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Chorlian DB, Kamarajan C, Meyers JL, Pandey AK, Zhang J, Kinreich S, Porjesz B. Non-linear development of EEG coherence in adolescents and young adults shown by the analysis of neurophysiological trajectories and their covariance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.13.584867. [PMID: 38559025 PMCID: PMC10980032 DOI: 10.1101/2024.03.13.584867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
To contribute to the understanding of changes in the factors governing the development of neural connectivity, the developmental structure of EEG coherence in adolescents and young adults was analyzed using the means, variances, and covariances of high alpha frequency band coherence measures from a set of 27 coherence pairs obtained from a sample of 1426 participants from the COGA study with 5006 observations over ages 12 through 31. Means and covariances were calculated at 96 age centers by a LOESS method. In the current study, trajectories of covariance matrices considered as individual units were determined by tensorial analysis: calculation of Riemannian geodesic (non-Euclidean) distances between matrices and application of both linear and non-linear dimension reduction techniques to these distances. Results were evaluated by bootstrap methods. Mean coherence trajectories for males and females were very similar, showing a steady upward trend from ages 12 to 20 which diminishes gradually from 20 to 25 and reaches stability from 25 to 31. In contrast, the individual covariance trajectories of males and female differed, with the male covariance levels becoming greater than that of females during the developmental process. Tensorial determination of the distances from the initial covariance matrix of subsequent covariance matrices to age 20 had the same trajectory as the mean coherence values. Tensorial determination of the trajectories of the covariance matrices of males and females based on their all pairs geodesic distances revealed a non-linear pattern in the multi-dimensional space of each of the trajectories: A steady increase in one dimension is accompanied by deviations from it peaking at age 20 which have both transient and lasting effects. There is a precise temporal parallelism of this pattern of covariance in males and females, while there is a consistent distance between the male and female covariance structures throughout the developmental process. Between region differences (anterior-posterior) within each sex are greater than between sex differences within regions. Examining development using multiple methods provides unique insight into the developmental process.
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Ardinger CE, Lapish CC, Linsenbardt DN. Repeated Binge Alcohol Drinking Leads to Reductions in Corticostriatal Theta Coherence in Female but not Male Mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.07.581791. [PMID: 38496601 PMCID: PMC10942409 DOI: 10.1101/2024.03.07.581791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Decreased functional connectivity between the striatum and frontal cortex is observed in individuals with alcohol use disorder (AUD), and predicts the probability of relapse in abstinent individuals with AUD. To further our understanding of how repeated alcohol (ethanol; EtOH) consumption impacts the corticostriatal circuit, extracellular electrophysiological recordings (local field potentials; LFPs) were gathered from the nucleus accumbens (NAc) and prefrontal cortex (PFC) of C57BL/6J mice voluntarily consuming EtOH or water using a 'drinking-in-the-dark' (DID) procedure. Following a three-day acclimation period wherein only water access was provided during DID, mice were given 15 consecutive days of access to EtOH. Each session consisted of a 30-minute baseline period where water was available and was followed immediately by a 2-hour period where sippers containing water were replaced with new sippers containing either unsweetened 20% (v/v) EtOH (days 4-18; DID) or water (days 1-3; acclimation). Our analyses focused primarily on theta coherence during bouts of drinking, as differences in this band are associated with several behavioral markers of AUD. Both sexes displayed decreases in theta coherence during the first day of binge EtOH consumption. However, only females displayed further decreases in theta coherence on the 14th day of EtOH access. No differences in theta coherence were observed between the first and final bout on any EtOH drinking days. These results provide additional support for decreases in the functional coupling of corticostriatal circuits as a consequence of alcohol consumption and suggests that female mice are uniquely vulnerable to these effects following repeated EtOH drinking.
