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Krause F, Linden DEJ, Hermans EJ. Getting stress-related disorders under control: the untapped potential of neurofeedback. Trends Neurosci 2024:S0166-2236(24)00150-4. [PMID: 39261131 DOI: 10.1016/j.tins.2024.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 07/05/2024] [Accepted: 08/16/2024] [Indexed: 09/13/2024]
Abstract
Stress-related disorders are among the biggest global health challenges. Despite significant progress in understanding their neurocognitive basis, the promise of applying insights from fundamental research to prevention and treatment remains largely unfulfilled. We argue that neurofeedback - a method for training voluntary control over brain activity - has the potential to fill this translational gap. We provide a contemporary perspective on neurofeedback as endogenous neuromodulation that can target complex brain network dynamics, is transferable to real-world scenarios outside a laboratory or treatment facility, can be trained prospectively, and is individually adaptable. This makes neurofeedback a prime candidate for a personalized preventive neuroscience-based intervention strategy that focuses on the ecological momentary neuromodulation of stress-related brain networks in response to actual stressors in real life.
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Affiliation(s)
- Florian Krause
- Donders Institute for Brain, Cognition and Behaviour, Radboud university medical center, Nijmegen, The Netherlands.
| | - David E J Linden
- Faculty of Health, Medicine and Life Sciences, Mental Health and Neuroscience Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Erno J Hermans
- Donders Institute for Brain, Cognition and Behaviour, Radboud university medical center, Nijmegen, The Netherlands
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2
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Godet A, Serrand Y, Léger B, Moirand R, Bannier E, Val-Laillet D, Coquery N. Functional near-infrared spectroscopy-based neurofeedback training targeting the dorsolateral prefrontal cortex induces changes in cortico-striatal functional connectivity. Sci Rep 2024; 14:20025. [PMID: 39198481 PMCID: PMC11358514 DOI: 10.1038/s41598-024-69863-w] [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: 07/06/2023] [Accepted: 08/09/2024] [Indexed: 09/01/2024] Open
Abstract
Due to its central role in cognitive control, the dorso-lateral prefrontal cortex (dlPFC) has been the target of multiple brain modulation studies. In the context of the present pilot study, the dlPFC was the target of eight repeated neurofeedback (NF) sessions with functional near infrared spectroscopy (fNIRS) to assess the brain responses during NF and with functional and resting state magnetic resonance imaging (task-based fMRI and rsMRI) scanning. Fifteen healthy participants were recruited. Cognitive task fMRI and rsMRI were performed during the 1st and the 8th NF sessions. During NF, our data revealed an increased activity in the dlPFC as well as in brain regions involved in cognitive control and self-regulation learning (pFWE < 0.05). Changes in functional connectivity between the 1st and the 8th session revealed increased connectivity between the posterior cingulate cortex and the dlPFC, and between the posterior cingulate cortex and the dorsal striatum (pFWE < 0.05). Decreased left dlPFC-left insula connectivity was also observed. Behavioural results revealed a significant effect of hunger and motivation on the participant control feeling and a lower control feeling when participants did not identify an effective mental strategy, providing new insights on the effects of behavioural factors that may affect the NF learning.
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Affiliation(s)
- A Godet
- INRAE, INSERM, CHU Rennes, Nutrition Metabolisms and Cancer, NuMeCan, Univ Rennes, Rennes, France
| | - Y Serrand
- INRAE, INSERM, CHU Rennes, Nutrition Metabolisms and Cancer, NuMeCan, Univ Rennes, Rennes, France
| | - B Léger
- INRAE, INSERM, CHU Rennes, Nutrition Metabolisms and Cancer, NuMeCan, Univ Rennes, Rennes, France
| | - R Moirand
- INRAE, INSERM, CHU Rennes, Nutrition Metabolisms and Cancer, NuMeCan, Univ Rennes, Rennes, France
- Unité d'Addictologie, CHU Rennes, Rennes, France
| | - E Bannier
- Inria, CRNS, Inserm, IRISA UMR 6074, Empenn U1228, Univ Rennes, Rennes, France.
- Radiology Department, CHU Rennes, Rennes, France.
| | - D Val-Laillet
- INRAE, INSERM, CHU Rennes, Nutrition Metabolisms and Cancer, NuMeCan, Univ Rennes, Rennes, France.
| | - N Coquery
- INRAE, INSERM, CHU Rennes, Nutrition Metabolisms and Cancer, NuMeCan, Univ Rennes, Rennes, France
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3
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Klugah-Brown B, Bore MC, Liu X, Gan X, Biswal BB, Kendrick KM, Chang DHF, Zhou B, Becker B. The neurostructural consequences of glaucoma and their overlap with disorders exhibiting emotional dysregulations: A voxel-based meta-analysis and tripartite system model. J Affect Disord 2024; 358:487-499. [PMID: 38705527 DOI: 10.1016/j.jad.2024.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/23/2024] [Accepted: 05/02/2024] [Indexed: 05/07/2024]
Abstract
BACKGROUND Glaucoma, a progressive neurodegenerative disorder leading to irreversible blindness, is associated with heightened rates of generalized anxiety and depression. This study aims to comprehensively investigate brain morphological changes in glaucoma patients, extending beyond visual processing areas, and explores overlaps with morphological alterations observed in anxiety and depression. METHODS A comparative meta-analysis was conducted, using case-control studies of brain structural integrity in glaucoma patients. We aimed to identify regions with gray matter volume (GMV) changes, examine their role within distinct large-scale networks, and assess overlap with alterations in generalized anxiety disorder (GAD) and major depressive disorder (MDD). RESULTS Glaucoma patients exhibited significant GMV reductions in visual processing regions (lingual gyrus, thalamus). Notably, volumetric reductions extended beyond visual systems, encompassing the left putamen and insula. Behavioral and functional network decoding revealed distinct large-scale networks, implicating visual, motivational, and affective domains. The insular region, linked to pain and affective processes, displayed reductions overlapping with alterations observed in GAD. LIMITATIONS While the study identified significant morphological alterations, the number of studies from both the glaucoma and GAD cohorts remains limited due to the lack of independent studies meeting our inclusion criteria. CONCLUSION The study proposes a tripartite brain model for glaucoma, with visual processing changes related to the lingual gyrus and additional alterations in the putamen and insular regions tied to emotional or motivational functions. These neuroanatomical changes extend beyond the visual system, implying broader implications for brain structure and potential pathological developments, providing insights into the overall neurological consequences of glaucoma.
