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Zhang G, Carrasco CD, Winsler K, Bahle B, Cong F, Luck SJ. Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data. Neuroimage 2024; 293:120625. [PMID: 38704056 PMCID: PMC11098681 DOI: 10.1016/j.neuroimage.2024.120625] [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: 02/28/2024] [Revised: 04/08/2024] [Accepted: 04/23/2024] [Indexed: 05/06/2024] Open
Abstract
Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.
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Affiliation(s)
- Guanghui Zhang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, Liaoning, 116029, China; Key Laboratory of Brain and Cognitive Neuroscience, Liaoning Province, Dalian, 116029, China; Center for Mind and Brain, University of California-Davis, Davis, CA, 95618, USA.
| | - Carlos D Carrasco
- Center for Mind and Brain, University of California-Davis, Davis, CA, 95618, USA
| | - Kurt Winsler
- Center for Mind and Brain, University of California-Davis, Davis, CA, 95618, USA
| | - Brett Bahle
- Center for Mind and Brain, University of California-Davis, Davis, CA, 95618, USA
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning, 116024, China; Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, 40014, Finland; Key Laboratory of Social Computing and Cognitive Intelligence, Ministry of Education, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Steven J Luck
- Center for Mind and Brain, University of California-Davis, Davis, CA, 95618, USA
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Marvi N, Haddadnia J, Fayyazi Bordbar MR. An automated drug dependence detection system based on EEG. Comput Biol Med 2023; 158:106853. [PMID: 37030264 DOI: 10.1016/j.compbiomed.2023.106853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/13/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVE Substance abuse causes damage to the brain structure and function. This research aim is to design an automated drug dependence detection system based on EEG signals in a Multidrug (MD) abuser. METHODS EEG signals were recorded from participants categorized into MD-dependents (n = 10) and Healthy Control (HC) (n = 12). The Recurrence Plot investigates the dynamic characteristics of the EEG signal. The entropy index (ENTR) measured from the Recurrence Quantification Analysis was considered the complexity index of the delta, theta, alpha, beta, gamma, and all-band EEG signals. Statistical analysis was performed by t-test. The support vector machine technique was used for the data classification. RESULTS The results show decreased ENTR indices in the delta, alpha, beta, gamma, and all-band EEG signal and increased theta band in MD abusers compared to the HC group. That indicated the reduction of complexity in the delta, alpha, beta, gamma, and all-band EEG signals in the MD group. Additionally, the SVM classifier distinguished the MD group from the HC group with 90% accuracy, 89.36% sensitivity, 90.7% specificity, and 89.8% F1 score. CONCLUSIONS AND SIGNIFICANCE The nonlinear analysis of brain data was used to build an automatic diagnostic aid system that could identify HC people apart from those who abuse MD.
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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: 0] [Impact Index Per Article: 0] [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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Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer. PERSONALITY NEUROSCIENCE 2021; 4:e6. [PMID: 34909565 PMCID: PMC8640675 DOI: 10.1017/pen.2021.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/30/2021] [Accepted: 04/12/2021] [Indexed: 12/13/2022]
Abstract
By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.
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Habelt B, Wirth C, Afanasenkau D, Mihaylova L, Winter C, Arvaneh M, Minev IR, Bernhardt N. A Multimodal Neuroprosthetic Interface to Record, Modulate and Classify Electrophysiological Biomarkers Relevant to Neuropsychiatric Disorders. Front Bioeng Biotechnol 2021; 9:770274. [PMID: 34805123 PMCID: PMC8595111 DOI: 10.3389/fbioe.2021.770274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/18/2021] [Indexed: 12/18/2022] Open
Abstract
Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox whose suitability for diagnosis and therapy of mental disorders has not yet been explored. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders.
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Affiliation(s)
- Bettina Habelt
- Department of Psychiatry and Psychotherapy, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Leibniz Institute of Polymer Research Dresden, Dresden, Germany
| | - Christopher Wirth
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - Dzmitry Afanasenkau
- Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden, Dresden, Germany
| | - Lyudmila Mihaylova
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - Christine Winter
- Department of Psychiatry and Psychotherapy, Charite University Medicine Berlin, Campus Mitte, Berlin, Germany
| | - Mahnaz Arvaneh
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - Ivan R. Minev
- Leibniz Institute of Polymer Research Dresden, Dresden, Germany
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - Nadine Bernhardt
- Department of Psychiatry and Psychotherapy, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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6
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Steele VR, Maxwell AM. Treating cocaine and opioid use disorder with transcranial magnetic stimulation: A path forward. Pharmacol Biochem Behav 2021; 209:173240. [PMID: 34298030 PMCID: PMC8445657 DOI: 10.1016/j.pbb.2021.173240] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 06/19/2021] [Accepted: 07/16/2021] [Indexed: 12/15/2022]
Abstract
Developing new, effective treatments for substance use disorders (SUDs), especially cocaine and opioid use disorders (CUD and OUD), are of immense importance. These are chronic, relapsing brain diseases characterized by dysregulated circuits manifesting from neuroplastic change brought on by repeated exposure to substances of abuse. A potential treatment is therapeutically inducing neuroplastic change in targeted dysregulated circuits. One such intervention, repetitive transcranial magnetic stimulation (rTMS) has gained traction over the past two decades as a method of noninvasively stimulating cortical structures in order to induce subcortical neuroplastic change. By doing so, rTMS ameliorates symptoms that are consequent of dysregulations in disease-related circuits, such as craving, and reduces drug use. Although rTMS has been successfully applied as a treatment for other clinical disorders, progress toward treatment applications for SUDs has been stymied by what we dub "known unknowns". These are fundamental lines of research within the rTMS-SUD field that have yet to be systematically understood which could help to optimize TMS as an intervention for SUDs. Because progress in treatment for CUD and OUD is imperative given the widespread severity of OUD and the lack of treatment for CUD, it is necessary to critically reflect on the ways in which rTMS research for these disorders can most effectively move forward to help patients. We articulate six "known unknowns" and outline a direction of research to address each. Briefly, the "known unknowns" in the field are: 1) Cortical target selection, 2) subcortical circuit engagement, 3) optimizing rTMS sequences, 4) rTMS as an adjuvant to existing interventions, 5) manipulating brain state, and 6) selecting outcome measures. We also outline research design approaches to address these "known unknowns" in the rTMS-SUDs field. Unification of efforts across research laboratories is necessary to develop empirically validated treatments that will benefit patients in a timely fashion.