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Affiliation(s)
- Cherish E Ardinger
- Addiction Neuroscience, Department of Psychology and Indiana Alcohol Research Center, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, 46202
| | - Christopher C Lapish
- Addiction Neuroscience, Department of Psychology and Indiana Alcohol Research Center, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, 46202
- Indiana University School of Medicine Stark Neuroscience Institute, Indianapolis, Indiana, 46202
| | - David N Linsenbardt
- Department of Neurosciences School of Medicine and Health Sciences Center, University of New Mexico, Albuquerque, New Mexico, 87131
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Yi C, Liu C, Zhang J, Zhang X, Jiang L, Si Y, He G, Ao M, Zhao Y, Yao D, Li F, Ma X, Xu P, He B. The long-term effect of modulated acoustic stimulation on alteration in EEG brain network of chronic tinnitus patients: An exploratory study. Brain Res Bull 2023; 205:110812. [PMID: 37951276 DOI: 10.1016/j.brainresbull.2023.110812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/04/2023] [Accepted: 11/09/2023] [Indexed: 11/13/2023]
Abstract
Acoustic stimulation is one of the most influential techniques for distressing tinnitus, while how it functions to reverse neural changes associated with tinnitus remains undisclosed. In this study, our objective is to investigate alterations in brain networks to shed light on the enigma of acoustic intervention for tinnitus. We designed a 75-day long-term acoustic intervention experiment, during which chronic tinnitus patients received daily modulated acoustic stimulation with each session lasting 15 days. Every 15 days, professional tinnitus assessments were conducted, collecting both electroencephalogram (EEG) and tinnitus handicap inventory (THI) data from the patients. Thereafter, we investigated the changes in EEG network organizations during continuous acoustic stimulation and their progressive evolution throughout long-term therapy, alongside exploring the associations between the evolving changes of the network alterations and THI. Our current study findings reveal reorganization in alpha/beta long-range frontal-parietal-occipital connections as well as local frontal and parietal-occipital regions induced by acoustic stimulation. Furthermore, we observed a decrease in modulation effects as therapy sessions progressed. These alterations in brain networks reflect the reversal of tinnitus-related neural activities, particularly distress and perception; thus contributing to tinnitus rehabilitation through long-term modulation effects. This study provides unique insights into how long-term acoustic intervention affects the network organizations of tinnitus patients and deepens our understanding of the pathophysiological mechanisms underlying tinnitus rehabilitation.
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Affiliation(s)
- Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chen Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jiamin Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiabing Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yajing Si
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Psychology, Xinxiang Medical University, Xinxiang 453003, China
| | - Gang He
- Otolaryngology Department of Sichuan Provincial People's Hospital, Chengdu 610072, China
| | - Min Ao
- Otolaryngology Department of Sichuan Provincial People's Hospital, Chengdu 610072, China
| | - Yong Zhao
- Betterlife Medical Chengdu Co., Ltd, Chengdu 610000, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Psychology, Xinxiang Medical University, Xinxiang 453003, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Xuntai Ma
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, China.
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China; Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, China.
| | - Baoming He
- Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China; Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China.
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Agrawal A, Brislin SJ, Bucholz KK, Dick D, Hart RP, Johnson EC, Meyers J, Salvatore J, Slesinger P, Almasy L, Foroud T, Goate A, Hesselbrock V, Kramer J, Kuperman S, Merikangas AK, Nurnberger JI, Tischfield J, Edenberg HJ, Porjesz B. The Collaborative Study on the Genetics of Alcoholism: Overview. GENES, BRAIN, AND BEHAVIOR 2023; 22:e12864. [PMID: 37736010 PMCID: PMC10550790 DOI: 10.1111/gbb.12864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 07/21/2023] [Accepted: 08/23/2023] [Indexed: 09/23/2023]
Abstract
Alcohol use disorders (AUD) are commonly occurring, heritable and polygenic disorders with etiological origins in the brain and the environment. To outline the causes and consequences of alcohol-related milestones, including AUD, and their related psychiatric comorbidities, the Collaborative Study on the Genetics of Alcoholism (COGA) was launched in 1989 with a gene-brain-behavior framework. COGA is a family based, diverse (~25% self-identified African American, ~52% female) sample, including data on 17,878 individuals, ages 7-97 years, in 2246 families of which a proportion are densely affected for AUD. All participants responded to questionnaires (e.g., personality) and the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) which gathers information on psychiatric diagnoses, conditions and related behaviors (e.g., parental monitoring). In addition, 9871 individuals have brain function data from electroencephalogram (EEG) recordings while 12,009 individuals have been genotyped on genome-wide association study (GWAS) arrays. A series of functional genomics studies examine the specific cellular and molecular mechanisms underlying AUD. This overview provides the framework for the development of COGA as a scientific resource in the past three decades, with individual reviews providing in-depth descriptions of data on and discoveries from behavioral and clinical, brain function, genetic and functional genomics data. The value of COGA also resides in its data sharing policies, its efforts to communicate scientific findings to the broader community via a project website and its potential to nurture early career investigators and to generate independent research that has broadened the impact of gene-brain-behavior research into AUD.
