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Li N, Yang J, Long C, Lei X. Test-Retest Reliability of EEG Aperiodic Components in Resting and Mental Task States. Brain Topogr 2024; 37:961-971. [PMID: 39017780 DOI: 10.1007/s10548-024-01067-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 07/03/2024] [Indexed: 07/18/2024]
Abstract
Aperiodic activity is derived from the electroencephalography (EEG) power spectrum and reflects changes in the slope and shifts of the broadband spectrum. Studies have shown inconsistent test-retest reliability of the aperiodic components. This study systematically measured how the test-retest reliability of the aperiodic components was affected by data duration (1, 2, 3, 4, and 5 min), states (resting with eyes closed, resting with eyes open, performing mental arithmetic, recalling the events of the day, and mentally singing songs), and methods (the Fitting Oscillations and One-Over-F (FOOOF) and Linear Mixed-Effects Regression (LMER)) at both short (90-min) and long (one-month) intervals. The results showed that aperiodic components had fair, good, or excellent test-retest reliability (ranging from 0.53 to 0.91) at both short and long intervals. It is recommended that better reliability of the aperiodic components be obtained using data durations longer than 3 min, the resting state with eyes closed, the mental arithmetic task state, and the LMER method.
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Affiliation(s)
- Na Li
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, Chongqing, 400715, China
| | - Jingqi Yang
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, Chongqing, 400715, China
| | - Changquan Long
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, Chongqing, 400715, China.
| | - Xu Lei
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, Chongqing, 400715, China
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2
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Fox NA, Pérez-Edgar K, Morales S, Brito NH, Campbell AM, Cavanagh JF, Gabard-Durnam LJ, Hudac CM, Key AP, Larson-Prior LJ, Pedapati EV, Norton ES, Reetzke R, Roberts TP, Rutter TM, Scott LS, Shuffrey LC, Antúnez M, Boylan MR, Garner BM, Learnard B, McNair S, McSweeney M, Castillo MIN, Norris J, Nyabingi OS, Pini N, Quinn A, Stosur R, Tan E, Troller-Renfree SV, Yoder L. The development and structure of the HEALthy Brain and Child Development (HBCD) Study EEG protocol. Dev Cogn Neurosci 2024; 69:101447. [PMID: 39305603 PMCID: PMC11439552 DOI: 10.1016/j.dcn.2024.101447] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 08/03/2024] [Accepted: 09/06/2024] [Indexed: 09/30/2024] Open
Abstract
The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Electroencephalography (EEG) is one of two brain imaging modalities central to the HBCD Study. EEG records electrical signals from the scalp that reflect electrical brain activity. In addition, the EEG signal can be synchronized to the presentation of discrete stimuli (auditory or visual) to measure specific cognitive processes with excellent temporal precision (e.g., event-related potentials; ERPs). EEG is particularly helpful for the HBCD Study as it can be used with awake, alert infants, and can be acquired continuously across development. The current paper reviews the HBCD Study's EEG/ERP protocol: (a) the selection and development of the tasks (Video Resting State, Visual Evoked Potential, Auditory Oddball, Face Processing); (b) the implementation of common cross-site acquisition parameters and hardware, site setup, training, and initial piloting; (c) the development of the preprocessing pipelines and creation of derivatives; and (d) the incorporation of equity and inclusion considerations. The paper also provides an overview of the functioning of the EEG Workgroup and the input from members across all steps of protocol development and piloting.
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Affiliation(s)
- Nathan A Fox
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA.