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Affiliation(s)
- Benjamin Klugah-Brown
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Mercy C Bore
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiqin Liu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Xianyang Gan
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat B Biswal
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, USA
| | - Keith M Kendrick
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dorita H F Chang
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Department of Psychology, The University of Hong Kong, Hong Kong, China
| | - Bo Zhou
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Department of Psychology, The University of Hong Kong, Hong Kong, China.
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Morrissey G, Tsuchiyagaito A, Takahashi T, McMillin J, Aupperle RL, Misaki M, Khalsa SS. Could neurofeedback improve therapist-patient communication? Considering the potential for neuroscience informed examinations of the psychotherapeutic relationship. Neurosci Biobehav Rev 2024; 161:105680. [PMID: 38641091 DOI: 10.1016/j.neubiorev.2024.105680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 03/22/2024] [Accepted: 04/16/2024] [Indexed: 04/21/2024]
Abstract
Empathic communication between a patient and therapist is an essential component of psychotherapy. However, finding objective neural markers of the quality of the psychotherapeutic relationship have been elusive. Here we conceptualize how a neuroscience-informed approach involving real-time neurofeedback, facilitated via existing functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) technologies, could provide objective information for facilitating therapeutic rapport. We propose several neurofeedback-assisted psychotherapy (NF-AP) approaches that could be studied as a way to optimize the experience of the individual patient and therapist across the spectrum of psychotherapeutic treatment. Finally, we consider how the possible strengths of these approaches are balanced by their current limitations and discuss the future prospects of NF-AP.
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Affiliation(s)
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK, USA
| | - Toru Takahashi
- Laureate Institute for Brain Research, Tulsa, OK, USA; Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo, Japan
| | - John McMillin
- Advocate Medical Group, Downers Grove, IL, USA; Department of Psychiatry, University of Oklahoma-Tulsa, Tulsa, OK, USA
| | - Robin L Aupperle
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK, USA.
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Pereira DJ, Pereira J, Sayal A, Morais S, Macedo A, Direito B, Castelo-Branco M. Functional and structural connectivity success predictors of real-time fMRI neurofeedback targeting DLPFC: Contributions from central executive, salience, and default mode networks. Netw Neurosci 2024; 8:81-95. [PMID: 38562293 PMCID: PMC10861170 DOI: 10.1162/netn_a_00338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 09/10/2023] [Indexed: 04/04/2024] Open
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF), a training method for the self-regulation of brain activity, has shown promising results as a neurorehabilitation tool, depending on the ability of the patient to succeed in neuromodulation. This study explores connectivity-based structural and functional success predictors in an NF n-back working memory paradigm targeting the dorsolateral prefrontal cortex (DLPFC). We established as the NF success metric the linear trend on the ability to modulate the target region during NF runs and performed a linear regression model considering structural and functional connectivity (intrinsic and seed-based) metrics. We found a positive correlation between NF success and the default mode network (DMN) intrinsic functional connectivity and a negative correlation with the DLPFC-precuneus connectivity during the 2-back condition, indicating that success is associated with larger uncoupling between DMN and the executive network. Regarding structural connectivity, the salience network emerges as the main contributor to success. Both functional and structural classification models showed good performance with 77% and 86% accuracy, respectively. Dynamic switching between DMN, salience network and central executive network seems to be the key for neurofeedback success, independently indicated by functional connectivity on the localizer run and structural connectivity data.
