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Luckett PH, Park KY, Lee JJ, Lenze EJ, Wetherell JL, Eyler L, Snyder AZ, Ances BM, Shimony JS, Leuthardt EC. Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning. J Neurosurg 2023; 139:1258-1269. [PMID: 37060318 PMCID: PMC10576012 DOI: 10.3171/2023.3.jns2314] [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: 01/04/2023] [Accepted: 03/01/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVE Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data. METHODS Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme. RESULTS The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations. CONCLUSIONS Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.
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Affiliation(s)
- Patrick H. Luckett
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Ki Yun Park
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
- Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri
| | - John J. Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Eric J Lenze
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Julie L Wetherell
- Mental Health Impact Unit 3, VA San Diego Healthcare System, San Diego, California
- Department of Psychiatry, University of California, San Diego, California
| | - Lisa Eyler
- Department of Psychiatry, University of California, San Diego, California
| | - Abraham Z. Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Beau M. Ances
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Joshua S. Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Eric C. Leuthardt
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
- Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO
- Center for Innovation in Neuroscience and Technology, Division of Neurotechnology, Washington University School of Medicine, St. Louis, MO
- Brain Laser Center, Washington University School of Medicine, St. Louis, Missouri
- National Center for Adaptive Neurotechnologies
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2
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Noro Y, Li R, Matsui T, Jimura K. A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations. Front Neuroinform 2023; 16:960607. [PMID: 36713290 PMCID: PMC9878402 DOI: 10.3389/fninf.2022.960607] [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: 06/03/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
Resting-state (rs) fMRI has been widely used to examine brain-wide large-scale spatiotemporal architectures, known as resting-state networks (RSNs). Recent studies have focused on the temporally evolving characteristics of RSNs, but it is unclear what temporal characteristics are reflected in the networks. To address this issue, we devised a novel method for voxel-based visualization of spatiotemporal characteristics of rs-fMRI with a time scale of tens of seconds. We first extracted clusters of dominant activity-patterns using a region-of-interest approach and then used these temporal patterns of the clusters to obtain voxel-based activation patterns related to the clusters. We found that activation patterns related to the clusters temporally evolved with a characteristic temporal structure and showed mutual temporal alternations over minutes. The voxel-based representation allowed the decoding of activation patterns of the clusters in rs-fMRI using a meta-analysis of functional activations. The activation patterns of the clusters were correlated with behavioral measures. Taken together, our analysis highlights a novel approach to examine brain activity dynamics during rest.
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Affiliation(s)
- Yusuke Noro
- Department of Biosciences and Informatics, Keio University, Yokohama, Japan
| | - Ruixiang Li
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan
| | - Teppei Matsui
- Department of Biology, Okayama University, Okayama, Japan,PRESTO, Japan Science and Technology Agency, Tokyo, Japan,Teppei Matsui ✉
| | - Koji Jimura
- Department of Informatics, Gunma University, Maebashi, Japan,*Correspondence: Koji Jimura ✉
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3
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O'Connor D, Mandino F, Shen X, Horien C, Ge X, Herman P, Hyder F, Crair M, Papademetris X, Lake E, Constable RT. Functional network properties derived from wide-field calcium imaging differ with wakefulness and across cell type. Neuroimage 2022; 264:119735. [PMID: 36347441 PMCID: PMC9808917 DOI: 10.1016/j.neuroimage.2022.119735] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/21/2022] [Accepted: 11/04/2022] [Indexed: 11/08/2022] Open
Abstract
To improve 'bench-to-bedside' translation, it is integral that knowledge flows bidirectionally-from animal models to humans, and vice versa. This requires common analytical frameworks, as well as open software and data sharing practices. We share a new pipeline (and test dataset) for the preprocessing of wide-field optical fluorescence imaging data-an emerging mode applicable in animal models-as well as results from a functional connectivity and graph theory analysis inspired by recent work in the human neuroimaging field. The approach is demonstrated using a dataset comprised of two test-cases: (1) data from animals imaged during awake and anesthetized conditions with excitatory neurons labeled, and (2) data from awake animals with different genetically encoded fluorescent labels that target either excitatory neurons or inhibitory interneuron subtypes. Both seed-based connectivity and graph theory measures (global efficiency, transitivity, modularity, and characteristic path-length) are shown to be useful in quantifying differences between wakefulness states and cell populations. Wakefulness state and cell type show widespread effects on canonical network connectivity with variable frequency band dependence. Differences between excitatory neurons and inhibitory interneurons are observed, with somatostatin expressing inhibitory interneurons emerging as notably dissimilar from parvalbumin and vasoactive polypeptide expressing cells. In sum, we demonstrate that our pipeline can be used to examine brain state and cell-type differences in mesoscale imaging data, aiding translational neuroscience efforts. In line with open science practices, we freely release the pipeline and data to encourage other efforts in the community.
