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Choi M, Kim HC, Youn I, Lee SJ, Lee JH. Use of functional magnetic resonance imaging to identify cortical loci for lower limb movements and their efficacy for individuals after stroke. J Neuroeng Rehabil 2024; 21:58. [PMID: 38627779 PMCID: PMC11020805 DOI: 10.1186/s12984-024-01319-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 01/29/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Identification of cortical loci for lower limb movements for stroke rehabilitation is crucial for better rehabilitation outcomes via noninvasive brain stimulation by targeting the fine-grained cortical loci of the movements. However, identification of the cortical loci for lower limb movements using functional MRI (fMRI) is challenging due to head motion and difficulty in isolating different types of movement. Therefore, we developed a custom-made MR-compatible footplate and leg cushion to identify the cortical loci for lower limb movements and conducted multivariate analysis on the fMRI data. We evaluated the validity of the identified loci using both fMRI and behavioral data, obtained from healthy participants as well as individuals after stroke. METHODS We recruited 33 healthy participants who performed four different lower limb movements (ankle dorsiflexion, ankle rotation, knee extension, and toe flexion) using our custom-built equipment while fMRI data were acquired. A subgroup of these participants (Dataset 1; n = 21) was used to identify the cortical loci associated with each lower limb movement in the paracentral lobule (PCL) using multivoxel pattern analysis and representational similarity analysis. The identified cortical loci were then evaluated using the remaining healthy participants (Dataset 2; n = 11), for whom the laterality index (LI) was calculated for each lower limb movement using the cortical loci identified for the left and right lower limbs. In addition, we acquired a dataset from 15 individuals with chronic stroke for regression analysis using the LI and the Fugl-Meyer Assessment (FMA) scale. RESULTS The cortical loci associated with the lower limb movements were hierarchically organized in the medial wall of the PCL following the cortical homunculus. The LI was clearer using the identified cortical loci than using the PCL. The healthy participants (mean ± standard deviation: 0.12 ± 0.30; range: - 0.63 to 0.91) exhibited a higher contralateral LI than the individuals after stroke (0.07 ± 0.47; - 0.83 to 0.97). The corresponding LI scores for individuals after stroke showed a significant positive correlation with the FMA scale for paretic side movement in ankle dorsiflexion (R2 = 0.33, p = 0.025) and toe flexion (R2 = 0.37, p = 0.016). CONCLUSIONS The cortical loci associated with lower limb movements in the PCL identified in healthy participants were validated using independent groups of healthy participants and individuals after stroke. Our findings suggest that these cortical loci may be beneficial for the neurorehabilitation of lower limb movement in individuals after stroke, such as in developing effective rehabilitation interventions guided by the LI scores obtained for neuronal activations calculated from the identified cortical loci across the paretic and non-paretic sides of the brain.
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Affiliation(s)
- Minseok Choi
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Hyun-Chul Kim
- Department of Artificial Intelligence, Kyungpook National University, Daegu, South Korea
| | - Inchan Youn
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, South Korea
| | - Song Joo Lee
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, South Korea.
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Boston, Massachusetts, USA.
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Zotev V, McQuaid JR, Robertson-Benta CR, Hittson AK, Wick TV, Ling JM, van der Horn HJ, Mayer AR. Validation of real-time fMRI neurofeedback procedure for cognitive training using counterbalanced active-sham study design. Neuroimage 2024; 290:120575. [PMID: 38479461 PMCID: PMC11060147 DOI: 10.1016/j.neuroimage.2024.120575] [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: 12/22/2023] [Revised: 03/10/2024] [Accepted: 03/11/2024] [Indexed: 03/17/2024] Open
Abstract
Investigation of neural mechanisms of real-time functional MRI neurofeedback (rtfMRI-nf) training requires an efficient study control approach. A common rtfMRI-nf study design involves an experimental group, receiving active rtfMRI-nf, and a control group, provided with sham rtfMRI-nf. We report the first study in which rtfMRI-nf procedure is controlled through counterbalancing training runs with active and sham rtfMRI-nf for each participant. Healthy volunteers (n = 18) used rtfMRI-nf to upregulate fMRI activity of an individually defined target region in the left dorsolateral prefrontal cortex (DLPFC) while performing tasks that involved mental generation of a random numerical sequence and serial summation of numbers in the sequence. Sham rtfMRI-nf was provided based on fMRI activity of a different brain region, not involved in these tasks. The experimental procedure included two training runs with the active rtfMRI-nf and two runs with the sham rtfMRI-nf, in a randomized order. The participants achieved significantly higher fMRI activation of the left DLPFC target region during the active rtfMRI-nf conditions compared to the sham rtfMRI-nf conditions. fMRI functional connectivity of the left DLPFC target region with the nodes of the central executive network was significantly enhanced during the active rtfMRI-nf conditions relative to the sham conditions. fMRI connectivity of the target region with the nodes of the default mode network was similarly enhanced. fMRI connectivity changes between the active and sham conditions exhibited meaningful associations with individual performance measures on the Working Memory Multimodal Attention Task, the Approach-Avoidance Task, and the Trail Making Test. Our results demonstrate that the counterbalanced active-sham study design can be efficiently used to investigate mechanisms of active rtfMRI-nf in direct comparison to those of sham rtfMRI-nf. Further studies with larger group sizes are needed to confirm the reported findings and evaluate clinical utility of this study control approach.