| | | | - Santiago Morales
- Department of Psychology, University of Southern California, USA
| | | | - Alana M Campbell
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | | | | | - Caitlin M Hudac
- Department of Psychology and Carolina Autism and Neurodevelopment Research Center, University of South Carolina, USA
| | - Alexandra P Key
- Department of Pediatrics and Marcus Autism Center, Emory University School of Medicine, USA
| | - Linda J Larson-Prior
- Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, USA
| | - Ernest V Pedapati
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, USA
| | - Elizabeth S Norton
- Roxelyn and Richard Pepper, Department of Communication Sciences and Disorders, Department of Medical Social Sciences, and Institute for Innovations in Developmental Sciences, Northwestern University, USA
| | - Rachel Reetzke
- Center for Autism Services, Science and Innovation, Kennedy Krieger Institute, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, USA
| | | | - Tara M Rutter
- Department of Psychiatry, Oregon Health and Science University, USA
| | - Lisa S Scott
- Department of Psychology, University of Florida, USA
| | - Lauren C Shuffrey
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, USA
| | - Martín Antúnez
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | | | | | | | - Savannah McNair
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | - Marco McSweeney
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | | | - Jessica Norris
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | | | - Nicolò Pini
- Department of Psychiatry, Columbia University Irving Medical Center; Division of Developmental Neuroscience, New York State Psychiatric Institute, USA
| | - Alena Quinn
- Department of Psychology, University of South Carolina, USA
| | - Rachel Stosur
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | - Enda Tan
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | | | - Lydia Yoder
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
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3
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Etkin A, Mathalon DH. Bringing Imaging Biomarkers Into Clinical Reality in Psychiatry. JAMA Psychiatry 2024:2822966. [PMID: 39230917 DOI: 10.1001/jamapsychiatry.2024.2553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Importance Advancing precision psychiatry, where treatments are based on an individual's biology rather than solely their clinical presentation, requires attention to several key attributes for any candidate biomarker. These include test-retest reliability, sensitivity to relevant neurophysiology, cost-effectiveness, and scalability. Unfortunately, these issues have not been systematically addressed by biomarker development efforts that use common neuroimaging tools like magnetic resonance imaging (MRI) and electroencephalography (EEG). Here, the critical barriers that neuroimaging methods will need to overcome to achieve clinical relevance in the near to intermediate term are examined. Observations Reliability is often overlooked, which together with sensitivity to key aspects of neurophysiology and replicated predictive utility, favors EEG-based methods. The principal barrier for EEG has been the lack of large-scale data collection among multisite psychiatric consortia. By contrast, despite its high reliability, structural MRI has not demonstrated clinical utility in psychiatry, which may be due to its limited sensitivity to psychiatry-relevant neurophysiology. Given the prevalence of structural MRIs, establishment of a compelling clinical use case remains its principal barrier. By contrast, low reliability and difficulty in standardizing collection are the principal barriers for functional MRI, along with the need for demonstration that its superior spatial resolution over EEG and ability to directly image subcortical regions in fact provide unique clinical value. Often missing, moreover, is consideration of how these various scientific issues can be balanced against practical economic realities of psychiatric health care delivery today, for which embedding economic modeling into biomarker development efforts may help direct research efforts. Conclusions and Relevance EEG seems most ripe for near- to intermediate-term clinical impact, especially considering its scalability and cost-effectiveness. Recent efforts to broaden its collection, as well as development of low-cost turnkey systems, suggest a promising pathway by which neuroimaging can impact clinical care. Continued MRI research focused on its key barriers may hold promise for longer-horizon utility.
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Affiliation(s)
- Amit Etkin
- Alto Neuroscience Inc, Los Altos, California
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
- Veterans Affairs San Francisco Health Care System, San Francisco, California
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Marsicano G, Bertini C, Ronconi L. Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data. Neurosci Biobehav Rev 2024; 164:105795. [PMID: 38977116 DOI: 10.1016/j.neubiorev.2024.105795] [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: 04/30/2024] [Revised: 06/21/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024]
Abstract
Multivariate pattern analysis (MVPA) of electroencephalographic (EEG) data represents a revolutionary approach to investigate how the brain encodes information. By considering complex interactions among spatio-temporal features at the individual level, MVPA overcomes the limitations of univariate techniques, which often fail to account for the significant inter- and intra-individual neural variability. This is particularly relevant when studying clinical populations, and therefore MVPA of EEG data has recently started to be employed as a tool to study cognition in brain disorders. Here, we review the insights offered by this methodology in the study of anomalous patterns of neural activity in conditions such as autism, ADHD, schizophrenia, dyslexia, neurological and neurodegenerative disorders, within different cognitive domains (perception, attention, memory, consciousness). Despite potential drawbacks that should be attentively addressed, these studies reveal a peculiar sensitivity of MVPA in unveiling dysfunctional and compensatory neurocognitive dynamics of information processing, which often remain blind to traditional univariate approaches. Such higher sensitivity in characterizing individual neurocognitive profiles can provide unique opportunities to optimise assessment and promote personalised interventions.