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Affiliation(s)
- Arpana Agrawal
- Department of PsychiatryWashington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Sarah J. Brislin
- Department of PsychiatryRutgers Robert Wood Johnson Medical SchoolPiscatawayNew JerseyUSA
| | - Kathleen K. Bucholz
- Department of PsychiatryWashington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Danielle Dick
- Department of PsychiatryRutgers Robert Wood Johnson Medical SchoolPiscatawayNew JerseyUSA
| | - Ronald P. Hart
- Department of Cell Biology and NeuroscienceRutgers UniversityPiscatawayNew JerseyUSA
| | - Emma C. Johnson
- Department of PsychiatryWashington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Jacquelyn Meyers
- Department of Psychiatry and Behavioral SciencesSUNY Downstate Health Sciences UniversityBrooklynNew YorkUSA
| | - Jessica Salvatore
- Department of PsychiatryRutgers Robert Wood Johnson Medical SchoolPiscatawayNew JerseyUSA
| | - Paul Slesinger
- Department of Neuroscience & Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Laura Almasy
- Department of Biomedical and Health InformaticsChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Genetics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tatiana Foroud
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Alison Goate
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Victor Hesselbrock
- Department of PsychiatryUniversity of Connecticut School of MedicineFarmingtonConnecticutUSA
| | - John Kramer
- Department of PsychiatryUniversity of Iowa Carver College of MedicineIowa CityIowaUSA
| | - Samuel Kuperman
- Department of PsychiatryUniversity of Iowa Carver College of MedicineIowa CityIowaUSA
| | - Alison K. Merikangas
- Department of Biomedical and Health InformaticsChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Genetics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Jay Tischfield
- Department of GeneticsRutgers UniversityPiscatawayNew JerseyUSA
| | - Howard J. Edenberg
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Biochemistry and Molecular BiologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral SciencesSUNY Downstate Health Sciences UniversityBrooklynNew YorkUSA
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Yan W, Yu L, Liu D, Sui J, Calhoun VD, Lin Z. Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG. Front Psychiatry 2023; 14:1202049. [PMID: 37441141 PMCID: PMC10333510 DOI: 10.3389/fpsyt.2023.1202049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/12/2023] [Indexed: 07/15/2023] Open
Abstract
Background Accurate classification based on affordable objective neuroimaging biomarkers are important steps toward designing individualized treatment. Methods In this work, we investigated a deep learning classification model, multi-scale convolutional recurrent neural network (MCRNN), to explore psychiatric disorder-related biomarkers by leveraging the spatiotemporal information of resting-state EEG (rsEEG) using a multiple psychiatric disorder database containing 327 individuals diagnosed with schizophrenia, bipolar, major depressive disorders, and healthy controls. All subjects were mapped to a shared low-dimensional subspace for intuitively interpreting the inter-relationship and separation of psychiatric disorders. Results Psychiatric disorders were identified using rsEEG with high accuracy ranged from 78.6 to 91.3% in patient vs. controls two-class classification, and 68.2% in four-class classification. The control-to-schizophrenia trajectory interpretated by the model was consistent with the disease severity in clinical observation. Conclusion The MsRNN demonstrated a capability in extracting discriminative rsEEG biomarkers for psychiatric disorder classification, indicating its potential to facilitate our understanding of psychiatric disorders and monitoring interventions.