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Affiliation(s)
- Vadim Zotev
- The Mind Research Network/LBRI, Albuquerque, NM, USA.
| | | | | | - Anne K Hittson
- The Mind Research Network/LBRI, Albuquerque, NM, USA; Department of Pediatrics, University of New Mexico, Albuquerque, NM, USA
| | - Tracey V Wick
- The Mind Research Network/LBRI, Albuquerque, NM, USA
| | - Josef M Ling
- The Mind Research Network/LBRI, Albuquerque, NM, USA
| | | | - Andrew R Mayer
- The Mind Research Network/LBRI, Albuquerque, NM, USA; Department of Psychiatry & Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA; Department of Psychology, University of New Mexico, Albuquerque, NM, USA; Department of Neurology, University of New Mexico, Albuquerque, NM, USA
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Lee J, Lee J. Discovering individual fingerprints in resting-state functional connectivity using deep neural networks. Hum Brain Mapp 2024; 45:e26561. [PMID: 38096866 PMCID: PMC10789221 DOI: 10.1002/hbm.26561] [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: 06/06/2023] [Revised: 11/13/2023] [Accepted: 11/28/2023] [Indexed: 01/16/2024] Open
Abstract
Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the "fingerprint of FC" (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.
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Affiliation(s)
- Juhyeon Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jong‐Hwan Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
- Interdisciplinary Program in Precision Public HealthKorea UniversitySeoulSouth Korea
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyBostonMassachusettsUSA
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Fagerland SM, Berntsen HR, Fredriksen M, Endestad T, Skouras S, Rootwelt-Revheim ME, Undseth RM. Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2024; 15:41-62. [PMID: 38827812 PMCID: PMC11141335 DOI: 10.2478/joeb-2024-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Indexed: 06/05/2024]
Abstract
Objective The goal of this study was to explore the development and implementation of a protocol for real-time fMRI neurofeedback (rtfMRI-nf) and to assess the potential for enhancing the selective brain activation using stimuli from Virtual Reality (VR). In this study we focused on two specific brain regions, supplementary motor area (SMA) and right inferior frontal gyrus (rIFG). Publications by other study groups have suggested impaired function in these specific brain regions in patients with the diagnoses Attention Deficit Hyperactivity Disorder (ADHD) and Tourette's Syndrome (TS). This study explored the development of a protocol to investigate if attention and contextual memory may be used to systematically strengthen the procedure of rtfMRI-nf. Methods We used open-science software and platforms for rtfMRI-nf and for developing a simulated repetition of the rtfMRI-nf brain training in VR. We conducted seven exploratory tests in which we updated the protocol at each step. During rtfMRI-nf, MRI images are analyzed live while a person is undergoing an MRI scan, and the results are simultaneously shown to the person in the MRI-scanner. By focusing the analysis on specific regions of the brain, this procedure can be used to help the person strengthen conscious control of these regions. The VR simulation of the same experience involved a walk through the hospital toward the MRI scanner where the training sessions were conducted, as well as a subsequent simulated repetition of the MRI training. The VR simulation was a 2D projection of the experience.The seven exploratory tests involved 19 volunteers. Through this exploration, methods for aiming within the brain (e.g. masks/algorithms for coordinate-system control) and calculations for the analyses (e.g. calculations based on connectivity versus activity) were updated by the project team throughout the project. The final procedure involved three initial rounds of rtfMRI-nf for learning brain strategies. Then, the volunteers were provided with VR headsets and given instructions for one week of use. Afterward, a new session with three rounds of rtfMRI-nf was conducted. Results Through our exploration of the indirect effect parameters - brain region activity (directed oxygenated blood flow), connectivity (degree of correlated activity in different regions), and neurofeedback score - the volunteers tended to increase activity in the reinforced brain regions through our seven tests. Updates of procedures and analyses were always conducted between pilots, and never within. The VR simulated repetition was tested in pilot 7, but the role of the VR contribution in this setting is unclear due to underpowered testing. Conclusion This proof-of-concept protocol implies how rtfMRI-nf may be used to selectively train two brain regions (SMA and rIFG). The method may likely be adapted to train any given region in the brain, but readers are advised to update and adapt the procedure to experimental needs.
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Affiliation(s)
- Steffen Maude Fagerland
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Department of Cognitive and Neuropsychology, Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Psychology, University of Oslo, Norway
| | - Henrik Røsholm Berntsen
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
| | - Mats Fredriksen
- Neuropsychatric Outpatient Clinic, Vestfold Hospital Trust, Tønsberg, Norway
| | - Tor Endestad
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Psychology, University of Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Norway
| | - Stavros Skouras
- Department of Fundamental Neurosciences, Faculty of Medicine, University of Geneva, Geneva, CH-1202, Switzerland
- Department of Biological and Medical Psychology, University of Bergen, Bergen, NO-5020, Norway
- Department of Neurology, Inselspital University Hospital Bern, Bern, CH-3010, Switzerland
| | - Mona Elisabeth Rootwelt-Revheim
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Ragnhild Marie Undseth
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Department of Cognitive and Neuropsychology, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Radiology Research, The Intervention Centre, Oslo University Hospital, Oslo, Norway
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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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Kim Y, Lee J, Tegethoff M, Meinlschmidt G, Yoo SS, Lee JH. Reliability of self-reported dispositional mindfulness scales and their association with working memory performance and functional connectivity. Brain Cogn 2023; 169:106001. [PMID: 37235929 DOI: 10.1016/j.bandc.2023.106001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 04/22/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023]
Abstract
We systematically investigated the link between trait mindfulness scores and functional connectivity (FC) features or behavioral data, to emphasize the importance of the reliability of self-report mindfulness scores. Sixty healthy young male participants underwent two functional MRI runs with three mindfulness or mind-wandering task blocks with an N-back task (NBT) block. The data from 49 participants (age: 23.3 ± 2.8) for whom two sets of the self-reported Mindfulness Attention Awareness Scale (MAAS) and NBT performance were available were analyzed. We divided participants into two groups based on the consistency level of their MAAS scores (i.e., a "consistent" and an "inconsistent" group). Then, the association between the MAAS scores and FC features or NBT performance was investigated using linear regression analysis with p-value correction and bootstrapping. Meaningful associations (a) between MAAS and NBT accuracy (slope = 0.41, CI = [0.10, 0.73], corrected p < 0.05), (b) between MAAS and the FC edges in the frontoparietal network, and (c) between the FC edges and NBT performance were only observed in the consistent group (n = 26). Our findings demonstrate the importance of appropriate screening mechanisms for self-report-based dispositional mindfulness scores when trait mindfulness scores are combined with neuronal features and behavioral data.
