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Drake KA, Talantov D, Tong GJ, Lin JT, Verheijden S, Katz S, Leung JM, Yuen B, Krishna V, Wu MJ, Sutherland AM, Short SA, Kheradpour P, Mumbach MR, Franz KM, Trifonov V, Lucas MV, Merson J, Kim CC. Multi-omic profiling reveals early immunological indicators for identifying COVID-19 Progressors. Clin Immunol 2023; 256:109808. [PMID: 37852344 DOI: 10.1016/j.clim.2023.109808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/25/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023]
Abstract
We sought to better understand the immune response during the immediate post-diagnosis phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. In lymphocytes, the CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. These early stage observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19. BACKGROUND: Much of the literature on immune response post-SARS-CoV-2 infection has been in the acute and post-acute phases of infection. TRANSLATIONAL SIGNIFICANCE: We found differences at early time points of infection in approximately 160 participants. We compared multi-omic signatures in immune cells between individuals progressing to needing more significant medical intervention and non-progressors. We observed widespread evidence of a state of increased inflammation associated with progression, supported by a range of epigenomic, transcriptomic, and proteomic signatures. The signatures we identified support other findings at later time points and serve as the basis for prognostic biomarker development or to inform interventional strategies.
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Affiliation(s)
- Katherine A Drake
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Dimitri Talantov
- Janssen Research & Development, LLC, San Diego, CA, United States of America
| | - Gary J Tong
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Jack T Lin
- Verily Life Sciences, South San Francisco, CA, United States of America
| | | | - Samuel Katz
- Verily Life Sciences, South San Francisco, CA, United States of America
| | | | - Benjamin Yuen
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Vinod Krishna
- Janssen Research & Development, LLC, San Diego, CA, United States of America
| | - Michelle J Wu
- Verily Life Sciences, South San Francisco, CA, United States of America
| | | | - Sarah A Short
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Pouya Kheradpour
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Maxwell R Mumbach
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Kate M Franz
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Vladimir Trifonov
- Janssen Research & Development, LLC, San Diego, CA, United States of America
| | - Molly V Lucas
- Janssen Research & Development, LLC, NJ, United States of America
| | - James Merson
- Janssen Research & Development, LLC, San Francisco, CA, United States of America
| | - Charles C Kim
- Verily Life Sciences, South San Francisco, CA, United States of America.
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Drake KA, Talantov D, Tong GJ, Lin JT, Verheijden S, Katz S, Leung JM, Yuen B, Krishna V, Wu MJ, Sutherland A, Short SA, Kheradpour P, Mumbach M, Franz K, Trifonov V, Lucas MV, Merson J, Kim CC. Multi-omic Profiling Reveals Early Immunological Indicators for Identifying COVID-19 Progressors. bioRxiv 2023:2023.05.25.542297. [PMID: 37292797 PMCID: PMC10246026 DOI: 10.1101/2023.05.25.542297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a rapid response by the scientific community to further understand and combat its associated pathologic etiology. A focal point has been on the immune responses mounted during the acute and post-acute phases of infection, but the immediate post-diagnosis phase remains relatively understudied. We sought to better understand the immediate post-diagnosis phase by collecting blood from study participants soon after a positive test and identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. Additionally, in the lymphocyte compartment, CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. Importantly, the identification of these cellular and molecular immune changes occurred at the early stages of COVID-19 disease. These observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19.
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Affiliation(s)
| | | | - Gary J Tong
- Verily Life Sciences, South San Francisco, CA
| | - Jack T Lin
- Verily Life Sciences, South San Francisco, CA
| | | | - Samuel Katz
- Verily Life Sciences, South San Francisco, CA
| | | | | | | | | | | | | | | | | | - Kate Franz
- Verily Life Sciences, South San Francisco, CA
| | | | | | - James Merson
- Janssen Research & Development, LLC, San Diego, CA
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Shen H, Wang SH, Zhang Y, Wang H, Li F, Lucas MV, Zhang YD, Liu Y, Yuan TF. Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning. BMC Psychiatry 2021; 21:522. [PMID: 34686178 PMCID: PMC8532270 DOI: 10.1186/s12888-021-03452-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 08/24/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Individuals with psychiatric disorders perceive the world differently. Previous studies indicated impaired color vision and weakened color discrimination ability in psychotic patients. Examining the paintings from psychotic patients can measure the visual-motor function. However, few studies examined the potential changes in the color painting behavior in these individuals. The current study aims to discriminate schizophrenia patients from healthy controls (HCs) and predict PANSS scores of schizophrenia patients according to their paintings. METHODS In the present study, we retrospectively analyzed the paintings colored by 281 chronic schizophrenia patients and 35 HCs. The images were scanned and processed using series of computational analyses. RESULTS The results showed that schizophrenia patients tend to use less color and exhibit different strokes compared to HCs. Using a deep learning residual neural network (ResNet), we were able to discriminate patients from HCs with over 90% accuracy. Further, we developed a novel convolutional neural network to predict PANSS positive, negative, general psychopathology, and total scores. The Root Mean Square Error (RMSE) of the prediction was low, which indicates higher accuracy of prediction. CONCLUSION In conclusion, the deep learning paradigm showed the large potential to discriminate schizophrenia patients from HCs based on color paintings. Besides, this color painting-based paradigm can effectively predict clinical symptom severity for chronic schizophrenia patients. The color paintings by schizophrenia patients show potential as a tool for clinical diagnosis and prognosis. These findings show potential as a tool for clinical diagnosis and prognosis among schizophrenia patients.