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Affiliation(s)
- Daniela Jardim Pereira
- Neurorradiology Functional Area, Imaging Department, Coimbra Hospital and University Center, Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - João Pereira
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Alexandre Sayal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Siemens Healthineers Portugal, Lisboa, Portugal
| | - Sofia Morais
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Psychiatry Department, Coimbra Hospital and University Center, Coimbra, Portugal
| | - António Macedo
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Psychiatry Department, Coimbra Hospital and University Center, Coimbra, Portugal
| | - Bruno Direito
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Instituto do Ambiente, Tecnologia e Vida (IATV), Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
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Wang JN, Wang M, Wu GW, Li WH, Lv ZL, Chen Q, Yang ZH, Li XH, Wang ZC, Li ZJ, Zhang P, Tang LR. Uncovering neural pathways underlying bulimia nervosa: resting-state neural connectivity disruptions correlate with maladaptive eating behaviors. Eat Weight Disord 2023; 28:91. [PMID: 37899387 PMCID: PMC10613592 DOI: 10.1007/s40519-023-01617-5] [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: 03/19/2023] [Accepted: 10/18/2023] [Indexed: 10/31/2023] Open
Abstract
PURPOSE Bulimia nervosa (BN) is characterized by recurrent binge-eating episodes and inappropriate compensatory behaviors. This study investigated alterations in resting-state surface-based neural activity in BN patients and explored correlations between brain activity and eating behavior. METHODS A total of 26 BN patients and 28 healthy controls were enrolled. Indirect measurement of cerebral cortical activity and functional connectivity (FC) analyses were performed in Surfstat. A principal component analysis (PCA) model was used to capture the commonalities within the behavioral questionnaires from the BN group. RESULTS Compared with the healthy control group, the BN group showed decreased surface-based two-dimensional regional homogeneity in the right superior parietal lobule (SPL). Additionally, the BN group showed decreased FC between the right SPL and the bilateral lingual gyrus and increased FC between the right SPL and the left caudate nucleus and right putamen. In the FC-behavior association analysis, the second principal component (PC2) was negatively correlated with FC between the right SPL and the left caudate nucleus. The third principal component (PC3) was negatively correlated with FC between the right SPL and the left lingual gyrus and positively correlated with FC between the right SPL and the right lingual gyrus. CONCLUSION We revealed that the right SPL undergoes reorganization with respect to specific brain regions at the whole-brain level in BN. In addition, our results suggest a correlation between brain reorganization and maladaptive eating behavior. These findings may provide useful information to better understand the neural mechanisms of BN. LEVEL OF EVIDENCE V, descriptive study.
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Affiliation(s)
- Jia-Ni Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, China
| | - Miao Wang
- Chinese Institute for Brain Research, Beijing, China
| | - Guo-Wei Wu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Wei-Hua Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, China
| | - Zi-Ling Lv
- Beijing Anding Hospital, Capital Medical University, Beijing, China
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, No. 5 Ankang Hutong, Xicheng District, Beijing, China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, China
| | - Zheng-Han Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, China
| | - Xiao-Hong Li
- Beijing Anding Hospital, Capital Medical University, Beijing, China
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, No. 5 Ankang Hutong, Xicheng District, Beijing, China
| | - Zhen-Chang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, China
| | - Zhan-Jiang Li
- Beijing Anding Hospital, Capital Medical University, Beijing, China.
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, No. 5 Ankang Hutong, Xicheng District, Beijing, China.
| | - Peng Zhang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, China.
| | - Li-Rong Tang
- Beijing Anding Hospital, Capital Medical University, Beijing, China.
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, No. 5 Ankang Hutong, Xicheng District, Beijing, China.
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Li K, Yang J, Becker B, Li X. Functional near-infrared spectroscopy neurofeedback of dorsolateral prefrontal cortex enhances human spatial working memory. NEUROPHOTONICS 2023; 10:025011. [PMID: 37275655 PMCID: PMC10234406 DOI: 10.1117/1.nph.10.2.025011] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 04/06/2023] [Accepted: 05/11/2023] [Indexed: 06/07/2023]
Abstract
Significance Spatial working memory (SWM) is essential for daily life and deficits in this domain represent a common impairment across aging and several mental disorders. Impaired SWM has been closely linked to dysregulations in dorsolateral prefrontal cortex (DLPFC) activation. Aim The present study evaluates the feasibility and maintenance of functional near-infrared spectroscopy neurofeedback (fNIRS-NF) training of the DLPFC to enhance SWM in healthy individuals using a real-time fNIRS-NF platform developed by the authors. Approach We used a randomized sham-controlled between-subject fNIRS-NF design with 60 healthy subjects as a sample. Training-induced changes in the DLPFC, SWM, and attention performance served as primary outcomes. Results Feedback from the target channel significantly increased regional-specific DLPFC activation over the fNIRS-NF training compared to sham NF. A significant group difference in NF-induced frontoparietal connectivity was observed. Compared to the control group, the experimental group demonstrated significantly improved SWM and attention performance that were maintained for 1 week. Furthermore, a mediation analysis demonstrated that increased DLPFC activation mediated the effects of fNIRS-NF treatment on better SWM performance. Conclusions The present results demonstrated that successful self-regulation of DLPFC activation may represent a long-lasting intervention to improve human SWM and has the potential for further applications.
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Affiliation(s)
- Keshuang Li
- East China Normal University, School of Psychology and Cognitive Science, Affiliated Mental Health Center, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Shanghai, China
| | - Jinhao Yang
- East China Normal University, School of Psychology and Cognitive Science, Affiliated Mental Health Center, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Shanghai, China
| | - Benjamin Becker
- University of Electronic Science and Technology of China, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Chengdu, China
| | - Xianchun Li
- East China Normal University, School of Psychology and Cognitive Science, Affiliated Mental Health Center, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Shanghai, China
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Uslu S, Vögele C. The more, the better? Learning rate and self-pacing in neurofeedback enhance cognitive performance in healthy adults. Front Hum Neurosci 2023; 17:1077039. [PMID: 36733608 PMCID: PMC9887027 DOI: 10.3389/fnhum.2023.1077039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 01/03/2023] [Indexed: 01/18/2023] Open
Abstract
Real time electroencephalogram (EEG) based neurofeedback has been shown to be effective in regulating brain activity, thereby modifying cognitive performance and behavior. Nevertheless, individual variations in neurofeedback learning rates limit the overall efficacy of EEG based neurofeedback. In the present study we investigated the effects of learning rate and control over training realized by self-pacing on cognitive performance and electrocortical activity. Using a double-blind design, we randomly allocated 60 participants to either individual upper alpha (IUA) or sham neurofeedback and subsequently to self- or externally paced training. Participants receiving IUA neurofeedback improved their IUA activity more than participants receiving sham neurofeedback. Furthermore, the learning rate predicted enhancements in resting-state activity and mental rotation ability. The direction of this linear relationship depended on the neurofeedback condition being positive for IUA and negative for sham neurofeedback. Finally, self-paced training increased higher-level cognitive skills more than externally paced training. These results underpin the important role of learning rate in enhancing both resting-state activity and cognitive performance. Our design allowed us to differentiate the effect of learning rate between neurofeedback conditions, and to demonstrate the positive effect of self-paced training on cognitive performance in IUA neurofeedback.