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Affiliation(s)
- D O'Connor
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - F Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - X Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - C Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
| | - X Ge
- Department of Physiology, School of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - P Herman
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - F Hyder
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - M Crair
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA; Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA; Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, USA
| | - X Papademetris
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Emr Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R T Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
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4
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Afrashteh N, Inayat S, Bermudez-Contreras E, Luczak A, McNaughton BL, Mohajerani MH. Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice. Cell Rep 2021; 37:110081. [PMID: 34879278 DOI: 10.1016/j.celrep.2021.110081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 05/25/2021] [Accepted: 11/10/2021] [Indexed: 11/22/2022] Open
Abstract
Stimuli-evoked and spontaneous brain activity propagates across the cortex in diverse spatiotemporal patterns. Despite extensive studies, the relationship between spontaneous and evoked activity is poorly understood. We investigate this relationship by comparing the amplitude, speed, direction, and complexity of propagation trajectories of spontaneous and evoked activity elicited with visual, auditory, and tactile stimuli using mesoscale wide-field imaging in mice. For both spontaneous and evoked activity, the speed and direction of propagation is modulated by the amplitude. However, spontaneous activity has a higher complexity of the propagation trajectories. For low stimulus strengths, evoked activity amplitude and speed is similar to that of spontaneous activity but becomes dissimilar at higher stimulus strengths. These findings are consistent with observations that primary sensory areas receive widespread inputs from other cortical regions, and during rest, the cortex tends to reactivate traces of complex multisensory experiences that might have occurred in exhibition of different behaviors.
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Affiliation(s)
- Navvab Afrashteh
- University of Lethbridge, Faculty of Arts and Sciences, Department of Neuroscience, 4401 University Dr. W., Lethbridge, Alberta T1K 3M4, Canada
| | - Samsoon Inayat
- University of Lethbridge, Faculty of Arts and Sciences, Department of Neuroscience, 4401 University Dr. W., Lethbridge, Alberta T1K 3M4, Canada
| | - Edgar Bermudez-Contreras
- University of Lethbridge, Faculty of Arts and Sciences, Department of Neuroscience, 4401 University Dr. W., Lethbridge, Alberta T1K 3M4, Canada
| | - Artur Luczak
- University of Lethbridge, Faculty of Arts and Sciences, Department of Neuroscience, 4401 University Dr. W., Lethbridge, Alberta T1K 3M4, Canada
| | - Bruce L McNaughton
- University of Lethbridge, Faculty of Arts and Sciences, Department of Neuroscience, 4401 University Dr. W., Lethbridge, Alberta T1K 3M4, Canada; Center for Neurobiology of Learning and Memory, Department of Neurobiology and Behavior, University of California, Irvine, CA 92603, USA
| | - Majid H Mohajerani
- University of Lethbridge, Faculty of Arts and Sciences, Department of Neuroscience, 4401 University Dr. W., Lethbridge, Alberta T1K 3M4, Canada.
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5
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Wu C, Ferreira F, Fox M, Harel N, Hattangadi-Gluth J, Horn A, Jbabdi S, Kahan J, Oswal A, Sheth SA, Tie Y, Vakharia V, Zrinzo L, Akram H. Clinical applications of magnetic resonance imaging based functional and structural connectivity. Neuroimage 2021; 244:118649. [PMID: 34648960 DOI: 10.1016/j.neuroimage.2021.118649] [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: 04/15/2021] [Revised: 09/24/2021] [Accepted: 10/10/2021] [Indexed: 12/23/2022] Open
Abstract
Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
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Affiliation(s)
- Chengyuan Wu
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, 909 Walnut Street, Third Floor, Philadelphia, PA 19107, USA; Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, 909 Walnut Street, First Floor, Philadelphia, PA 19107, USA.
| | - Francisca Ferreira
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, 2021 Sixth Street S.E., Minneapolis, MN 55455, USA.
| | - Jona Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, Center for Precision Radiation Medicine, University of California, San Diego, 3855 Health Sciences Drive, La Jolla, CA 92037, USA.
| | - Andreas Horn
- Neurology Department, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Charitéplatz 1, D-10117, Berlin, Germany.
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK.
| | - Joshua Kahan
- Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10065, USA.
| | - Ashwini Oswal
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Mansfield Rd, Oxford OX1 3TH, UK.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 7200 Cambridge, Ninth Floor, Houston, TX 77030, USA.
| | - Yanmei Tie
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Vejay Vakharia
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK.
