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Zopfs M, Jindrová M, Gurevitch G, Keynan JN, Hendler T, Baumeister S, Aggensteiner PM, Cornelisse S, Brandeis D, Schmahl C, Paret C. Amygdala-related electrical fingerprint is modulated with neurofeedback training and correlates with deep-brain activation: proof-of-concept in borderline personality disorder. Psychol Med 2024; 54:1651-1660. [PMID: 38131344 DOI: 10.1017/s0033291723003549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
BACKGROUND The modulation of brain circuits of emotion is a promising pathway to treat borderline personality disorder (BPD). Precise and scalable approaches have yet to be established. Two studies investigating the amygdala-related electrical fingerprint (Amyg-EFP) in BPD are presented: one study addressing the deep-brain correlates of Amyg-EFP, and a second study investigating neurofeedback (NF) as a means to improve brain self-regulation. METHODS Study 1 combined electroencephalography (EEG) and simultaneous functional magnetic resonance imaging to investigate the replicability of Amyg-EFP-related brain activation found in the reference dataset (N = 24 healthy subjects, 8 female; re-analysis of published data) in the replication dataset (N = 16 female individuals with BPD). In the replication dataset, we additionally explored how the Amyg-EFP would map to neural circuits defined by the research domain criteria. Study 2 investigated a 10-session Amyg-EFP NF training in parallel to a 12-weeks residential dialectical behavior therapy (DBT) program. Fifteen patients with BPD completed the training, N = 15 matched patients served as DBT-only controls. RESULTS Study 1 replicated previous findings and showed significant amygdala blood oxygenation level dependent activation in a whole-brain regression analysis with the Amyg-EFP. Neurocircuitry activation (negative affect, salience, and cognitive control) was correlated with the Amyg-EFP signal. Study 2 showed Amyg-EFP modulation with NF training, but patients received reversed feedback for technical reasons, which limited interpretation of results. CONCLUSIONS Recorded via scalp EEG, the Amyg-EFP picks up brain activation of high relevance for emotion. Administering Amyg-EFP NF in addition to standardized BPD treatment was shown to be feasible. Clinical utility remains to be investigated.
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Affiliation(s)
- Malte Zopfs
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Miroslava Jindrová
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Guy Gurevitch
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Jackob N Keynan
- Brain Stimulation Lab, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Talma Hendler
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv, Israel
- School of Psychological Sciences and Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Pascal-M Aggensteiner
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Sven Cornelisse
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Christian Schmahl
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Christian Paret
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
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Fleury M, Figueiredo P, Vourvopoulos A, Lécuyer A. Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review. J Neural Eng 2023; 20:051003. [PMID: 37879343 DOI: 10.1088/1741-2552/ad06e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 10/25/2023] [Indexed: 10/27/2023]
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.
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Affiliation(s)
- Mathis Fleury
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Athanasios Vourvopoulos
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Anatole Lécuyer
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
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Tene O, Bleich Cohen M, Helpman L, Fine N, Halevy A, Goldway N, Perry D, Bary P, Aisenberg Romano G, Ben-Zion Z, Hendler T, Bloch M. Limbic self-neuromodulation as a novel treatment option for emotional dysregulation in premenstrual dysphoric disorder (PMDD); a proof-of-concept study. Psychiatry Clin Neurosci 2023; 77:550-558. [PMID: 37354437 DOI: 10.1111/pcn.13574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/07/2023] [Accepted: 06/19/2023] [Indexed: 06/26/2023]
Abstract
AIM To assess the efficacy of a novel neurofeedback (NF) method, targeting limbic activity, to treat emotional dysregulation related to premenstrual dysphoric disorder (PMDD). METHODS We applied a NF probe targeting limbic activity using a functional magnetic resonance imaging-inspired electroencephalogram model (termed Amyg-EFP-NF) in a double-blind randomized controlled trial. A frontal alpha asymmetry probe (AAS-NF), served as active control. Twenty-seven participants diagnosed with PMDD (mean age = 33.57 years, SD = 5.67) were randomly assigned to Amyg-EFP-NF or AAS-NF interventions with a 2:1 ratio, respectively. The treatment protocol consisted of 11 NF sessions through three menstrual cycles, and a follow-up assessment 3 months thereafter. The primary outcome measure was improvement in the Revised Observer Version of the Premenstrual Tension Syndrome Rating Scale (PMTS-OR). RESULTS A significant group by time effect was observed for the core symptom subscale of the PMTS-OR, with significant improvement observed at follow-up for the Amyg-EFP group compared with the AAS group [F(1, 15)=4.968, P = 0.042]. This finding was specifically robust for reduction in anger [F(1, 15) = 22.254, P < 0.001]. A significant correlation was found between learning scores and overall improvement in core symptoms (r = 0.514, P = 0.042) suggesting an association between mechanism of change and clinical improvement. CONCLUSION Our preliminary findings suggest that Amyg-EFP-NF may serve as an affordable and accessible non-invasive treatment option for emotional dysregulation in women suffering from PMDD. Our main limitations were the relatively small number of participants and the lack of a sham-NF placebo arm.