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Affiliation(s)
- Yeji Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Juhyeon Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Marion Tegethoff
- Division of Clinical Psychology and Psychiatry, Department of Psychology, University of Basel, Basel, Switzerland; Institute of Psychology, RWTH Aachen University, Germany
| | - Gunther Meinlschmidt
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Basel, Switzerland; Department of Psychosomatic Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Division of Clinical Psychology and Cognitive Behavioral Therapy, International Psychoanalytic University, Berlin, Germany
| | - Seung-Schik Yoo
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
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Dourron HM, Strauss C, Hendricks PS. Self-Entropic Broadening Theory: Toward a New Understanding of Self and Behavior Change Informed by Psychedelics and Psychosis. Pharmacol Rev 2022; 74:982-1027. [DOI: 10.1124/pharmrev.121.000514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 06/14/2022] [Accepted: 06/16/2022] [Indexed: 11/22/2022] Open
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Ganesan S, Beyer E, Moffat B, Van Dam NT, Lorenzetti V, Zalesky A. Focused attention meditation in healthy adults: A systematic review and meta-analysis of cross-sectional functional MRI studies. Neurosci Biobehav Rev 2022; 141:104846. [PMID: 36067965 DOI: 10.1016/j.neubiorev.2022.104846] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/17/2022] [Accepted: 08/26/2022] [Indexed: 10/14/2022]
Abstract
Meditation trains the mind to focus attention towards an object or experience. Among different meditation techniques, focused attention meditation is considered foundational for more advanced practices. Despite renewed interest in its functional neural correlates, there is no unified neurocognitive model of focused attention meditation developed via quantitative synthesis of contemporary literature. Hence, we performed a quantitative systematic review and meta-analysis of all functional MRI studies examining focussed attention meditation. Following PRISMA guidelines, 28 studies were included in this review, of which 10 studies (200 participants) were amenable to activation likelihood estimation meta-analysis. We found that regions comprising three key functional brain networks i.e., Default-mode, Salience, and Executive Control, were consistently implicated in focused attention meditation. Furthermore, meditation expertise, mindfulness levels and attentional skills were found to significantly influence the magnitude, but not regional extent, of activation and functional connectivity in these networks. Aggregating all evidence, we present a unified neurocognitive brain-network model of focused attention meditation.
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Affiliation(s)
- Saampras Ganesan
- Melbourne Neuropsychiatry Centre, Carlton, Victoria 3053, Australia; Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia.
| | - Emillie Beyer
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Fitzroy, Victoria 3065, Australia.
| | - Bradford Moffat
- Melbourne Brain Centre Imaging Unit, Department of Radiology, The University of Melbourne, Parkville, Victoria 3052, Australia.
| | - Nicholas T Van Dam
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria 3010, Australia.
| | - Valentina Lorenzetti
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Fitzroy, Victoria 3065, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Carlton, Victoria 3053, Australia; Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia.
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Gu YQ, Zhu Y. Underlying mechanisms of mindfulness meditation: Genomics, circuits, and networks. World J Psychiatry 2022; 12:1141-1149. [PMID: 36186506 PMCID: PMC9521538 DOI: 10.5498/wjp.v12.i9.1141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/29/2022] [Accepted: 08/18/2022] [Indexed: 02/05/2023] Open
Abstract
Understanding neuropsychological mechanisms of mindfulness meditation (MM) has been a hot topic in recent years. This review was conducted with the goal of synthesizing empirical relationships via the genomics, circuits and networks between MM and mental disorders. We describe progress made in assessing the effects of MM on gene expression in immune cells, with particular focus on stress-related inflammatory markers and associated biological pathways. We then focus on key brain circuits associated with mindfulness practices and effects on symptoms of mental disorders, and expand our discussion to identify three key brain networks associated with mindfulness practices including default mode network, central executive network, and salience network. More research efforts need to be devoted into identifying underlying neuropsychological mechanisms of MM on how it alleviates the symptoms of mental disorders.
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Affiliation(s)
- Ying-Qi Gu
- Department of Psychology, Zhejiang Sci-Tech University, Hangzhou 310018, Zhejiang Province, China
| | - Yi Zhu
- School of Psychology, Hainan Medical University, Haikou 571199, Hainan Province, China
- Department of Psychology, The First Affiliated Hospital of Hainan Medical University, Haikou 570102, Hainan Province, China
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Kim HC, Lee JH. Spectral dynamic causal modeling of mindfulness, mind-wandering, and resting-state in the triple network using fMRI. Neuroreport 2022; 33:221-226. [PMID: 35287148 PMCID: PMC8893127 DOI: 10.1097/wnr.0000000000001772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 01/19/2022] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Functional connectivity in intrinsic brain networks, namely, the triple network, which includes the salience network, default mode network (DMN) and central executive network (CEN), has been suggested as prominent, major networks involved in human cognition and mental state-mindfulness, mind-wandering and resting-state. Despite the established roles of functional connections within and between intrinsic networks, there has been limited research on the effective connectivity of mindfulness, mind-wandering and resting-state using the triple network, as well as on their direct comparisons. METHODS We employed spectral dynamic causal modeling to compare effective connectivity patterns across mindfulness (i.e. attention focused on physical sensations of breathing), mind-wandering (i.e. connecting thoughts) and resting-state (i.e. relaxing while remaining calm and awake) conditions using functional MRI data of healthy subjects who underwent ambulatory training by practicing mindfulness and mind-wandering (N = 59). RESULTS When comparing mindfulness and mindwandering conditions, our analysis results revealed that salience network and CEN interacted depending on mindfulness or mind-wandering. When mindfulness or mind-wandering was compared to resting-state, mindfulness increased the effective connectivity from the left CEN to salience network through DMN, whereas mindwandering increased the effective connectivity from the DMN to right CEN. CONCLUSION To the best of our knowledge, this is the first study to examine possible differences in effective connectivity patterns among mindfulness, mind-wandering and resting-state using the triple network. We believe that our findings will provide deeper insights into the neural substrates of mindfulness compared to mind-wandering and resting-state.