| | - Ludvic Zrinzo
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Harith Akram
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
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6
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Manan HA, Franz EA, Yahya N. The utilisation of resting-state fMRI as a pre-operative mapping tool in patients with brain tumours in comparison to task-based fMRI and intraoperative mapping: A systematic review. Eur J Cancer Care (Engl) 2021; 30:e13428. [PMID: 33592671 DOI: 10.1111/ecc.13428] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 01/25/2021] [Accepted: 01/28/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is suggested to be a viable option for pre-operative mapping for patients with brain tumours. However, it remains an open issue whether the tool is useful in the clinical setting compared to task-based fMRI (T-fMRI) and intraoperative mapping. Thus, a systematic review was conducted to investigate the usefulness of this technique. METHODS A systematic literature search of rs-fMRI methods applied as a pre-operative mapping tool was conducted using the PubMed/MEDLINE and Cochrane Library electronic databases following PRISMA guidelines. RESULTS Results demonstrated that 50% (six out of twelve) of the studies comparing rs-fMRI and T-fMRI showed good concordance for both language and sensorimotor networks. In comparison to intraoperative mapping, 86% (six out of seven) studies found a good agreement to rs-fMRI. Finally, 87% (twenty out of twenty-three) studies agreed that rs-fMRI is a suitable and useful pre-operative mapping tool. CONCLUSIONS rs-fMRI is a promising technique for pre-operative mapping in assessing the functional brain areas. However, the agreement between rs-fMRI with other techniques, including T-fMRI and intraoperative maps, is not yet optimal. Studies to ascertain and improve the sophistication in pre-processing of rs-fMRI imaging data are needed.
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Affiliation(s)
- Hanani Abdul Manan
- Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Elizabeth A Franz
- Department of Psychology and fMRIotago, University of Otago, Dunedin, New Zealand
| | - Noorazrul Yahya
- Diagnostic Imaging & Radiotherapy Program, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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7
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Sui Y, Tian Y, Ko WKD, Wang Z, Jia F, Horn A, De Ridder D, Choi KS, Bari AA, Wang S, Hamani C, Baker KB, Machado AG, Aziz TZ, Fonoff ET, Kühn AA, Bergman H, Sanger T, Liu H, Haber SN, Li L. Deep Brain Stimulation Initiative: Toward Innovative Technology, New Disease Indications, and Approaches to Current and Future Clinical Challenges in Neuromodulation Therapy. Front Neurol 2021; 11:597451. [PMID: 33584498 PMCID: PMC7876228 DOI: 10.3389/fneur.2020.597451] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 11/23/2020] [Indexed: 01/17/2023] Open
Abstract
Deep brain stimulation (DBS) is one of the most important clinical therapies for neurological disorders. DBS also has great potential to become a great tool for clinical neuroscience research. Recently, the National Engineering Laboratory for Neuromodulation at Tsinghua University held an international Deep Brain Stimulation Initiative workshop to discuss the cutting-edge technological achievements and clinical applications of DBS. We specifically addressed new clinical approaches and challenges in DBS for movement disorders (Parkinson's disease and dystonia), clinical application toward neurorehabilitation for stroke, and the progress and challenges toward DBS for neuropsychiatric disorders. This review highlighted key developments in (1) neuroimaging, with advancements in 3-Tesla magnetic resonance imaging DBS compatibility for exploration of brain network mechanisms; (2) novel DBS recording capabilities for uncovering disease pathophysiology; and (3) overcoming global healthcare burdens with online-based DBS programming technology for connecting patient communities. The successful event marks a milestone for global collaborative opportunities in clinical development of neuromodulation to treat major neurological disorders.
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Affiliation(s)
- Yanan Sui
- National Engineering Laboratory for Neuromodulation, Tsinghua University, Beijing, China
| | - Ye Tian
- National Engineering Laboratory for Neuromodulation, Tsinghua University, Beijing, China
| | - Wai Kin Daniel Ko
- National Engineering Laboratory for Neuromodulation, Tsinghua University, Beijing, China
| | - Zhiyan Wang
- National Engineering Laboratory for Neuromodulation, Tsinghua University, Beijing, China
| | - Fumin Jia
- National Engineering Laboratory for Neuromodulation, Tsinghua University, Beijing, China
| | - Andreas Horn
- Charité, Department of Neurology, Movement Disorders and Neuromodulation Unit, University Medicine Berlin, Berlin, Germany
| | - Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Ki Sueng Choi
- Department of Psychiatry and Behavioural Science, Emory University, Atlanta, GA, United States.,Department of Radiology, Mount Sinai School of Medicine, New York, NY, United States.,Department of Neurosurgery, Mount Sinai School of Medicine, New York, NY, United States
| | - Ausaf A Bari
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States
| | - Shouyan Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Clement Hamani
- Harquail Centre for Neuromodulation, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Kenneth B Baker
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States.,Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Andre G Machado
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States.,Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Tipu Z Aziz
- Department of Neurosurgery, John Radcliffe Hospital, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Erich Talamoni Fonoff
- Department of Neurology, University of São Paulo Medical School, São Paulo, Brazil.,Hospital Sírio-Libanês and Hospital Albert Einstein, São Paulo, Brazil
| | - Andrea A Kühn
- Charité, Department of Neurology, Movement Disorders and Neuromodulation Unit, University Medicine Berlin, Berlin, Germany
| | - Hagai Bergman
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada (IMRIC), Faculty of Medicine, Jerusalem, Israel.,The Edmond and Lily Safra Center for Brain Research (ELSC), The Hebrew University and Department of Neurosurgery, Hadassah Medical Center, Hebrew University, Jerusalem, Israel
| | - Terence Sanger
- University of Southern California, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Hesheng Liu
- Department of Neuroscience, College of Medicine, Medical University of South Carolina, Charleston, SC, United States
| | - Suzanne N Haber
- Department of Pharmacology and Physiology, University of Rochester School of Medicine & Dentistry, Rochester, NY, United States.,McLean Hospital and Harvard Medical School, Belmont, MA, United States