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Affiliation(s)
- Oren Tene
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Maya Bleich Cohen
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Liat Helpman
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Department of Counseling and Human Development, Faculty of Education, University of Haifa, Haifa, Israel
| | - Naomi Fine
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Anat Halevy
- Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel
| | - Noam Goldway
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Department of Psychology, New York University, New York City, New York, USA
| | - Daniella Perry
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Plia Bary
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Gabi Aisenberg Romano
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Ziv Ben-Zion
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Departments of Comparative Medicine and Psychiatry, Yale School of Medicine, Yale University, New Haven, Connecticut, USA
- Clinical Neuroscience Division, US Department of Veterans Affairs National Center for PTSD, VA Connecticut Healthcare System, West Haven, Connecticut, USA
| | - Talma Hendler
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Miki Bloch
- Department of Psychiatry and Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Sagol School of Neuroscience and Faculty of Social Sciences, Tel-Aviv University, Tel-Aviv, Israel
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Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity. Neuroimage 2023; 266:119822. [PMID: 36535325 DOI: 10.1016/j.neuroimage.2022.119822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 11/17/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed "Electrical FingerPrint of rIFG"; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe.
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Kinreich S, Meyers JL, Maron-Katz A, Kamarajan C, Pandey AK, Chorlian DB, Zhang J, Pandey G, Subbie-Saenz de Viteri S, Pitti D, Anokhin AP, Bauer L, Hesselbrock V, Schuckit MA, Edenberg HJ, Porjesz B. Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study. Mol Psychiatry 2021; 26:1133-1141. [PMID: 31595034 PMCID: PMC7138692 DOI: 10.1038/s41380-019-0534-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 09/11/2019] [Accepted: 09/20/2019] [Indexed: 11/09/2022]
Abstract
Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development.
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Affiliation(s)
- Sivan Kinreich
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA.
| | - Jacquelyn L Meyers
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Chella Kamarajan
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Ashwini K Pandey
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - David B Chorlian
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Jian Zhang
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Gayathri Pandey
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | | | - Dan Pitti
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Andrey P Anokhin
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Lance Bauer
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Bernice Porjesz
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
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Kinreich S, McCutcheon VV, Aliev F, Meyers JL, Kamarajan C, Pandey AK, Chorlian DB, Zhang J, Kuang W, Pandey G, Viteri SSSD, Francis MW, Chan G, Bourdon JL, Dick DM, Anokhin AP, Bauer L, Hesselbrock V, Schuckit MA, Nurnberger JI, Foroud TM, Salvatore JE, Bucholz KK, Porjesz B. Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach. Transl Psychiatry 2021; 11:166. [PMID: 33723218 PMCID: PMC7960734 DOI: 10.1038/s41398-021-01281-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 12/07/2020] [Accepted: 12/16/2020] [Indexed: 12/02/2022] Open
Abstract
Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.