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Affiliation(s)
- Hyun-Chul Kim
- Department of Artificial Intelligence, Kyungpook National University, Daehak-ro, Buk-gu, Daegu
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seongbuk-gu, Seoul, Republic of Korea
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Kim DY, Jang Y, Heo DW, Jo S, Kim HC, Lee JH. Electronic Cigarette Vaping Did Not Enhance the Neural Process of Working Memory for Regular Cigarette Smokers. Front Hum Neurosci 2022; 16:817538. [PMID: 35250518 PMCID: PMC8894252 DOI: 10.3389/fnhum.2022.817538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 01/20/2022] [Indexed: 12/01/2022] Open
Abstract
Background Electronic cigarettes (e-cigs) as substitute devices for regular tobacco cigarettes (r-cigs) have been increasing in recent times. We investigated neuronal substrates of vaping e-cigs and smoking r-cigs from r-cig smokers. Methods Twenty-two r-cig smokers made two visits following overnight smoking cessation. Functional magnetic resonance imaging (fMRI) data were acquired while participants watched smoking images. Participants were then allowed to smoke either an e-cig or r-cig until satiated and fMRI data were acquired. Their craving levels and performance on the Montreal Imaging Stress Task and a 3-back alphabet/digit recognition task were obtained and analyzed using two-way repeated-measures analysis of variance. Regions-of-interest (ROIs) were identified by comparing the abstained and satiated conditions. Neuronal activation within ROIs was regressed on the craving and behavioral data separately. Results Craving was more substantially reduced by smoking r-cigs than by vaping e-cigs. The response time (RT) for the 3-back task was significantly shorter following smoking r-cigs than following vaping e-cigs (interaction: F (1, 17) = 5.3, p = 0.035). Neuronal activations of the right vermis (r = 0.43, p = 0.037, CI = [-0.05, 0.74]), right caudate (r = 0.51, p = 0.015, CI = [0.05, 0.79]), and right superior frontal gyrus (r = −0.70, p = 0.001, CI = [−0.88, −0.34]) were significantly correlated with the RT for the 3-back task only for smoking r-cigs. Conclusion Our findings suggest that insufficient satiety from vaping e-cigs for r-cigs smokers may be insignificant effect on working memory function.
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Affiliation(s)
- Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, United States
| | - Yujin Jang
- Department of Psychology, Korea University, Seoul, South Korea
| | - Da-Woon Heo
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Sungman Jo
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Hyun-Chul Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Department of Artificial Intelligence, Kyungpook National University, Daegu, South Korea
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- *Correspondence: Jong-Hwan Lee,
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12
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Li Y, Yan J, Cui L, Chu J, Wang X, Huang X, Li Y, Cui Y. Protocol of a randomized controlled trial to investigate the efficacy and neural correlates of mindfulness-based habit reversal training in children with Tourette syndrome. Front Psychiatry 2022; 13:938103. [PMID: 36479556 PMCID: PMC9719972 DOI: 10.3389/fpsyt.2022.938103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 11/07/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Tourette syndrome (TS) is a developmental neuropsychiatric disorder. Behavior therapy, especially habit reversal training (HRT), has gradually become regarded as one of the core therapies for TS. Mindfulness approaches can improve psychological adjustment and reduce stress and anxiety, suggesting potential benefits when incorporated into behavior therapy. To improve the efficacy of HRT, we combined it with mindfulness, an approach named mindfulness-based habitual reversal training (MHRT). The aim of this protocol is to investigate the efficacy and neural mechanisms of MHRT for TS. METHODS/DESIGN We will perform a randomized control trial (RCT) to evaluate the efficacy and neural mechanisms of MHRT. The sample will include 160 participants (including 120 patients with TS and 40 healthy controls). The patient sample will be randomly divided into three groups exposed to three different types of training: MHRT, HRT, and psychoeducation and supportive therapy (PST). Participants will be assessed and undergo resting-state fMRI scans at baseline and at the end of the 12-week training. The Yale Global Tic Severity Scale (YGTSS) and Premonitory Urge for Tic Scale (PUTS) will be used to assess the severity of tic symptoms and premonitory urges. The primary outcomes are change scores on the YGTSS and other assessments from baseline and the end of the training. The secondary outcomes are the neural correlates of these trainings among these groups based on graph theory, which is used to characterize brain functional connectivity networks. The default mode network (DMN) and the salience network (SN) will be assessed (which have been associated with mindfulness as well as the generation of tic symptoms) by network parameters, including clustering coefficients and shortest path lengths. Changes in these network parameters will be regarded as the neural correlates of the behavioral training. DISCUSSION MHRT was newly developed for the treatment of TS. MHRT may lead to greater reductions in tic severity than traditional HRT. Changes in the network parameters of the DMN and SN may show associations with the efficacy of MHRT. CLINICAL TRIAL REGISTRATION http://www.chictr.org.cn, ChiCTR2100053077, China.