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Zhou Z, Luo Y, Gao X, Zhu Y, Bai X, Yang H, Bi Q, Chen S, Duan L, Wang L, Gong F, Feng F, Gong G, Zhu H, Pan H. Alterations in brain structure and function associated with pediatric growth hormone deficiency: A multi-modal magnetic resonance imaging study. Front Neurosci 2023; 16:1043857. [PMID: 36685242 PMCID: PMC9853296 DOI: 10.3389/fnins.2022.1043857] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/02/2022] [Indexed: 01/07/2023] Open
Abstract
Introduction Pediatric growth hormone deficiency (GHD) is a disease resulting from impaired growth hormone/insulin-like growth factor-1 (IGF-1) axis but the effects of GHD on children's cognitive function, brain structure and brain function were not yet fully illustrated. Methods Full Wechsler Intelligence Scales for Children, structural imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging were assessed in 11 children with GHD and 10 matched healthy controls. Results (1) The GHD group showed moderate cognitive impairment, and a positive correlation existed between IGF-1 levels and cognitive indices. (2) Mean diffusivity was significantly increased in both corticospinal tracts in GHD group. (3) There were significant positive correlations between IGF-1 levels and volume metrics of left thalamus, left pallidum and right putamen but a negative correlation between IGF-1 levels and cortical thickness of the occipital lobe. And IGF-1 levels negatively correlated with fractional anisotropy in the superior longitudinal fasciculus and right corticospinal tract. (4) Regional homogeneity (ReHo) in the left hippocampus/parahippocampal gyrus was negatively correlated with IGF-1 levels; the amplitude of low-frequency fluctuation (ALFF) and ReHo in the paracentral lobe, postcentral gyrus and precentral gyrus were also negatively correlated with IGF-1 levels, in which region ALFF fully mediates the effect of IGF-1 on working memory index. Conclusion Multiple subcortical, cortical structures, and regional neural activities might be influenced by serum IGF-1 levels. Thereinto, ALFF in the paracentral lobe, postcentral gyrus and precentral gyrus fully mediates the effect of IGF-1 on the working memory index.
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Affiliation(s)
- Zhibo Zhou
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yunyun Luo
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoxing Gao
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanlin Zhu
- Beijing Normal University, Beijing, China
| | - Xi Bai
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongbo Yang
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qiuhui Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Shi Chen
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lian Duan
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Linjie Wang
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fengying Gong
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huijuan Zhu
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,Huijuan Zhu,
| | - Hui Pan
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,*Correspondence: Hui Pan,
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10
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Caria A, Grecucci A. Neuroanatomical predictors of real‐time
fMRI
‐based anterior insula regulation. A supervised machine learning study. Psychophysiology 2022; 60:e14237. [PMID: 36523140 DOI: 10.1111/psyp.14237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/18/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022]
Abstract
Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including fronto-occipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix significantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings highlighted specific neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms.
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Affiliation(s)
- Andrea Caria
- Department of Psychology and Cognitive Science University of Trento Rovereto Italy
| | - Alessandro Grecucci
- Department of Psychology and Cognitive Science University of Trento Rovereto Italy
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11
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Sanders ZB, Fleming MK, Smejka T, Marzolla MC, Zich C, Rieger SW, Lührs M, Goebel R, Sampaio-Baptista C, Johansen-Berg H. Self-modulation of motor cortex activity after stroke: a randomized controlled trial. Brain 2022; 145:3391-3404. [PMID: 35960166 PMCID: PMC9586541 DOI: 10.1093/brain/awac239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 06/01/2022] [Accepted: 06/20/2022] [Indexed: 11/14/2022] Open
Abstract
Real-time functional MRI neurofeedback allows individuals to self-modulate their ongoing brain activity. This may be a useful tool in clinical disorders that are associated with altered brain activity patterns. Motor impairment after stroke has previously been associated with decreased laterality of motor cortex activity. Here we examined whether chronic stroke survivors were able to use real-time fMRI neurofeedback to increase laterality of motor cortex activity and assessed effects on motor performance and on brain structure and function. We carried out a randomized, double-blind, sham-controlled trial (ClinicalTrials.gov: NCT03775915) in which 24 chronic stroke survivors with mild to moderate upper limb impairment experienced three training days of either Real (n = 12) or Sham (n = 12) neurofeedback. Assessments of brain structure, brain function and measures of upper-limb function were carried out before and 1 week after neurofeedback training. Additionally, measures of upper-limb function were repeated 1 month after neurofeedback training. Primary outcome measures were (i) changes in lateralization of motor cortex activity during movements of the stroke-affected hand throughout neurofeedback training days; and (ii) changes in motor performance of the affected limb on the Jebsen Taylor Test (JTT). Stroke survivors were able to use Real neurofeedback to increase laterality of motor cortex activity within (P = 0.019), but not across, training days. There was no group effect on the primary behavioural outcome measure, which was average JTT performance across all subtasks (P = 0.116). Secondary analysis found improvements in the performance of the gross motor subtasks of the JTT in the Real neurofeedback group compared to Sham (P = 0.010). However, there were no improvements on the Action Research Arm Test or the Upper Extremity Fugl-Meyer score (both P > 0.5). Additionally, decreased white-matter asymmetry of the corticospinal tracts was detected 1 week after neurofeedback training (P = 0.008), indicating that the tracts become more similar with Real neurofeedback. Changes in the affected corticospinal tract were positively correlated with participants neurofeedback performance (P = 0.002). Therefore, here we demonstrate that chronic stroke survivors are able to use functional MRI neurofeedback to self-modulate motor cortex activity in comparison to a Sham control, and that training is associated with improvements in gross hand motor performance and with white matter structural changes.