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, Tsinghua University, Beijing, China
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8
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Yan Y, Dahmani L, Ren J, Shen L, Peng X, Wang R, He C, Jiang C, Gong C, Tian Y, Zhang J, Guo Y, Lin Y, Li S, Wang M, Li L, Hong B, Liu H. Reconstructing lost BOLD signal in individual participants using deep machine learning. Nat Commun 2020; 11:5046. [PMID: 33028816 PMCID: PMC7542429 DOI: 10.1038/s41467-020-18823-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 09/14/2020] [Indexed: 11/09/2022] Open
Abstract
Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles governing BOLD activity in one dataset and reconstruct artificially compromised regions in an independent dataset, frame by frame. Intriguingly, BOLD time series extracted from reconstructed frames are correlated with the original time series, even though the frames do not independently carry any temporal information. Moreover, reconstructed functional connectivity maps exhibit good correspondence with the original connectivity maps, indicating that the model recovers functional relationships among brain regions. We replicated this result in two healthy datasets and in patients whose scans suffered signal loss due to intracortical electrodes. Critically, the reconstructions capture individual-specific information. Deep machine learning thus presents a unique opportunity to reconstruct compromised BOLD signal while capturing features of an individual's own functional brain organization.
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Affiliation(s)
- Yuxiang Yan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Louisa Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Radiology, Zhengzhou University People Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Jianxun Ren
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Lunhao Shen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Xiaolong Peng
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Ruiqi Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Changgeng He
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Changqing Jiang
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Chen Gong
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Ye Tian
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yi Guo
- Department of Neurosurgery, Peking Union Medical College Hospital, Beijing, China
| | - Yuanxiang Lin
- Department of Neurosurgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Shijun Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Meiyun Wang
- Department of Radiology, Zhengzhou University People Hospital & Henan Provincial People's Hospital, Zhengzhou, China.
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China.
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China.
| | - Bo Hong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China.
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA.
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9
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Yan Y, Qian T, Xu X, Han H, Ling Z, Zhou W, Liu H, Hong B. Human cortical networking by probabilistic and frequency-specific coupling. Neuroimage 2020; 207:116363. [PMID: 31740339 DOI: 10.1016/j.neuroimage.2019.116363] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 11/03/2019] [Accepted: 11/13/2019] [Indexed: 11/26/2022] Open
Abstract
Large-scale cortical networking patterns have been established based on the correlation of slow fluctuations of resting fMRI signals. However, the electrophysiological mechanism of cortical networking remained to be elucidated. With large-scale human ECoG recording, we developed a novel approach for functional network parcellation on the basis of probabilistic co-activation of cortical sites in spatio-temporal microstates. The parcellated networks were verified by electrical cortical stimulation (ECS) and somatosensory evoked potentials recording, which showed significantly higher accuracy than the traditional long-term correlation method. This provides direct electrophysiological evidence supporting the dynamic nature of cortical networking. Further analysis revealed that the brain-wide connectivity is likely established on the coupling of ECoG power envelop over a common carrier frequency ranging from alpha to low-beta (8-32Hz). Surprisingly, the cortical networking pattern over this specific frequency was found to be consistent across various tasks, which resembles the resting networks. The high similarity between the above functional network parcellation and the fMRI resting network atlas in individuals also suggested the slow power-envelope coupling of band-limited neural oscillations as the electrophysiological basis of spontaneous BOLD signals. Collectively, our findings on direct human recording revealed a probabilistic and frequency specific coupling mechanism for large-scale cortical networking shared by task and resting brain.
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Affiliation(s)
- Yuxiang Yan
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Tianyi Qian
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Xin Xu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, 100853, China
| | - Hao Han
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Zhipei Ling
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, 100853, China
| | - Wenjin Zhou
- Epilepsy Center, Yuquan Hospital, Tsinghua University, Beijing, 100040, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, 02129, USA.
| | - Bo Hong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China.
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10
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Azad TD, Duffau H. Limitations of functional neuroimaging for patient selection and surgical planning in glioma surgery. Neurosurg Focus 2020; 48:E12. [DOI: 10.3171/2019.11.focus19769] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Accepted: 11/01/2019] [Indexed: 11/06/2022]
Abstract
The optimal surgical management of gliomas requires a balance between surgical cytoreduction and preservation of neurological function. Preoperative functional neuroimaging, such as functional MRI (fMRI) and diffusion tensor imaging (DTI), has emerged as a possible tool to inform patient selection and surgical planning. However, evidence that preoperative fMRI or DTI improves extent of resection, limits neurological morbidity, and broadens surgical indications in classically eloquent areas is lacking. In this review, the authors describe facets of functional neuroimaging techniques that may limit their impact on neurosurgical oncology and critically evaluate the evidence supporting fMRI and DTI for patient selection and operative planning in glioma surgery. The authors also propose alternative applications for functional neuroimaging in the care of glioma patients.
