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Wojciechowski J, Jurewicz K, Dzianok P, Antonova I, Paluch K, Wolak T, Kublik E. Common and distinct BOLD correlates of Simon and flanker conflicts which can(not) be reduced to time-on-task effects. Hum Brain Mapp 2024; 45:e26549. [PMID: 38224538 PMCID: PMC10777776 DOI: 10.1002/hbm.26549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 10/25/2023] [Accepted: 11/16/2023] [Indexed: 01/17/2024] Open
Abstract
The ability to identify and resolve conflicts between standard, well-trained behaviors and behaviors required by the current context is an essential feature of cognitive control. To date, no consensus has been reached on the brain mechanisms involved in exerting such control: while some studies identified diverse patterns of activity across different conflicts, other studies reported common resources across conflict tasks or even across simple tasks devoid of the conflict component. The latter reports attributed the entire activity observed in the presence of conflict to longer time spent on the task (i.e., to the so-called time-on-task effects). Here, we used an extended Multi-Source Interference Task (MSIT) which combines Simon and flanker types of interference to determine shared and conflict-specific mechanisms of conflict resolution in fMRI and their separability from the time-on-task effects. Large portions of the activity in the dorsal attention network and decreases of activity in the default mode network were shared across the tasks and scaled in parallel with increasing reaction times. Importantly, the activity in the sensory and sensorimotor cortices, as well as in the posterior medial frontal cortex (pMFC) - a key region implicated in conflict processing - could not be exhaustively explained by the time-on-task effects.
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Affiliation(s)
- Jakub Wojciechowski
- Neurobiology of Emotions LaboratoryNencki Institute of Experimental Biology, Polish Academy of SciencesWarsawPoland
- Bioimaging Research CenterInstitute of Physiology and Pathology of HearingWarsawPoland
| | - Katarzyna Jurewicz
- Neurobiology of Emotions LaboratoryNencki Institute of Experimental Biology, Polish Academy of SciencesWarsawPoland
- Department of PhysiologyFaculty of Medicine and Health Sciences, McGill UniversityMontrealQuebecCanada
| | - Patrycja Dzianok
- Neurobiology of Emotions LaboratoryNencki Institute of Experimental Biology, Polish Academy of SciencesWarsawPoland
| | - Ingrida Antonova
- Neurobiology of Emotions LaboratoryNencki Institute of Experimental Biology, Polish Academy of SciencesWarsawPoland
- Laboratory of NeuroinformaticsNencki Institute of Experimental Biology, Polish Academy of SciencesWarsawPoland
| | - Katarzyna Paluch
- Neurobiology of Emotions LaboratoryNencki Institute of Experimental Biology, Polish Academy of SciencesWarsawPoland
- Laboratory of Neurophysiology of MindCenter of Excellence for Neural Plasticity and Brain Disorders: BRAINCITY, Nencki Institute of Experimental Biology, Polish Academy of SciencesWarsawPoland
| | - Tomasz Wolak
- Bioimaging Research CenterInstitute of Physiology and Pathology of HearingWarsawPoland
| | - Ewa Kublik
- Neurobiology of Emotions LaboratoryNencki Institute of Experimental Biology, Polish Academy of SciencesWarsawPoland
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2
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Bloom PA, Pagliaccio D, Zhang J, Bauer CCC, Kyler M, Greene KD, Treves I, Morfini F, Durham K, Cherner R, Bajwa Z, Wool E, Olafsson V, Lee RF, Bidmead F, Cardona J, Kirshenbaum JS, Ghosh S, Hinds O, Wighton P, Galfalvy H, Simpson HB, Whitfield-Gabrieli S, Auerbach RP. Mindfulness-based real-time fMRI neurofeedback: a randomized controlled trial to optimize dosing for depressed adolescents. BMC Psychiatry 2023; 23:757. [PMID: 37848857 PMCID: PMC10580563 DOI: 10.1186/s12888-023-05223-8] [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: 09/23/2023] [Accepted: 09/26/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Adolescence is characterized by a heightened vulnerability for Major Depressive Disorder (MDD) onset, and currently, treatments are only effective for roughly half of adolescents with MDD. Accordingly, novel interventions are urgently needed. This study aims to establish mindfulness-based real-time fMRI neurofeedback (mbNF) as a non-invasive approach to downregulate the default mode network (DMN) in order to decrease ruminatory processes and depressive symptoms. METHODS Adolescents (N = 90) with a current diagnosis of MDD ages 13-18-years-old will be randomized in a parallel group, two-arm, superiority trial to receive either 15 or 30 min of mbNF with a 1:1 allocation ratio. Real-time neurofeedback based on activation of the frontoparietal network (FPN) relative to the DMN will be displayed to participants via the movement of a ball on a computer screen while participants practice mindfulness in the scanner. We hypothesize that within-DMN (medial prefrontal cortex [mPFC] with posterior cingulate cortex [PCC]) functional connectivity will be reduced following mbNF (Aim 1: Target Engagement). Additionally, we hypothesize that participants in the 30-min mbNF condition will show greater reductions in within-DMN functional connectivity (Aim 2: Dosing Impact on Target Engagement). Aim 1 will analyze data from all participants as a single-group, and Aim 2 will leverage the randomized assignment to analyze data as a parallel-group trial. Secondary analyses will probe changes in depressive symptoms and rumination. DISCUSSION Results of this study will determine whether mbNF reduces functional connectivity within the DMN among adolescents with MDD, and critically, will identify the optimal dosing with respect to DMN modulation as well as reduction in depressive symptoms and rumination. TRIAL REGISTRATION This study has been registered with clinicaltrials.gov, most recently updated on July 6, 2023 (trial identifier: NCT05617495).
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Affiliation(s)
- Paul A Bloom
- Department of Psychiatry, Columbia University, New York, NY, USA.