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Affiliation(s)
- Yanlin Li
- Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Junjuan Yan
- Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Linyu Cui
- Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Jiahui Chu
- Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Xianbin Wang
- Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Xi Huang
- Cloud Services Innovation Laboratory, Institute of Intelligent Science and Technology, China Electronics Technology Group Corporation, Beijing, China
| | - Ying Li
- Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yonghua Cui
- Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
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13
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Gu Y, Zhu Y, Brown KW. Mindfulness and Attention Deficit Hyperactivity Disorder: A Neuropsychological Perspective. J Nerv Ment Dis 2021; 209:796-801. [PMID: 34292276 DOI: 10.1097/nmd.0000000000001388] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
ABSTRACT Understanding the underlying mechanisms of mindfulness has been a hot topic in recent years, not only in clinical fields but also in neuroscience. Most neuroimaging findings demonstrate that critical brain regions involved in mindfulness are responsible for cognitive functions and mental states. However, the brain is a complex system operating via multiple circuits and networks, rather than isolated brain regions solely responsible for specific functions. Mindfulness-based treatments for attention deficit hyperactivity disorder (ADHD) have emerged as promising adjunctive or alternative intervention approaches. We focus on four key brain circuits associated with mindfulness practices and effects on symptoms of ADHD and its cognitive dysfunction, including executive attention circuit, sustained attention circuit, impulsivity circuit, and hyperactivity circuit. We also expand our discussion to identify three key brain networks associated with mindfulness practices, including central executive network, default mode network, and salience network. We conclude by suggesting that more research efforts need to be devoted into identifying putative neuropsychological mechanisms of mindfulness on how it alleviates ADHD symptoms.
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Affiliation(s)
- Yingqi Gu
- Department of Psychology, Zhejiang Sci-Tech University, Hangzhou
| | | | - Kirk Warren Brown
- Department of Psychology, Virginia Commonwealth University, Richmond, Virginia
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14
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Krause F, Kogias N, Krentz M, Lührs M, Goebel R, Hermans EJ. Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback. Neuroimage 2021; 243:118527. [PMID: 34469815 DOI: 10.1016/j.neuroimage.2021.118527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 08/23/2021] [Accepted: 08/25/2021] [Indexed: 10/20/2022] Open
Abstract
It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.
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Affiliation(s)
- Florian Krause
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - Nikos Kogias
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Martin Krentz
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Research and Development, Brain Innovation B.V., Maastricht, the Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Research and Development, Brain Innovation B.V., Maastricht, the Netherlands
| | - Erno J Hermans
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
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15
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Zhang Y, Jiang X, Qiao L, Liu M. Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification. Front Neurosci 2021; 15:720909. [PMID: 34421530 PMCID: PMC8374334 DOI: 10.3389/fnins.2021.720909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 07/09/2021] [Indexed: 02/04/2023] Open
Abstract
Function brain network (FBN) analysis has shown great potential in identifying brain diseases, such as Alzheimer's disease (AD) and its prodromal stage, namely mild cognitive impairment (MCI). It is essential to identify discriminative and interpretable features from function brain networks, so as to improve classification performance and help us understand the pathological mechanism of AD-related brain disorders. Previous studies usually extract node statistics or edge weights from FBNs to represent each subject. However, these methods generally ignore the topological structure (such as modularity) of FBNs. To address this issue, we propose a modular-LASSO feature selection (MLFS) framework that can explicitly model the modularity information to identify discriminative and interpretable features from FBNs for automated AD/MCI classification. Specifically, the proposed MLFS method first searches the modular structure of FBNs through a signed spectral clustering algorithm, and then selects discriminative features via a modularity-induced group LASSO method, followed by a support vector machine (SVM) for classification. To evaluate the effectiveness of the proposed method, extensive experiments are performed on 563 resting-state functional MRI scans from the public ADNI database to identify subjects with AD/MCI from normal controls and predict the future progress of MCI subjects. Experimental results demonstrate that our method is superior to previous methods in both tasks of AD/MCI identification and MCI conversion prediction, and also helps discover discriminative brain regions and functional connectivities associated with AD.
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Affiliation(s)
- Yangyang Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Xiao Jiang
- School of Mathematics Science, Liaocheng University, Liaocheng, China.,School of Science and Technology, University of Camerino, Camerino, Italy
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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16
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Jo S, Kim HC, Lustig N, Chen G, Lee JH. Mixed-effects multilevel analysis followed by canonical correlation analysis is an effective fMRI tool for the investigation of idiosyncrasies. Hum Brain Mapp 2021; 42:5374-5396. [PMID: 34415651 PMCID: PMC8519860 DOI: 10.1002/hbm.25627] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
We report that regions-of-interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed-effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). A real-world dataset for the neuronal response to nicotine use was acquired using a custom-made MRI-compatible apparatus for the smoking of electronic cigarettes (e-cigarettes). Nineteen participants smoked e-cigarettes in an MRI scanner using the apparatus with two experimental conditions: e-cigarettes with nicotine (ECIG) and sham e-cigarettes without nicotine (SCIG) and subjective ratings were collected. The right insula was identified in the ECIG condition from the χ2 -test of the MEMA but not from the t-test, and the corresponding activations were significantly associated with the similarity scores (r = -.52, p = .041, confidence interval [CI] = [-0.78, -0.17]) and the urge-to-smoke scores (r = .73, p <.001, CI = [0.52, 0.88]). From the contrast between the two conditions (i.e., ECIG > SCIG), the right orbitofrontal cortex was identified from the χ2 -tests, and the corresponding neuronal activations showed a statistically meaningful association with similarity (r = -.58, p = .01, CI = [-0.84, -0.17]) and the urge to smoke (r = .34, p = .15, CI = [0.09, 0.56]). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was further evaluated using the fMRI and behavioral data acquired from the working memory and gambling tasks available from the Human Connectome Project.