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Affiliation(s)
- Tej D. Azad
- 1Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, Maryland; and
| | - Hugues Duffau
- 2Department of Neurosurgery, Hôpital Gui de Chauliac, Montpellier, France
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11
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Han M, Yang G, Li H, Zhou S, Xu B, Jiang J, Men W, Ge J, Gong G, Liu H, Gao JH. Individualized Cortical Parcellation Based on Diffusion MRI Tractography. Cereb Cortex 2019; 30:3198-3208. [PMID: 31814022 DOI: 10.1093/cercor/bhz303] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/24/2019] [Accepted: 11/11/2019] [Indexed: 12/29/2022] Open
Abstract
The spatial topological properties of cortical regions vary across individuals. Connectivity-based functional and anatomical cortical mapping in individuals will facilitate research on structure-function relationships. However, individual-specific cortical topographic properties derived from anatomical connectivity are less explored than those based on functional connectivity. We aimed to develop a novel individualized anatomical connectivity-based parcellation framework and investigate individual differences in spatial topographic features of cortical regions using diffusion magnetic resonance imaging (dMRI) tractography. Using a high-quality, repeated-session dMRI dataset (42 subjects, 2 sessions per subject), cortical parcels were derived through in vivo anatomical connectivity-based parcellation. These individual-specific parcels demonstrated good within-individual reproducibility and reflected interindividual differences in anatomical brain organization. Connectivity in these individual-specific parcels was significantly more homogeneous than that based on the group atlas. We found that the position, size, and topography of these anatomical parcels were highly variable across individuals and demonstrated nonredundant information about individual differences. Finally, we found that intersubject variability in anatomical connectivity was correlated with the diversity of anatomical connectivity patterns. Overall, we identified cortical parcels that show homogeneous anatomical connectivity patterns. These parcels displayed marked intersubject spatial variability, which may be used in future functional studies to reveal structure-function relationships in the human brain.
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Affiliation(s)
- Meizhen Han
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Guoyuan Yang
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Hai Li
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China.,Beijing Intelligent Brain Cloud Inc., Beijing 100036, China
| | - Sizhong Zhou
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Boyan Xu
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Jun Jiang
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Weiwei Men
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Jianqiao Ge
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Gaolang Gong
- National Key Laboratory of Cognitive Neuroscience and Learning, School of Brain and Cognitive Sciences, Beijing Normal University, Beijing 100875, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing 100069, China
| | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
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12
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Li M, Wang D, Ren J, Langs G, Stoecklein S, Brennan BP, Lu J, Chen H, Liu H. Performing group-level functional image analyses based on homologous functional regions mapped in individuals. PLoS Biol 2019; 17:e2007032. [PMID: 30908490 PMCID: PMC6448916 DOI: 10.1371/journal.pbio.2007032] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 04/04/2019] [Accepted: 03/05/2019] [Indexed: 12/13/2022] Open
Abstract
Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the "localizer" to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations.
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Affiliation(s)
- Meiling Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
| | - Jianxun Ren
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
| | - Sophia Stoecklein
- Institute of Clinical Radiology, Ludwig-Maximilians University of Munich, Munich Germany
| | - Brian P. Brennan
- McLean Hospital, Harvard Medical School, Belmont, Massachusetts, United States of America
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Beijing, China
| | - Huafu Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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13
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Brennan BP, Wang D, Li M, Perriello C, Ren J, Elias JA, Van Kirk NP, Krompinger JW, Pope HG, Haber SN, Rauch SL, Baker JT, Liu H. Use of an Individual-Level Approach to Identify Cortical Connectivity Biomarkers in Obsessive-Compulsive Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 4:27-38. [PMID: 30262337 DOI: 10.1016/j.bpsc.2018.07.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/25/2018] [Accepted: 07/26/2018] [Indexed: 02/05/2023]
Abstract
BACKGROUND Existing functional connectivity studies of obsessive-compulsive disorder (OCD) support a model of circuit dysfunction. However, these group-level observations have failed to yield neuroimaging biomarkers sufficient to serve as a test for the OCD diagnosis, predict current or future symptoms, or predict treatment response, perhaps because these studies failed to account for the substantial intersubject variability in structural and functional brain organization. METHODS We used functional regions, localized in each of 41 individual OCD patients, to identify cortical connectivity biomarkers of both global and dimension-specific symptom severity and to detect functional connections that track changes in symptom severity following intensive residential treatment. RESULTS Global OCD symptom severity was directly linked to dysconnectivity between large-scale intrinsic brain networks-particularly among the dorsal attention, default, and frontoparietal networks. Changes within a subset of connections among these networks were associated with symptom resolution. Additionally, distinct and nonoverlapping cortical connectivity biomarkers were identified that were significantly associated with the severity of contamination/washing and responsibility for harm/checking symptoms, highlighting the contribution of dissociable neural networks to specific OCD symptom dimensions. By contrast, when we defined functional regions conventionally, using a population-level brain atlas, we could no longer identify connectivity biomarkers of severity or improvement for any of the symptom dimensions. CONCLUSIONS Our findings would seem to encourage the use of individual-level approaches to connectivity analyses to better delineate the cortical and subcortical networks underlying symptom severity and improvement at the dimensional level in OCD patients.