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Affiliation(s)
- Vaughn R Steele
- Yale University, School of Medicine, Department of Psychiatry, New Haven CT, USA.
| | - Andrea M Maxwell
- Medical Scientist Training Program, University of Minnesota, Minneapolis MN, USA
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Yip SW, Kiluk B, Scheinost D. Toward Addiction Prediction: An Overview of Cross-Validated Predictive Modeling Findings and Considerations for Future Neuroimaging Research. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:748-758. [PMID: 31932230 PMCID: PMC8274215 DOI: 10.1016/j.bpsc.2019.11.001] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/28/2019] [Accepted: 11/03/2019] [Indexed: 11/26/2022]
Abstract
Substance use is a leading cause of disability and death worldwide. Despite the existence of evidence-based treatments, clinical outcomes are highly variable across individuals, and relapse rates following treatment remain high. Within this context, methods to identify individuals at particular risk for unsuccessful treatment (i.e., limited within-treatment abstinence), or for relapse following treatment, are needed to improve outcomes. Cumulatively, the literature generally supports the hypothesis that individual differences in brain function and structure are linked to differences in treatment outcomes, although anatomical loci and directions of associations have differed across studies. However, this work has almost entirely used methods that may overfit the data, leading to inflated effect size estimates and reduced likelihood of reproducibility in novel clinical samples. In contrast, cross-validated predictive modeling (i.e., machine learning) approaches are designed to overcome limitations of traditional approaches by focusing on individual differences and generalization to novel subjects (i.e., cross-validation), thereby increasing the likelihood of replication and potential translation to novel clinical settings. Here, we review recent studies using these approaches to generate brain-behavior models of treatment outcomes in addictions and provide recommendations for further work using these methods.
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Affiliation(s)
- Sarah W Yip
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut.
| | - Brian Kiluk
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
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8
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Tortora L, Meynen G, Bijlsma J, Tronci E, Ferracuti S. Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective. Front Psychol 2020; 11:220. [PMID: 32256422 PMCID: PMC7090235 DOI: 10.3389/fpsyg.2020.00220] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Accepted: 01/31/2020] [Indexed: 01/21/2023] Open
Abstract
Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as 'A.I. neuroprediction,' and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.
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Affiliation(s)
- Leda Tortora
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Gerben Meynen
- Willem Pompe Institute for Criminal Law and Criminology/Utrecht Centre for Accountability and Liability Law (UCALL), Utrecht University, Utrecht, Netherlands
- Faculty of Humanities, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Johannes Bijlsma
- Willem Pompe Institute for Criminal Law and Criminology/Utrecht Centre for Accountability and Liability Law (UCALL), Utrecht University, Utrecht, Netherlands
| | - Enrico Tronci
- Department of Computer Science, Sapienza University of Rome, Rome, Italy
| | - Stefano Ferracuti
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
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9
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Habelt B, Arvaneh M, Bernhardt N, Minev I. Biomarkers and neuromodulation techniques in substance use disorders. Bioelectron Med 2020; 6:4. [PMID: 32232112 PMCID: PMC7098236 DOI: 10.1186/s42234-020-0040-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 01/29/2020] [Indexed: 01/10/2023] Open
Abstract
Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders.
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Affiliation(s)
- Bettina Habelt
- Department of Psychiatry and Psychotherapy, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Mahnaz Arvaneh
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
| | - Nadine Bernhardt
- Department of Psychiatry and Psychotherapy, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ivan Minev
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
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10
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Rowlands A, Fisher M, Mishra J, Nahum M, Brandrett B, Reinke M, Caldwell M, Kiehl KA, Vinogradov S. Cognitive Training for Very High Risk Incarcerated Adolescent Males. Front Psychiatry 2020; 11:225. [PMID: 32351409 PMCID: PMC7175987 DOI: 10.3389/fpsyt.2020.00225] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 03/09/2020] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE Persistent violent and antisocial behavior, as manifested in conduct disorder (CD) traits, are associated with a range of cognitive deficits. Individuals with more severe cognitive deficits are more likely to commit violent crimes. Currently, no treatments target improving cognition in high-risk CD youth. This pilot study tests the feasibility and efficacy of delivering intensive tablet-based cognitive training (CT) to adolescent males incarcerated in a youth maximum-security prison. METHODS Participants were fourteen adolescent males, diagnosed with CD. All participants completed up to 30 h of unsupervised, intensive, adaptive CT exercises that targeted multiple neurocognitive domains, as well as a battery of standardized neurocognitive measures and computerized assessments at baseline and post-training. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE At baseline, participants exhibited significant impairments on neurocognitive measures, relative to age-matched healthy controls. Twelve participants completed training and showed evidence of target engagement, as indexed by improvement in cognitive processing speed. Significant gains were observed in measures of global cognition, with additional gains in cognitive flexibility at trend level significance. Improvements in these measures were positively related to total training time. In summary, both assessments and intervention appear to be feasible, tolerable, and acceptable in incarcerated youth. Intensive CT shows preliminary efficacy in improving neurocognitive performance in key domains, with large effect sizes, and significant performance improvement associations with the time in training.