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Affiliation(s)
- Sungman Jo
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Hyun-Chul Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Niv Lustig
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, Maryland
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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17
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Kim DY, Tegethoff M, Meinlschmidt G, Yoo SS, Lee JH. Cigarette craving modulation is more feasible than resistance modulation for heavy cigarette smokers: empirical evidence from functional MRI data. Neuroreport 2021; 32:762-770. [PMID: 33901056 DOI: 10.1097/wnr.0000000000001653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Modulation of cigarette craving and neuronal activations from nicotine-dependent cigarette smokers using real-time functional MRI (rtfMRI)-based neurofeedback (rtfMRI-NF) has been previously reported. OBJECTIVES The aim of this study was to evaluate the efficacy of rtfMRI-NF training in reducing cigarette cravings using fMRI data acquired before and after training. METHODS Treatment-seeking male heavy cigarette smokers (N = 14) were enrolled and randomly assigned to two conditions related to rtfMRI-NF training aiming at resisting the urge to smoke. In one condition, subjects underwent conventional rtfMRI-NF training using neuronal activity as the neurofeedback signal (activity-based) within regions-of-interest (ROIs) implicated in cigarette craving. In another condition, subjects underwent rtfMRI-NF training with additional functional connectivity information included in the neurofeedback signal (functional connectivity-added). Before and after rtfMRI-NF training at each of two visits, participants underwent two fMRI runs with cigarette smoking stimuli and were asked to crave or resist the urge to smoke without neurofeedback. Cigarette craving-related or resistance-related regions were identified using a general linear model followed by paired t-tests and were evaluated using regression analysis on the basis of neuronal activation and subjective craving scores (CRSs). RESULTS Visual areas were mainly implicated in craving, whereas the superior frontal areas were associated with resistance. The degree of (a) CRS reduction and (b) the correlation between neuronal activation and CRSs were statistically significant (P < 0.05) in the functional connectivity-added neurofeedback group for craving-related ROIs. CONCLUSION Our study demonstrated the feasibility of altering cigarette craving in craving-related ROIs but not in resistance-related ROIs via rtfMRI-NF training.
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Affiliation(s)
- Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
| | - Marion Tegethoff
- Institute of Psychology, RWTH Aachen, Jägerstrasse, Aachen, Germany
- Division of Clinical Psychology and Psychiatry, Department of Psychology, University of Basel, Missionsstrasse, Basel, Switzerland
| | - Gunther Meinlschmidt
- Division of Clinical Psychology and Cognitive Behavioral Therapy, International Psychoanalytic University, Stromstrasse, Berlin, Germany
- Department of Psychosomatic Medicine, University Hospital Basel and University of Basel, Hebelstrasse, Basel, Switzerland
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Missionsstrasse, Basel, Switzerland
| | - Seung-Schik Yoo
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
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18
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Doborjeh Z, Doborjeh M, Crook-Rumsey M, Taylor T, Wang GY, Moreau D, Krägeloh C, Wrapson W, Siegert RJ, Kasabov N, Searchfield G, Sumich A. Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture. SENSORS (BASEL, SWITZERLAND) 2020; 20:E7354. [PMID: 33371459 PMCID: PMC7767448 DOI: 10.3390/s20247354] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 12/16/2020] [Accepted: 12/17/2020] [Indexed: 01/05/2023]
Abstract
Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanisms underpinning these changes are uncertain. This study uses a novel brain-inspired artificial neural network to investigate the effect of mindfulness training on electroencephalographic function. Participants completed a 4-tone auditory oddball task (that included targets and physically similar distractors) at three assessment time points. In Group A (n = 10), these tasks were given immediately prior to 6-week mindfulness training, immediately after training and at a 3-week follow-up; in Group B (n = 10), these were during an intervention waitlist period (3 weeks prior to training), pre-mindfulness training and post-mindfulness training. Using a spiking neural network (SNN) model, we evaluated concurrent neural patterns generated across space and time from features of electroencephalographic data capturing the neural dynamics associated with the event-related potential (ERP). This technique capitalises on the temporal dynamics of the shifts in polarity throughout the ERP and spatially across electrodes. Findings support anteriorisation of connection weights in response to distractors relative to target stimuli. Right frontal connection weights to distractors were associated with trait mindfulness (positively) and depression (inversely). Moreover, mindfulness training was associated with an increase in connection weights to targets (bilateral frontal, left frontocentral, and temporal regions only) and distractors. SNN models were superior to other machine learning methods in the classification of brain states as a function of mindfulness training. Findings suggest SNN models can provide useful information that differentiates brain states based on distinct task demands and stimuli, as well as changes in brain states as a function of psychological intervention.
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Affiliation(s)
- Zohreh Doborjeh
- Faculty of Medical and Health Sciences, School of Population Health, Section of Audiology, The University of Auckland, Auckland 1142, New Zealand;
- Eisdell Moore Centre, The University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland 1142, New Zealand;
| | - Maryam Doborjeh
- Information Technology and Software Engineering Department, Auckland University of Technology, Auckland 1010, New Zealand;
| | - Mark Crook-Rumsey
- School of Psychology, Nottingham Trent University, Nottingham NG25 0QF, UK; (M.C.-R.); (A.S.)
| | - Tamasin Taylor
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1142, New Zealand;
| | - Grace Y. Wang
- Department of Psychology and Neuroscience, Auckland University of Technology, Auckland 0627, New Zealand; (G.Y.W.); (C.K.); (R.J.S.)
| | - David Moreau
- Centre for Brain Research, The University of Auckland, Auckland 1142, New Zealand;
- School of Psychology, The University of Auckland, Auckland 1142, New Zealand
| | - Christian Krägeloh
- Department of Psychology and Neuroscience, Auckland University of Technology, Auckland 0627, New Zealand; (G.Y.W.); (C.K.); (R.J.S.)
| | - Wendy Wrapson
- School of Public Health and Interdisciplinary Studies, Auckland University of Technology, Auckland 0627, New Zealand;
| | - Richard J. Siegert
- Department of Psychology and Neuroscience, Auckland University of Technology, Auckland 0627, New Zealand; (G.Y.W.); (C.K.); (R.J.S.)
| | - Nikola Kasabov
- Intelligent Systems Research Centre, Ulster University, Londonderry BT48 7JL, UK
- School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
| | - Grant Searchfield
- Faculty of Medical and Health Sciences, School of Population Health, Section of Audiology, The University of Auckland, Auckland 1142, New Zealand;
- Eisdell Moore Centre, The University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland 1142, New Zealand;
| | - Alexander Sumich
- School of Psychology, Nottingham Trent University, Nottingham NG25 0QF, UK; (M.C.-R.); (A.S.)