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Affiliation(s)
- Hui Shen
- grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shui-Hua Wang
- grid.9918.90000 0004 1936 8411School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH UK
| | - Yi Zhang
- grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haixia Wang
- grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Feng Li
- grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Molly V. Lucas
- grid.168010.e0000000419368956Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA USA
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
| | - Yan Liu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu, China. .,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, China.
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Shu H, Gu L, Yang P, Lucas MV, Gao L, Zhang H, Zhang H, Xu Z, Wu W, Li L, Zhang Z. Disturbed temporal dynamics of episodic retrieval activity with preserved spatial activity pattern in amnestic mild cognitive impairment: A simultaneous EEG-fMRI study. Neuroimage Clin 2021; 30:102572. [PMID: 33548865 PMCID: PMC7868727 DOI: 10.1016/j.nicl.2021.102572] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 12/31/2020] [Accepted: 01/16/2021] [Indexed: 11/23/2022]
Abstract
The HC and aMCI subjects showed similar retrieval success patterns in fMRI analysis. The aMCI showed diminished ERP old/new effects within the retrieval success pattern. Disturbed fMRI correlate of ERP recollection component was related to EM function. The aMCI showed disturbed cognitive processes despite of the preserved fMRI pattern.
Episodic memory (EM) deficit is the core cognitive dysfunction of amnestic mild cognitive impairment (aMCI). However, the episodic retrieval pattern detected by functional MRI (fMRI) appears preserved in aMCI subjects. To address this discrepancy, simultaneous electroencephalography (EEG)-fMRI recording was employed to determine whether temporal dynamics of brain episodic retrieval activity were disturbed in patients with aMCI. Twenty-six aMCI and 29 healthy control (HC) subjects completed a word-list memory retrieval task during simultaneous EEG-fMRI. The retrieval success activation pattern was detected with fMRI analysis, and the familiarity- and recollection-related components of episodic retrieval activity were identified using event-related potential (ERP) analysis. The fMRI-constrained ERP analysis explored the temporal dynamics of brain activity in the retrieval success pattern, and the ERP-informed fMRI analysis detected fMRI correlates of the ERP components related to familiarity and recollection processes. The two groups exhibited similar retrieval success patterns in the bilateral posteromedial parietal cortex, the left inferior parietal lobule (IPL), and the left lateral prefrontal cortex (LPFC). The fMRI-constrained ERP analysis showed that the aMCI group did not exhibit old/new effects in the IPL and LPFC that were observed in the HC group. In addition, the aMCI group showed disturbed fMRI correlate of ERP recollection component that was associated with inferior EM performance. Therefore, in this study, we identified disturbed temporal dynamics in episodic retrieval activity with a preserved spatial activity pattern in aMCI. Taken together, the simultaneous EEG-fMRI technique demonstrated the potential to identify individuals with a high risk of cognitive deterioration.
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Affiliation(s)
- Hao Shu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Neuropsychiatric Institute, The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210009, China
| | - Lihua Gu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Neuropsychiatric Institute, The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210009, China
| | - Ping Yang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Molly V Lucas
- Department of Psychiatry and Behavioral Sciences and Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA 94394, USA
| | - Lijuan Gao
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Neuropsychiatric Institute, The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210009, China
| | - Hongxing Zhang
- Department of Psychiatry, The Second Affiliated Hospital of Xingxiang Medical University, Xinxiang, Henan 453002, China
| | - Haisan Zhang
- Department of Psychiatry, The Second Affiliated Hospital of Xingxiang Medical University, Xinxiang, Henan 453002, China
| | - Zhan Xu
- Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Wei Wu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China; Department of Psychiatry and Behavioral Sciences and Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA 94394, USA.
| | - Ling Li
- Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Neuropsychiatric Institute, The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210009, China; Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; Department of Psychiatry, The Second Affiliated Hospital of Xingxiang Medical University, Xinxiang, Henan 453002, China.