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Affiliation(s)
- Zeena-Britt Sanders
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Melanie K Fleming
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Tom Smejka
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Marilien C Marzolla
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Catharina Zich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Sebastian W Rieger
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, 6229 EV Maastricht, The Netherlands
- Research Department, Brain Innovation B.V., 6229 EV Maastricht, The Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, 6229 EV Maastricht, The Netherlands
- Research Department, Brain Innovation B.V., 6229 EV Maastricht, The Netherlands
| | - Cassandra Sampaio-Baptista
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G61 1QH, UK
| | - Heidi Johansen-Berg
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
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12
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Liu X, Klugah-Brown B, Zhang R, Chen H, Zhang J, Becker B. Pathological fear, anxiety and negative affect exhibit distinct neurostructural signatures: evidence from psychiatric neuroimaging meta-analysis. Transl Psychiatry 2022; 12:405. [PMID: 36151073 PMCID: PMC9508096 DOI: 10.1038/s41398-022-02157-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 09/04/2022] [Accepted: 09/06/2022] [Indexed: 11/24/2022] Open
Abstract
Internalizing disorders encompass anxiety, fear and depressive disorders, which exhibit overlap at both conceptual and symptom levels. Given that a neurobiological evaluation is lacking, we conducted a Seed-based D-Mapping comparative meta-analysis including coordinates as well as original statistical maps to determine common and disorder-specific gray matter volume alterations in generalized anxiety disorder (GAD), fear-related anxiety disorders (FAD, i.e., social anxiety disorder, specific phobias, panic disorder) and major depressive disorder (MDD). Results showed that GAD exhibited disorder-specific altered volumes relative to FAD including decreased volumes in left insula and lateral/medial prefrontal cortex as well as increased right putamen volume. Both GAD and MDD showed decreased prefrontal volumes compared to controls and FAD. While FAD showed less robust alterations in lingual gyrus compared to controls, this group presented intact frontal integrity. No shared structural abnormalities were found. Our study is the first to provide meta-analytic evidence for distinct neuroanatomical abnormalities underlying the pathophysiology of anxiety-, fear-related and depressive disorders. These findings may have implications for determining promising target regions for disorder-specific neuromodulation interventions (e.g. transcranial magnetic stimulation or neurofeedback).
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Affiliation(s)
- Xiqin Liu
- grid.54549.390000 0004 0369 4060The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People’s Hospital, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, 611731 Chengdu, P. R. China
| | - Benjamin Klugah-Brown
- grid.54549.390000 0004 0369 4060The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People’s Hospital, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, 611731 Chengdu, P. R. China
| | - Ran Zhang
- grid.54549.390000 0004 0369 4060The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People’s Hospital, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, 611731 Chengdu, P. R. China
| | - Huafu Chen
- grid.54549.390000 0004 0369 4060The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People’s Hospital, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, 611731 Chengdu, P. R. China
| | - Jie Zhang
- grid.8547.e0000 0001 0125 2443Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, 200433 Shanghai, P. R. China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, 200433 Shanghai, P. R. China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, 611731, Chengdu, P. R. China.
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13
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Choice of Voxel-based Morphometry processing pipeline drives variability in the location of neuroanatomical brain markers. Commun Biol 2022; 5:913. [PMID: 36068295 PMCID: PMC9448776 DOI: 10.1038/s42003-022-03880-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 08/23/2022] [Indexed: 11/12/2022] Open
Abstract
Fundamental and clinical neuroscience has benefited tremendously from the development of automated computational analyses. In excess of 600 human neuroimaging papers using Voxel-based Morphometry (VBM) are now published every year and a number of different automated processing pipelines are used, although it remains to be systematically assessed whether they come up with the same answers. Here we examined variability between four commonly used VBM pipelines in two large brain structural datasets. Spatial similarity and between-pipeline reproducibility of the processed gray matter brain maps were generally low between pipelines. Examination of sex-differences and age-related changes revealed considerable differences between the pipelines in terms of the specific regions identified. Machine learning-based multivariate analyses allowed accurate predictions of sex and age, however accuracy differed between pipelines. Our findings suggest that the choice of pipeline alone leads to considerable variability in brain structural markers which poses a serious challenge for reproducibility and interpretation. Four common processing pipelines tested on two Voxel-based Morphometry (VBM) datasets yield considerable variations in results, raising issues on the interpretability and robustness of VBM results.