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Affiliation(s)
- Brian P Brennan
- Biological Psychiatry Laboratory, McLean Hospital, Belmont, Massachusetts; Obsessive-Compulsive Disorder Institute, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Meiling Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu
| | - Chris Perriello
- Biological Psychiatry Laboratory, McLean Hospital, Belmont, Massachusetts
| | - Jianxun Ren
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jason A Elias
- Obsessive-Compulsive Disorder Institute, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Nathaniel P Van Kirk
- Obsessive-Compulsive Disorder Institute, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Jason W Krompinger
- Obsessive-Compulsive Disorder Institute, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Harrison G Pope
- Biological Psychiatry Laboratory, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Suzanne N Haber
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Scott L Rauch
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Justin T Baker
- Psychotic Disorders Division, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China; Institute for Research and Medical Consultations, Imam Abdulahman Bin Faisal University, Dammam, Saudi Arabia.
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14
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Sensorimotor network parcellation for pre-surgical patients using low-pass filtered fMRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:4479-4482. [PMID: 29060892 DOI: 10.1109/embc.2017.8037851] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Pre-surgical mapping of sensorimotor and language functions is crucial to reduce neurological deficits in epilepsy and tumor resection surgery. As non-invasive mapping, both resting-state and task-evoked functional MRI has been explored in pre-surgical mapping. In lack of standardized test paradigm, the reliability of fMRI mapping is still a concern for clinical use. In this study, to improve the reliability of fMRI based mapping, task fMRI data from all available task paradigms (motor movement, word repeating and picture naming) were low-pass filtered in the band of resting-state fMRI (0.01-0.08Hz) and concatenated to get more time points. With K-means clustering, it was shown that the sensorimotor network could be reliably parcellated into hand and tongue sub-regions. The resulted parcellations were further verified with invasive ECoG and ECS mapping. Both the accuracy and specificity were better than using the motor-task fMRI only. Especially, for those patients who failed in task fMRI mapping, our method was able to provide accurate mapping as well. Our results also indicate that cortical sensorimotor network pattern is intrinsic and always present during various tasks, which supports the physiological link between the spontaneous and the task-evoked BOLD signals.
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15
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Temporal reliability of ultra-high field resting-state MRI for single-subject sensorimotor and language mapping. Neuroimage 2018; 168:499-508. [DOI: 10.1016/j.neuroimage.2016.11.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 10/29/2016] [Accepted: 11/12/2016] [Indexed: 11/19/2022] Open
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16
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Jin C, Li H, Li X, Wang M, Liu C, Guo J, Yang J. Temporary Hearing Threshold Shift in Healthy Volunteers with Hearing Protection Caused by Acoustic Noise Exposure during 3-T Multisequence MR Neuroimaging. Radiology 2018; 286:602-608. [PMID: 28813235 DOI: 10.1148/radiol.2017161622] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Chao Jin
- From the Department of Diagnostic Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an 710061, PR China (C.J., H.L., X.L., M.W., C.L., J.G., J.Y.); and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, PR China (X.L., J.Y.)
| | - Huan Li
- From the Department of Diagnostic Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an 710061, PR China (C.J., H.L., X.L., M.W., C.L., J.G., J.Y.); and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, PR China (X.L., J.Y.)
| | - Xianjun Li
- From the Department of Diagnostic Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an 710061, PR China (C.J., H.L., X.L., M.W., C.L., J.G., J.Y.); and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, PR China (X.L., J.Y.)
| | - Miaomiao Wang
- From the Department of Diagnostic Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an 710061, PR China (C.J., H.L., X.L., M.W., C.L., J.G., J.Y.); and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, PR China (X.L., J.Y.)
| | - Congcong Liu
- From the Department of Diagnostic Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an 710061, PR China (C.J., H.L., X.L., M.W., C.L., J.G., J.Y.); and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, PR China (X.L., J.Y.)
| | - Jianxin Guo
- From the Department of Diagnostic Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an 710061, PR China (C.J., H.L., X.L., M.W., C.L., J.G., J.Y.); and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, PR China (X.L., J.Y.)
| | - Jian Yang
- From the Department of Diagnostic Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an 710061, PR China (C.J., H.L., X.L., M.W., C.L., J.G., J.Y.); and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, PR China (X.L., J.Y.)