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Affiliation(s)
- Gianluca Marsicano
- Department of Psychology, University of Bologna, Viale Berti Pichat 5, Bologna 40121, Italy; Centre for Studies and Research in Cognitive Neuroscience, University of Bologna, Via Rasi e Spinelli 176, Cesena 47023, Italy.
| | - Caterina Bertini
- Department of Psychology, University of Bologna, Viale Berti Pichat 5, Bologna 40121, Italy; Centre for Studies and Research in Cognitive Neuroscience, University of Bologna, Via Rasi e Spinelli 176, Cesena 47023, Italy.
| | - Luca Ronconi
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Etkin A, Powell J, Savitz AJ. Opportunities for use of neuroimaging in de-risking drug development and improving clinical outcomes in psychiatry: an industry perspective. Neuropsychopharmacology 2024:10.1038/s41386-024-01970-8. [PMID: 39169213 DOI: 10.1038/s41386-024-01970-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/30/2024] [Accepted: 08/14/2024] [Indexed: 08/23/2024]
Abstract
Neuroimaging, across positron emission tomography (PET), electroencephalography (EEG), and magnetic resonance imaging (MRI), has been a mainstay of clinical neuroscience research for decades, yet has penetrated little into psychiatric drug development beyond often underpowered phase 1 studies, or into clinical care. Simultaneously, there is a pressing need to improve the probability of success in drug development, increase mechanistic diversity, and enhance clinical efficacy. These goals can be achieved by leveraging neuroimaging in a precision psychiatry framework, wherein effects of drugs on the brain are measured early in clinical development to understand dosing and indication, and then in later-stage trials to identify likely drug responders and enrich clinical trials, ultimately improving clinical outcomes. Here we examine the key variables important for success in using neuroimaging for precision psychiatry from the lens of biotechnology and pharmaceutical companies developing and deploying new drugs in psychiatry. We argue that there are clear paths for incorporating different neuroimaging modalities to de-risk subsequent development phases in the near to intermediate term, culminating in use of select neuroimaging modalities in clinical care for prescription of new precision drugs. Better outcomes through neuroimaging biomarkers, however, require a wholesale commitment to a precision psychiatry approach and will necessitate a cultural shift to align biopharma and clinical care in psychiatry to a precision orientation already routine in other areas of medicine.
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Affiliation(s)
- Amit Etkin
- Alto Neuroscience Inc., Los Altos, CA, 94022, USA.
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94304, USA.
| | | | - Adam J Savitz
- Alto Neuroscience Inc., Los Altos, CA, 94022, USA
- Department of Psychiatry, Weill Cornell Medical College, New York, NY, 10021, USA
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6
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Bagdasarov A, Brunet D, Michel CM, Gaffrey MS. Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability. Brain Topogr 2024; 37:496-513. [PMID: 38430283 PMCID: PMC11199263 DOI: 10.1007/s10548-024-01043-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 02/16/2024] [Indexed: 03/03/2024]
Abstract
Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.
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Affiliation(s)
- Armen Bagdasarov
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA.
| | - Denis Brunet
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne, EPFL AVP CP CIBM Station 6, Lausanne, 1015, Switzerland
| | - Christoph M Michel
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne, EPFL AVP CP CIBM Station 6, Lausanne, 1015, Switzerland
| | - Michael S Gaffrey
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA
- Children's Wisconsin, 9000 W. Wisconsin Avenue, Milwaukee, WI, 53226, USA
- Medical College of Wisconsin, Division of Pediatric Psychology and Developmental Medicine, Department of Pediatrics, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
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7
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Yener G, Kıyı İ, Düzenli-Öztürk S, Yerlikaya D. Age-Related Aspects of Sex Differences in Event-Related Brain Oscillatory Responses: A Turkish Study. Brain Sci 2024; 14:567. [PMID: 38928567 PMCID: PMC11202018 DOI: 10.3390/brainsci14060567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 05/28/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024] Open
Abstract
Earlier research has suggested gender differences in event-related potentials/oscillations (ERPs/EROs). Yet, the alteration in event-related oscillations (EROs) in the delta and theta frequency bands have not been explored between genders across the three age groups of adulthood, i.e., 18-50, 51-65, and >65 years. Data from 155 healthy elderly participants who underwent a neurological examination, comprehensive neuropsychological assessment (including attention, memory, executive function, language, and visuospatial skills), and magnetic resonance imaging (MRI) from past studies were used. The delta and theta ERO powers across the age groups and between genders were compared and correlational analyses among the ERO power, age, and neuropsychological tests were performed. The results indicated that females displayed higher theta ERO responses than males in the frontal, central, and parietal regions but not in the occipital location between 18 and 50 years of adulthood. The declining theta power of EROs in women reached that of men after the age of 50 while the theta ERO power was more stable across the age groups in men. Our results imply that the cohorts must be recruited at specified age ranges across genders, and clinical trials using neurophysiological biomarkers as an intervention endpoint should take gender into account in the future.