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Affiliation(s)
- Abby Rowlands
- School of Nursing, University of Maryland Medical Center, Baltimore, MD, United States
| | - Melissa Fisher
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
| | - Jyoti Mishra
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Mor Nahum
- Posit Science Inc., San Francisco, CA, United States
| | - Benjamin Brandrett
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Michael Reinke
- Medical School, University of Minnesota, Minneapolis, MN, United States
| | - Michael Caldwell
- Department of Psychology, University of Wisconsin, Madison, WI, United States
| | - Kent A Kiehl
- Department of Psychology, University of New Mexico and Mind Research Network, Albuquerque, NM, United States
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
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11
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Stewart JL, May AC, Paulus MP. Bouncing back: Brain rehabilitation amid opioid and stimulant epidemics. NEUROIMAGE-CLINICAL 2019; 24:102068. [PMID: 31795056 PMCID: PMC6978215 DOI: 10.1016/j.nicl.2019.102068] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 08/20/2019] [Accepted: 11/03/2019] [Indexed: 12/18/2022]
Abstract
Frontoparietal event related potentials predict/track recovery. Frontostriatal functional magnetic resonance imaging signals predict/track recovery. Transcranial magnetic left prefrontal stimulation reduces craving and drug use.
Recent methamphetamine and opioid use epidemics are a major public health concern. Chronic stimulant and opioid use are characterized by significant psychosocial, physical and mental health costs, repeated relapse, and heightened risk of early death. Neuroimaging research highlights deficits in brain processes and circuitry that are linked to responsivity to drug cues over natural rewards as well as suboptimal goal-directed decision-making. Despite the need for interventions, little is known about (1) how the brain changes with prolonged abstinence or as a function of various treatments; and (2) how symptoms change as a result of neuromodulation. This review focuses on the question: What do we know about changes in brain function during recovery from opioids and stimulants such as methamphetamine and cocaine? We provide a detailed overview and critique of published research employing a wide array of neuroimaging methods – functional and structural magnetic resonance imaging, electroencephalography, event-related potentials, diffusion tensor imaging, and multiple brain stimulation technologies along with neurofeedback – to track or induce changes in drug craving, abstinence, and treatment success in stimulant and opioid users. Despite the surge of methamphetamine and opioid use in recent years, most of the research on neuroimaging techniques for recovery focuses on cocaine use. This review highlights two main findings: (1) interventions can lead to improvements in brain function, particularly in frontal regions implicated in goal-directed behavior and cognitive control, paired with reduced drug urges/craving; and (2) the targeting of striatal mechanisms implicated in drug reward may not be as cost-effective as prefrontal mechanisms, given that deep brain stimulation methods require surgery and months of intervention to produce effects. Overall, more studies are needed to replicate and confirm findings, particularly for individuals with opioid and methamphetamine use disorders.
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Affiliation(s)
- Jennifer L Stewart
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Community Medicine, University of Tulsa, Tulsa, OK, United States.
| | - April C May
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
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12
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Steele VR, Maxwell AM, Ross TJ, Stein EA, Salmeron BJ. Accelerated Intermittent Theta-Burst Stimulation as a Treatment for Cocaine Use Disorder: A Proof-of-Concept Study. Front Neurosci 2019; 13:1147. [PMID: 31736689 PMCID: PMC6831547 DOI: 10.3389/fnins.2019.01147] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 10/11/2019] [Indexed: 11/15/2022] Open
Abstract
There are no effective treatments for cocaine use disorder (CUD), a chronic, relapsing brain disease characterized by dysregulated circuits related to cue reactivity, reward processing, response inhibition, and executive control. Transcranial magnetic stimulation (TMS) has the potential to modulate circuits and networks implicated in neuropsychiatric disorders, including addiction. Although acute applications of TMS have reduced craving in urine-negative cocaine users, the tolerability and safety of administering accelerated TMS to cocaine-positive individuals is unknown. As such, we performed a proof-of-concept study employing an intermittent theta-burst stimulation (iTBS) protocol in an actively cocaine-using sample. Although our main goal was to assess the tolerability and safety of administering three iTBS sessions daily, we also hypothesized that iTBS would reduce cocaine use in this non-treatment seeking cohort. We recruited 19 individuals with CUD to receive three open-label iTBS sessions per day, with approximately a 60-min interval between sessions, for 10 days over a 2-week period (30 total iTBS sessions). iTBS was delivered to left dorsolateral prefrontal cortex (dlPFC) with neuronavigation guidance. Compliance and safety were assessed throughout the trial. Cocaine use behavior was assessed before, during, and after the intervention and at 1- and 4-week follow-up visits. Of the 335 iTBS sessions applied, 73% were performed on participants with cocaine-positive urine tests. Nine of the 14 participants who initiated treatment received at least 26 of 30 iTBS sessions and returned for the 4-week follow-up visit. These individuals reduced their weekly cocaine consumption by 78% in amount of dollars spent and 70% in days of use relative to pre-iTBS cocaine use patterns. Similarly, individuals reduced their weekly consumption of nicotine, alcohol, and THC, suggesting iTBS modulated a common circuit across drugs of abuse. iTBS was well-tolerated, despite the expected occasional headaches. A single participant developed a transient neurological event of uncertain etiology on iTBS day 9 and cocaine-induced psychosis 2 weeks after discontinuation. It thus appears that accelerated iTBS to left dlPFC administered in active, chronic cocaine users is both feasible and tolerable in actively using cocaine participants with preliminary indications of efficacy in reducing both the amount and frequency of cocaine (and other off target drug) use. The neural underpinnings of these behavioral changes could help in the future development of effective treatment of CUD.
