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Rojas Bernal LA, Santamaría García H, Castaño Pérez GA. Electrophysiological biomarkers in dual pathology. REVISTA COLOMBIANA DE PSIQUIATRIA (ENGLISH ED.) 2024; 53:93-102. [PMID: 38677941 DOI: 10.1016/j.rcpeng.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 01/12/2022] [Indexed: 04/29/2024]
Abstract
INTRODUCTION The co-occurrence of substance use disorder with at least one other mental disorder is called dual pathology, which in turn is characterised by heterogeneous symptoms that are difficult to diagnose and have a poor response to treatment. For this reason, the identification and validation of biomarkers is necessary. Within this group, possible electroencephalographic biomarkers have been reported to be useful in diagnosis, treatment and follow-up, both in neuropsychiatric conditions and in substance use disorders. This article aims to review the existing literature on electroencephalographic biomarkers in dual pathology. METHODS A narrative review of the literature. A bibliographic search was performed on the PubMed, Science Direct, OVID, BIREME and Scielo databases, with the keywords: electrophysiological biomarker and substance use disorder, electrophysiological biomarker and mental disorders, biomarker and dual pathology, biomarker and substance use disorder, electroencephalography, and substance use disorder or comorbid mental disorder. RESULTS Given the greater amount of literature found in relation to electroencephalography as a biomarker of mental illness and substance use disorders, and the few articles found on dual pathology, the evidence is organised as a biomarker in psychiatry for the diagnosis and prediction of risk and as a biomarker for dual pathology. CONCLUSIONS Although the evidence is not conclusive, it suggests the existence of a subset of sites and mechanisms where the effects of psychoactive substances and the neurobiology of some mental disorders could overlap or interact.
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Affiliation(s)
| | - Hernando Santamaría García
- Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Departamento de Psiquiatría y Fisiología, Universidad Pontificia Javeriana, Bogotá, Colombia
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Zhang J, Liu T, Shi Z, Tan S, Suo D, Dai C, Wang L, Wu J, Funahashi S, Liu M. Impaired Self-Referential Cognitive Processing in Bipolar Disorder: A Functional Connectivity Analysis. Front Aging Neurosci 2022; 14:754600. [PMID: 35197839 PMCID: PMC8859154 DOI: 10.3389/fnagi.2022.754600] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 01/10/2022] [Indexed: 11/21/2022] Open
Abstract
Patients with bipolar disorder have deficits in self-referenced information. The brain functional connectivity during social cognitive processing in bipolar disorder is unclear. Electroencephalogram (EEG) was recorded in 23 patients with bipolar disorder and 19 healthy comparison subjects. We analyzed the time-frequency distribution of EEG power for each electrode associated with self, other, and font reflection conditions and used the phase lag index to characterize the functional connectivity between electrode pairs for 4 frequency bands. Then, the network properties were assessed by graph theoretic analysis. The results showed that bipolar disorder induced a weaker response power and phase lag index values over the whole brain in both self and other reflection conditions. Moreover, the characteristic path length was increased in patients during self-reflection processing, whereas the global efficiency and the node degree were decreased. In addition, when discriminating patients from normal controls, we found that the classification accuracy was high. These results suggest that patients have impeded integration of attention, memory, and other resources of the whole brain, resulting in a deficit of efficiency and ability in self-referential processing.
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Affiliation(s)
- Jian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Tiantian Liu
- School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Zhongyan Shi
- School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Shuping Tan
- Center for Psychiatric Research, Beijing Huilongguan Hospital, Beijing, China
| | - Dingjie Suo
- School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Chunyang Dai
- School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Li Wang
- School of Life Sciences, Beijing Institute of Technology, Beijing, China
- *Correspondence: Li Wang,
| | - Jinglong Wu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
- Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing, China
| | - Miaomiao Liu
- School of Psychology, Shenzhen University, Shenzhen, China
- Miaomiaos Liu,
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Manchia M, Vieta E, Smeland OB, Altimus C, Bechdolf A, Bellivier F, Bergink V, Fagiolini A, Geddes JR, Hajek T, Henry C, Kupka R, Lagerberg TV, Licht RW, Martinez-Cengotitabengoa M, Morken G, Nielsen RE, Pinto AG, Reif A, Rietschel M, Ritter P, Schulze TG, Scott J, Severus E, Yildiz A, Kessing LV, Bauer M, Goodwin GM, Andreassen OA. Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action. Eur Neuropsychopharmacol 2020; 36:121-136. [PMID: 32536571 DOI: 10.1016/j.euroneuro.2020.05.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/15/2020] [Accepted: 05/24/2020] [Indexed: 12/15/2022]
Abstract
Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is much smaller than its economic impact appears to demand. There is a consensus that the accurate identification of the underlying pathophysiology for BD is fundamental to realize major health benefits through better treatment and preventive regimens. However, to achieve these goals requires coordinated action and innovative approaches to boost the discovery of the neurobiological underpinnings of BD, and rapid translation of research findings into development and testing of better and more specific treatments. To this end, we here propose that only a large-scale coordinated action can be successful in integrating international big-data approaches with real-world clinical interventions. This could be achieved through the creation of a Global Bipolar Disorder Foundation, which could bring government, industry and philanthropy together in common cause. A global initiative for BD research would come at a highly opportune time given the seminal advances promised for our understanding of the genetic and brain basis of the disease and the obvious areas of unmet clinical need. Such an endeavour would embrace the principles of open science and see the strong involvement of user groups and integration of dissemination and public involvement with the research programs. We believe the time is right for a step change in our approach to understanding, treating and even preventing BD effectively.