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17
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Cochereau J, Deverdun J, Herbet G, Charroud C, Boyer A, Moritz-Gasser S, Le Bars E, Molino F, Bonafé A, Menjot de Champfleur N, Duffau H. Comparison between resting state fMRI networks and responsive cortical stimulations in glioma patients. Hum Brain Mapp 2018; 37:3721-3732. [PMID: 27246771 DOI: 10.1002/hbm.23270] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2015] [Revised: 05/01/2016] [Accepted: 05/17/2016] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVES To validate the functional relevance of resting state networks (RSNs) by means of a comparison of resting state connectivity (RSC) between language regions elicited by direct cortical stimulation versus RSC between random regions; and to evaluate the accuracy of resting state fMRI in surgical planning by assessing the overlap between RSNs and intraoperative functional mapping results. METHODS Sensorimotor and language eloquent sites were identified by direct electrical cortical stimulation in 98 patients with a diffuse low-grade glioma. A seed to voxel analysis with inter-language stimulation point connectivity versus inter-random ROIs connectivity was performed (19 patients). An independant component analysis (ICA) was also applied to rsfMRI data. Language and sensorimotor components were selected over 20 independent components and compared to the corresponding stimulation points and resected cortex masks (31 and 90 patients, respectively). RESULTS Mean connectivity value between language seeds was significantly higher than the one between random seeds (0.68 ± 0.39 and 0.12 ± 0.21 respectively, P < 10-10 ). 96 ± 11% of sensorimotor stimulation points were located within 10 mm from sensorimotor ICA maps versus 92 ± 21% for language. 3.1 and 15% of resected cortex overlapped sensorimotor and language networks, respectively. Mean sensorimotor stimulation points and resected cortex z-scores were 2.0 ± 1.2 and -0.050 ± 0.60, respectively (P < 10-10 ). Mean language stimulation points and resected cortex z-scores were 1.6 ± 1.9 and 0.68 ± 0.91, respectively, P < 0.005. CONCLUSION The significantly higher RSC between language seeds than between random seeds validated the functional relevance of RSC. ICA partly succeeded to distinguish eloquent versus surgically removable areas and may be possibly used as a complementary tool to intraoperative mapping. Hum Brain Mapp 37:3721-3732, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Jérôme Cochereau
- Department of Neurosurgery, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Unité I2FH, Institut d'Imagerie Fonctionnelle Humaine, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors", INSERM U1051, Institute of Neurosciences of Montpellier, Montpellier, France.,Department of Neuroradiology, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France
| | - Jérémy Deverdun
- Unité I2FH, Institut d'Imagerie Fonctionnelle Humaine, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Department of Neuroradiology, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Institut de Génomique Fonctionnelle, Unité UMR 5203 - INSERM U661 - Université Montpellier II - Université Montpellier I, France.,Laboratoire Charles Coulomb, Unité CNRS UMR 5221 - Université Montpellier II, Montpellier, France
| | - Guillaume Herbet
- Department of Neurosurgery, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors", INSERM U1051, Institute of Neurosciences of Montpellier, Montpellier, France
| | - Céline Charroud
- Unité I2FH, Institut d'Imagerie Fonctionnelle Humaine, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Department of Neuroradiology, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France
| | - Anthony Boyer
- Unité I2FH, Institut d'Imagerie Fonctionnelle Humaine, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,University of Montpellier 2, LIRMM laboratory, DEMAR Team, CNRS, INRIA, Montpellier, 34095, France
| | - Sylvie Moritz-Gasser
- Department of Neurosurgery, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors", INSERM U1051, Institute of Neurosciences of Montpellier, Montpellier, France
| | - Emmanuelle Le Bars
- Unité I2FH, Institut d'Imagerie Fonctionnelle Humaine, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Department of Neuroradiology, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Laboratoire Charles Coulomb, Unité CNRS UMR 5221 - Université Montpellier II, Montpellier, France
| | - François Molino
- Institut de Génomique Fonctionnelle, Unité UMR 5203 - INSERM U661 - Université Montpellier II - Université Montpellier I, France.,Laboratoire Charles Coulomb, Unité CNRS UMR 5221 - Université Montpellier II, Montpellier, France
| | - Alain Bonafé
- Unité I2FH, Institut d'Imagerie Fonctionnelle Humaine, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors", INSERM U1051, Institute of Neurosciences of Montpellier, Montpellier, France.,Department of Neuroradiology, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France
| | - Nicolas Menjot de Champfleur
- Unité I2FH, Institut d'Imagerie Fonctionnelle Humaine, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors", INSERM U1051, Institute of Neurosciences of Montpellier, Montpellier, France.,Department of Neuroradiology, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Laboratoire Charles Coulomb, Unité CNRS UMR 5221 - Université Montpellier II, Montpellier, France
| | - Hugues Duffau
- Department of Neurosurgery, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France.,Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors", INSERM U1051, Institute of Neurosciences of Montpellier, Montpellier, France
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18
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Abstract
Maximal safe resection is the cornerstone of treatment for low-grade and high-grade gliomas. In addition to high-resolution anatomic MRI studies that highlight tumor architecture, it is important to determine the relationship of the tumor to the eloquent cortical and subcortical areas to avoid introducing or exacerbating a neurologic deficit. The goal of this review was to highlight imaging modalities that provide functional information and can be integrated with intraoperative MRI navigation to maximize the extent of resection while preserving a patient's neurologic function.