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Affiliation(s)
- Vaughn R Steele
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, United States.,Center on Compulsive Behaviors, Intramural Research Program, National Institutes of Health, Bethesda, MD, United States
| | - Andrea M Maxwell
- Medical Scientist Training Program, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Thomas J Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, United States
| | - Elliot A Stein
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, United States
| | - Betty Jo Salmeron
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, United States
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13
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Ekhtiari H, Tavakoli H, Addolorato G, Baeken C, Bonci A, Campanella S, Castelo-Branco L, Challet-Bouju G, Clark VP, Claus E, Dannon PN, Del Felice A, den Uyl T, Diana M, di Giannantonio M, Fedota JR, Fitzgerald P, Gallimberti L, Grall-Bronnec M, Herremans SC, Herrmann MJ, Jamil A, Khedr E, Kouimtsidis C, Kozak K, Krupitsky E, Lamm C, Lechner WV, Madeo G, Malmir N, Martinotti G, McDonald WM, Montemitro C, Nakamura-Palacios EM, Nasehi M, Noël X, Nosratabadi M, Paulus M, Pettorruso M, Pradhan B, Praharaj SK, Rafferty H, Sahlem G, Salmeron BJ, Sauvaget A, Schluter RS, Sergiou C, Shahbabaie A, Sheffer C, Spagnolo PA, Steele VR, Yuan TF, van Dongen JDM, Van Waes V, Venkatasubramanian G, Verdejo-García A, Verveer I, Welsh JW, Wesley MJ, Witkiewitz K, Yavari F, Zarrindast MR, Zawertailo L, Zhang X, Cha YH, George TP, Frohlich F, Goudriaan AE, Fecteau S, Daughters SB, Stein EA, Fregni F, Nitsche MA, Zangen A, Bikson M, Hanlon CA. Transcranial electrical and magnetic stimulation (tES and TMS) for addiction medicine: A consensus paper on the present state of the science and the road ahead. Neurosci Biobehav Rev 2019; 104:118-140. [PMID: 31271802 PMCID: PMC7293143 DOI: 10.1016/j.neubiorev.2019.06.007] [Citation(s) in RCA: 171] [Impact Index Per Article: 34.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 05/30/2019] [Accepted: 06/08/2019] [Indexed: 12/21/2022]
Abstract
There is growing interest in non-invasive brain stimulation (NIBS) as a novel treatment option for substance-use disorders (SUDs). Recent momentum stems from a foundation of preclinical neuroscience demonstrating links between neural circuits and drug consuming behavior, as well as recent FDA-approval of NIBS treatments for mental health disorders that share overlapping pathology with SUDs. As with any emerging field, enthusiasm must be tempered by reason; lessons learned from the past should be prudently applied to future therapies. Here, an international ensemble of experts provides an overview of the state of transcranial-electrical (tES) and transcranial-magnetic (TMS) stimulation applied in SUDs. This consensus paper provides a systematic literature review on published data - emphasizing the heterogeneity of methods and outcome measures while suggesting strategies to help bridge knowledge gaps. The goal of this effort is to provide the community with guidelines for best practices in tES/TMS SUD research. We hope this will accelerate the speed at which the community translates basic neuroscience into advanced neuromodulation tools for clinical practice in addiction medicine.
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Affiliation(s)
| | - Hosna Tavakoli
- Institute for Cognitive Science Studies (ICSS), Iran; Iranian National Center for Addiction Studies (INCAS), Iran
| | - Giovanni Addolorato
- Alcohol Use Disorder Unit, Division of Internal Medicine, Gastroenterology and Hepatology Unit, Catholic University of Rome, A. Gemelli Hospital, Rome, Italy; Fondazione Policlinico Universitario A Gemelli IRCCS, Rome, Italy
| | - Chris Baeken
- Department of Psychiatry and Medical Psychology, University Hospital Ghent, Ghent, Belgium
| | - Antonello Bonci
- Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | | | - Vincent P Clark
- University of New Mexico, USA; The Mind Research Network, USA
| | | | | | - Alessandra Del Felice
- University of Padova, Department of Neuroscience, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | | | - Marco Diana
- 'G. Minardi' Laboratory of Cognitive Neuroscience, Department of Chemistry and Pharmacy, University of Sassari, Italy
| | | | - John R Fedota
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | | | - Luigi Gallimberti
- Novella Fronda Foundation, Human Science and Brain Research, Padua, Italy
| | | | - Sarah C Herremans
- Department of Psychiatry and Medical Psychology, University Hospital Ghent, Ghent, Belgium
| | - Martin J Herrmann
- Center of Mental Health, Department of Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of Würzburg, Würzburg, Germany
| | - Asif Jamil
- Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany
| | | | | | - Karolina Kozak
- University of Toronto, Canada; Centre for Addiction and Mental Health (CAMH), Canada
| | - Evgeny Krupitsky
- V. M. Bekhterev National Medical Research Center for Psychiatry and Neurology, St.-Petersburg, Russia; St.-Petersburg First Pavlov State Medical University, Russia
| | - Claus Lamm
- Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Austria
| | | | - Graziella Madeo
- Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | | | | | - William M McDonald
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Chiara Montemitro
- Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA; University G.d'Annunzio of Chieti-Pescara, Italy
| | | | - Mohammad Nasehi
- Cognitive and Neuroscience Research Center (CNRC), Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Xavier Noël
- Université Libre de Bruxelles (ULB), Belgium
| | | | | | | | | | - Samir K Praharaj
- Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Haley Rafferty
- Spaulding Rehabilitation Hospital, Harvard Medical School, USA
| | | | - Betty Jo Salmeron
- Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Anne Sauvaget
- Laboratory «Movement, Interactions, Performance» (E.A. 4334), University of Nantes, 25 Bis Boulevard Guy Mollet, BP 72206, 44322, Nantes Cedex 3, France; CHU de Nantes Addictology and Liaison Psychiatry Department, University Hospital Nantes, Nantes Cedex 3, France
| | - Renée S Schluter
- Laureate Institute for Brain Research, USA; Institute for Cognitive Science Studies (ICSS), Iran
| | | | - Alireza Shahbabaie
- Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany
| | | | | | - Vaughn R Steele
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Ti-Fei Yuan