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19
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Kim DY, Jung EK, Zhang J, Lee SY, Lee JH. Functional magnetic resonance imaging multivoxel pattern analysis reveals neuronal substrates for collaboration and competition with myopic and predictive strategic reasoning. Hum Brain Mapp 2020; 41:4314-4331. [PMID: 32633451 PMCID: PMC7502831 DOI: 10.1002/hbm.25127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 11/18/2022] Open
Abstract
Competition and collaboration are strategies that can be used to optimize the outcomes of social interactions. Research into the neuronal substrates underlying these aspects of social behavior has been limited due to the difficulty in distinguishing complex activation via univariate analysis. Therefore, we employed multivoxel pattern analysis of functional magnetic resonance imaging to reveal the neuronal activations underlying competitive and collaborative processes when the collaborator/opponent used myopic/predictive reasoning. Twenty‐four healthy subjects participated in 2 × 2 matrix‐based sequential‐move games. Searchlight‐based multivoxel patterns were used as input for a support vector machine using nested cross‐validation to distinguish game conditions, and identified voxels were validated via the regression of the behavioral data with bootstrapping. The left anterior insula (accuracy = 78.5%) was associated with competition, and middle frontal gyrus (75.1%) was associated with predictive reasoning. The inferior/superior parietal lobules (84.8%) and middle frontal gyrus (84.7%) were associated with competition, particularly in trials with a predictive opponent. The visual/motor areas were related to response time as a proxy for visual attention and task difficulty. Our results suggest that multivoxel patterns better represent the neuronal substrates underlying the social cognition of collaboration and competition intermixed with myopic and predictive reasoning than do univariate features.
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Affiliation(s)
- Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Eun Kyung Jung
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Jun Zhang
- Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA
| | - Soo-Young Lee
- Department of Electrical Engineering, KAIST, Daejeon, South Korea.,Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
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20
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Vu H, Kim HC, Jung M, Lee JH. fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations. Neuroimage 2020; 223:117328. [PMID: 32896633 DOI: 10.1016/j.neuroimage.2020.117328] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 07/16/2020] [Accepted: 08/20/2020] [Indexed: 12/22/2022] Open
Abstract
Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area of the input sample and moves across the sample to provide a feature map for the subsequent layers. In our study, we hypothesized that a 3D-CNN model with down-sampling operations such as pooling and/or stride would have the ability to extract robust feature maps from the shifted and scaled neuronal activations in a single functional MRI (fMRI) volume for the classification of task information associated with that volume. Thus, the 3D-CNN model would be able to ameliorate the potential misalignment of neuronal activations and over-/under-activation in local brain regions caused by imperfections in spatial alignment algorithms, confounded by variability in blood-oxygenation-level-dependent (BOLD) responses across sessions and/or subjects. To this end, the fMRI volumes acquired from four sensorimotor tasks (left-hand clenching, right-hand clenching, auditory attention, and visual stimulation) were used as input for our 3D-CNN model to classify task information using a single fMRI volume. The classification performance of the 3D-CNN was systematically evaluated using fMRI volumes obtained from various minimal preprocessing scenarios applied to raw fMRI volumes that excluded spatial normalization to a template and those obtained from full preprocessing that included spatial normalization. Alternative classifier models such as the 1D fully connected DNN (1D-fcDNN) and support vector machine (SVM) were also used for comparison. The classification performance was also assessed for several k-fold cross-validation (CV) schemes, including leave-one-subject-out CV (LOOCV). Overall, the classification results of the 3D-CNN model were superior to that of the 1D-fcDNN and SVM models. When using the fully-processed fMRI volumes with LOOCV, the mean error rates (± the standard error of the mean) for the 3D-CNN, 1D-fcDNN, and SVM models were 2.1% (± 0.9), 3.1% (± 1.2), and 4.1% (± 1.5), respectively (p = 0.041 from a one-way ANOVA). The error rates for 3-fold CV were higher (2.4% ± 1.0, 4.2% ± 1.3, and 10.1% ± 2.0; p < 0.0003 from a one-way ANOVA). The mean error rates also increased considerably using the raw fMRI 3D volume data without preprocessing (26.2% for the 3D-CNN, 75.0% for the 1D-fcDNN, and 75.0% for the SVM). Furthermore, the ability of the pre-trained 3D-CNN model to handle shifted and scaled neuronal activations was demonstrated in an online scenario for five-class classification (i.e., four sensorimotor tasks and the resting state) using the real-time fMRI of three participants. The resulting classification accuracy was 78.5% (± 1.4), 26.7% (± 5.9), and 21.5% (± 3.1) for the 3D-CNN, 1D-fcDNN, and SVM models, respectively. The superior performance of the 3D-CNN compared to the 1D-fcDNN was verified by analyzing the resulting feature maps and convolution filters that handled the shifted and scaled neuronal activations and by utilizing an independent public dataset from the Human Connectome Project.
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Affiliation(s)
- Hanh Vu
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Hyun-Chul Kim
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Minyoung Jung
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea.