| | - David Pagliaccio
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Jiahe Zhang
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Clemens C C Bauer
- Department of Psychology, Northeastern University, Boston, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mia Kyler
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Keara D Greene
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Isaac Treves
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Katherine Durham
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Rachel Cherner
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Zia Bajwa
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Emma Wool
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Valur Olafsson
- Northeastern University Biomedical Imaging Center, Boston, MA, USA
| | - Ray F Lee
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA
| | - Fred Bidmead
- Northeastern University Biomedical Imaging Center, Boston, MA, USA
| | - Jonathan Cardona
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA
| | | | | | | | - Paul Wighton
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Hanga Galfalvy
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - H Blair Simpson
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, MA, USA
- Northeastern University Biomedical Imaging Center, Boston, MA, USA
| | - Randy P Auerbach
- Department of Psychiatry, Columbia University, New York, NY, USA
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Pamplona GSP, Heldner J, Langner R, Koush Y, Michels L, Ionta S, Salmon CEG, Scharnowski F. Preliminary findings on long-term effects of fMRI neurofeedback training on functional networks involved in sustained attention. Brain Behav 2023; 13:e3217. [PMID: 37594145 PMCID: PMC10570501 DOI: 10.1002/brb3.3217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 07/25/2023] [Accepted: 07/29/2023] [Indexed: 08/19/2023] Open
Abstract
INTRODUCTION Neurofeedback based on functional magnetic resonance imaging allows for learning voluntary control over one's own brain activity, aiming to enhance cognition and clinical symptoms. We previously reported improved sustained attention temporarily by training healthy participants to up-regulate the differential activity of the sustained attention network minus the default mode network (DMN). However, the long-term brain and behavioral effects of this training have not yet been studied. In general, despite their relevance, long-term learning effects of neurofeedback training remain under-explored. METHODS Here, we complement our previously reported results by evaluating the neurofeedback training effects on functional networks involved in sustained attention and by assessing behavioral and brain measures before, after, and 2 months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self-regulation runs without feedback (i.e., transfer runs) and during resting-state runs from 15 healthy individuals. RESULTS Neurally, we found that participants maintained their ability to control the differential activity during follow-up sessions. Further, exploratory analyses showed that the training increased the functional connectivity between the DMN and the occipital gyrus, which was maintained during follow-up transfer runs but not during follow-up resting-state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow-up. CONCLUSION The discrepancy between lasting regulation-related brain changes but transient behavioral and resting-state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior in the long term, a better understanding of the brain-behavior associations during and after neurofeedback training is needed to develop its full potential as a promising scientific and clinical tool.
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Affiliation(s)
- Gustavo Santo Pedro Pamplona
- Sensory‐Motor Laboratory (SeMoLa), Jules‐Gonin Eye Hospital/Fondation Asile des AveuglesDepartment of Ophthalmology/University of LausanneLausanneSwitzerland
- InBrain Lab, Department of PhysicsUniversity of Sao PauloRibeirao PretoBrazil
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric HospitalUniversity of ZurichZurichSwitzerland
- Rehabilitation Engineering Laboratory (RELab), Department of Health Sciences and TechnologyETH ZurichZurichSwitzerland
| | - Jennifer Heldner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric HospitalUniversity of ZurichZurichSwitzerland
| | - Robert Langner
- Institute of Systems NeuroscienceHeinrich Heine University DusseldorfDusseldorfGermany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JulichJulichGermany
| | - Yury Koush
- Department of Radiology and Biomedical Imaging, Yale School of MedicineYale UniversityNew HavenConnecticutUSA
| | - Lars Michels
- Department of NeuroradiologyUniversity Hospital ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of Zurich and Swiss Federal Institute of TechnologyZurichSwitzerland
| | - Silvio Ionta
- Sensory‐Motor Laboratory (SeMoLa), Jules‐Gonin Eye Hospital/Fondation Asile des AveuglesDepartment of Ophthalmology/University of LausanneLausanneSwitzerland
| | | | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric HospitalUniversity of ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of Zurich and Swiss Federal Institute of TechnologyZurichSwitzerland
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of PsychologyUniversity of ViennaViennaAustria
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Roye S, Calamia M, Robinson A. Examining patterns of executive functioning across dimensions of psychopathology. J Behav Ther Exp Psychiatry 2022; 77:101778. [PMID: 36113913 DOI: 10.1016/j.jbtep.2022.101778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 08/15/2022] [Accepted: 08/20/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND AND OBJECTIVES The current study examined relationships between psychopathology and individual domains of executive functioning (EF) amongst adults. While previous studies have examined these relationships using diagnostic groups, we compared factor structures of both dimensional psychopathology and EF and used an approach to better isolate EF-specific task variance within each domain. METHODS This study analyzed the data of 722 individuals between the ages of 18-59 years, who took part in the Nathan Kline Institute (NKI)-Rockland project. Confirmatory factor analyses were used to derive a three-factor model of EF (i.e., inhibition, shifting, and fluency) proposed by Karr et al. (2019) with scores primarily from the Delis-Kaplan Executive Function System (D-KEFS), as well as a three-factor model of psychopathology (i.e., internalizing, externalizing, and thought disorder symptoms) from the Adult Self-Report (ASR) and Peter's et al. Delusions Inventory (PDI). These models were compared using structural equation modeling. RESULTS Results demonstrated an adequate fit for both model structures and indicated that internalizing and externalizing psychopathology had positive and negative relationships with different factors of EF, while thought disorder traits were not related to EF. LIMITATIONS This study examines pathological traits within a non-clinical sample that excluded individuals with severe mental illness. Additionally, analyses were limited by the availability of certain variables, and potential shared method variance within factors. CONCLUSIONS Patterns of associations with EF were unique to all three aspects of dimensional psychopathology. When examined together, different dimensions of psychopathology were related to both better and worse EF performance.
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Affiliation(s)
- Scott Roye
- University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
| | - Matthew Calamia
- Department of Psychology, Louisiana State University, Baton Rouge, LA, United States
| | - Anthony Robinson
- Department of Psychology, Louisiana State University, Baton Rouge, LA, United States
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5
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Wang X, Li XH, Cho JW, Russ BE, Rajamani N, Omelchenko A, Ai L, Korchmaros A, Sawiak S, Benn RA, Garcia-Saldivar P, Wang Z, Kalin NH, Schroeder CE, Craddock RC, Fox AS, Evans AC, Messinger A, Milham MP, Xu T. U-net model for brain extraction: Trained on humans for transfer to non-human primates. Neuroimage 2021; 235:118001. [PMID: 33789137 PMCID: PMC8529630 DOI: 10.1016/j.neuroimage.2021.118001] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/22/2021] [Accepted: 03/25/2021] [Indexed: 01/21/2023] Open
Abstract
Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.
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Affiliation(s)
- Xindi Wang
- Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Xin-Hui Li
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Jae Wook Cho
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Brian E Russ
- Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, NY, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, USA; Department of Psychiatry, New York University School of Medicine, New York City, NY, USA
| | - Nanditha Rajamani
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Alisa Omelchenko
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Lei Ai
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | | | - Stephen Sawiak
- Translational Neuroimaging Laboratory, Department of Physiology, Development and Neuroscience University of Cambridge, Cambridge CB2 3EG, UK
| | - R Austin Benn
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Pamela Garcia-Saldivar
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, México
| | - Zheng Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Science, Shanghai, China; University of Chinese Academy of Science, China
| | - Ned H Kalin
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, 6001 Research Park Blvd, Madison, WI 53719, USA
| | - Charles E Schroeder
- Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, NY, USA; Departments of Psychiatry and Neurology, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA
| | - R Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, USA
| | - Andrew S Fox
- Department of Psychology, and the California National Primate Research Center, University of California, Davis, One Shields Ave., Davis, CA 95616, USA
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, USA
| | - Michael P Milham
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA; Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, NY, USA
| | - Ting Xu
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA.