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, China
| | | | - Vincent Van Waes
- Laboratoire de Neurosciences Intégratives et Cliniques EA481, Université Bourgogne Franche-Comté, Besançon, France
| | | | | | | | - Justine W Welsh
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | | | | | - Fatemeh Yavari
- Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany
| | - Mohammad-Reza Zarrindast
- Department of Pharmacology School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Laurie Zawertailo
- University of Toronto, Canada; Centre for Addiction and Mental Health (CAMH), Canada
| | - Xiaochu Zhang
- University of Science and Technology of China, China
| | | | - Tony P George
- University of Toronto, Canada; Centre for Addiction and Mental Health (CAMH), Canada
| | | | - Anna E Goudriaan
- Department of Psychiatry, Amsterdam Institute for Addiction Research, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands; Arkin, Department of Research and Quality of Care, Amsterdam, The Netherlands
| | | | | | - Elliot A Stein
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Felipe Fregni
- Spaulding Rehabilitation Hospital, Harvard Medical School, USA
| | - Michael A Nitsche
- Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany; University Medical Hospital Bergmannsheil, Dept. Neurology, Bochum, Germany
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14
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Pasion R, Prata C, Fernandes M, Almeida R, Garcez H, Araújo C, Barbosa F. N2 amplitude modulation across the antisocial spectrum: a meta-analysis. Rev Neurosci 2019; 30:781-794. [DOI: 10.1515/revneuro-2018-0116] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 02/08/2019] [Indexed: 02/04/2023]
Abstract
Abstract
Despite the accumulated knowledge on antisocial behavior and the positive event-related potential peaking around 300 ms (P3), less is known about the preceding negative electrophysiological response around 200 ms (N2). A systematic search of the literature was conducted to analyze the N2 modulation across the antisocial spectrum. Thirty-seven studies (n = 1199) were retrieved to the quantitative analysis. Reduced N2 amplitudes were found in the more severe antisocial manifestations (violent behavior and antisocial personality disorder), which is consistent with previous findings on P3 alterations and N2 reduced amplitudes in externalizing disorders. Findings on psychopathy were mixed, also in accordance with previous P3 results. From a dimensional lens, this supports the heterogeneity of the psychopathic personality structure: impulsivity features are a closer attribute of antisocial behavior and thus may be associated with N2 reduction, while adaptive psychopathic traits may be associated with intact (or even increased) N2 amplitude. The increased N2 amplitudes observed in impulsive behavior challenge, however, the previous meta-analytic findings. As most of the studies on impulsivity include subclinical samples, it leads to the hypothesis that some compensatory mechanisms can still occur at a subclinical level, reflecting the need for heightened allocation of brain resources to yield similar performances. Importantly, inhibition was the core deficit to explain N2 blunted amplitudes, alongside with deficits in the frontal brain region. From our findings, the reduction in P3 amplitude across the antisocial spectrum may be detected in the previous N2 time window.
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Affiliation(s)
- Rita Pasion
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto , Rua Alfredo Allen, 535 , 4200-135 Porto , Portugal
| | - Catarina Prata
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto , Rua Alfredo Allen, 535 , 4200-135 Porto , Portugal
| | - Marisa Fernandes
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto , Rua Alfredo Allen, 535 , 4200-135 Porto , Portugal
| | - Rita Almeida
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto , Rua Alfredo Allen, 535 , 4200-135 Porto , Portugal
| | - Helena Garcez
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto , Rua Alfredo Allen, 535 , 4200-135 Porto , Portugal
| | - Carolina Araújo
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto , Rua Alfredo Allen, 535 , 4200-135 Porto , Portugal
| | - Fernando Barbosa
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto , Rua Alfredo Allen, 535 , 4200-135 Porto , Portugal
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15
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Fede SJ, Grodin EN, Dean SF, Diazgranados N, Momenan R. Resting state connectivity best predicts alcohol use severity in moderate to heavy alcohol users. Neuroimage Clin 2019; 22:101782. [PMID: 30921611 PMCID: PMC6438989 DOI: 10.1016/j.nicl.2019.101782] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 02/07/2019] [Accepted: 03/14/2019] [Indexed: 01/12/2023]
Abstract
BACKGROUND In the United States, 13% of adults are estimated to have alcohol use disorder (AUD). Most studies examining the neurobiology of AUD treat individuals with this disorder as a homogeneous group; however, the theories of the neurocircuitry of AUD call for a quantitative and dimensional approach. Previous imaging studies find differences in brain structure, function, and resting-state connectivity in AUD, but few use a multimodal approach to understand the association between severity of alcohol use and the brain differences. METHODS Adults (ages 22-60) with problem drinking patterns (n = 59) completed a behavioral and neuroimaging protocol at the National Institutes of Health. Alcohol severity was quantified with the Alcohol Use Disorders Identification Test (AUDIT). In a 3 T MRI scanner, participants underwent a structural MRI as well as resting-state, monetary incentive delay, and face matching fMRI scans. Machine learning was applied and trained using the neural data from MRI scanning. The model was tested for generalizability in a validation sample (n = 24). RESULTS The resting state-connectivity features model best predicted AUD severity in the naïve sample, compared to task fMRI, structural MRI, combined MRI features, or demographic features. Network connectivity features between salience network, default mode network, executive control network, and sensory networks explained 33% of the variance associated with AUDIT in this model. CONCLUSIONS These findings indicate that the neural effects of AUD vary according to severity. Our results emphasize the utility of resting state fMRI as a neuroimaging biomarker for quantitative clinical evaluation of AUD.