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Affiliation(s)
- Sivan Kinreich
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA.
| | - Vivia V McCutcheon
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Fazil Aliev
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
- Faculty of Business, Karabuk University, Karabük, Turkey
| | - Jacquelyn L Meyers
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Chella Kamarajan
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Ashwini K Pandey
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - David B Chorlian
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Jian Zhang
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Weipeng Kuang
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Gayathri Pandey
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | | | - Meredith W Francis
- Brown School of Social Work / Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - Grace Chan
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Jessica L Bourdon
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Danielle M Dick
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Andrey P Anokhin
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Lance Bauer
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - John I Nurnberger
- Departments of Psychiatry and Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tatiana M Foroud
- Department of Medical and Molecular Genetics at Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jessica E Salvatore
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Bernice Porjesz
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
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Non-Pharmacological Management of Painful Peripheral Neuropathies: A Systematic Review. Adv Ther 2020; 37:4096-4106. [PMID: 32809209 DOI: 10.1007/s12325-020-01462-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Peripheral neuropathic pain (PNP) is defined as the neuropathic pain that arises either acutely or in the chronic phase of a lesion or disease affecting the peripheral nervous system. PNP is associated with a remarkable disease burden, and there is an increasing demand for new therapies to be used in isolation or combination with currently available treatments. The aim of this systematic review was to evaluate the current evidence, derived from randomized controlled trials (RCTs) that assess non-pharmacological interventions for the treatment of PNP. METHODS After a systematic Medline search, we identified 18 papers eligible to be included. RESULTS The currently best available evidence (level II of evidence) exist for painful diabetic peripheral neuropathy. In particular, spinal cord stimulation as adjuvant to conventional medical treatment can be effectively used for the management of patients with refractory pain. Similarly, adjuvant repetitive transcranial magnetic stimulation of the motor cortex is effective in reducing the overall pain intensity, whereas adjuvant static magnetic field therapy can lead to a significant decrease in exercise-induced pain. Weaker evidence (level III of evidence) exists for the use of acupuncture as a monotherapy and neurofeedback, either as an add-on or a monotherapy approach, for treatment of painful chemotherapy-induced peripheral neuropathy CONCLUSIONS: Future RCTs should be conducted to shed more light in the use of non-pharmacological approaches in patients with PNP.
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Goldway N, Ablin J, Lubin O, Zamir Y, Keynan JN, Or-Borichev A, Cavazza M, Charles F, Intrator N, Brill S, Ben-Simon E, Sharon H, Hendler T. Volitional limbic neuromodulation exerts a beneficial clinical effect on Fibromyalgia. Neuroimage 2019; 186:758-770. [DOI: 10.1016/j.neuroimage.2018.11.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 10/03/2018] [Accepted: 11/01/2018] [Indexed: 12/18/2022] Open
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Prinsloo S, Novy D, Driver L, Lyle R, Ramondetta L, Eng C, Lopez G, Li Y, Cohen L. The Long-Term Impact of Neurofeedback on Symptom Burden and Interference in Patients With Chronic Chemotherapy-Induced Neuropathy: Analysis of a Randomized Controlled Trial. J Pain Symptom Manage 2018; 55:1276-1285. [PMID: 29421164 DOI: 10.1016/j.jpainsymman.2018.01.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 01/05/2018] [Accepted: 01/11/2018] [Indexed: 12/19/2022]
Abstract
CONTEXT Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect of cancer treatment and may adversely affect quality of life (QOL) for years. OBJECTIVES We explored the long-term effects of electroencephalographic neurofeedback (NFB) as a treatment for CIPN and other aspects of QOL. METHODS Seventy-one cancer survivors (mean age 62.5; 87% females) with CIPN were randomized to NFB or to a waitlist control (WLC) group. The NFB group underwent 20 sessions of NFB where rewards were given for voluntary changes in electroencephalography. Measurements of pain, cancer-related symptoms, QOL, sleep, and fatigue were obtained at baseline, end of treatment, and one and four months later. RESULTS Seventy one participants enrolled in the study. At the end of treatment, 30 in the NFB group and 32 in the WLC group completed assessments; at four months, 23 in the NFB group and 28 in the WLC completed assessments. Linear mixed model analysis revealed significant group × time interaction for pain severity. A general linear model determined that the NFB group had greater improvements in worst pain (primary outcome) and other symptoms such as numbness, cancer-related symptom severity, symptom interference, physical functioning, general health, and fatigue compared with the WLC group at the end of treatment and four months (all P < 0.05). Effect sizes were moderate or large for most measures. CONCLUSION NFB appears to result in long-term reduction in multiple CIPN symptoms and improved postchemotherapy QOL and fatigue.