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5
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Wu W, Zhang Y, Jiang J, Lucas MV, Fonzo GA, Rolle CE, Cooper C, Chin-Fatt C, Krepel N, Cornelssen CA, Wright R, Toll RT, Trivedi HM, Monuszko K, Caudle TL, Sarhadi K, Jha MK, Trombello JM, Deckersbach T, Adams P, McGrath PJ, Weissman MM, Fava M, Pizzagalli DA, Arns M, Trivedi MH, Etkin A. An electroencephalographic signature predicts antidepressant response in major depression. Nat Biotechnol 2020; 38:439-447. [PMID: 32042166 PMCID: PMC7145761 DOI: 10.1038/s41587-019-0397-3] [Citation(s) in RCA: 126] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 12/06/2019] [Accepted: 12/17/2019] [Indexed: 12/21/2022]
Abstract
Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.
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Affiliation(s)
- Wei Wu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510640, China
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Yu Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Jing Jiang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Molly V. Lucas
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Gregory A. Fonzo
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Camarin E. Rolle
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Crystal Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Cherise Chin-Fatt
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Noralie Krepel
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA 02478
| | - Carena A. Cornelssen
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Rachael Wright
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Russell T. Toll
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Hersh M. Trivedi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Karen Monuszko
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Trevor L. Caudle
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Kamron Sarhadi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Manish K. Jha
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Joseph M. Trombello
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Thilo Deckersbach
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Phil Adams
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Patrick J. McGrath
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Myrna M. Weissman
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Maurizio Fava
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Diego A. Pizzagalli
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Martijn Arns
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA 02478
- Department of Experimental Psychology, Utrecht University, Utrecht, the Netherlands
- neuroCare Group Netherlands, Nijmegen, the Netherlands
| | - Madhukar H. Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
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Yuan J, Liu W, Liang Q, Cao X, Lucas MV, Yuan TF. Effect of Low-Frequency Repetitive Transcranial Magnetic Stimulation on Impulse Inhibition in Abstinent Patients With Methamphetamine Addiction: A Randomized Clinical Trial. JAMA Netw Open 2020; 3:e200910. [PMID: 32167568 PMCID: PMC7070234 DOI: 10.1001/jamanetworkopen.2020.0910] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
IMPORTANCE Impulsivity during periods of abstinence is a critical symptom of patients who use methamphetamine (MA). OBJECTIVE To evaluate changes in impulse inhibition elicited by repetitive transcranial magnetic stimulation (rTMS) in patients with MA addiction. DESIGN, SETTING, AND PARTICIPANTS This randomized clinical trial was conducted in Da Lian Shan Addiction Rehabilitation Center, Nanjing, China, from December 1, 2018, to April 20, 2019. Effects of the intervention were examined at 3 time points: after a single session (day 1), 24 hours after 10 repeated sessions (day 11), and at 3 weeks of follow-up (day 31). Men with MA addiction and healthy male control participants were recruited for this study. Data analysis was performed from March 2019 to October 2019. INTERVENTIONS Patients who use MA were randomized to undergo sham rTMS (36 patients) and or 1-Hz rTMS (37 patients) to the left prefrontal cortex, receiving daily TMS treatments for 10 consecutive days. MAIN OUTCOMES AND MEASURES The primary outcome was impulse inhibition, which is primarily embodied by accuracy reduction (ie, accuracy cost) from standard to deviant trials in a 2-choice oddball task (80% standard and 20% deviant trials). RESULT The study included 73 men with MA addiction (mean [SD] age, 38.49 [7.69] years) and 33 male healthy control participants without MA addiction (mean [SD] age, 35.15 [9.68] years). The mean (SD) duration of abstinence for the men with MA addiction was 9.27 (4.61) months. Compared with the control group, patients with MA addiction exhibited greater impulsivity (accuracy cost, 3.3% vs 6.2%). The single session of 1-Hz rTMS over the left prefrontal cortex significantly increased accuracy from 91.4% to 95.7% (F1,36 = 9.58; P < .001) and reaction time delay from 50 milliseconds to 77 milliseconds (F1,36 = 22.66; P < .001) in deviant trials. These effects were seen consistently after 10 sessions of 1-Hz rTMS treatment (day 11 vs day 1, t26 = 1.59; P = .12), and the behavioral improvement was maintained at least for 3 weeks after treatment (day 31 vs day 1, t26 = 0.26; P = .80). These improvement effects of impulse inhibition were coupled with a reduction in addictive symptoms as measured by cue-induced craving. The pretest accuracy cost was positively correlated with the change in impulse inhibition (r = 0.615; P < .001) and change in craving (r = 0.334; P = .01), suggesting that these 2 behaviors may be modified simultaneously. CONCLUSIONS AND RELEVANCE These findings suggest that repeated rTMS sessions have sustained effects on impulse inhibition in patients with MA addiction and provide novel data on impulsivity management strategies for addiction rehabilitation. TRIAL REGISTRATION ChiCTR-ROC-16008541.