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Affiliation(s)
- Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy; Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy; Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Eduard Vieta
- Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Olav B Smeland
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | | | - Andreas Bechdolf
- Vivantes Klinikum im Friedrichshain, Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Charité-Universitätsmedizin, Berlin, Germany; Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany; ORYGEN, The National Centre of Excellence in Youth Mental Health, Melbourne, Victoria, Australia
| | - Frank Bellivier
- Université de Paris and INSERM UMRS 1144, Paris, France; AP-HP, Groupe Hospitalo-Universitaire AP-HP Nord, Hopital Fernand Widal, DMU Neurosciences, Département de Psychiatrie et de Médecine Addictologique, Paris, France
| | - Veerle Bergink
- Department of Psychiatry - Erasmus Medical Center, Rotterdam, the Netherlands; Department of Psychiatry, Department of Obstetrics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Andrea Fagiolini
- Department of Molecular Medicine, University of Siena, Siena, Italy
| | - John R Geddes
- Department of Psychiatry and Oxford Health NHS Foundation Trust, University of Oxford, Oxford, United Kingdom
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada; National Institute of Mental Health, Klecany, Czech Republic
| | - Chantal Henry
- Department of Psychiatry, Service Hospitalo-Universitaire, GHU Paris Psychiatrie & Neurosciences, F-75014 Paris, France
| | - Ralph Kupka
- Amsterdam UMC, Vrije Universiteit, Department of Psychiatry, Amsterdam, Netherlands
| | - Trine V Lagerberg
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Rasmus W Licht
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark; Psychiatry - Aalborg University Hospital, Aalborg, Denmark
| | | | - Gunnar Morken
- Østmarka Department of Psychiatry, St Olav University Hospital, Trondheim, Norway; Department of Mental Health, Faculty of Medicine and Healthsciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - René E Nielsen
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark; Psychiatry - Aalborg University Hospital, Aalborg, Denmark
| | - Ana Gonzalez Pinto
- Hospital Universitario de Alava. BIOARABA, UPV/EHU. CIBERSAM. Vitoria, Spain
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany and German Society for Bipolar Disorders (DGBS), Frankfurt am Main, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Phillip Ritter
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig-Maximilian University of Munich, Munich, Germany; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University of Munich, Munich, Germany; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland, USA; Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Jan Scott
- AP-HP, Groupe Hospitalo-Universitaire AP-HP Nord, Hopital Fernand Widal, DMU Neurosciences, Département de Psychiatrie et de Médecine Addictologique, Paris, France; Department of Mental Health, Faculty of Medicine and Healthsciences, Norwegian University of Science and Technology, Trondheim, Norway; Academic Psychiatry, Institute of Neuroscience, Newcastle University, UK
| | - Emanuel Severus
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Aysegul Yildiz
- Dokuz Eylül University Department of Psychiatry, Izmir, Turkey
| | - Lars Vedel Kessing
- Psychiatric Center Copenhagen and University of Copenhagen, Faculty of Health and Medical Sciences, Denmark
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Guy M Goodwin
- Department of Psychiatry and Oxford Health NHS Foundation Trust, University of Oxford, Oxford, United Kingdom
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
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Painold A, Faber PL, Reininghaus EZ, Mörkl S, Holl AK, Achermann P, Saletu B, Saletu-Zyhlarz G, Anderer P, Dalkner N, Birner A, Bengesser S, Kapfhammer HP, Milz P. Reduced Brain Electric Activity and Functional Connectivity in Bipolar Euthymia: An sLORETA Source Localization Study. Clin EEG Neurosci 2020; 51:155-166. [PMID: 31845595 DOI: 10.1177/1550059419893472] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Bipolar disorder (BD) is a chronic illness with a relapsing and remitting time course. Relapses are manic or depressive in nature and intermitted by euthymic states. During euthymic states, patients lack the criteria for a manic or depressive diagnosis, but still suffer from impaired cognitive functioning as indicated by difficulties in executive and language-related processing. The present study investigated whether these deficits are reflected by altered intracortical activity in or functional connectivity between brain regions involved in these processes such as the prefrontal and the temporal cortices. Vigilance-controlled resting state EEG of 13 euthymic BD patients and 13 healthy age- and sex-matched controls was analyzed. Head-surface EEG was recomputed into intracortical current density values in 8 frequency bands using standardized low-resolution electromagnetic tomography. Intracortical current densities were averaged in 19 evenly distributed regions of interest (ROIs). Lagged coherences were computed between each pair of ROIs. Source activity and coherence measures between patients and controls were compared (paired t tests). Reductions in temporal cortex activity and in large-scale functional connectivity in patients compared to controls were observed. Activity reductions affected all 8 EEG frequency bands. Functional connectivity reductions affected the delta, theta, alpha-2, beta-2, and gamma band and involved but were not limited to prefrontal and temporal ROIs. The findings show reduced activation of the temporal cortex and reduced coordination between many brain regions in BD euthymia. These activation and connectivity changes may disturb the continuous frontotemporal information flow required for executive and language-related processing, which is impaired in euthymic BD patients.
