{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T17:47:57Z","timestamp":1790012877683,"version":"4.0.1"},"reference-count":42,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T00:00:00Z","timestamp":1631232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Electroencephalography (EEG) likely reflects activity of cortical neurocircuits, making it an insightful estimation for mental health in patients with substance use disorder (SUD). EEG signals are recorded as sinusoidal waves, containing spectral amplitudes across several frequency bands with high spatio-temporal resolution. Prior work on EEG signal analysis has been made mainly at individual electrodes. These signals can be evaluated from advanced aspects, including sub-regional and hemispheric analyses. Due to limitation of computational techniques, few studies in earlier work could conduct data analyses from these aspects. Therefore, EEG in patients with SUD is not fully understood. In the present retrospective study, spectral powers from a data house containing opioid (OUD), methamphetamine\/stimulants (MUD), and alcohol use disorder (AUD) were extracted, and then converted into five distinct topographic data (i.e., electrode-based, cortical subregion-based, left-right hemispheric, anterior-posterior based, and total cortex-based analyses). We found that data conversion and reorganization in the topographic way had an impact on EEG spectral powers in patients with OUD significantly different from those with MUD or AUD. Differential changes were observed from multiple perspectives, including individual electrodes, subregions, hemispheres, anterior-posterior cortices, and across the cortex as a whole. Understanding the differential changes in EEG signals may be useful for future work with machine learning and artificial intelligence (AI), not only for diagnostic but also for prognostic purposes in patients with SUD.<\/jats:p>","DOI":"10.1371\/journal.pone.0248794","type":"journal-article","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T13:28:28Z","timestamp":1631280508000},"page":"e0248794","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":17,"title":["Characteristic changes in EEG spectral powers of patients with opioid-use disorder as compared with those with methamphetamine- and alcohol-use disorders"],"prefix":"10.1371","volume":"16","author":[{"given":"Christopher","family":"Minnerly","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim M.","family":"Shokry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"To","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John J.","family":"Callanan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3814-9367","authenticated-orcid":true,"given":"Rui","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"pone.0248794.ref001","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1097\/WNP.0b013e31827edb2d","article-title":"Early history of electroencephalography and establishment of the American Clinical Neurophysiology Society","volume":"30","author":"JL Stone","year":"2013","journal-title":"J Clinic Neurophysiol"},{"key":"pone.0248794.ref002","first-page":"58","article-title":"Finding synchrony in the desynchronized EEG: the history and interpretation of gamma rhythms","volume":"7","author":"OJ Ahmed","year":"2013","journal-title":"Front Integrat Neurosci"},{"key":"pone.0248794.ref003","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.bspc.2015.10.010","article-title":"A comparison of five different algorithms for EEG signal analysis in artifacts rejection for monitoring depth of anesthesia","volume":"25","author":"Q Liu","year":"2016","journal-title":"Biomed Signal Proces"},{"key":"pone.0248794.ref004","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.4049\/jimmunol.172.5.2731","article-title":"Of mince and Not men: differences between mouse and human immunology","volume":"172","author":"Hughes CW Mestas j","year":"2004","journal-title":"J Immunol"},{"key":"pone.0248794.ref005","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.schres.2014.09.024","article-title":"The utility of P300 as a schizophrenia endophenotype and predictive biomarker: Clinical and socio-demographic modulators in COGS-2","volume":"163","author":"BI Turetsky","year":"2015","journal-title":"Schiz Res"},{"key":"pone.0248794.ref006","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.schres.2013.10.020","article-title":"Can P300 distinguish among schizophrenia, schizoaffective and bipolar I disorders? An ERP study of response inhibition.","volume":"151","author":"J Chun","year":"2013","journal-title":"Schiz Res"},{"key":"pone.0248794.ref007","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0123033","article-title":"Schizophrenia Detection and Classification by Advanced Analysis of EEG Recordings Using a Single Electrode Approach.","volume":"10","author":"Z Dvey-Aharon","year":"2015","journal-title":"PloS One."