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Affiliation(s)
- Samantha J Fede
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States.
| | - Erica N Grodin
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Sarah F Dean
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Nancy Diazgranados
- Office of Clinical Director, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States.
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16
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Stewart JL, May AC, Aupperle RL, Bodurka J. Forging Neuroimaging Targets for Recovery in Opioid Use Disorder. Front Psychiatry 2019; 10:117. [PMID: 30899231 PMCID: PMC6417368 DOI: 10.3389/fpsyt.2019.00117] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 02/15/2019] [Indexed: 01/01/2023] Open
Abstract
The United States is in the midst of an opioid epidemic and lacks a range of successful interventions to reduce this public health burden. Many individuals with opioid use disorder (OUD) consume drugs to relieve physical and/or emotional pain, a pattern that may increasingly result in death. The field of addiction research lacks a comprehensive understanding of physiological and neural mechanisms instantiating this cycle of Negative Reinforcement in OUD, resulting in limited interventions that successfully promote abstinence and recovery. Given the urgency of the opioid crisis, the present review highlights faulty brain circuitry and processes associated with OUD within the context of the Three-Stage Model of Addiction (1). This model underscores Negative Reinforcement processes as crucial to the maintenance and exacerbation of chronic substance use together with Binge/Intoxication and Preoccupation/Anticipation processes. This review focuses on cross-sectional as well as longitudinal studies of relapse and treatment outcome that employ magnetic resonance imaging (MRI), functional near-infrared spectroscopy (fNIRs), brain stimulation methods, and/or electroencephalography (EEG) explored in frequency and time domains (the latter measured by event-related potentials, or ERPs). We discuss strengths and limitations of this neuroimaging work with respect to study design and individual differences that may influence interpretation of findings (e.g., opioid use chronicity/recency, comorbid symptoms, and biological sex). Lastly, we translate gaps in the OUD literature, particularly with respect to Negative Reinforcement processes, into future research directions involving operant and classical conditioning involving aversion/stress. Overall, opioid-related stimuli may lessen their hold on frontocingulate mechanisms implicated in Preoccupation/Anticipation as a function of prolonged abstinence and that degree of frontocingulate impairment may predict treatment outcome. In addition, longitudinal studies suggest that brain stimulation/drug treatments and prolonged abstinence can change brain responses during Negative Reinforcement and Preoccupation/Anticipation to reduce salience of drug cues, which may attenuate further craving and relapse. Incorporating this neuroscience-derived knowledge with the Three-Stage Model of Addiction may offer a useful plan for delineating specific neurobiological targets for OUD treatment.
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Affiliation(s)
- Jennifer L Stewart
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
| | - April C May
- Joint Doctoral Program in Clinical Psychology, San Diego State University, University of California, San Diego, San Diego, CA, United States
| | - Robin L Aupperle
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States
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17
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Diehl MM, Lempert KM, Parr AC, Ballard I, Steele VR, Smith DV. Toward an integrative perspective on the neural mechanisms underlying persistent maladaptive behaviors. Eur J Neurosci 2018; 48:1870-1883. [PMID: 30044022 PMCID: PMC6113118 DOI: 10.1111/ejn.14083] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2018] [Revised: 06/13/2018] [Accepted: 06/26/2018] [Indexed: 01/29/2023]
Affiliation(s)
- Maria M. Diehl
- Department of Psychiatry, University of Puerto Rico School of Medicine, San Juan, PR 00936
| | - Karolina M. Lempert
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
| | - Ashley C. Parr
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario
| | - Ian Ballard
- Neurosciences Graduate Training Program, Stanford University, Stanford, CA 94305
| | - Vaughn R. Steele
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - David V. Smith
- Department of Psychology, Temple University, Philadelphia, PA 19122
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18
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Ramlakhan JU, Zomorrodi R, Downar J, Blumberger DM, Daskalakis ZJ, George TP, Kiang M, Barr MS. Using Mismatch Negativity to Investigate the Pathophysiology of Substance Use Disorders and Comorbid Psychosis. Clin EEG Neurosci 2018; 49:226-237. [PMID: 29502434 DOI: 10.1177/1550059418760077] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Substance use disorders (SUDs) have a devastating impact on society and place a heavy burden on health care systems. Given that alcohol, tobacco, and cannabis use have the highest prevalence, further understanding of the underlying pathophysiology of these SUDs is crucial. Electroencephalography is an inexpensive, temporally superior, and translatable technique which enables investigation of the pathobiology of SUDs through the evaluation of various event-related potential components, including mismatch negativity (MMN). The goals of this review were to investigate the effects of acute and chronic alcohol, tobacco, and cannabis use on MMN among nonpsychiatric populations and patients with comorbid psychosis. A literature search was performed using the database PubMed, and 36 articles met our inclusion and exclusion criteria. We found a pattern of attenuation of MMN amplitude among patients with alcoholism across acute and chronic alcohol use, and this dysregulation was not heritable. Reports were limited, and results were mixed on the effects of acute and chronic tobacco and cannabis use on MMN. Reports on comorbid SUDs and psychosis were even fewer, and also presented mixed findings. These preliminary results suggest that MMN deficits may be associated with SUDs, specifically alcohol use disorder, and serve as a possible biomarker for treating these common disorders.