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Heunis S, Breeuwer M, Caballero-Gaudes C, Hellrung L, Huijbers W, Jansen JF, Lamerichs R, Zinger S, Aldenkamp AP. The effects of multi-echo fMRI combination and rapid T 2*-mapping on offline and real-time BOLD sensitivity. Neuroimage 2021; 238:118244. [PMID: 34116148 DOI: 10.1016/j.neuroimage.2021.118244] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/11/2021] [Accepted: 06/04/2021] [Indexed: 12/25/2022] Open
Abstract
A variety of strategies are used to combine multi-echo functional magnetic resonance imaging (fMRI) data, yet recent literature lacks a systematic comparison of the available options. Here we compare six different approaches derived from multi-echo data and evaluate their influences on BOLD sensitivity for offline and in particular real-time use cases: a single-echo time series (based on Echo 2), the real-time T2*-mapped time series (T2*FIT) and four combined time series (T2*-weighted, tSNR-weighted, TE-weighted, and a new combination scheme termed T2*FIT-weighted). We compare the influences of these six multi-echo derived time series on BOLD sensitivity using a healthy participant dataset (N = 28) with four task-based fMRI runs and two resting state runs. We show that the T2*FIT-weighted combination yields the largest increase in temporal signal-to-noise ratio across task and resting state runs. We demonstrate additionally for all tasks that the T2*FIT time series consistently yields the largest offline effect size measures and real-time region-of-interest based functional contrasts and temporal contrast-to-noise ratios. These improvements show the promising utility of multi-echo fMRI for studies employing real-time paradigms, while further work is advised to mitigate the decreased tSNR of the T2*FIT time series. We recommend the use and continued exploration of T2*FIT for offline task-based and real-time region-based fMRI analysis. Supporting information includes: a data repository (https://dataverse.nl/dataverse/rt-me-fmri), an interactive web-based application to explore the data (https://rt-me-fmri.herokuapp.com/), and further materials and code for reproducibility (https://github.com/jsheunis/rt-me-fMRI).
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Affiliation(s)
- Stephan Heunis
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, the Netherlands; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Germany; Department of Psychology, Education and Child studies, Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands.
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Philips Healthcare, Best, the Netherlands
| | | | - Lydia Hellrung
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Switzerland
| | - Willem Huijbers
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Philips Research, Eindhoven, the Netherlands
| | - Jacobus Fa Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands; School for Mental Health and Neuroscience, Maastricht, the Netherlands
| | - Rolf Lamerichs
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, the Netherlands; Philips Research, Eindhoven, the Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, the Netherlands
| | - Albert P Aldenkamp
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, the Netherlands; School for Mental Health and Neuroscience, Maastricht, the Netherlands; Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Ghent University Hospital, Ghent, Belgium; Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
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7
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Chen S, Wang M, Dong H, Wang L, Jiang Y, Hou X, Zhuang Q, Dong GH. Internet gaming disorder impacts gray matter structural covariance organization in the default mode network. J Affect Disord 2021; 288:23-30. [PMID: 33839555 DOI: 10.1016/j.jad.2021.03.077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/19/2021] [Accepted: 03/23/2021] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Although previous studies have revealed that dysfunctional brain organization is associated with internet gamingdisorder (IGD), the neuroanatomical basis that underlies IGD remains elusive. In this work, we aimed to investigate gray matter (GM) volume alterations and structural covariance patterns in relation to IGD severity. METHODS Structural magnetic resonance imaging data were acquired from two hundred and thirty young adults encompassing a wide range of IGD severity. Voxel-based morphometry (VBM) analysis was applied to examine GM volume changes associated with IGD severity. Furthermore, the organization of whole-brain structural covariance network (SCN) was analyzed using the regions identified as seeds from VBM analysis. RESULTS Individuals with greater IGD severity had increased GM volumes in the midline components of the default mode network (DMN), namely, the right medial prefrontal cortex (mPFC) and precuneus. More importantly, the SCN results revealed impaired patterns of structural covariance between the DMN-related regions and areas associated with visuospatial attention and reward craving processing as the addiction severity of IGD worsened. LIMITATIONS Only young Chinese adults were enrolled in our study andthe extent to which findings generalize to samples in other age groups and diverse cultures is unclear. CONCLUSIONS These results showed volume expansion of the DMN components and its weakened structural association with visuospatial attention and motivational craving regions with increasing IGD severity. This study deepens our understanding of the underlying neuroanatomical correlates of IGD, which may help to explain why some individuals are more vulnerable to compulsive gaming usage than others.
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Affiliation(s)
- Shuaiyu Chen
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China
| | - Min Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China
| | - Haohao Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China
| | - Yuchao Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
| | - Xin Hou
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qian Zhuang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
| | - Guang-Heng Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China.
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8
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Haugg A, Renz FM, Nicholson AA, Lor C, Götzendorfer SJ, Sladky R, Skouras S, McDonald A, Craddock C, Hellrung L, Kirschner M, Herdener M, Koush Y, Papoutsi M, Keynan J, Hendler T, Cohen Kadosh K, Zich C, Kohl SH, Hallschmid M, MacInnes J, Adcock RA, Dickerson KC, Chen NK, Young K, Bodurka J, Marxen M, Yao S, Becker B, Auer T, Schweizer R, Pamplona G, Lanius RA, Emmert K, Haller S, Van De Ville D, Kim DY, Lee JH, Marins T, Megumi F, Sorger B, Kamp T, Liew SL, Veit R, Spetter M, Weiskopf N, Scharnowski F, Steyrl D. Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis. Neuroimage 2021; 237:118207. [PMID: 34048901 DOI: 10.1016/j.neuroimage.2021.118207] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 05/14/2021] [Accepted: 05/24/2021] [Indexed: 12/12/2022] Open
Abstract
Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
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Affiliation(s)
- Amelie Haugg
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria.