},{"key":"pone.0248794.ref008","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.schres.2016.05.007","article-title":"Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features","volume":"176","author":"M Shim","year":"2016","journal-title":"Schiz Res"},{"key":"pone.0248794.ref009","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1016\/j.mcm.2008.07.017","article-title":"Analysis of brain electrical topography by spatio-temporal wavelet decomposition","volume":"49","author":"AD Duru","year":"2009","journal-title":"Math Comput Model"},{"key":"pone.0248794.ref010","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s12021-019-09447-6","article-title":"Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M\/EEG Statistics","volume":"18","author":"JA Taylor","year":"2020","journal-title":"Neuroinformatics"},{"key":"pone.0248794.ref011","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1093\/brain\/awz015","article-title":"Interictal and ictal source localization for epilepsy surgery using high-density EEG with MEG: a prospective long-term study","volume":"142","author":"C Plummer","year":"2019","journal-title":"Brain"},{"key":"pone.0248794.ref012","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2014.00031","article-title":"The role of functional neuroimaging in pre-surgical epilepsy evaluation","volume":"5","author":"F Pittau","year":"2014","journal-title":"Front Neurol"},{"key":"pone.0248794.ref013","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1073\/pnas.0406797102","article-title":"Mu-opioid receptors modulate the stability of dendritic spines","volume":"102","author":"D Liao","year":"2005","journal-title":"PNAS"},{"key":"pone.0248794.ref014","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1038\/npp.2015.222","article-title":"GABA-Mediated Inactivation of Medial Prefrontal and Agranular Insular Cortex in the Rat: Contrasting Effects on Hunger- and Palatability-Driven Feeding","volume":"41","author":"BA Baldo","year":"2016","journal-title":"Neuropsychopharmacol."},{"key":"pone.0248794.ref015","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.clinph.2014.08.021","article-title":"Changes in resting EEG following methadone treatment in opiate addicts","volume":"126","author":"GY Wang","year":"2015","journal-title":"Clin Neurophysiol"},{"key":"pone.0248794.ref016","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.1007\/s00213-018-5035-0","article-title":"Acute effects of methadone on EEG power spectrum and event-related potentials among heroin dependents","volume":"235","author":"F Motlagh","year":"2018","journal-title":"Psychopharmacol."},{"key":"pone.0248794.ref017","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.pnpbp.2003.09.022","article-title":"EEG spectral power and mean frequencies in early heroin abstinence.","volume":"28","author":"AG Polunina","year":"2004","journal-title":"Prog Neuro-psychopharmacol Bio Psych"},{"key":"pone.0248794.ref018","doi-asserted-by":"crossref","first-page":"8586153","DOI":"10.1155\/2019\/8586153","article-title":"Estimating Mental Health Conditions of Patients with Opioid Use Disorder","volume":"2019","author":"C Minnerly","year":"2019","journal-title":"J Addict"},{"key":"pone.0248794.ref019","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1038\/sj.npp.1300596","article-title":"Mu-opioid self-administration vs passive administration in heroin abusers produces differential EEG activation","volume":"30","author":"MK Greenwald","year":"2005","journal-title":"Neuropsychopharmacol."},{"key":"pone.0248794.ref020","doi-asserted-by":"crossref","first-page":"8417","DOI":"10.1523\/JNEUROSCI.18-20-08417.1998","article-title":"Reduced striatal dopamine transporter density in abstinent methamphetamine and methcathinone users: evidence from positron emission tomography studies with [11C]WIN-35,428","volume":"18","author":"UD McCann","year":"1998","journal-title":"J Neurosci"},{"key":"pone.0248794.ref021","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/S1388-2457(02)00409-1","article-title":"Quantitative EEG abnormalities in recently abstinent methamphetamine dependent individuals","volume":"114","author":"TF Newton","year":"2003","journal-title":"Clin Neurophysiol"},{"key":"pone.0248794.ref022","doi-asserted-by":"crossref","first-page":"e0226249","DOI":"10.1371\/journal.pone.0226249","article-title":"Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG","volume":"14","author":"H Khajehpour","year":"2019","journal-title":"PloS One"},{"key":"pone.0248794.ref023","doi-asserted-by":"crossref","DOI":"10.1186\/s13041-017-0325-8","article-title":"Role of GABAA receptors in alcohol use disorders suggested by chronic intermittent ethanol (CIE) rodent model","volume":"10","author":"RW Olsen","year":"2017","journal-title":"Mol Brain."},{"key":"pone.0248794.ref024","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/S0893-133X(01)00236-6","article-title":"Predicting relapse to alcohol and drug abuse via quantitative electroencephalography","volume":"25","author":"LO Bauer","year":"2001","journal-title":"Neuropsychopharmacol"},{"key":"pone.0248794.ref025","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.anr.2018.05.003","article-title":"Effects of Quantitative Electroencephalography Based Neurofeedback Training on Autonomous Regulations in Patients with Alcohol Use Disorder","volume":"12","author":"S Ko","year":"2018","journal-title":"Asian Nurs Res"},{"key":"pone.0248794.ref026","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.clinph.2005.12.021","article-title":"Clinical correlates of quantitative EEG alterations in alcoholic patients","volume":"117","author":"P Coutin-Churchman","year":"2006","journal-title":"Clin