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14
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Qu JF, Zhou YQ, Liu JF, Hu HH, Cheng WY, Lu ZH, Shi L, Luo YS, Zhao L, Chen YK. Right Cortical Infarction and a Reduction in Putamen Volume May Be Correlated with Empathy in Patients after Subacute Ischemic Stroke—A Multimodal Magnetic Resonance Imaging Study. J Clin Med 2022; 11:jcm11154479. [PMID: 35956096 PMCID: PMC9369598 DOI: 10.3390/jcm11154479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/12/2022] [Accepted: 07/28/2022] [Indexed: 02/04/2023] Open
Abstract
Empathy has not been well studied in patients following ischemic stroke. We aimed to evaluate the relationships of multimodal neuroimaging parameters with the impairment of empathy in patients who had experienced subacute ischemic stroke. Patients who had experienced a first-event acute ischemic stroke were recruited, and we assessed their empathy using the Chinese version of the Empathy Quotient (EQ) 3 months after the index stroke. Multimodal magnetic resonance imaging (MRI) was conducted in all the participants to identify acute infarction and assess brain volumes, white matter integrity, and other preexisting abnormalities. We quantified the brain volumes of various subcortical structures, the ventricles, and cortical lobar atrophy. The microstructural integrity of the white matter was reflected in the mean fractional anisotropy (FA) and mean diffusivity (MD), and the regional mean values of FA and MD were quantified after mapping using the ICBM_DTI_81 Atlas. Twenty-three (56.1%) men and 18 (43.9%) women (mean age: 61.73 years, range: 41–77 years) were included. The median National Institutes of Health Stroke Scale (NIHSS) score at discharge was 1 (range: 0–4). On univariate analysis, the EQ was correlated with right cortical infarction (r = −0.39, p = 0.012), putamen volume (r = 0.382, p = 0.014), right putamen volume (r = 0.338, p = 0.031), and the FA value of the right sagittal stratum. EQ did not correlated with the MD value in any region of interest or pre-existing brain abnormalities. Multiple stepwise linear regression models were used to identify factors associated with EQ. After adjusting for age and the NIHSS score on admission, the frequency of right cortical infarcts negatively correlated with EQ (standardized β = −0.358, 95% confidence interval =−0.708 to −0.076, p = 0.016), and the putamen volume positively correlated with EQ (standardized β = 0.328, 95% confidence interval =0.044 to 0.676, p = 0.027). In conclusion, in patients who have experienced subacute ischemic stroke, right cortical infarction and a smaller putamen volume are associated with the impairment of empathy.
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Affiliation(s)
- Jian-Feng Qu
- Department of Neurology, Dongguan People’s Hospital (Affiliated Dongguan Hospital, Southern Medical University), Dongguan 523108, China; (J.-F.Q.); (Y.-Q.Z.); (J.-F.L.); (H.-H.H.); (W.-Y.C.); (Z.-H.L.)
| | - Yue-Qiong Zhou
- Department of Neurology, Dongguan People’s Hospital (Affiliated Dongguan Hospital, Southern Medical University), Dongguan 523108, China; (J.-F.Q.); (Y.-Q.Z.); (J.-F.L.); (H.-H.H.); (W.-Y.C.); (Z.-H.L.)
- Faculty of Neurology, Graduate School of Guangdong Medical University, Zhanjiang 524013, China
| | - Jian-Fei Liu
- Department of Neurology, Dongguan People’s Hospital (Affiliated Dongguan Hospital, Southern Medical University), Dongguan 523108, China; (J.-F.Q.); (Y.-Q.Z.); (J.-F.L.); (H.-H.H.); (W.-Y.C.); (Z.-H.L.)
- Faculty of Neurology, Graduate School of Southern Medical University, Guangzhou 510505, China
| | - Hui-Hong Hu
- Department of Neurology, Dongguan People’s Hospital (Affiliated Dongguan Hospital, Southern Medical University), Dongguan 523108, China; (J.-F.Q.); (Y.-Q.Z.); (J.-F.L.); (H.-H.H.); (W.-Y.C.); (Z.-H.L.)
| | - Wei-Yang Cheng
- Department of Neurology, Dongguan People’s Hospital (Affiliated Dongguan Hospital, Southern Medical University), Dongguan 523108, China; (J.-F.Q.); (Y.-Q.Z.); (J.-F.L.); (H.-H.H.); (W.-Y.C.); (Z.-H.L.)
| | - Zhi-Hao Lu
- Department of Neurology, Dongguan People’s Hospital (Affiliated Dongguan Hospital, Southern Medical University), Dongguan 523108, China; (J.-F.Q.); (Y.-Q.Z.); (J.-F.L.); (H.-H.H.); (W.-Y.C.); (Z.-H.L.)
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong, China;
| | - Yi-Shan Luo
- BrainNow Research Institute, Shenzhen 518000, China; (Y.-S.L.); (L.Z.)
| | - Lei Zhao
- BrainNow Research Institute, Shenzhen 518000, China; (Y.-S.L.); (L.Z.)
| | - Yang-Kun Chen
- Department of Neurology, Dongguan People’s Hospital (Affiliated Dongguan Hospital, Southern Medical University), Dongguan 523108, China; (J.-F.Q.); (Y.-Q.Z.); (J.-F.L.); (H.-H.H.); (W.-Y.C.); (Z.-H.L.)