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Affiliation(s)
- Jiajin Yuan
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Weijun Liu
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Qiongdan Liang
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xinyu Cao
- Da Lian Shan Institute of Addiction Rehabilitation, Nanjing, China
| | - Molly V. Lucas
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, California
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu, China
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Naze S, Caggiano V, Sun Y, Lucas MV, Etkin A, Kozloski JR. Classification of TMS evoked potentials using ERP time signatures and SVM versus deep learning. Annu Int Conf IEEE Eng Med Biol Soc 2019; 2019:3539-3542. [PMID: 31946642 DOI: 10.1109/embc.2019.8857583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Modeling transcranial magnetic stimulation (TMS) evoked potentials (TEP) begins with classification of stereotypical single-pulse TMS responses in order to select validation targets for generative dynamical models. Several dimensionality reduction techniques are commonly in use to extract statistically independent features from experimental data for regression against model parameters. Here, we first designed a 3-dimensional feature space based on commonly described event-related potentials (ERP) from the literature. We then compared classification schemes which take as inputs either the 3D projection space or the original full rank input space. Their ability to discriminate TEP recorded from different brain regions given a stimulus site were evaluated. We show that a deep learning architecture, employing Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP), yields better accuracy than the 3D projection and raw TEP input combined with Support Vector Machines. Such supervised feature extraction models may therefore be useful for scoring neural circuit simulations based on their ability to reproduce the underlying dynamical processes responsible for differential TEP responses.
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Lucas MV, Anderson LC, Bolling DZ, Pelphrey KA, Kaiser MD. Dissociating the Neural Correlates of Experiencing and Imagining Affective Touch. Cereb Cortex 2014; 25:2623-30. [PMID: 24700583 DOI: 10.1093/cercor/bhu061] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
This functional magnetic resonance imaging (fMRI) study examined experiencing and imagining gentle arm and palm touch to determine whether these processes activate overlapping or distinct brain regions. Although past research shows brain responses to experiencing and viewing touch, this study investigates neural processing of touch absent of visual stimulation. C-tactile (CT) nerves, present in hairy skin, respond specifically to caress-like touch. CT-targeted touch activates "social brain" regions including insula, right posterior superior temporal sulcus, amygdala, temporal poles, and orbitofrontal cortex ( McGlone et al. 2012). We addressed whether activations reflect sensory input-driven mechanisms, cognitive-based mechanisms, or both. We identified a functional dissociation between insula regions. Posterior insula responded during experienced touch. Anterior insula responded during both experienced and imagined touch. To isolate stimulus-independent mechanisms recruited during physical experience of CT-targeted touch, we identified regions active to experiencing and imagining such touch. These included amygdala and temporal pole. We posit that the dissociation of insula function suggests posterior and anterior insula involvement in distinct yet interacting processes: coding physical stimulation and affective interpretation of touch. Regions active during experiencing and imagining CT-targeted touch are associated with social processes indicating that imagining touch conjures affective aspects of experiencing such touch.
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Affiliation(s)
- Molly V Lucas
- Yale Child Study Center, Yale University, New Haven, CT 06520, USA
| | - Laura C Anderson
- Department of Psychology, University of Maryland, College Park, MD 20742, USA
| | | | - Kevin A Pelphrey
- Yale Child Study Center, Yale University, New Haven, CT 06520, USA
| | - Martha D Kaiser
- Yale Child Study Center, Yale University, New Haven, CT 06520, USA
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Morris ED, Lucas MV, Petrulli JR, Cosgrove KP. How to design PET experiments to study neurochemistry: application to alcoholism. Yale J Biol Med 2014; 87:33-54. [PMID: 24600335 PMCID: PMC3941463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Positron Emission Tomography (PET) (and the related Single Photon Emission Computed Tomography) is a powerful imaging tool with a molecular specificity and sensitivity that are unique among imaging modalities. PET excels in the study of neurochemistry in three ways: 1) It can detect and quantify neuroreceptor molecules; 2) it can detect and quantify changes in neurotransmitters; and 3) it can detect and quantify exogenous drugs delivered to the brain. To carry out any of these applications, the user must harness the power of kinetic modeling. Further, the quality of the information gained is only as good as the soundness of the experimental design. This article reviews the concepts behind the three main uses of PET, the rationale behind kinetic modeling of PET data, and some of the key considerations when planning a PET experiment. Finally, some examples of PET imaging related to the study of alcoholism are discussed and critiqued.