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Affiliation(s)
- Sarah Prinsloo
- Department of Palliative, Rehabilitation, and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
| | - Diane Novy
- Department of Pain Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Larry Driver
- Department of Pain Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Randall Lyle
- Department of Marriage and Family Therapy, Mount Mercy University, Cedar Rapids, Iowa, USA
| | - Lois Ramondetta
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Cathy Eng
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gabriel Lopez
- Department of Palliative, Rehabilitation, and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Yisheng Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lorenzo Cohen
- Department of Palliative, Rehabilitation, and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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10
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D'Atri A, Romano C, Gorgoni M, Scarpelli S, Alfonsi V, Ferrara M, Ferlazzo F, Rossini PM, De Gennaro L. Bilateral 5 Hz transcranial alternating current stimulation on fronto-temporal areas modulates resting-state EEG. Sci Rep 2017; 7:15672. [PMID: 29142322 PMCID: PMC5688177 DOI: 10.1038/s41598-017-16003-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 11/03/2017] [Indexed: 02/08/2023] Open
Abstract
Rhythmic non-invasive brain stimulations are promising tools to modulate brain activity by entraining neural oscillations in specific cortical networks. The aim of the study was to assess the possibility to influence the neural circuits of the wake-sleep transition in awake subjects via a bilateral transcranial alternating current stimulation at 5 Hz (θ-tACS) on fronto-temporal areas. 25 healthy volunteers participated in two within-subject sessions (θ-tACS and sham), one week apart and in counterbalanced order. We assessed the stimulation effects on cortical EEG activity (28 derivations) and self-reported sleepiness (Karolinska Sleepiness Scale). θ-tACS induced significant increases of the theta activity in temporo-parieto-occipital areas and centro-frontal increases in the alpha activity compared to sham but failed to induce any online effect on sleepiness. Since the total energy delivered in the sham condition was much less than in the active θ-tACS, the current data are unable to isolate the specific effect of entrained theta oscillatory activity per se on sleepiness scores. On this basis, we concluded that θ-tACS modulated theta and alpha EEG activity with a topography consistent with high sleep pressure conditions. However, no causal relation can be traced on the basis of the current results between these rhythms and changes on sleepiness.
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Affiliation(s)
- Aurora D'Atri
- Department of Psychology, University of Rome "Sapienza", Via dei Marsi 78, 00185, Rome, Italy
- IRCCS San Raffaele Pisana, Via della Pisana 235, 00163, Rome, Italy
| | - Claudia Romano
- Department of Psychology, University of Rome "Sapienza", Via dei Marsi 78, 00185, Rome, Italy
| | - Maurizio Gorgoni
- Department of Psychology, University of Rome "Sapienza", Via dei Marsi 78, 00185, Rome, Italy
| | - Serena Scarpelli
- Department of Psychology, University of Rome "Sapienza", Via dei Marsi 78, 00185, Rome, Italy
| | - Valentina Alfonsi
- Department of Psychology, University of Rome "Sapienza", Via dei Marsi 78, 00185, Rome, Italy
| | - Michele Ferrara
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Via Vetoio (Coppito 2), 67100 Coppito, L'Aquila, Italy
| | - Fabio Ferlazzo
- Department of Psychology, University of Rome "Sapienza", Via dei Marsi 78, 00185, Rome, Italy
| | - Paolo Maria Rossini
- IRCCS San Raffaele Pisana, Via della Pisana 235, 00163, Rome, Italy
- Institute of Neurology, Catholic University of The Sacred Heart, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Luigi De Gennaro
- Department of Psychology, University of Rome "Sapienza", Via dei Marsi 78, 00185, Rome, Italy.