Neurophysiol"},{"key":"pone.0248794.ref027","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1080\/10874201003773500","article-title":"Validity and reliability of quantitative electroencephalography","volume":"14","author":"RW Thatcher","year":"2010","journal-title":"J Neurother"},{"key":"pone.0248794.ref028","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1016\/j.pnpbp.2006.06.005","article-title":"Reorganization of the composition of brain oscillations and their temporal characteristics in opioid dependent patients","volume":"30","author":"AA Fingelkurts","year":"2006","journal-title":"Prog Neuro-psychopharmacol Bio Psychiat"},{"key":"pone.0248794.ref029","doi-asserted-by":"crossref","first-page":"2466","DOI":"10.1111\/ejn.13840","article-title":"What does scalp electroencephalogram coherence tell us about long-range cortical networks?","volume":"48","author":"AC Snyder","year":"2018","journal-title":"Eur J Neurosci"},{"key":"pone.0248794.ref030","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.1152\/jn.00427.2015","article-title":"Stimulus-dependent spiking relationships with the EEG","volume":"114","author":"AC Snyder","year":"2015","journal-title":"J Neurophysiol"},{"key":"pone.0248794.ref031","doi-asserted-by":"crossref","first-page":"1495982","DOI":"10.1080\/20018525.2018.1495982","article-title":"ECG with alternating electric axis in relation to left-sided tension pneumothorax: a case report and review of the literature","volume":"5","author":"DC Schmidt","year":"2018","journal-title":"Eur Clin Respir J"},{"key":"pone.0248794.ref032","doi-asserted-by":"crossref","first-page":"e35673","DOI":"10.1371\/journal.pone.0035673","article-title":"EEG-based functional brain networks: does the network size matter?","volume":"7","author":"A Joudaki","year":"2012","journal-title":"PloS One"},{"key":"pone.0248794.ref033","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3389\/fninf.2019.00066","article-title":"Exploring EEG Effective Connectivity Network in Estimating Influence of Color on Emotion and Memory","volume":"13","author":"MT Chai","year":"2019","journal-title":"Front Neuroinform."},{"key":"pone.0248794.ref034","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neuropsychologia.2017.11.020","article-title":"Chromaticity separation and the alpha response","volume":"108","author":"SM Haigh","year":"2018","journal-title":"Neuropsychologia"},{"key":"pone.0248794.ref035","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.tics.2012.10.007","article-title":"Alpha-band oscillations, attention, and controlled access to stored information","volume":"16","author":"W. Klimesch","year":"2012","journal-title":"Trends Cognitive Sci"},{"key":"pone.0248794.ref036","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1002\/hbm.20150","article-title":"EEG alpha synchronization and functional coupling during top-down processing in a working memory task","volume":"26","author":"P Sauseng","year":"2005","journal-title":"Human Brain Mapping"},{"key":"pone.0248794.ref037","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.neuroimage.2015.03.001","article-title":"Distinguishing rhythmic from non-rhythmic brain activity during rest in healthy neurocognitive aging","volume":"112","author":"JB Caplan","year":"2015","journal-title":"NeuroImage"},{"key":"pone.0248794.ref038","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1093\/sleep\/20.7.523","article-title":"Topographical characteristics and principal component structure of the hypnagogic EEG","volume":"20","author":"H Tanaka","year":"1997","journal-title":"Sleep"},{"key":"pone.0248794.ref039","doi-asserted-by":"crossref","first-page":"5218","DOI":"10.1038\/s41598-020-62154-0","article-title":"Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications","volume":"10","author":"H Hinrichs","year":"2020","journal-title":"Scientific Reports"},{"key":"pone.0248794.ref040","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/S0361-9230(01)00763-8","article-title":"Dynamic changes and spatial correlation of EEG activities during cold pressor test in man","volume":"57","author":"PF Chang","year":"2002","journal-title":"Brain Res Bull"},{"key":"pone.0248794.ref041","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1038\/s41467-017-00282-4","article-title":"Dendritic calcium spikes are clearly detectable at the cortical surface","volume":"8","author":"M Suzuki","year":"2017","journal-title":"Nature Communications"},{"key":"pone.0248794.ref042","doi-asserted-by":"crossref","first-page":"76","DOI":"10.3389\/fnhum.2019.00076","article-title":"Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures","volume":"13","author":"M Golmohammadi","year":"2019","journal-title":"Front Human Neurosci"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0248794","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T13:30:58Z","timestamp":1631280658000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0248794"}},"subtitle":[],"editor":[{"given":"Wajid","family":"Mumtaz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,9,10]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9,10]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0248794","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.04.13.439656","asserted-by":"object"}]},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,10]]}}}