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19
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Groppe DM, Bickel S, Dykstra AR, Wang X, Mégevand P, Mercier MR, Lado FA, Mehta AD, Honey CJ. iELVis: An open source MATLAB toolbox for localizing and visualizing human intracranial electrode data. J Neurosci Methods 2017; 281:40-48. [PMID: 28192130 DOI: 10.1016/j.jneumeth.2017.01.022] [Citation(s) in RCA: 131] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 01/31/2017] [Accepted: 01/31/2017] [Indexed: 11/30/2022]
Abstract
BACKGROUND Intracranial electrical recordings (iEEG) and brain stimulation (iEBS) are invaluable human neuroscience methodologies. However, the value of such data is often unrealized as many laboratories lack tools for localizing electrodes relative to anatomy. To remedy this, we have developed a MATLAB toolbox for intracranial electrode localization and visualization, iELVis. NEW METHOD: iELVis uses existing tools (BioImage Suite, FSL, and FreeSurfer) for preimplant magnetic resonance imaging (MRI) segmentation, neuroimaging coregistration, and manual identification of electrodes in postimplant neuroimaging. Subsequently, iELVis implements methods for correcting electrode locations for postimplant brain shift with millimeter-scale accuracy and provides interactive visualization on 3D surfaces or in 2D slices with optional functional neuroimaging overlays. iELVis also localizes electrodes relative to FreeSurfer-based atlases and can combine data across subjects via the FreeSurfer average brain. RESULTS It takes 30-60min of user time and 12-24h of computer time to localize and visualize electrodes from one brain. We demonstrate iELVis's functionality by showing that three methods for mapping primary hand somatosensory cortex (iEEG, iEBS, and functional MRI) provide highly concordant results. COMPARISON WITH EXISTING METHODS: iELVis is the first public software for electrode localization that corrects for brain shift, maps electrodes to an average brain, and supports neuroimaging overlays. Moreover, its interactive visualizations are powerful and its tutorial material is extensive. CONCLUSIONS iELVis promises to speed the progress and enhance the robustness of intracranial electrode research. The software and extensive tutorial materials are freely available as part of the EpiSurg software project: https://github.com/episurg/episurg.
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Affiliation(s)
- David M Groppe
- Department of Psychology, University of Toronto, Toronto, ON M5SSG3, Canada; Department of Neurosurgery, Hofstra Northwell School of Medicine, and Feinstein Institute for Medical Research, Manhasset, NY 11030, USA.
| | - Stephan Bickel
- Department of Neurology, Montefiore Medical Center, Bronx, NY 10467, USA; Department of Neurology, Stanford University, Stanford, CA 94305, USA
| | - Andrew R Dykstra
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, 69120 Heidelberg, Germany
| | - Xiuyuan Wang
- Department of Neurology, New York University School of Medicine, New York, NY 10016, USA; Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Pierre Mégevand
- Department of Neurosurgery, Hofstra Northwell School of Medicine, and Feinstein Institute for Medical Research, Manhasset, NY 11030, USA; Division of Neurology, Department of Clinical Neuroscience, Hôpitaux Universitaires de Genève, Geneva 1211, Switzerland
| | - Manuel R Mercier
- Department of Neurology, Montefiore Medical Center, Bronx, NY 10467, USA; Centre de Recherche Cerveau et Cognition (CerCo), CNRS, Université Paul Sabatier, UMR5549, CHU Purpan, Toulouse, France; Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Fred A Lado
- Department of Neurology, Montefiore Medical Center, Bronx, NY 10467, USA; Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Ashesh D Mehta
- Department of Neurosurgery, Hofstra Northwell School of Medicine, and Feinstein Institute for Medical Research, Manhasset, NY 11030, USA
| | - Christopher J Honey
- Department of Psychology, University of Toronto, Toronto, ON M5SSG3, Canada; Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA
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Wang D, Buckner RL, Fox MD, Holt DJ, Holmes AJ, Stoecklein S, Langs G, Pan R, Qian T, Li K, Baker JT, Stufflebeam SM, Wang K, Wang X, Hong B, Liu H. Parcellating cortical functional networks in individuals. Nat Neurosci 2015; 18:1853-60. [PMID: 26551545 PMCID: PMC4661084 DOI: 10.1038/nn.4164] [Citation(s) in RCA: 323] [Impact Index Per Article: 35.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Accepted: 10/14/2015] [Indexed: 12/19/2022]
Abstract
The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.
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Affiliation(s)
- Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Randy L. Buckner
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Michael D. Fox
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daphne J. Holt
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Avram J. Holmes
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Sophia Stoecklein
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Ludwig Maximilians University Munich, Institute of Clinical Radiology, Munich, Germany
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ruiqi Pan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Tianyi Qian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
- Siemens Healthcare, MR Collaboration NE Asia, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Justin T. Baker
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Cambridge, MA, USA
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaomin Wang
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Bo Hong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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