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Affiliation(s)
- Weizheng Yan
- Department of Psychiatry, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Linzhen Yu
- Department of Psychiatry, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dandan Liu
- Department of Psychiatry, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Zheng Lin
- Department of Psychiatry, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Zhang X, Lu B, Chen C, Yang L, Chen W, Yao D, Hou J, Qiu J, Li F, Xu P. The correlation between upper body grip strength and resting-state EEG network. Med Biol Eng Comput 2023:10.1007/s11517-023-02865-4. [PMID: 37338738 DOI: 10.1007/s11517-023-02865-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 06/07/2023] [Indexed: 06/21/2023]
Abstract
Current research in the field of neuroscience primarily focuses on the analysis of electroencephalogram (EEG) activities associated with movement within the central nervous system. However, there is a dearth of studies investigating the impact of prolonged individual strength training on the resting state of the brain. Therefore, it is crucial to examine the correlation between upper body grip strength and resting-state EEG networks. In this study, coherence analysis was utilized to construct resting-state EEG networks using the available datasets. A multiple linear regression model was established to examine the correlation between the brain network properties of individuals and their maximum voluntary contraction (MVC) during gripping tasks. The model was used to predict individual MVC. The beta and gamma frequency bands showed significant correlation between RSN connectivity and MVC (p < 0.05), particularly in left hemisphere frontoparietal and fronto-occipital connectivity. RSN properties were consistently correlated with MVC in both bands, with correlation coefficients greater than 0.60 (p < 0.01). Additionally, predicted MVC positively correlated with actual MVC, with a coefficient of 0.70 and root mean square error of 5.67 (p < 0.01). The results show that the resting-state EEG network is closely related to upper body grip strength, which can indirectly reflect an individual's muscle strength through the resting brain network.
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Affiliation(s)
- Xiabing Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bin Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chunli Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Lei Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Wanjun Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Jingming Hou
- Department of Rehabilitation, Southwest Hospital, Army Medical University, Chongqing, 400038, China
| | - Jing Qiu
- Robotics Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China.
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China.
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610041, China.
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The potential of electroencephalography coherence to predict the outcome of repetitive transcranial magnetic stimulation in insomnia disorder. J Psychiatr Res 2023; 160:56-63. [PMID: 36774831 DOI: 10.1016/j.jpsychires.2023.02.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/27/2023] [Accepted: 02/04/2023] [Indexed: 02/09/2023]
Abstract
BACKGROUND It is unknown whether repetitive Transcranial Magnetic Stimulation (rTMS) could improve sleep quality by modulating electroencephalography (EEG) connectivity of insomnia disorder (ID) patients. Great heterogeneity had been found in the clinical outcomes of rTMS for ID. The study aimed to investigate the potential mechanisms of rTMS therapy for ID and develop models to predict clinical outcomes. METHODS In Study 1, 50 ID patients were randomly divided into active and sham groups, and subjected to 20 sessions of treatment with 1 Hz rTMS over the left dorsolateral prefrontal cortex. EEG during awake, Polysomnography, and clinical assessment were collected and analyzed before and after rTMS. In Study 2, 120 ID patients were subjected to active rTMS stimulation and were then separated into optimal and sub-optimal groups due to the median of Pittsburgh Sleep Quality Index reduction rate. Machine learning models were developed based on baseline EEG coherence to predict rTMS treatment effects. RESULTS In Study 1, decreased EEG coherence in theta and alpha bands were observed after rTMS treatment, and changes in theta band (F7-O1) coherence were correlated with changes in sleep efficiency. In Study 2, baseline EEG coherence in theta, alpha, and beta bands showed the potential to predict the treatment effects of rTMS for ID. CONCLUSION rTMS improved sleep quality of ID patients by modulating the abnormal EEG coherence. Baseline EEG coherence between certain channels in theta, alpha, and beta bands could act as potential biomarkers to predict the therapeutic effects.