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Affiliation(s)
- Evan D. Morris
- Department of Psychiatry, Yale University, New Haven, Connecticut,Department of Diagnostic Radiology, Yale University, New Haven, Connecticut,Department of Biomedical Engineering, Yale University, New Haven, Connecticut,Yale PET Center, Yale University, New Haven, Connecticut,To whom all correspondence should be addressed: Evan Morris, Yale University Departments of Psychiatry, Diagnostic Radiology, Biomedical Engineering and Yale PET Center, 333 Cedar St., New Haven, CT 06520-8040; Tele: 203-737-5752;
| | - Molly V. Lucas
- Department of Psychology, Yale University, New Haven, Connecticut,Yale PET Center, Yale University, New Haven, Connecticut
| | - J. Ryan Petrulli
- Department of Diagnostic Radiology, Yale University, New Haven, Connecticut,Department of Biomedical Engineering, Yale University, New Haven, Connecticut,Yale PET Center, Yale University, New Haven, Connecticut
| | - Kelly P. Cosgrove
- Department of Psychiatry, Yale University, New Haven, Connecticut,Department of Diagnostic Radiology, Yale University, New Haven, Connecticut,Yale PET Center, Yale University, New Haven, Connecticut
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Gordon I, Vander Wyk BC, Bennett RH, Cordeaux C, Lucas MV, Eilbott JA, Zagoory-Sharon O, Leckman JF, Feldman R, Pelphrey KA. Oxytocin enhances brain function in children with autism. Proc Natl Acad Sci U S A 2013; 110:20953-8. [PMID: 24297883 PMCID: PMC3876263 DOI: 10.1073/pnas.1312857110] [Citation(s) in RCA: 216] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Following intranasal administration of oxytocin (OT), we measured, via functional MRI, changes in brain activity during judgments of socially (Eyes) and nonsocially (Vehicles) meaningful pictures in 17 children with high-functioning autism spectrum disorder (ASD). OT increased activity in the striatum, the middle frontal gyrus, the medial prefrontal cortex, the right orbitofrontal cortex, and the left superior temporal sulcus. In the striatum, nucleus accumbens, left posterior superior temporal sulcus, and left premotor cortex, OT increased activity during social judgments and decreased activity during nonsocial judgments. Changes in salivary OT concentrations from baseline to 30 min postadministration were positively associated with increased activity in the right amygdala and orbitofrontal cortex during social vs. nonsocial judgments. OT may thus selectively have an impact on salience and hedonic evaluations of socially meaningful stimuli in children with ASD, and thereby facilitate social attunement. These findings further the development of a neurophysiological systems-level understanding of mechanisms by which OT may enhance social functioning in children with ASD.
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Affiliation(s)
- Ilanit Gordon
- Center for Translational Developmental Neuroscience, Yale Child Study Center, Yale University, New Haven, CT 06520
- Department of Psychology, and
| | - Brent C. Vander Wyk
- Center for Translational Developmental Neuroscience, Yale Child Study Center, Yale University, New Haven, CT 06520
| | - Randi H. Bennett
- Center for Translational Developmental Neuroscience, Yale Child Study Center, Yale University, New Haven, CT 06520
| | - Cara Cordeaux
- Center for Translational Developmental Neuroscience, Yale Child Study Center, Yale University, New Haven, CT 06520
| | - Molly V. Lucas
- Center for Translational Developmental Neuroscience, Yale Child Study Center, Yale University, New Haven, CT 06520
| | - Jeffrey A. Eilbott
- Center for Translational Developmental Neuroscience, Yale Child Study Center, Yale University, New Haven, CT 06520
| | - Orna Zagoory-Sharon
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 52900, Israel; and
| | - James F. Leckman
- Yale Child Study Center, School of Medicine, Yale University, New Haven, CT 06520
| | - Ruth Feldman
- Department of Psychology, and
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 52900, Israel; and
- Yale Child Study Center, School of Medicine, Yale University, New Haven, CT 06520
| | - Kevin A. Pelphrey
- Center for Translational Developmental Neuroscience, Yale Child Study Center, Yale University, New Haven, CT 06520
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