| | - Fabian M Renz
- Faculty of Psychology, University of Vienna, Austria
| | | | - Cindy Lor
- Faculty of Psychology, University of Vienna, Austria
| | | | - Ronald Sladky
- Faculty of Psychology, University of Vienna, Austria
| | - Stavros Skouras
- Department of Biological and Medical Psychology, University of Bergen, Norway
| | - Amalia McDonald
- Department of Psychology, University of Virginia, United States
| | - Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, United States
| | - Lydia Hellrung
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Switzerland
| | - Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Canada
| | - Marcus Herdener
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland
| | - Yury Koush
- Department of Radiology and Biomedical Imaging, Yale University, United States
| | - Marina Papoutsi
- UCL Huntington's Disease Centre, Institute of Neurology, University College London, United Kingdom; IXICO plc, United Kingdom
| | - Jackob Keynan
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
| | - Talma Hendler
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
| | | | - Catharina Zich
- Nuffiled Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Simon H Kohl
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Germany
| | - Manfred Hallschmid
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Germany; German Center for Diabetes Research (DZD), Germany
| | - Jeff MacInnes
- Institute for Learning and Brain Sciences, University of Washington, United States
| | - R Alison Adcock
- Duke Institute for Brain Sciences, Duke University, United States; Department of Psychiatry and Behavioral Sciences, Duke University, United States
| | - Kathryn C Dickerson
- Department of Psychiatry and Behavioral Sciences, Duke University, United States
| | - Nan-Kuei Chen
- Department of Biomedical Engineering, University of Arizona, United States
| | - Kymberly Young
- Department of Psychiatry, School of Medicine, University of Pittsburgh, United States
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, United States; Stephenson School of Biomedical Engineering, University of Oklahoma, United States
| | - Michael Marxen
- Department of Psychiatry, Technische Universität Dresden, Germany
| | - Shuxia Yao
- Clinical Hospital of the Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, China
| | - Benjamin Becker
- Clinical Hospital of the Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, China
| | - Tibor Auer
- School of Psychology, University of Surrey, United Kingdom
| | | | - Gustavo Pamplona
- Department of Ophthalmology, University of Lausanne and Fondation Asile des Aveugles, Switzerland
| | - Ruth A Lanius
- Department of Psychiatry, University of Western Ontario, Canada
| | - Kirsten Emmert
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel University, Germany
| | - Sven Haller
- Department of Surgical Sciences, Radiology, Uppsala University, Sweden
| | - Dimitri Van De Ville
- Center for Neuroprosthetics, Ecole polytechnique féderale de Lausanne, Switzerland; Faculty of Medicine, University of Geneva, Switzerland
| | - Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Korea
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Korea
| | - Theo Marins
- D'Or Institute for Research and Education, Brazil
| | | | - Bettina Sorger
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | - Tabea Kamp
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | | | - Ralf Veit
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Germany; German Center for Diabetes Research (DZD), Germany; High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Germany
| | - Maartje Spetter
- School of Psychology, University of Birmingham, United Kingdom
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Germany
| | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria
| | - David Steyrl
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria
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9
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de Albuquerque D, Goffinet J, Wright R, Pearson J. Deep Generative Analysis for Task-Based Functional MRI Experiments. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2021; 149:146-175. [PMID: 35224507 PMCID: PMC8871581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
While functional magnetic resonance imaging (fMRI) remains one of the most widespread and important methods in basic and clinical neuroscience, the data it produces-time series of brain volumes-continue to pose daunting analysis challenges. The current standard ("mass univariate") approach involves constructing a matrix of task regressors, fitting a separate general linear model at each volume pixel ("voxel"), computing test statistics for each model, and correcting for false positives post hoc using bootstrap or other resampling methods. Despite its simplicity, this approach has enjoyed great success over the last two decades due to: 1) its ability to produce effect maps highlighting brain regions whose activity significantly correlates with a given variable of interest; and 2) its modeling of experimental effects as separable and thus easily interpretable. However, this approach suffers from several well-known drawbacks, namely: inaccurate assumptions of linearity and noise Gaussianity; a limited ability to capture individual effects and variability; and difficulties in performing proper statistical testing secondary to independently fitting voxels. In this work, we adopt a different approach, modeling entire volumes directly in a manner that increases model flexibility while preserving interpretability. Specifically, we use a generalized additive model (GAM) in which the effects of each regressor remain separable, the product of a spatial map produced by a variational autoencoder and a (potentially nonlinear) gain modeled by a covariate-specific Gaussian Process. The result is a model that yields group-level effect maps comparable or superior to the ones obtained with standard fMRI analysis software while also producing single-subject effect maps capturing individual differences. This suggests that generative models with a decomposable structure might offer a more flexible alternative for the analysis of task-based fMRI data.
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Affiliation(s)
- Daniela de Albuquerque
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Jack Goffinet
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Rachael Wright
- Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA
| | - John Pearson
- Department of Biostatistics & Bioinformatics, Department of Electrical and Computer Engineering, Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA
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10
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Pamplona GSP, Vieira BH, Scharnowski F, Salmon CEG. Personode: A Toolbox for ICA Map Classification and Individualized ROI Definition. Neuroinformatics 2020; 18:339-349. [PMID: 31900722 DOI: 10.1007/s12021-019-09449-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Canonical resting state networks (RSNs) can be obtained through independent component analysis (ICA). RSNs are reproducible across subjects but also present inter-individual differences, which can be used to individualize regions-of-interest (ROI) definition, thus making fMRI analyses more accurate. Unfortunately, no automatic tool for defining subject-specific ROIs exists, making the classification of ICAs as representatives of RSN time-consuming and largely dependent on visual inspection. Here, we present Personode, a user-friendly and open source MATLAB-based toolbox that semi-automatically performs the classification of RSN and allows for defining subject- and group-specific ROIs. To validate the applicability of our new approach and to assess potential improvements compared to previous approaches, we applied Personode to both task-related activation and resting-state data. Our analyses show that for task-related activation analyses, subject-specific spherical ROIs defined with Personode produced higher activity contrasts compared to ROIs derived from single-study and meta-analytic coordinates. We also show that subject-specific irregular ROIs defined with Personode improved ROI-to-ROI functional connectivity analyses.Hence, Personode might be a useful toolbox for ICA map classification into RSNs and group- as well as subject-specific ROI definitions, leading to improved analyses of task-related activation and functional connectivity.
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Affiliation(s)
- Gustavo S P Pamplona
- Sensory-Motor Laboratory (SeMoLa), Jules-Gonin Eye Hospital/Fondation Asile des Aveugles, Department of Ophthalmology/University of Lausanne, Avenue de France 15, 1004, Lausanne, Switzerland. .,Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital, University of Zürich, Lenggstr. 31, 8032, Zürich, Switzerland. .,Inbrain Lab, Department of Physics, University of São Paulo, Av. Bandeirantes 3900, Ribeirão Preto, 14040-900, Brazil.