- Correspondence: ; Tel.: +86-13713135765
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15
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Rao B, Cheng H, Xu H, Peng Y. Random Network and Non-rich-club Organization Tendency in Children With Non-syndromic Cleft Lip and Palate After Articulation Rehabilitation: A Diffusion Study. Front Neurol 2022; 13:790607. [PMID: 35185761 PMCID: PMC8847279 DOI: 10.3389/fneur.2022.790607] [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: 10/07/2021] [Accepted: 01/03/2022] [Indexed: 11/30/2022] Open
Abstract
Objective The neuroimaging pattern in brain networks after articulation rehabilitation can be detected using graph theory and multivariate pattern analysis (MVPA). In this study, we hypothesized that the characteristics of the topology pattern of brain structural network in articulation-rehabilitated children with non-syndromic cleft lip and palate (NSCLP) were similar to that in healthy comparisons. Methods A total of 28 children with NSCLP and 28 controls with typical development were scanned for diffusion tensor imaging on a 3T MRI scanner. Structural networks were constructed, and their topological properties were obtained. Besides, the Chinese language clear degree scale (CLCDS) scores were used for correlation analysis with topological features in patients with NSCLP. Results The NSCLP group showed a similar rich-club connection pattern, but decreased small-world index, normalized rich-club coefficient, and increased connectivity strength of connections compared to controls. The univariate and multivariate patterns of the structural network in articulation-rehabilitated children were primarily in the feeder and local connections, covering sensorimotor, visual, frontoparietal, default mode, salience, and language networks, and orbitofrontal cortex. In addition, the connections that were significantly correlated with the CLCDS scores, as well as the weighted regions for classification, were chiefly distributed in the dorsal and ventral stream associated with the language networks of the non-dominant hemisphere. Conclusion The average level rich-club connection pattern and the compensatory of the feeder and local connections mainly covering language networks may be related to the CLCDS in articulation-rehabilitated children with NSCLP. However, the patterns of small-world and rich-club structural organization in the articulation-rehabilitated children exhibited a random network and non-rich-club organization tendency. These findings enhanced the understanding of neuroimaging patterns in children with NSCLP after articulation rehabilitation.
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Affiliation(s)
- Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
| | - Hua Cheng
- Department of Radiology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
- *Correspondence: Haibo Xu
| | - Yun Peng
- Department of Radiology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
- Yun Peng
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16
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Weiss F, Zhang J, Aslan A, Kirsch P, Gerchen MF. Feasibility of training the dorsolateral prefrontal-striatal network by real-time fMRI neurofeedback. Sci Rep 2022; 12:1669. [PMID: 35102203 PMCID: PMC8803939 DOI: 10.1038/s41598-022-05675-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/17/2022] [Indexed: 12/17/2022] Open
Abstract
Real-time fMRI neurofeedback (rt-fMRI NF) is a promising non-invasive technique that enables volitional control of usually covert brain processes. While most rt-fMRI NF studies so far have demonstrated the ability of the method to evoke changes in brain activity and improve symptoms of mental disorders, a recently evolving field is network-based functional connectivity (FC) rt-fMRI NF. However, FC rt-fMRI NF has methodological challenges such as respirational artefacts that could potentially bias the training if not controlled. In this randomized, double-blind, yoke-controlled, pre-registered FC rt-fMRI NF study with healthy participants (N = 40) studied over three training days, we tested the feasibility of an FC rt-fMRI NF approach with online global signal regression (GSR) to control for physiological artefacts for up-regulation of connectivity in the dorsolateral prefrontal-striatal network. While our pre-registered null hypothesis significance tests failed to reach criterion, we estimated the FC training effect at a medium effect size at the end of the third training day after rigorous control of physiological artefacts in the offline data. This hints at the potential of FC rt-fMRI NF for the development of innovative transdiagnostic circuit-specific interventional approaches for mental disorders and the effect should now be confirmed in a well-powered study.
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Affiliation(s)
- Franziska Weiss
- Department of Clinical Psychology, Central Institute of Mental Health (ZI), Heidelberg University/Medical Faculty Mannheim, J5, 68159, Mannheim, Germany
| | - Jingying Zhang
- Department of Clinical Psychology, Central Institute of Mental Health (ZI), Heidelberg University/Medical Faculty Mannheim, J5, 68159, Mannheim, Germany
| | - Acelya Aslan
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Heidelberg University/Medical Faculty Mannheim, Mannheim, Germany
| | - Peter Kirsch
- Department of Clinical Psychology, Central Institute of Mental Health (ZI), Heidelberg University/Medical Faculty Mannheim, J5, 68159, Mannheim, Germany.,Bernstein Center for Computational Neuroscience Heidelberg/Mannheim, Mannheim, Germany.,Department of Psychology, Heidelberg University, Heidelberg, Germany
| | - Martin Fungisai Gerchen
- Department of Clinical Psychology, Central Institute of Mental Health (ZI), Heidelberg University/Medical Faculty Mannheim, J5, 68159, Mannheim, Germany. .,Bernstein Center for Computational Neuroscience Heidelberg/Mannheim, Mannheim, Germany. .,Department of Psychology, Heidelberg University, Heidelberg, Germany.
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17
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Nakano T, Takamura M, Nishimura H, Machizawa MG, Ichikawa N, Yoshino A, Okada G, Okamoto Y, Yamawaki S, Yamada M, Suhara T, Yoshimoto J. Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training. Neuroimage 2021; 245:118733. [PMID: 34800664 DOI: 10.1016/j.neuroimage.2021.118733] [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: 01/29/2021] [Revised: 10/27/2021] [Accepted: 11/13/2021] [Indexed: 11/19/2022] Open
Abstract
Neurofeedback (NF) aptitude, which refers to an individual's ability to change brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical applications to screen patients suitable for NF treatment. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude, independent of NF-targeting brain regions. We combined the data from fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the multiple regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Subsequently, the reproducibility of the prediction model was validated using independent test data from another site. The identified FC model revealed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting that NF aptitude may be involved in the attentional mode-orientation modulation system's characteristics in task-free resting-state brain activity.