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Affiliation(s)
- Jessica U Ramlakhan
- 1 Temerty Centre for Therapeutic Brain Intervention, Division of Mood and Anxiety, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,2 Biobehavioural Addictions and Concurrent Disorders Research Laboratory (BACDRL), Additions Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Reza Zomorrodi
- 1 Temerty Centre for Therapeutic Brain Intervention, Division of Mood and Anxiety, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Jonathan Downar
- 3 Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.,4 Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,5 Division of Brain and Therapeutics, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Daniel M Blumberger
- 1 Temerty Centre for Therapeutic Brain Intervention, Division of Mood and Anxiety, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,4 Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,5 Division of Brain and Therapeutics, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Zafiris J Daskalakis
- 1 Temerty Centre for Therapeutic Brain Intervention, Division of Mood and Anxiety, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,4 Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,5 Division of Brain and Therapeutics, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Tony P George
- 2 Biobehavioural Addictions and Concurrent Disorders Research Laboratory (BACDRL), Additions Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,4 Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,5 Division of Brain and Therapeutics, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Michael Kiang
- 1 Temerty Centre for Therapeutic Brain Intervention, Division of Mood and Anxiety, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,4 Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,5 Division of Brain and Therapeutics, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Mera S Barr
- 1 Temerty Centre for Therapeutic Brain Intervention, Division of Mood and Anxiety, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,4 Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,5 Division of Brain and Therapeutics, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
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19
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Maurer JM, Steele VR, Fink BC, Vincent GM, Calhoun VD, Kiehl KA. Investigating error-related processing in incarcerated adolescents with self-report psychopathy measures. Biol Psychol 2018; 132:96-105. [PMID: 29180243 PMCID: PMC6047355 DOI: 10.1016/j.biopsycho.2017.11.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2017] [Revised: 09/01/2017] [Accepted: 11/20/2017] [Indexed: 01/17/2023]
Abstract
Disparate results have been found in previous reports when incorporating both interview-based and self-report measures of psychopathic traits within the same sample, suggesting such assessments should not be used interchangeably. We previously found Total and Facet 4 scores from Hare's Psychopathy Checklist: Youth Version (PCL:YV) were negatively related to amplitude of the error-related positivity (Pe) event-related potential (ERP) component. Here, we investigated using the same previously published sample whether scores on four different self-report measures of adolescent psychopathic traits (the Antisocial Process Screening Device [APSD], Child Psychopathy Scale [CPS], Inventory of Callous-Unemotional Traits [ICU], and Youth Psychopathic Traits Inventory [YPI]) were similarly associated with reduced Pe amplitude. Unlike our previous results, adolescent self-report psychopathy scores were not associated with reduced Pe amplitude in multiple regression analyses. Results obtained in the current report support previous research observing incongruent findings when incorporating different assessment types within the same sample.
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Affiliation(s)
- J Michael Maurer
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States; The Mind Research Network (MRN) & Lovelace Biomedical and Environmental Research Institute (LBERI), Albuquerque, NM, United States.
| | - Vaughn R Steele
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, United States
| | - Brandi C Fink
- Department of Psychiatry and Behavioral Sciences, Clinical and Translational Science Center, University of New Mexico, Albuquerque, NM, United States
| | - Gina M Vincent
- Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, United States
| | - Vince D Calhoun
- The Mind Research Network (MRN) & Lovelace Biomedical and Environmental Research Institute (LBERI), Albuquerque, NM, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Kent A Kiehl
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States; The Mind Research Network (MRN) & Lovelace Biomedical and Environmental Research Institute (LBERI), Albuquerque, NM, United States.
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Houston RJ, Schlienz NJ. Event-Related Potentials as Biomarkers of Behavior Change Mechanisms in Substance Use Disorder Treatment. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 3:30-40. [PMID: 29397076 DOI: 10.1016/j.bpsc.2017.09.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 09/15/2017] [Accepted: 09/16/2017] [Indexed: 12/21/2022]
Abstract
Substance use disorders (SUDs) are one of the most prevalent psychiatric conditions and represent a significant public health concern. Substantial research has identified key processes related to reinforcement and cognition for the development and maintenance of SUDs, and these processes represent viable treatment targets for psychosocial and pharmacological interventions. Research on SUD treatments has suggested that most approaches are comparable in effectiveness. As a result, recent work has focused on delineating the underlying mechanisms of behavior change that drive SUD treatment outcome. Given the rapid fluctuations associated with the key neurocognitive processes associated with SUDs, high-temporal-resolution measures of human brain processing, namely event-related potentials (ERPs), are uniquely suited to expand our understanding of the underlying neural mechanisms of change during and after SUD treatment. The value of ERPs in the context of SUD treatment are discussed along with work demonstrating the predictive validity of ERPs as biomarkers of SUD treatment response. Example associations between multiple ERP components and psychosocial and/or pharmacological treatment outcome include the P3a and P3b (in response to neutral and substance-related cues), the attention-related negativities (e.g., N170, N200), the late positive potential, and the error-related negativity. Also addressed are limitations of the biomarker approach to underscore the need for research programs evaluating mechanisms of change. Finally, we emphasize the advantages of ERPs as indices of behavior change in SUD treatment and outline issues relevant for future directions in this context.