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Galkin SA, Levchuk LA, Simutkin GG, Ivanova SA, Bokhan NA. [Coherence of the electroencephalogram and peripheral markers of nerve tissue damage in depressive disorders]. Zh Nevrol Psikhiatr Im S S Korsakova 2023; 123:82-87. [PMID: 36946402 DOI: 10.17116/jnevro202312303182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
OBJECTIVE To evaluate the coherence parameters of the electroencephalogram and the level of peripheral markers of nerve tissue damage in patients with depressive disorders. MATERIAL AND METHODS Thirty patients with a diagnosis from a cluster of mood disorders: affective disorder within a single depressive episode and recurrent depressive disorder were examined. A control group consisted of 30 healthy individuals, comparable in sex and age to the main group. The bioelectric activity of the brain was recorded and analyzed with the calculation of the averaged coefficients of intra- and interhemispheric coherence. The concentration of calcium-binding protein S100b, the main protein of myelin MBP and glial fibrillar acid protein GFAP was determined in blood sera by solid-phase enzyme immunoassay. RESULTS Patients with depressive disorders showed signi cantly lower coefficients of interhemispheric coherence of alpha (p=0.003), beta (p=0.042) and theta rhythms (p=0.041), as well as intrahemispheric coherence of alpha rhythm in the left (p=0.016) and right hemispheres (p=0.026), beta rhythm in the right hemisphere (p=0.034) compared to the control group. Patients with depressive disorders showed a higher concentration of MBP compared to the control group (p=0.008). Additionally, we identified statistically significant correlations between EEG coherence coefficients and serum markers in patients with depressive disorders. CONCLUSION The results clearly confirm the presence of inflammatory changes in the brain in patients with depression, which is reflected in structural and functional changes.
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Affiliation(s)
- S A Galkin
- Mental Health Research Institute - Tomsk National Research Medical Center Russian Academy of Science, Tomsk, Russia
| | - L A Levchuk
- Mental Health Research Institute - Tomsk National Research Medical Center Russian Academy of Science, Tomsk, Russia
| | - G G Simutkin
- Mental Health Research Institute - Tomsk National Research Medical Center Russian Academy of Science, Tomsk, Russia
| | - S A Ivanova
- Mental Health Research Institute - Tomsk National Research Medical Center Russian Academy of Science, Tomsk, Russia
- Siberian State Medical University, Tomsk, Russia
| | - N A Bokhan
- Mental Health Research Institute - Tomsk National Research Medical Center Russian Academy of Science, Tomsk, Russia
- Siberian State Medical University, Tomsk, Russia
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9
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Bel-Bahar TS, Khan AA, Shaik RB, Parvaz MA. A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment. Front Hum Neurosci 2022; 16:995534. [PMID: 36325430 PMCID: PMC9619053 DOI: 10.3389/fnhum.2022.995534] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 09/20/2022] [Indexed: 11/24/2022] Open
Abstract
Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.
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Affiliation(s)
- Tarik S. Bel-Bahar
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Anam A. Khan
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Riaz B. Shaik
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Muhammad A. Parvaz
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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10
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Li Z, Yi C, Chen C, Liu C, Zhang S, Li S, Gao D, Cheng L, Zhang X, Sun J, He Y, Xu P. Predicting individual muscle fatigue tolerance by resting-state EEG brain network. J Neural Eng 2022; 19. [PMID: 35901723 DOI: 10.1088/1741-2552/ac8502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 07/28/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and fatigue tolerance varies across individuals. Various studies have emphasized the close relationships between muscle fatigue and the brain. However, the relationships between the resting-state electroencephalogram (rsEEG) brain network and individual muscle fatigue tolerance remain unexplored. APPROACH Eighteen elite water polo athletes took part in our experiment. Five-minute before- and after-fatigue-exercise rsEEG and fatiguing task (i.e., elbow flexion and extension) electromyography (EMG) data were recorded. Based on the graph theory, we constructed the before- and after-task rsEEG coherence network and compared the network differences between them. Then, the correlation between the before-fatigue rsEEG network properties and the EMG fatigue indexes when a subject cannot keep on exercising anymore was profiled. Finally, a prediction model based on the before-fatigue rsEEG network properties was established to predict fatigue tolerance. MAIN RESULTS Results of this study revealed the significant differences between the before- and after-exercise rsEEG brain network and found significant high correlations between before-exercise rsEEG network properties in the beta band and individual muscle fatigue tolerance. Finally, an efficient support vector regression (SVR) model based on the before-exercise rsEEG network properties in the beta band was constructed and achieved the accurate prediction of individual fatigue tolerance. Similar results were also revealed on another thirty-subject swimmer data set further demonstrating the reliability of predicting fatigue tolerance based on the rsEEG network. SIGNIFICANCE Our study investigates the relationship between the rsEEG brain network and individual muscle fatigue tolerance and provides a potential objective physiological biomarker for tolerance prediction and the regulation of muscle fatigue.
