of the participants recruited, 9 withdrew after first dose owing to reported adverse effects, and 221 participants (150 women; mean [SD] age, 37.8 [12.7] years) underwent EEG recordings and had high-quality pretreatment EEG data. After correction for multiple comparisons, connectome-wide analyses revealed moderation by connections within and between widespread cortical regions-most prominently parietal-for both the antidepressant and placebo groups. Greater alpha-band and lower gamma-band connectivity predicted better placebo outcomes and worse antidepressant outcomes. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia. Connectivity features that moderate treatment response differentially by treatment group were distinct from connectivity features that change from baseline to 1 week into treatment. The group mean (SD) score on the 17-item Hamilton Rating Scale for Depression was 18.35 (4.58) at baseline and 26.14 (30.37) across all time points. CONCLUSIONS AND RELEVANCE These findings establish the utility of EEG-based network functional connectivity analyses for differentiating between responses to an antidepressant vs placebo. A role emerged for parietal cortical regions in predicting placebo outcome. From a treatment perspective, capitalizing on the therapeutic components leading to placebo response differentially from antidepressant response should provide an alternative direction toward establishing a placebo signature in clinical trials, thereby enhancing the signal detection in randomized clinical trials. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01407094.
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Affiliation(s)
- Camarin E. Rolle
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California
| | - Gregory A. Fonzo
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California,Department of Psychiatry, Dell Medical School, The University of Texas at Austin
| | - Wei Wu
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Russ Toll
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California
| | - Manish K. Jha
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Crystal Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Cherise Chin-Fatt
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | | | - Joseph M. Trombello
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Thilo Deckersbach
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Maurizio Fava
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Myrna M. Weissman
- New York State Psychiatric Institute, Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York
| | - Madhukar H. Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California,now at Alto Neuroscience Inc, Los Altos, California
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33
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Seligowski AV, Harnett NG, Merker JB, Ressler KJ. Nervous and Endocrine System Dysfunction in Posttraumatic Stress Disorder: An Overview and Consideration of Sex as a Biological Variable. Biol Psychiatry Cogn Neurosci Neuroimaging 2020; 5:381-391. [PMID: 32033924 PMCID: PMC7150641 DOI: 10.1016/j.bpsc.2019.12.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 12/06/2019] [Indexed: 12/12/2022]
Abstract
Decades of research into the biological mechanisms of posttraumatic stress disorder (PTSD) suggests that chronic activation of the stress response leads to long-lasting changes in the structure and function of the nervous and endocrine systems. While the prevalence of PTSD is twice as high in females as males, little is known about how sex differences in neuroendocrine systems may contribute to PTSD. In response to the paucity of research on sex-related mechanisms, the National Institutes of Health created a policy that asks researchers to consider sex as a biological variable. This review provides an overview of the current understanding of nervous and endocrine dysfunction in PTSD (e.g., neural circuitry, autonomic arousal, hormonal response), highlighting areas where the influence of sex has been characterized and where further research is needed. We also provide recommendations for using the sex-as-a-biological-variable policy to address specific gaps in PTSD neuroscience research.
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Affiliation(s)
- Antonia V Seligowski
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts.
| | - Nathaniel G Harnett
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts
| | - Julia B Merker
- Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts
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Affiliation(s)
- Ned H Kalin
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison
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Abstract
Anxiety and fear-related disorders are common and disabling, and they significantly increase risk for suicide and other causes of morbidity and mortality. However, there is tremendous potential for translational neuroscience to advance our understanding of these disorders, leading to novel and powerful interventions and even to preventing their initial development. This overview examines the general circuits and processes thought to underlie fear and anxiety, along with the promise of translational research. It then examines some of the data-driven "next-generation" approaches that are needed for discovery and understanding but that do not always fit neatly into established models. From one perspective, these disorders offer among the most tractable problems in psychiatry, with a great deal of accumulated understanding, across species, of neurocircuit, behavioral, and, increasingly, genetic mechanisms, of how dysregulation of fear and threat processes contributes to anxiety-related disorders. One example is the progressively sophisticated understanding of how extinction underlies the exposure therapy component of cognitive-behavioral therapy approaches, which are ubiquitously used across anxiety and fear-related disorders. However, it is also critical to examine gaps in our understanding between reasonably well-replicated examples of successful translation, areas of significant deficits in knowledge, and the role of large-scale data-driven approaches in future progress and discovery. Although a tremendous amount of progress is still needed, translational approaches to understanding, treating, and even preventing anxiety and fear-related disorders offer great opportunities for successfully bridging neuroscience discovery to clinical practice.
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