- IRCCS San Raffaele Pisana, Via della Pisana 235, 00163, Rome, Italy.
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11
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Ben Simon E, Maron-Katz A, Lahav N, Shamir R, Hendler T. Tired and misconnected: A breakdown of brain modularity following sleep deprivation. Hum Brain Mapp 2017; 38:3300-3314. [PMID: 28370703 DOI: 10.1002/hbm.23596] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Revised: 02/10/2017] [Accepted: 03/20/2017] [Indexed: 12/18/2022] Open
Abstract
Sleep deprivation (SD) critically affects a range of cognitive and affective functions, typically assessed during task performance. Whether such impairments stem from changes to the brain's intrinsic functional connectivity remain largely unknown. To examine this hypothesis, we applied graph theoretical analysis on resting-state fMRI data derived from 18 healthy participants, acquired during both sleep-rested and sleep-deprived states. We hypothesized that parameters indicative of graph connectivity, such as modularity, will be impaired by sleep deprivation and that these changes will correlate with behavioral outcomes elicited by sleep loss. As expected, our findings point to a profound reduction in network modularity without sleep, evident in the limbic, default-mode, salience and executive modules. These changes were further associated with behavioral impairments elicited by SD: a decrease in salience module density was associated with worse task performance, an increase in limbic module density was predictive of stronger amygdala activation in a subsequent emotional-distraction task and a shift in frontal hub lateralization (from left to right) was associated with increased negative mood. Altogether, these results portray a loss of functional segregation within the brain and a shift towards a more random-like network without sleep, already detected in the spontaneous activity of the sleep-deprived brain. Hum Brain Mapp 38:3300-3314, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Eti Ben Simon
- Functional Brain Center, Wohl Institute for Advanced Imaging Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Adi Maron-Katz
- Functional Brain Center, Wohl Institute for Advanced Imaging Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Nir Lahav
- Department of Physics, Bar-Ilan University, Ramat Gan, Israel
| | - Ron Shamir
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Talma Hendler
- Functional Brain Center, Wohl Institute for Advanced Imaging Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,School of Psychological Sciences, Tel Aviv University, Tel-Aviv, Israel.,Sagol school of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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12
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Keynan JN, Meir-Hasson Y, Gilam G, Cohen A, Jackont G, Kinreich S, Ikar L, Or-Borichev A, Etkin A, Gyurak A, Klovatch I, Intrator N, Hendler T. Limbic Activity Modulation Guided by Functional Magnetic Resonance Imaging-Inspired Electroencephalography Improves Implicit Emotion Regulation. Biol Psychiatry 2016; 80:490-496. [PMID: 26996601 DOI: 10.1016/j.biopsych.2015.12.024] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Revised: 12/16/2015] [Accepted: 12/17/2015] [Indexed: 01/19/2023]
Abstract
The amygdala has a pivotal role in processing traumatic stress; hence, gaining control over its activity could facilitate adaptive mechanism and recovery. To date, amygdala volitional regulation could be obtained only via real-time functional magnetic resonance imaging (fMRI), a highly inaccessible procedure. The current article presents high-impact neurobehavioral implications of a novel imaging approach that enables bedside monitoring of amygdala activity using fMRI-inspired electroencephalography (EEG), hereafter termed amygdala-electrical fingerprint (amyg-EFP). Simultaneous EEG/fMRI indicated that the amyg-EFP reliably predicts amygdala-blood oxygen level-dependent activity. Implementing the amyg-EFP in neurofeedback demonstrated that learned downregulation of the amyg-EFP facilitated volitional downregulation of amygdala-blood oxygen level-dependent activity via real-time fMRI and manifested as reduced amygdala reactivity to visual stimuli. Behavioral evidence further emphasized the therapeutic potential of this approach by showing improved implicit emotion regulation following amyg-EFP neurofeedback. Additional EFP models denoting different brain regions could provide a library of localized activity for low-cost and highly accessible brain-based diagnosis and treatment.