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Affiliation(s)
- Zhiwei Li
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Chanlin Yi
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Chunli Chen
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Chen Liu
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Shu Zhang
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Shunchang Li
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Dongrui Gao
- Chengdu University of Information Technology, No.24 Block 1, Xuefu Road, Chengdu, Sichuan, 610225, CHINA
| | - Liang Cheng
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Xiabing Zhang
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Junzhi Sun
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Ying He
- Small Ball Department of Physical Education and Sport Sciences, Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Peng Xu
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
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11
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Babiloni C, Ferri R, Noce G, Lizio R, Lopez S, Lorenzo I, Tucci F, Soricelli A, Nobili F, Arnaldi D, Famà F, Orzi F, Buttinelli C, Giubilei F, Cipollini V, Marizzoni M, Güntekin B, Aktürk T, Hanoğlu L, Yener G, Özbek Y, Stocchi F, Vacca L, Frisoni GB, Del Percio C. Resting State Alpha Electroencephalographic Rhythms Are Differently Related to Aging in Cognitively Unimpaired Seniors and Patients with Alzheimer's Disease and Amnesic Mild Cognitive Impairment. J Alzheimers Dis 2021; 82:1085-1114. [PMID: 34151788 DOI: 10.3233/jad-201271] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND In relaxed adults, staying in quiet wakefulness at eyes closed is related to the so-called resting state electroencephalographic (rsEEG) rhythms, showing the highest amplitude in posterior areas at alpha frequencies (8-13 Hz). OBJECTIVE Here we tested the hypothesis that age may affect rsEEG alpha (8-12 Hz) rhythms recorded in normal elderly (Nold) seniors and patients with mild cognitive impairment due to Alzheimer's disease (ADMCI). METHODS Clinical and rsEEG datasets in 63 ADMCI and 60 Nold individuals (matched for demography, education, and gender) were taken from an international archive. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands, as well as fixed beta (14-30 Hz) and gamma (30-40 Hz) bands. Each group was stratified into three subgroups based on age ranges (i.e., tertiles). RESULTS As compared to the younger Nold subgroups, the older one showed greater reductions in the rsEEG alpha rhythms with major topographical effects in posterior regions. On the contrary, in relation to the younger ADMCI subgroups, the older one displayed a lesser reduction in those rhythms. Notably, the ADMCI subgroups pointed to similar cerebrospinal fluid AD diagnostic biomarkers, gray and white matter brain lesions revealed by neuroimaging, and clinical and neuropsychological scores. CONCLUSION The present results suggest that age may represent a deranging factor for dominant rsEEG alpha rhythms in Nold seniors, while rsEEG alpha rhythms in ADMCI patients may be more affected by the disease variants related to earlier versus later onset of the AD.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy.,San Raffaele of Cassino, Cassino (FR), Italy
| | | | | | | | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | | | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Andrea Soricelli
- IRCCS SDN, Napoli, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Flavio Nobili
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Dario Arnaldi
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Francesco Famà
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Francesco Orzi
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Virginia Cipollini
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey.,REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Laboratory, Istanbul Medipol University, Istanbul, Turkey
| | - Tuba Aktürk
- REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Laboratory, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Görsev Yener
- Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Izmir, Turkey.,Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Yağmur Özbek
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Laura Vacca
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
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12
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Stier C, Elshahabi A, Li Hegner Y, Kotikalapudi R, Marquetand J, Braun C, Lerche H, Focke NK. Heritability of Magnetoencephalography Phenotypes Among Patients With Genetic Generalized Epilepsy and Their Siblings. Neurology 2021; 97:e166-e177. [PMID: 34045271 PMCID: PMC8279565 DOI: 10.1212/wnl.0000000000012144] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 04/07/2021] [Indexed: 11/15/2022] Open
Abstract
Objective To assess whether neuronal signals in patients with genetic generalized epilepsy (GGE) are heritable, we examined magnetoencephalography resting-state recordings in patients and their healthy siblings. Methods In a prospective, cross-sectional design, we investigated source-reconstructed power and functional connectivity in patients, siblings, and controls. We analyzed 5 minutes of cleaned and awake data without epileptiform discharges in 6 frequency bands (1–40 Hz). We further calculated intraclass correlations to estimate heritability for the imaging patterns within families. Results Compared with controls (n = 45), patients with GGE (n = 25) showed widespread increased functional connectivity (θ to γ frequency bands) and power (δ to γ frequency bands) across the spectrum. Siblings (n = 18) fell between the levels of patients and controls. Heritability of the imaging metrics was observed in regions where patients strongly differed from controls, mainly in β frequencies, but also for δ and θ power. Network connectivity in GGE was heritable in frontal, central, and inferior parietal brain areas and power in central, temporo-parietal, and subcortical structures. Presence of generalized spike-wave activity during recordings and medication were associated with the network patterns, whereas other clinical factors such as age at onset, disease duration, or seizure control were not. Conclusion Metrics of brain oscillations are well suited to characterize GGE and likely relate to genetic factors rather than the active disease or treatment. High power and connectivity levels co-segregated in patients with GGE and healthy siblings, predominantly in the β band, representing an endophenotype of GGE.