| | - Bruno H Vieira
- Inbrain Lab, Department of Physics, University of São Paulo, Av. Bandeirantes 3900, Ribeirão Preto, 14040-900, Brazil
| | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital, University of Zürich, Lenggstr. 31, 8032, Zürich, Switzerland.,Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Winterthurerstr. 190, 8057, Zürich, Switzerland.,Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, 8057, Zürich, Switzerland.,Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Liebiggasse 5, 1010, Vienna, Austria
| | - Carlos E G Salmon
- Inbrain Lab, Department of Physics, University of São Paulo, Av. Bandeirantes 3900, Ribeirão Preto, 14040-900, Brazil
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11
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Tursic A, Eck J, Lührs M, Linden DEJ, Goebel R. A systematic review of fMRI neurofeedback reporting and effects in clinical populations. Neuroimage Clin 2020; 28:102496. [PMID: 33395987 PMCID: PMC7724376 DOI: 10.1016/j.nicl.2020.102496] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 10/29/2020] [Accepted: 11/02/2020] [Indexed: 12/22/2022]
Abstract
Real-time fMRI-based neurofeedback is a relatively young field with a potential to impact the currently available treatments of various disorders. In order to evaluate the evidence of clinical benefits and investigate how consistently studies report their methods and results, an exhaustive search of fMRI neurofeedback studies in clinical populations was performed. Reporting was evaluated using a limited number of Consensus on the reporting and experimental design of clinical and cognitive-behavioral neurofeedback studies (CRED-NF checklist) items, which was, together with a statistical power and sensitivity calculation, used to also evaluate the existing evidence of the neurofeedback benefits on clinical measures. The 62 found studies investigated regulation abilities and/or clinical benefits in a wide range of disorders, but with small sample sizes and were therefore unable to detect small effects. Most points from the CRED-NF checklist were adequately reported by the majority of the studies, but some improvements are suggested for the reporting of group comparisons and relations between regulation success and clinical benefits. To establish fMRI neurofeedback as a clinical tool, more emphasis should be placed in the future on using larger sample sizes determined through a priori power calculations and standardization of procedures and reporting.
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Affiliation(s)
- Anita Tursic
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands.
| | - Judith Eck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands.
| | - Michael Lührs
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands.
| | - David E J Linden
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Department of Neuroimaging and Neuromodeling, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands.
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12
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Haugg A, Sladky R, Skouras S, McDonald A, Craddock C, Kirschner M, Herdener M, Koush Y, Papoutsi M, Keynan JN, Hendler T, Cohen Kadosh K, Zich C, MacInnes J, Adcock RA, Dickerson K, Chen N, Young K, Bodurka J, Yao S, Becker B, Auer T, Schweizer R, Pamplona G, Emmert K, Haller S, Van De Ville D, Blefari M, Kim D, Lee J, Marins T, Fukuda M, Sorger B, Kamp T, Liew S, Veit R, Spetter M, Weiskopf N, Scharnowski F. Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity? Hum Brain Mapp 2020; 41:3839-3854. [PMID: 32729652 PMCID: PMC7469782 DOI: 10.1002/hbm.25089] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/18/2020] [Accepted: 05/26/2020] [Indexed: 12/31/2022] Open
Abstract
Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.
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Affiliation(s)
- Amelie Haugg
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Ronald Sladky
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Stavros Skouras
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
| | - Amalia McDonald
- Department of PsychologyUniversity of VirginiaCharlottesvilleVirginia
| | - Cameron Craddock
- Department of Diagnostic MedicineThe University of Texas at Austin Dell Medical SchoolAustinTexas
| | - Matthias Kirschner
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- McConnell Brain Imaging CentreMontréal Neurological Institute, McGill UniversityMontrealCanada
| | - Marcus Herdener
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
| | - Yury Koush
- Magnetic Resonance Research Center, Department of Radiology & Biomedical ImagingYale UniversityNew HavenConnecticut
| | - Marina Papoutsi
- UCL Huntington's Disease CentreInstitute of Neurology, University College LondonLondonEngland
| | - Jackob N. Keynan
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | - Talma Hendler
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | | | - Catharina Zich
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordEngland
| | - Jeff MacInnes
- Institute for Learning and Brain SciencesUniversity of WashingtonSeattleWashington
| | - R. Alison Adcock
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Kathryn Dickerson
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Nan‐Kuei Chen
- Department of Biomedical EngineeringUniversity of ArizonaTucsonArizona
| | - Kymberly Young
- Department of Psychiatry, School of MedicineUniversity of PittsburghPittsburghPennsylvania
| | | | - Shuxia Yao
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Benjamin Becker
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordEngland
| | - Renate Schweizer
- Functional Imaging LaboratoryGerman Primate CenterGöttingenGermany
| | - Gustavo Pamplona
- Hôpital and Ophtalmique Jules GoninUniversity of LausanneLausanneSwitzerland
| | - Kirsten Emmert
- Department of NeurologyUniversity Medical Center Schleswig‐Holstein, Kiel UniversityKielGermany
| | - Sven Haller
- Radiology‐Department of Surgical SciencesUppsala UniversityUppsalaSweden
| | - Dimitri Van De Ville
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
- Department of Radiology and Medical Informatics, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
| | - Maria‐Laura Blefari
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
| | - Dong‐Youl Kim
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Jong‐Hwan Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Theo Marins
- D'Or Institute for Research and Education (IDOR)Rio de JaneiroBrazil
| | - Megumi Fukuda
- School of Fundamental Science and EngineeringWaseda UniversityTokyoJapan
| | - Bettina Sorger
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Tabea Kamp
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Sook‐Lei Liew
- Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Ralf Veit
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center MunichUniversity of TübingenTübingenGermany
| | - Maartje Spetter
- School of PsychologyUniversity of BirminghamBirminghamEngland
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Frank Scharnowski
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
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13
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Heunis S, Lamerichs R, Zinger S, Caballero‐Gaudes C, Jansen JFA, Aldenkamp B, Breeuwer M. Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review. Hum Brain Mapp 2020; 41:3439-3467. [PMID: 32333624 PMCID: PMC7375116 DOI: 10.1002/hbm.25010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/13/2020] [Accepted: 04/03/2020] [Indexed: 01/31/2023] Open
Abstract
Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.