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Affiliation(s)
- Rebecca J Houston
- Health and Addictions Research Center, Department of Psychology, Rochester Institute of Technology, Rochester, New York.
| | - Nicolas J Schlienz
- Behavioral Pharmacology Research Unit, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Steele VR, Maurer JM, Arbabshirani MR, Claus ED, Fink BC, Rao V, Calhoun VD, Kiehl KA. Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017. [PMID: 29529409 DOI: 10.1016/j.bpsc.2017.07.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Successfully treating illicit drug use has become paramount, yet elusive. Devising specialized treatment interventions could increase positive outcomes, but it is necessary to identify risk factors of poor long-term outcomes to develop specialized, efficacious treatments. We investigated whether functional network connectivity (FNC) measures were predictive of substance abuse treatment completion using machine learning pattern classification of functional magnetic resonance imaging data. METHODS Treatment-seeking stimulant- or heroin-dependent incarcerated participants (n = 139; 89 women) volunteered for a 12-week substance abuse treatment program. Participants performed a response inhibition Go/NoGo functional magnetic resonance imaging task prior to onset of the substance abuse treatment. We tested whether FNC related to the anterior cingulate cortex would be predictive of those who would or would not complete a 12-week substance abuse treatment program. RESULTS Machine learning pattern classification models using FNC between networks incorporating the anterior cingulate cortex, striatum, and insula predicted which individuals would (sensitivity: 81.31%) or would not (specificity: 78.13%) complete substance abuse treatment. FNC analyses predicted treatment completion above and beyond other clinical assessment measures, including age, sex, IQ, years of substance use, psychopathy, anxiety and depressive symptomatology, and motivation for change. CONCLUSIONS Aberrant neural network connections predicted substance abuse treatment outcomes, which could illuminate new targets for developing interventions designed to reduce or eliminate substance use while facilitating long-term outcomes. This work represents the first application of machine-learning models of FNC analyses of functional magnetic resonance imaging data to predict which substance abusers would or would not complete treatment.
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Affiliation(s)
- Vaughn R Steele
- Intramural Research Program, Neuroimaging Research Branch, National Institute of Drug Abuse, National Institutes of Health, Baltimore, Maryland.
| | - J Michael Maurer
- Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Department of Psychology, University of New Mexico, Albuquerque, New Mexico
| | - Mohammad R Arbabshirani
- Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Institute for Advanced Application, Geisinger Health System, Danville, Pennsylvania
| | - Eric D Claus
- Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Brandi C Fink
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico
| | - Vikram Rao
- Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Vince D Calhoun
- Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico; Department of Neurosciences, University of New Mexico, Albuquerque, New Mexico
| | - Kent A Kiehl
- Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Department of Psychology, University of New Mexico, Albuquerque, New Mexico; Department of Neurosciences, University of New Mexico, Albuquerque, New Mexico
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Fink BC, Steele VR, Maurer MJ, Fede SJ, Calhoun VD, Kiehl KA. Brain potentials predict substance abuse treatment completion in a prison sample. Brain Behav 2016; 6:e00501. [PMID: 27547503 PMCID: PMC4893048 DOI: 10.1002/brb3.501] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 04/15/2016] [Accepted: 04/22/2016] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION National estimates suggest that up to 80% of prison inmates meet diagnostic criteria for a substance use disorder. Because more substance abuse treatment while incarcerated is associated with better post-release outcomes, including a reduced risk of accidental overdose death, the stakes are high in developing novel predictors of substance abuse treatment completion in inmate populations. METHODS Using electroencephalography (EEG), this study investigated stimulus-locked ERP components elicited by distractor stimuli in three tasks (VO-Distinct, VO-Repeated, Go/NoGo) as a predictor of treatment discontinuation in a sample of male and female prison inmates. We predicted that those who discontinued treatment early would exhibit a less positive P3a amplitude elicited by distractor stimuli. RESULTS Our predictions regarding ERP components were partially supported. Those who discontinued treatment early exhibited a less positive P3a amplitude and a less positive PC4 in the VO-D task. In the VO-R task, however, those who discontinued treatment early exhibited a more negative N200 amplitude rather than the hypothesized less positive P3a amplitude. The discontinuation group also displayed less positive PC4 amplitude. Surprisingly, there were no time-domain or principle component differences among the groups in the Go/NoGo task. Support Vector Machine (SVM) models of the three tasks accurately classified individuals who discontinued treatment with the best model accurately classifying 75% of inmates. PCA techniques were more sensitive in differentiating groups than the classic time-domain windowed approach. CONCLUSIONS Our pattern of findings are consistent with the context-updating theory of P300 and may help identify subtypes of ultrahigh-risk substance abusers who need specialized treatment programs.
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Affiliation(s)
- Brandi C. Fink
- Department of Psychiatry and Behavioral SciencesClinical and Translational Science CenterThe University of New MexicoAlbuquerqueNew Mexico
| | - Vaughn R. Steele
- Intramural Research ProgramNeuroimaging Research BranchNational Institute of Drug AbuseNational Institutes of HealthBaltimoreMaryland
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerqueNew Mexico
- Department of PsychologyThe University of New MexicoAlbuquerqueNew Mexico
| | - Michael J. Maurer
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerqueNew Mexico
- Department of PsychologyThe University of New MexicoAlbuquerqueNew Mexico
| | - Samantha J. Fede
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerqueNew Mexico
- Department of PsychologyThe University of New MexicoAlbuquerqueNew Mexico
| | - Vince D. Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerqueNew Mexico
- Department of Electrical and Computer EngineeringThe University of New MexicoAlbuquerqueNew Mexico
| | - Kent A. Kiehl
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerqueNew Mexico
- Department of PsychologyThe University of New MexicoAlbuquerqueNew Mexico
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