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21
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A naturalistic viewing paradigm using 360° panoramic video clips and real-time field-of-view changes with eye-gaze tracking. Neuroimage 2020; 216:116617. [DOI: 10.1016/j.neuroimage.2020.116617] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 02/05/2020] [Indexed: 11/18/2022] Open
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22
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Chand T, Li M, Jamalabadi H, Wagner G, Lord A, Alizadeh S, Danyeli LV, Herrmann L, Walter M, Sen ZD. Heart Rate Variability as an Index of Differential Brain Dynamics at Rest and After Acute Stress Induction. Front Neurosci 2020; 14:645. [PMID: 32714132 PMCID: PMC7344021 DOI: 10.3389/fnins.2020.00645] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 05/25/2020] [Indexed: 11/17/2022] Open
Abstract
The brain continuously receives input from the internal and external environment. Using this information, the brain exerts its influence on both itself and the body to facilitate an appropriate response. The dynamic interplay between the brain and the heart and how external conditions modulate this relationship deserves attention. In high-stress situations, synchrony between various brain regions such as the prefrontal cortex and the heart may alter. This flexibility is believed to facilitate transitions between functional states related to cognitive, emotional, and especially autonomic activity. This study examined the dynamic temporal functional association of heart rate variability (HRV) with the interaction between three main canonical brain networks in 38 healthy male subjects at rest and directly after a psychosocial stress task. A sliding window approach was used to estimate the functional connectivity (FC) among the salience network (SN), central executive network (CEN), and default mode network (DMN) in 60-s windows on time series of blood-oxygen-level dependent (BOLD) signal. FC between brain networks was calculated by Pearson correlation. A multilevel linear mixed model was conducted to examine the window-by-window association between the root mean square of successive differences between normal heartbeats (RMSSD) and FC of network-pairs across sessions. Our findings showed that the minute-by-minute correlation between the FC and RMSSD was significantly stronger between DMN and CEN than for SN and CEN in the baseline session [b = 4.36, t(5025) = 3.20, p = 0.006]. Additionally, this differential relationship between network pairs and RMSSD disappeared after the stress task; FC between DMN and CEN showed a weaker correlation with RMSSD in comparison to baseline [b = −3.35, t(5025) = −3.47, p = 0.006]. These results suggest a dynamic functional interplay between HRV and the functional association between brain networks that varies depending on the needs created by changing conditions.
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Affiliation(s)
- Tara Chand
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.,Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.,Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.,Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.,Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany
| | - Gerd Wagner
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Anton Lord
- Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany.,QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sarah Alizadeh
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.,Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany
| | - Lena V Danyeli
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.,Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany.,Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Luisa Herrmann
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.,Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.,Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.,Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.,Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany.,Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Zumrut D Sen
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.,Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.,Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany
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23
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Bauer CCC, Okano K, Gosh SS, Lee YJ, Melero H, de los Angeles C, Nestor PG, del Re EC, Northoff G, Niznikiewicz MA, Whitfield-Gabrieli S. Real-time fMRI neurofeedback reduces auditory hallucinations and modulates resting state connectivity of involved brain regions: Part 2: Default mode network -preliminary evidence. Psychiatry Res 2020; 284:112770. [PMID: 32004893 PMCID: PMC7046150 DOI: 10.1016/j.psychres.2020.112770] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 01/04/2020] [Accepted: 01/05/2020] [Indexed: 01/02/2023]
Abstract
Auditory hallucinations (AHs) are one of the most distressing symptoms of schizophrenia (SZ) and are often resistant to medication. Imaging studies of individuals with SZ show hyperactivation of the default mode network (DMN) and the superior temporal gyrus (STG). Studies in SZ show DMN hyperconnectivity and reduced anticorrelation between DMN and the central executive network (CEN). DMN hyperconnectivity has been associated with positive symptoms such as AHs while reduced DMN anticorrelations with cognitive impairment. Using real-time fMRI neurofeedback (rt-fMRI-NFB) we trained SZ patients to modulate DMN and CEN networks. Meditation is effective in reducing AHs in SZ and to modulate brain network integration and increase DMN anticorrelations. Consequently, patients were provided with meditation strategies to enhance their abilities to modulate DMN/CEN. Results show a reduction of DMN hyperconnectivity and increase in DMNCEN anticorrelation. Furthermore, the change in individual DMN connectivity significantly correlated with reductions in AHs. This is the first time that meditation enhanced through rt-fMRI-NFB is used to reduce AHs in SZ. Moreover, it provides the first empirical evidence for a direct causal relation between meditation enhanced rt-fMRI-NFB modulation of DMNCEN activity and post-intervention modulation of resting state networks ensuing in reductions in frequency and severity of AHs.
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Affiliation(s)
- Clemens C. C. Bauer
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology. Cambridge, MA 02139, USA,Northeastern University, Boston, MA 02139, USA,Please address correspondence to Clemens Bauer, Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, 43 Vassar St. 46-4037C Massachusetts Institute of Technology. Cambridge, MA 02139, USA Telephone: +1 (617) 324 5124,
| | - Kana Okano
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology. Cambridge, MA 02139, USA
| | - Satrajit S. Gosh
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology. Cambridge, MA 02139, USA
| | - Yoon Ji Lee
- Northeastern University, Boston, MA 02139, USA
| | - Helena Melero
- Northeastern University, Boston, MA 02139, USA,Medical Image Analysis Laboratory (LAIMBIO), Rey Juan Carlos University, Madrid, Spain
| | - Carlo de los Angeles
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology. Cambridge, MA 02139, USA
| | - Paul G. Nestor
- Harvard Medical School. Boston, MA 02115, USA,Boston VA Healthcare System. Boston, MA 02130, USA,University of Massachusetts, Boston, Boston MA 02215, USA
| | - Elisabetta C. del Re
- Harvard Medical School. Boston, MA 02115, USA,Boston VA Healthcare System. Boston, MA 02130, USA,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA, USA
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | - Margaret A. Niznikiewicz
- Harvard Medical School. Boston, MA 02115, USA,Boston VA Healthcare System. Boston, MA 02130, USA,Beth Israel Deaconess Medical Center. Boston, MA 02215, USA
| | - Susan Whitfield-Gabrieli
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology. Cambridge, MA 02139, USA,Northeastern University, Boston, MA 02139, USA
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24
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Affiliation(s)
- Michelle Hampson
- Department of Radiology and Biomedical Imaging, Department of Psychiatry, and the Child Study Center, Yale University School of Medicine, New Haven, CT, USA.
| | - Sergio Ruiz
- Department of Psychiatry, Medicine School, and Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Junichi Ushiba
- Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Japan.
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