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Affiliation(s)
- Annamaria Painold
- Department of Psychiatry and Psychotherapy, Medical University of Graz, Graz, Austria
| | - Pascal L Faber
- Department of Psychiatry, Psychotherapy and Psychosomatics, The KEY Institute for Brain-Mind Research, University Hospital of Psychiatry, Zurich, Switzerland
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapy, Medical University of Graz, Graz, Austria
| | - Sabrina Mörkl
- Department of Psychiatry and Psychotherapy, Medical University of Graz, Graz, Austria
| | - Anna K Holl
- Department of Psychiatry and Psychotherapy, Medical University of Graz, Graz, Austria
| | - Peter Achermann
- Department of Psychiatry, Psychotherapy and Psychosomatics, The KEY Institute for Brain-Mind Research, University Hospital of Psychiatry, Zurich, Switzerland
| | - Bernd Saletu
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Gerda Saletu-Zyhlarz
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Peter Anderer
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Nina Dalkner
- Department of Psychiatry and Psychotherapy, Medical University of Graz, Graz, Austria
| | - Armin Birner
- Department of Psychiatry and Psychotherapy, Medical University of Graz, Graz, Austria
| | - Susanne Bengesser
- Department of Psychiatry and Psychotherapy, Medical University of Graz, Graz, Austria
| | - Hans-Peter Kapfhammer
- Department of Psychiatry and Psychotherapy, Medical University of Graz, Graz, Austria
| | - Patricia Milz
- Department of Psychiatry, Psychotherapy and Psychosomatics, The KEY Institute for Brain-Mind Research, University Hospital of Psychiatry, Zurich, Switzerland
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Teixeira AL, Colpo GD, Fries GR, Bauer IE, Selvaraj S. Biomarkers for bipolar disorder: current status and challenges ahead. Expert Rev Neurother 2018; 19:67-81. [PMID: 30451546 DOI: 10.1080/14737175.2019.1550361] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Bipolar disorder (BD) is a chronic psychiatric disorder marked by clinical and pathophysiological heterogeneity. There is a high expectation that personalized approaches can improve the management of patients with BD. For that, identification and validation of potential biomarkers are fundamental. Areas covered: This manuscript will critically review the current status of different biomarkers for BD, including peripheral, genetic, neuroimaging, and neurophysiological candidates, discussing the challenges to move the field forward. Expert commentary: There are no lab or complementary tests currently recommended for the diagnosis or management of patients with BD. Panels composed by multiple biomarkers will probably contribute to stratifying patients according to their clinical stage, therapeutic response, and prognosis.
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Affiliation(s)
- Antonio L Teixeira
- a Department of Psychiatry & Behavioral Sciences , McGovern Medical School, UT Health , Houston , TX , USA.,b Laboratório Interdisciplinar de Investigação Médica, Faculdade de Medicina , Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte , Brazil
| | - Gabriela D Colpo
- a Department of Psychiatry & Behavioral Sciences , McGovern Medical School, UT Health , Houston , TX , USA
| | - Gabriel R Fries
- a Department of Psychiatry & Behavioral Sciences , McGovern Medical School, UT Health , Houston , TX , USA
| | - Isabelle E Bauer
- a Department of Psychiatry & Behavioral Sciences , McGovern Medical School, UT Health , Houston , TX , USA
| | - Sudhakar Selvaraj
- a Department of Psychiatry & Behavioral Sciences , McGovern Medical School, UT Health , Houston , TX , USA
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Schetinin V, Jakaite L, Nyah N, Novakovic D, Krzanowski W. Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification. Int J Neural Syst 2018; 28:1750064. [DOI: 10.1142/s0129065717500642] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique “brain print”, which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.
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Affiliation(s)
- Vitaly Schetinin
- School of Computer Science and Technology, University of Bedfordshire, Park Square, Luton, UK
| | - Livija Jakaite
- School of Computer Science and Technology, University of Bedfordshire, Park Square, Luton, UK
| | - Ndifreke Nyah
- School of Computer Science and Technology, University of Bedfordshire, Park Square, Luton, UK
| | - Dusica Novakovic
- School of Computer Science and Technology, University of Bedfordshire, Park Square, Luton, UK
| | - Wojtek Krzanowski
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
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