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Affiliation(s)
- Stephan Heunis
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | - Rolf Lamerichs
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- Philips ResearchEindhovenThe Netherlands
| | - Svitlana Zinger
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | | | - Jacobus F. A. Jansen
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of RadiologyMaastricht University Medical CentreMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
| | - Bert Aldenkamp
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and NeuropsychologyGhent University HospitalGhentBelgium
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Marcel Breeuwer
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Philips HealthcareBestThe Netherlands
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14
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Fede SJ, Dean SF, Manuweera T, Momenan R. A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review. Front Hum Neurosci 2020; 14:60. [PMID: 32161529 PMCID: PMC7052377 DOI: 10.3389/fnhum.2020.00060] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/07/2020] [Indexed: 11/26/2022] Open
Abstract
Background: Although biofeedback using electrophysiology has been explored extensively, the approach of using neurofeedback corresponding to hemodynamic response is a relatively young field. Real time functional magnetic resonance imaging-based neurofeedback (rt-fMRI-NF) uses sensory feedback to operantly reinforce patterns of neural response. It can be used, for example, to alter visual perception, increase brain connectivity, and reduce depression symptoms. Within recent years, interest in rt-fMRI-NF in both research and clinical contexts has expanded considerably. As such, building a consensus regarding best practices is of great value. Objective: This systematic review is designed to describe and evaluate the variations in methodology used in previous rt-fMRI-NF studies to provide recommendations for rt-fMRI-NF study designs that are mostly likely to elicit reproducible and consistent effects of neurofeedback. Methods: We conducted a database search for fMRI neurofeedback papers published prior to September 26th, 2019. Of 558 studies identified, 146 met criteria for inclusion. The following information was collected from each study: sample size and type, task used, neurofeedback calculation, regulation procedure, feedback, whether feedback was explicitly related to changing brain activity, feedback timing, control group for active neurofeedback, how many runs and sessions of neurofeedback, if a follow-up was conducted, and the results of neurofeedback training. Results: rt-fMRI-NF is typically upregulation practice based on hemodynamic response from a specific region of the brain presented using a continually updating thermometer display. Most rt-fMRI-NF studies are conducted in healthy samples and half evaluate its effect on immediate changes in behavior or affect. The most popular control group method is to provide sham signal from another region; however, many studies do not compare use a comparison group. Conclusions: We make several suggestions for designs of future rt-fMRI-NF studies. Researchers should use feedback calculation methods that consider neural response across regions (i.e., SVM or connectivity), which should be conveyed as intermittent, auditory feedback. Participants should be given explicit instructions and should be assessed on individual differences. Future rt-fMRI-NF studies should use clinical samples; effectiveness of rt-fMRI-NF should be evaluated on clinical/behavioral outcomes at follow-up time points in comparison to both a sham and no feedback control group.
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Affiliation(s)
| | | | | | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States
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15
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Mayeli A, Misaki M, Zotev V, Tsuchiyagaito A, Al Zoubi O, Phillips R, Smith J, Stewart JL, Refai H, Paulus MP, Bodurka J. Self-regulation of ventromedial prefrontal cortex activation using real-time fMRI neurofeedback-Influence of default mode network. Hum Brain Mapp 2020; 41:342-352. [PMID: 31633257 PMCID: PMC7267960 DOI: 10.1002/hbm.24805] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 09/12/2019] [Accepted: 09/12/2019] [Indexed: 02/03/2023] Open
Abstract
The ventromedial prefrontal cortex (vmPFC) is involved in regulation of negative emotion and decision-making, emotional and behavioral control, and active resilient coping. This pilot study examined the feasibility of training healthy subjects (n = 27) to self-regulate the vmPFC activity using a real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf). Participants in the experimental group (EG, n = 18) were provided with an ongoing vmPFC hemodynamic activity (rtfMRI-nf signal represented as variable-height bar). Individuals were instructed to raise the bar by self-relevant value-based thinking. Participants in the control group (CG, n = 9) performed the same task; however, they were provided with computer-generated sham neurofeedback signal. Results demonstrate that (a) both the CG and the EG show a higher vmPFC fMRI signal at the baseline than during neurofeedback training; (b) no significant positive training effect was seen in the vmPFC across neurofeedback runs; however, the medial prefrontal cortex, middle temporal gyri, inferior frontal gyri, and precuneus showed significant decreasing trends across the training runs only for the EG; (c) the vmPFC rtfMRI-nf signal associated with the fMRI signal across the default mode network (DMN). These findings suggest that it may be difficult to modulate a single DMN region without affecting other DMN regions. Observed decreased vmPFC activity during the neurofeedback task could be due to interference from the fMRI signal within other DMN network regions, as well as interaction with task-positive networks. Even though participants in the EG did not show significant positive increase in the vmPFC activity among neurofeedback runs, they were able to learn to accommodate the demand of self-regulation task to maintain the vmPFC activity with the help of a neurofeedback signal.
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Affiliation(s)
- Ahmad Mayeli
- Laureate Institute for Brain ResearchTulsaOklahoma
- Electrical and Computer Engineering DepartmentUniversity of OklahomaTulsaOklahoma
| | | | - Vadim Zotev
- Laureate Institute for Brain ResearchTulsaOklahoma
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain ResearchTulsaOklahoma
- Japan Society for the Promotion ScienceTokyoJapan
- Research Center for Child DevelopmentChiba UniversityChibaJapan
| | - Obada Al Zoubi
- Laureate Institute for Brain ResearchTulsaOklahoma
- Electrical and Computer Engineering DepartmentUniversity of OklahomaTulsaOklahoma
| | | | - Jared Smith
- Laureate Institute for Brain ResearchTulsaOklahoma
| | | | - Hazem Refai
- Electrical and Computer Engineering DepartmentUniversity of OklahomaTulsaOklahoma
| | | | - Jerzy Bodurka
- Laureate Institute for Brain ResearchTulsaOklahoma
- Stephenson School of Biomedical EngineeringUniversity of OklahomaTulsaOklahoma
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16
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Skottnik L, Linden DEJ. Mental Imagery and Brain Regulation-New Links Between Psychotherapy and Neuroscience. Front Psychiatry 2019; 10:779. [PMID: 31736799 PMCID: PMC6831624 DOI: 10.3389/fpsyt.2019.00779] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 09/30/2019] [Indexed: 01/23/2023] Open
Abstract
Mental imagery is a promising tool and mechanism of psychological interventions, particularly for mood and anxiety disorders. In parallel developments, neuromodulation techniques have shown promise as add-on therapies in psychiatry, particularly non-invasive brain stimulation for depression. However, these techniques have not yet been combined in a systematic manner. One novel technology that may be able to achieve this is neurofeedback, which entails the self-regulation of activation in specific brain areas or networks (or the self-modulation of distributed activation patterns) by the patients themselves, through real-time feedback of brain activation (for example, from functional magnetic resonance imaging). One of the key mechanisms by which patients learn such self-regulation is mental imagery. Here, we will first review the main mental imagery approaches in psychotherapy and the implicated brain networks. We will then discuss how these networks can be targeted with neuromodulation (neurofeedback or non-invasive or invasive brain stimulation). We will review the clinical evidence for neurofeedback and discuss possible ways of enhancing it through systematic combination with psychological interventions, with a focus on depression, anxiety disorders, and addiction. The overarching aim of this perspective paper will be to open a debate on new ways of developing neuropsychotherapies.