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Affiliation(s)
- Christina Stier
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Adham Elshahabi
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Yiwen Li Hegner
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Raviteja Kotikalapudi
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Justus Marquetand
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Christoph Braun
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Holger Lerche
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Niels K Focke
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy.
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13
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Bavkar S, Iyer B, Deosarkar S. Optimal EEG channels selection for alcoholism screening using EMD domain statistical features and harmony search algorithm. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2020.11.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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14
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Epigenetic and non-coding regulation of alcohol abuse and addiction. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2020; 156:63-86. [PMID: 33461665 DOI: 10.1016/bs.irn.2020.08.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Alcohol use disorder is a chronic debilitated condition adversely affecting the lives of millions of individuals throughout the modern world. Individuals suffering from an alcohol use disorder diagnosis frequently have serious cooccurring conditions, which often further exacerbates problematic drinking behavior. Comprehending the biochemical processes underlying the progression and perpetuation of disease is essential for mitigating maladaptive behavior in order to restore both physiological and psychological health. The range of cellular and biological systems contributing to, and affected by, alcohol use disorder and other comorbid disorders necessitates a fundamental grasp of intricate functional relationships that govern molecular biology. Epigenetic factors are recognized as essential mediators of cellular behavior, orchestrating a symphony of gene expression changes within multicellular environments that are ultimately responsible for directing human behavior. Understanding the epigenetic and transcriptional regulatory mechanisms involved in the pathogenesis of disease is important for improving available pharmacotherapies and reducing the incidence of alcohol abuse and cooccurring conditions.
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15
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Parker CC, Lusk R, Saba LM. Alcohol Sensitivity as an Endophenotype of Alcohol Use Disorder: Exploring Its Translational Utility between Rodents and Humans. Brain Sci 2020; 10:E725. [PMID: 33066036 PMCID: PMC7600833 DOI: 10.3390/brainsci10100725] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/06/2020] [Accepted: 10/09/2020] [Indexed: 12/21/2022] Open
Abstract
Alcohol use disorder (AUD) is a complex, chronic, relapsing disorder with multiple interacting genetic and environmental influences. Numerous studies have verified the influence of genetics on AUD, yet the underlying biological pathways remain unknown. One strategy to interrogate complex diseases is the use of endophenotypes, which deconstruct current diagnostic categories into component traits that may be more amenable to genetic research. In this review, we explore how an endophenotype such as sensitivity to alcohol can be used in conjunction with rodent models to provide mechanistic insights into AUD. We evaluate three alcohol sensitivity endophenotypes (stimulation, intoxication, and aversion) for their translatability across human and rodent research by examining the underlying neurobiology and its relationship to consumption and AUD. We show examples in which results gleaned from rodents are successfully integrated with information from human studies to gain insight in the genetic underpinnings of AUD and AUD-related endophenotypes. Finally, we identify areas for future translational research that could greatly expand our knowledge of the biological and molecular aspects of the transition to AUD with the broad hope of finding better ways to treat this devastating disorder.
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Affiliation(s)
- Clarissa C. Parker
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, VT 05753, USA
| | - Ryan Lusk
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Laura M. Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
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