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Affiliation(s)
- Jackob N Keynan
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Tel-Aviv, Israel; The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | | | - Gadi Gilam
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Tel-Aviv, Israel; The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Avihay Cohen
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Tel-Aviv, Israel
| | - Gilan Jackont
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Tel-Aviv, Israel; The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Sivan Kinreich
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Tel-Aviv, Israel; The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Limor Ikar
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Tel-Aviv, Israel
| | - Ayelet Or-Borichev
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Tel-Aviv, Israel; The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford; Sierra-Pacific Mental Illness Research, Education, and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Anett Gyurak
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford; Sierra-Pacific Mental Illness Research, Education, and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Ilana Klovatch
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Tel-Aviv, Israel
| | - Nathan Intrator
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel; Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Talma Hendler
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Tel-Aviv, Israel; The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel; Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
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13
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Meir-Hasson Y, Keynan JN, Kinreich S, Jackont G, Cohen A, Podlipsky-Klovatch I, Hendler T, Intrator N. One-Class FMRI-Inspired EEG Model for Self-Regulation Training. PLoS One 2016; 11:e0154968. [PMID: 27163677 PMCID: PMC4862623 DOI: 10.1371/journal.pone.0154968] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Accepted: 04/21/2016] [Indexed: 11/18/2022] Open
Abstract
Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations.
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Affiliation(s)
- Yehudit Meir-Hasson
- The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
- * E-mail:
| | - Jackob N. Keynan
- The Functional Brain Imaging Unit, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Sivan Kinreich
- The Functional Brain Imaging Unit, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Gilan Jackont
- The Functional Brain Imaging Unit, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Avihay Cohen
- The Functional Brain Imaging Unit, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | | | - Talma Hendler
- The Functional Brain Imaging Unit, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
- The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Nathan Intrator
- The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
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14
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Schedlowski M, Enck P, Rief W, Bingel U. Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses: Implications for Clinical Trials and Clinical Practice. Pharmacol Rev 2016; 67:697-730. [PMID: 26126649 DOI: 10.1124/pr.114.009423] [Citation(s) in RCA: 197] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The placebo effect has often been considered a nuisance in basic and particularly clinical research. This view has gradually changed in recent years due to deeper insight into the neuro-bio-behavioral mechanisms steering both the placebo and nocebo responses, the evil twin of placebo. For the neuroscientist, placebo and nocebo responses have evolved as indispensable tools to understand brain mechanisms that link cognitive and emotional factors with symptom perception as well as peripheral physiologic systems and end organ functioning. For the clinical investigator, better understanding of the mechanisms driving placebo and nocebo responses allow the control of these responses and thereby help to more precisely define the efficacy of a specific pharmacological intervention. Finally, in the clinical context, the systematic exploitation of these mechanisms will help to maximize placebo responses and minimize nocebo responses for the patient's benefit. In this review, we summarize and critically examine the neuro-bio-behavioral mechanisms underlying placebo and nocebo responses that are currently known in terms of different diseases and physiologic systems. We subsequently elaborate on the consequences of this knowledge for pharmacological treatments of patients and the implications for pharmacological research, the training of healthcare professionals, and for the health care system and future research strategies on placebo and nocebo responses.
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Affiliation(s)
- Manfred Schedlowski
- Institute of Medical Psychology and Behavioral Immunobiology (M.S.) and Department of Neurology (U.B.), University Clinic Essen, Essen, Germany; Department of Internal Medicine VI, Psychosomatic Medicine, University Hospital Tübingen, Tübingen, Germany (P.E.); and Department of Psychology, University of Marburg, Marburg, Germany (W.R.)
| | - Paul Enck
- Institute of Medical Psychology and Behavioral Immunobiology (M.S.) and Department of Neurology (U.B.), University Clinic Essen, Essen, Germany; Department of Internal Medicine VI, Psychosomatic Medicine, University Hospital Tübingen, Tübingen, Germany (P.E.); and Department of Psychology, University of Marburg, Marburg, Germany (W.R.)
| | - Winfried Rief
- Institute of Medical Psychology and Behavioral Immunobiology (M.S.) and Department of Neurology (U.B.), University Clinic Essen, Essen, Germany; Department of Internal Medicine VI, Psychosomatic Medicine, University Hospital Tübingen, Tübingen, Germany (P.E.); and Department of Psychology, University of Marburg, Marburg, Germany (W.R.)
| | - Ulrike Bingel
- Institute of Medical Psychology and Behavioral Immunobiology (M.S.) and Department of Neurology (U.B.), University Clinic Essen, Essen, Germany; Department of Internal Medicine VI, Psychosomatic Medicine, University Hospital Tübingen, Tübingen, Germany (P.E.); and Department of Psychology, University of Marburg, Marburg, Germany (W.R.)