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Affiliation(s)
| | - David E. J. Linden
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
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17
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Misaki M, Phillips R, Zotev V, Wong CK, Wurfel BE, Krueger F, Feldner M, Bodurka J. Brain activity mediators of PTSD symptom reduction during real-time fMRI amygdala neurofeedback emotional training. Neuroimage Clin 2019; 24:102047. [PMID: 31711031 PMCID: PMC6849428 DOI: 10.1016/j.nicl.2019.102047] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 10/08/2019] [Accepted: 10/21/2019] [Indexed: 11/20/2022]
Abstract
Self-regulation of brain activation with real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) is emerging as a promising treatment for psychiatric disorders. The association between the regulation and symptom reduction, however, has not been consistent, and the mechanisms underlying the symptom reduction remain poorly understood. The present study investigated brain activity mediators of the amygdala rtfMRI-nf training effect on combat veterans' PTSD symptom reduction. The training was designed to increase a neurofeedback signal either from the left amygdala (experimental group; EG) or from a control region not implicated in emotion regulation (control group; CG) during positive autobiographical memory recall. We employed a structural equation model mapping analysis to identify brain regions that mediated the effects of the rtfMRI-nf training on PTSD symptoms. Symptom reduction was mediated by low activation in the dorsomedial prefrontal cortex (DMPFC) and the middle cingulate cortex. There was a trend toward less activation in these regions for the EG compared to the CG. Low activation in the precuneus, the right superior parietal, the right insula, and the right cerebellum also mediated symptom reduction while their effects were moderated by the neurofeedback signal; a higher signal was linked to less effect on symptom reduction. This moderation was not specific to the EG. MDD comorbidity was associated with high DMPFC activation, which resulted in less effective regulation of the feedback signal. These results indicated that symptom reduction due to the neurofeedback training was not specifically mediated by the neurofeedback target activity, but broad regions were involved in the process.
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Affiliation(s)
- Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Raquel Phillips
- Laureate Psychiatric Clinic and Hospital, Tulsa, OK, United States
| | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Chung-Ki Wong
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Brent E Wurfel
- Laureate Institute for Brain Research, Tulsa, OK, United States; Laureate Psychiatric Clinic and Hospital, Tulsa, OK, United States
| | - Frank Krueger
- Neuroscience Department, George Mason University, Fairfax, VA, United States
| | - Matthew Feldner
- Department of Psychological Science, University of Arkansas, Fayetteville, AR, United States
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States; Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States.
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18
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Skouras S, Scharnowski F. The effects of psychiatric history and age on self-regulation of the default mode network. Neuroimage 2019; 198:150-159. [PMID: 31103786 DOI: 10.1016/j.neuroimage.2019.05.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 04/22/2019] [Accepted: 05/03/2019] [Indexed: 12/16/2022] Open
Abstract
Real-time neurofeedback enables human subjects to learn to regulate their brain activity, effecting behavioral changes and improvements of psychiatric symptomatology. Neurofeedback up-regulation and down-regulation have been assumed to share common neural correlates. Neuropsychiatric pathology and aging incur suboptimal functioning of the default mode network. Despite the exponential increase in real-time neuroimaging studies, the effects of aging, pathology and the direction of regulation on neurofeedback performance remain largely unknown. Using real-time fMRI data shared through the Rockland Sample Real-Time Neurofeedback project (N = 136) and open-access analyses, we first modeled neurofeedback performance and learning in a group of subjects with psychiatric history (na = 74) and a healthy control group (nb = 62). Subsequently, we examined the relationship between up-regulation and down-regulation learning, the relationship between age and neurofeedback performance in each group and differences in neurofeedback performance between the two groups. For interpretative purposes, we also investigated functional connectomics prior to neurofeedback. Results show that in an initial session of default mode network neurofeedback with real-time fMRI, up-regulation and down-regulation learning scores are negatively correlated. This finding is related to resting state differences in the eigenvector centrality of the posterior cingulate cortex. Moreover, age correlates negatively with default mode network neurofeedback performance, only in absence of psychiatric history. Finally, adults with psychiatric history outperform healthy controls in default mode network up-regulation. Interestingly, the performance difference is related to no up-regulation learning in controls. This finding is supported by marginally higher default mode network centrality during resting state, in the presence of psychiatric history.
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Affiliation(s)
- Stavros Skouras
- Neuroimaging Unit, Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, 08005, Spain; Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, 08005, Spain.
| | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, 8032, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Zürich, 8057, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, Zürich, 8057, Switzerland; Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria
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19
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Rubia K, Criaud M, Wulff M, Alegria A, Brinson H, Barker G, Stahl D, Giampietro V. Functional connectivity changes associated with fMRI neurofeedback of right inferior frontal cortex in adolescents with ADHD. Neuroimage 2019; 188:43-58. [PMID: 30513395 PMCID: PMC6414400 DOI: 10.1016/j.neuroimage.2018.11.055] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 11/28/2018] [Accepted: 11/29/2018] [Indexed: 11/21/2022] Open
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is associated with poor self-control, underpinned by inferior fronto-striatal deficits. We showed previously that 18 ADHD adolescents over 11 runs of 8.5 min of real-time functional magnetic resonance neurofeedback of the right inferior frontal cortex (rIFC) progressively increased activation in 2 regions of the rIFC which was associated with clinical symptom improvement. In this study, we used functional connectivity analyses to investigate whether fMRI-Neurofeedback of rIFC resulted in dynamic functional connectivity changes in underlying neural networks. Whole-brain seed-based functional connectivity analyses were conducted using the two clusters showing progressively increased activation in rIFC as seed regions to test for changes in functional connectivity before and after 11 fMRI-Neurofeedback runs. Furthermore, we tested whether the resulting functional connectivity changes were associated with clinical symptom improvements and whether they were specific to fMRI-Neurofeedback of rIFC when compared to a control group who had to self-regulate another region. rIFC showed increased positive functional connectivity after relative to before fMRI-Neurofeedback with dorsal caudate and anterior cingulate and increased negative functional connectivity with regions of the default mode network (DMN) such as posterior cingulate and precuneus. Furthermore, the functional connectivity changes were correlated with clinical improvements and the functional connectivity and correlation findings were specific to the rIFC-Neurofeedback group. The findings show for the first time that fMRI-Neurofeedback of a typically dysfunctional frontal region in ADHD adolescents leads to strengthening within fronto-cingulo-striatal networks and to weakening of functional connectivity with posterior DMN regions and that this may be underlying clinical improvement.