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Affiliation(s)
- Takashi Nakano
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan; School of Medicine, Fujita Health University, Toyoake 470-1192, Japan
| | - Masahiro Takamura
- Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Hiroshima 734-8551, Japan
| | - Haruki Nishimura
- Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan; Department of Functional Brain Imaging, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Maro G Machizawa
- Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Hiroshima 734-8551, Japan; Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan; Department of Functional Brain Imaging, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Naho Ichikawa
- Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Hiroshima 734-8551, Japan
| | - Atsuo Yoshino
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima 734-8551, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima 734-8551, Japan
| | - Yasumasa Okamoto
- Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Hiroshima 734-8551, Japan; Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima 734-8551, Japan
| | - Shigeto Yamawaki
- Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Hiroshima 734-8551, Japan
| | - Makiko Yamada
- Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan; Department of Functional Brain Imaging, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Tetsuya Suhara
- Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Junichiro Yoshimoto
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan.
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18
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Neurofeedback for cognitive enhancement and intervention and brain plasticity. Rev Neurol (Paris) 2021; 177:1133-1144. [PMID: 34674879 DOI: 10.1016/j.neurol.2021.08.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/27/2021] [Indexed: 12/18/2022]
Abstract
In recent years, neurofeedback has been used as a cognitive training tool to improve brain functions for clinical or recreational purposes. It is based on providing participants with feedback about their brain activity and training them to control it, initiating directional changes. The overarching hypothesis behind this method is that this control results in an enhancement of the cognitive abilities associated with this brain activity, and triggers specific structural and functional changes in the brain, promoted by learning and neuronal plasticity effects. Here, we review the general methodological principles behind neurofeedback and we describe its behavioural benefits in clinical and experimental contexts. We review the non-specific effects of neurofeedback on the reinforcement learning striato-frontal networks as well as the more specific changes in the cortical networks on which the neurofeedback control is exerted. Last, we analyse the current challenges faces by neurofeedback studies, including the quantification of the temporal dynamics of neurofeedback effects, the generalisation of its behavioural outcomes to everyday life situations, the design of appropriate controls to disambiguate placebo from true neurofeedback effects and the development of more advanced cortical signal processing to achieve a finer-grained real-time modelling of cognitive functions.
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A Multivariate Randomized Controlled Experiment about the Effects of Mindfulness Priming on EEG Neurofeedback Self-Regulation Serious Games. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Neurofeedback training (NFT) is a technique often proposed to train brain activity SR with promising results. However, some criticism has been raised due to the lack of evaluation, reliability, and validation of its learning effects. The current work evaluates the hypothesis that SR learning may be improved by priming the subject before NFT with guided mindfulness meditation (MM). The proposed framework was tested in a two-way parallel-group randomized controlled intervention with a single session alpha NFT, in a simplistic serious game design. Sixty-two healthy naïve subjects, aged between 18 and 43 years, were divided into MM priming and no-priming groups. Although both the EG and CG successfully attained the up-regulation of alpha rhythms (F(1,59) = 20.67, p < 0.001, ηp2 = 0.26), the EG showed a significantly enhanced ability (t(29) = 4.38, p < 0.001) to control brain activity, compared to the CG (t(29) = 1.18, p > 0.1). Furthermore, EG superior performance on NFT seems to be explained by the subject’s lack of awareness at pre-intervention, less vigour at post-intervention, increased task engagement, and a relaxed non-judgemental attitude towards the NFT tasks. This study is a preliminary validation of the proposed assisted priming framework, advancing some implicit and explicit metrics about its efficacy on NFT performance, and a promising tool for improving naïve “users” self-regulation ability.
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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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Zhao Z, Yao S, Zweerings J, Zhou X, Zhou F, Kendrick KM, Chen H, Mathiak K, Becker B. Putamen volume predicts real-time fMRI neurofeedback learning success across paradigms and neurofeedback target regions. Hum Brain Mapp 2021; 42:1879-1887. [PMID: 33400306 PMCID: PMC7978128 DOI: 10.1002/hbm.25336] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/17/2020] [Accepted: 12/22/2020] [Indexed: 12/11/2022] Open
Abstract
Real-time fMRI guided neurofeedback training has gained increasing interest as a noninvasive brain regulation technique with the potential to modulate functional brain alterations in therapeutic contexts. Individual variations in learning success and treatment response have been observed, yet the neural substrates underlying the learning of self-regulation remain unclear. Against this background, we explored potential brain structural predictors for learning success with pooled data from three real-time fMRI data sets. Our analysis revealed that gray matter volume of the right putamen could predict neurofeedback learning success across the three data sets (n = 66 in total). Importantly, the original studies employed different neurofeedback paradigms during which different brain regions were trained pointing to a general association with learning success independent of specific aspects of the experimental design. Given the role of the putamen in associative learning this finding may reflect an important role of instrumental learning processes and brain structural variations in associated brain regions for successful acquisition of fMRI neurofeedback-guided self-regulation.
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Affiliation(s)
- Zhiying Zhao
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jana Zweerings
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Xinqi Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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