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15
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Zotev V, Yuan H, Misaki M, Phillips R, Young KD, Feldner MT, Bodurka J. Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression. NEUROIMAGE-CLINICAL 2016; 11:224-238. [PMID: 26958462 PMCID: PMC4773387 DOI: 10.1016/j.nicl.2016.02.003] [Citation(s) in RCA: 106] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 01/29/2016] [Accepted: 02/10/2016] [Indexed: 10/25/2022]
Abstract
Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging approach for studies and novel treatments of major depressive disorder (MDD). EEG performed simultaneously with an rtfMRI-nf procedure allows an independent evaluation of rtfMRI-nf brain modulation effects. Frontal EEG asymmetry in the alpha band is a widely used measure of emotion and motivation that shows profound changes in depression. However, it has never been directly related to simultaneously acquired fMRI data. We report the first study investigating electrophysiological correlates of the rtfMRI-nf procedure, by combining the rtfMRI-nf with simultaneous and passive EEG recordings. In this pilot study, MDD patients in the experimental group (n = 13) learned to upregulate BOLD activity of the left amygdala using an rtfMRI-nf during a happy emotion induction task. MDD patients in the control group (n = 11) were provided with a sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the upper alpha band and BOLD activity across the brain were examined. Average individual changes in frontal EEG asymmetry during the rtfMRI-nf task for the experimental group showed a significant positive correlation with the MDD patients' depression severity ratings, consistent with an inverse correlation between the depression severity and frontal EEG asymmetry at rest. The average asymmetry changes also significantly correlated with the amygdala BOLD laterality. Temporal correlations between frontal EEG asymmetry and BOLD activity were significantly enhanced, during the rtfMRI-nf task, for the amygdala and many regions associated with emotion regulation. Our findings demonstrate an important link between amygdala BOLD activity and frontal EEG asymmetry during emotion regulation. Our EEG asymmetry results indicate that the rtfMRI-nf training targeting the amygdala is beneficial to MDD patients. They further suggest that EEG-nf based on frontal EEG asymmetry in the alpha band would be compatible with the amygdala-based rtfMRI-nf. Combination of the two could enhance emotion regulation training and benefit MDD patients.
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Affiliation(s)
- Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, OK, USA.
| | - Han Yuan
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | | | | | - Matthew T Feldner
- Department of Psychological Science, University of Arkansas, Fayetteville, AR, USA
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, USA; Center for Biomedical Engineering, University of Oklahoma, Norman, OK, USA; College of Engineering, University of Oklahoma, Norman, OK, USA.
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16
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Neurofeedback Treatment and Posttraumatic Stress Disorder: Effectiveness of Neurofeedback on Posttraumatic Stress Disorder and the Optimal Choice of Protocol. J Nerv Ment Dis 2016; 204:69-77. [PMID: 26825263 DOI: 10.1097/nmd.0000000000000418] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Neurofeedback is an alternative, noninvasive approach used in the treatment of a wide range of neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). Many different neurofeedback protocols and methods exist. Likewise, PTSD is a heterogeneous disorder. To review the evidence on effectiveness and preferred protocol when using neurofeedback treatment on PTSD, a systematic search of PubMed, PsychInfo, Embase, and Cochrane databases was undertaken. Five studies were included in this review. Neurofeedback had a statistically significant effect in three studies. Neurobiological changes were reported in three studies. Interpretation of results is, however, limited by differences between the studies and several issues regarding design. The optimistic results presented here qualify neurofeedback as probably efficacious for PTSD treatment.
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