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Affiliation(s)
- K Rubia
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - M Criaud
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - M Wulff
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - A Alegria
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - H Brinson
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - G Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - D Stahl
- Department of Biostatistics & Health Informatics, King's College London, UK
| | - V Giampietro
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
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20
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Atwi S, Metcalfe AWS, Robertson AD, Rezmovitz J, Anderson ND, MacIntosh BJ. Attention-Related Brain Activation Is Altered in Older Adults With White Matter Hyperintensities Using Multi-Echo fMRI. Front Neurosci 2018; 12:748. [PMID: 30405336 PMCID: PMC6200839 DOI: 10.3389/fnins.2018.00748] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 09/28/2018] [Indexed: 11/19/2022] Open
Abstract
Cognitive decline is often undetectable in the early stages of accelerated vascular aging. Attentional processes are particularly affected in older adults with white matter hyperintensities (WMH), although specific neurovascular mechanisms have not been elucidated. We aimed to identify differences in attention-related neurofunctional activation and behavior between adults with and without WMH. Older adults with moderate to severe WMH (n = 18, mean age = 70 years), age-matched adults (n = 28, mean age = 72), and healthy younger adults (n = 19, mean age = 25) performed a modified flanker task during multi-echo blood oxygenation level dependent functional magnetic resonance imaging. Task-related activation was assessed using a weighted-echo approach. Healthy older adults had more widespread response and higher amplitude of activation compared to WMH adults in fronto-temporal and parietal cortices. Activation associated with processing speed was absent in the WMH group, suggesting attention-related activation deficits that may be a consequence of cerebral small vessel disease. WMH adults had greater executive contrast activation in the precuneous and posterior cingulate gyrus compared to HYA, despite no performance benefits, reinforcing the network dysfunction theory in WMH.
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Affiliation(s)
- Sarah Atwi
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Arron W S Metcalfe
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Centre for Youth Bipolar Disorder, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Andrew D Robertson
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Jeremy Rezmovitz
- Department of Family and Community Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Nicole D Anderson
- Department of Psychiatry and Psychology, University of Toronto, Toronto, ON, Canada.,Rotman Research Institute, Baycrest Centre, University of Toronto, Toronto, ON, Canada
| | - Bradley J MacIntosh
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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21
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Milham MP, Craddock RC, Son JJ, Fleischmann M, Clucas J, Xu H, Koo B, Krishnakumar A, Biswal BB, Castellanos FX, Colcombe S, Di Martino A, Zuo XN, Klein A. Assessment of the impact of shared brain imaging data on the scientific literature. Nat Commun 2018; 9:2818. [PMID: 30026557 PMCID: PMC6053414 DOI: 10.1038/s41467-018-04976-1] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 06/05/2018] [Indexed: 01/14/2023] Open
Abstract
Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice. To justify the significant effort required for sharing data, funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a case study that provides direct evidence of the impact of open sharing on brain imaging data use and resulting peer-reviewed publications. We demonstrate that openly shared data can increase the scale of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings dispel the myth that scientific findings using shared data cannot be published in high-impact journals, suggest the transformative power of data sharing for accelerating science, and underscore the need for implementing data sharing universally.
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Affiliation(s)
- Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA.
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, 10962, NY, USA.
| | - R Cameron Craddock
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, 10962, NY, USA
| | - Jake J Son
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
| | - Michael Fleischmann
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
| | - Jon Clucas
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
| | - Helen Xu
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
| | - Bonhwang Koo
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
| | - Anirudh Krishnakumar
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
- Centre de Recherches Interdisciplinaires, INSERM U1001, Dpt Frontières du Vivant et de l'Apprendre, University Paris Descartes, Sorbonne Paris Cité, Paris, 75014, France
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, 07102, NJ, USA
| | - F Xavier Castellanos
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, 10962, NY, USA
- Department of Child and Adolescent Psychiatry, Hassenfeld Children's Hospital at NYU Langone, New York, 10016, NY, USA
| | - Stan Colcombe
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, 10962, NY, USA
| | - Adriana Di Martino
- Department of Child and Adolescent Psychiatry, Hassenfeld Children's Hospital at NYU Langone, New York, 10016, NY, USA
| | - Xi-Nian Zuo
- Department of Psychology, University of Chinese Academy of Sciences (CAS), Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
- Research Center for Lifespan Development of Mind and Brain (CLIMB) and Magnetic Resonance Imaging Research Center, Institute of Psychology, Beijing, 100101, China
- Key Laboratory for Brain and Education Sciences, Guangxi Teachers Education University, Nanning, 530001, China
| | - Arno Klein
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
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22
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Doruyter A, Groenewold NA, Dupont P, Stein DJ, Warwick JM. Resting-state fMRI and social cognition: An opportunity to connect. Hum Psychopharmacol 2017; 32. [PMID: 28766324 DOI: 10.1002/hup.2627] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 04/26/2017] [Accepted: 06/27/2017] [Indexed: 01/05/2023]
Abstract
Many psychiatric disorders are characterized by altered social cognition. The importance of social cognition has previously been recognized by the National Institute of Mental Health Research Domain Criteria project, in which it features as a core domain. Social task-based functional magnetic resonance imaging (fMRI) currently offers the most direct insight into how the brain processes social information; however, resting-state fMRI may be just as important in understanding the biology and network nature of social processing. Resting-state fMRI allows researchers to investigate the functional relationships between brain regions in a neutral state: so-called resting functional connectivity (RFC). There is evidence that RFC is predictive of how the brain processes information during social tasks. This is important because it shifts the focus from possibly context-dependent aberrations to context-independent aberrations in functional network architecture. Rather than being analysed in isolation, the study of resting-state brain networks shows promise in linking results of task-based fMRI results, structural connectivity, molecular imaging findings, and performance measures of social cognition-which may prove crucial in furthering our understanding of the social brain.
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Affiliation(s)
- Alex Doruyter
- Division of Nuclear Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Nynke A Groenewold
- Department of Psychiatry, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Patrick Dupont
- Department of Neurosciences, Laboratory of Cognitive Neurology, KU Leuven, Leuven, Belgium
| | - Dan J Stein
- MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - James M Warwick
- Division of Nuclear Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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23
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Lorenz R, Hampshire A, Leech R. Neuroadaptive Bayesian Optimization and Hypothesis Testing. Trends Cogn Sci 2017; 21:155-167. [PMID: 28236531 DOI: 10.1016/j.tics.2017.01.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Revised: 01/06/2017] [Accepted: 01/09/2017] [Indexed: 01/22/2023]
Abstract
Cognitive neuroscientists are often interested in broad research questions, yet use overly narrow experimental designs by considering only a small subset of possible experimental conditions. This limits the generalizability and reproducibility of many research findings. Here, we propose an alternative approach that resolves these problems by taking advantage of recent developments in real-time data analysis and machine learning. Neuroadaptive Bayesian optimization is a powerful strategy to efficiently explore more experimental conditions than is currently possible with standard methodology. We argue that such an approach could broaden the hypotheses considered in cognitive science, improving the generalizability of findings. In addition, Bayesian optimization can be combined with preregistration to cover exploration, mitigating researcher bias more broadly and improving reproducibility.
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Affiliation(s)
- Romy Lorenz
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, W12 0NN, UK; Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Adam Hampshire
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, W12 0NN, UK
| | - Robert Leech
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, W12 0NN, UK.
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