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Affiliation(s)
- Görsev Yener
- Faculty of Medicine, Izmir University of Economics, 35330 Balçova, Turkey
- Izmir Biomedicine and Genome Center, 35340 İzmir, Turkey
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, 35210 İzmir, Turkey; (İ.K.); (D.Y.)
| | - İlayda Kıyı
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, 35210 İzmir, Turkey; (İ.K.); (D.Y.)
| | - Seren Düzenli-Öztürk
- Department of Speech and Language Therapy, Faculty of Health Sciences, Izmir Bakırçay University, 35665 İzmir, Turkey;
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, 35210 İzmir, Turkey; (İ.K.); (D.Y.)
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McKeown DJ, Finley AJ, Kelley NJ, Cavanagh JF, Keage HAD, Baumann O, Schinazi VR, Moustafa AA, Angus DJ. Test-retest reliability of spectral parameterization by 1/f characterization using SpecParam. Cereb Cortex 2024; 34:bhad482. [PMID: 38100367 PMCID: PMC10793580 DOI: 10.1093/cercor/bhad482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
SpecParam (formally known as FOOOF) allows for the refined measurements of electroencephalography periodic and aperiodic activity, and potentially provides a non-invasive measurement of excitation: inhibition balance. However, little is known about the psychometric properties of this technique. This is integral for understanding the usefulness of SpecParam as a tool to determine differences in measurements of cognitive function, and electroencephalography activity. We used intraclass correlation coefficients to examine the test-retest reliability of parameterized activity across three sessions (90 minutes apart and 30 days later) in 49 healthy young adults at rest with eyes open, eyes closed, and during three eyes closed cognitive tasks including subtraction (Math), music recall (Music), and episodic memory (Memory). Intraclass correlation coefficients were good for the aperiodic exponent and offset (intraclass correlation coefficients > 0.70) and parameterized periodic activity (intraclass correlation coefficients > 0.66 for alpha and beta power, central frequency, and bandwidth) across conditions. Across all three sessions, SpecParam performed poorly in eyes open (40% of participants had poor fits over non-central sites) and had poor test-retest reliability for parameterized periodic activity. SpecParam mostly provides reliable metrics of individual differences in parameterized neural activity. More work is needed to understand the suitability of eyes open resting data for parameterization using SpecParam.
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Affiliation(s)
- Daniel J McKeown
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Anna J Finley
- Institute on Aging, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Nicholas J Kelley
- School of Psychology, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - James F Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, NM 87106, United States
| | - Hannah A D Keage
- School of Psychology, University of South Australia, Adelaide, SA 5001, Australia
| | - Oliver Baumann
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Victor R Schinazi
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Ahmed A Moustafa
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Douglas J Angus
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
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9
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Põld T, Päeske L, Hinrikus H, Lass J, Bachmann M. Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people. Sci Rep 2023; 13:21996. [PMID: 38081954 PMCID: PMC10713782 DOI: 10.1038/s41598-023-49237-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 12/06/2023] [Indexed: 12/18/2023] Open
Abstract
Mental disorders, especially depression, have become a rising problem in modern society. The development of methods and markers for the early detection of mental disorders is an actual problem. Psychological questionnaires are the only tools for evaluating the symptoms of mental disorders in clinical practice today. The electroencephalography (EEG) based non-invasive and cost-effective method seems feasible for the early detection of depression in occupational and family medicine centers and personal monitoring. The reliability of the EEG markers in the early detection of depression assumes their high temporal stability and correlation with the scores of depression questionnaires. The study was been performed on 17 healthy people over three years. Two hypotheses have been evaluated in the current study: first, the temporal stability of EEG markers is close to the stability of the scores of depression questionnaires, and second, EEG markers and depression questionnaires' scores are not correlated in healthy people. The results of the performed study support both hypotheses: the temporal stability of EEG markers is high and close to the stability of depression questionnaires scores and the correlation between the EEG markers and depression questionnaires scores is not detected in healthy people. The results of the current study contribute to the interpretation of results in depression EEG studies and to the feasibility of EEG markers in the detection of depression.
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Affiliation(s)
- Toomas Põld
- Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, 5 Ehitajate Rd, 19086, Tallinn, Estonia
- Meliva Unimed Qvalitas Medical Centre, Tallinn, Estonia
| | - Laura Päeske
- Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, 5 Ehitajate Rd, 19086, Tallinn, Estonia
| | - Hiie Hinrikus
- Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, 5 Ehitajate Rd, 19086, Tallinn, Estonia.
| | - Jaanus Lass
- Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, 5 Ehitajate Rd, 19086, Tallinn, Estonia
| | - Maie Bachmann
- Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, 5 Ehitajate Rd, 19086, Tallinn, Estonia
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