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      Dynamic brain-to-brain concordance and behavioral mirroring as a mechanism of the patient-clinician interaction

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          Abstract

          Brain activity concordance between patients and clinicians supports therapeutic alliance and treatment relief of pain.

          Abstract

          The patient-clinician interaction can powerfully shape treatment outcomes such as pain but is often considered an intangible “art of medicine” and has largely eluded scientific inquiry. Although brain correlates of social processes such as empathy and theory of mind have been studied using single-subject designs, specific behavioral and neural mechanisms underpinning the patient-clinician interaction are unknown. Using a two-person interactive design, we simultaneously recorded functional magnetic resonance imaging (hyperscanning) in patient-clinician dyads, who interacted via live video, while clinicians treated evoked pain in patients with chronic pain. Our results show that patient analgesia is mediated by patient-clinician nonverbal behavioral mirroring and brain-to-brain concordance in circuitry implicated in theory of mind and social mirroring. Dyad-based analyses showed extensive dynamic coupling of these brain nodes with the partners’ brain activity, yet only in dyads with pre-established clinical rapport. These findings introduce a putatively key brain-behavioral mechanism for therapeutic alliance and psychosocial analgesia.

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          Advances in functional and structural MR image analysis and implementation as FSL.

          The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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            Fast robust automated brain extraction.

            An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.
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              Improved optimization for the robust and accurate linear registration and motion correction of brain images.

              Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                October 2020
                21 October 2020
                : 6
                : 43
                : eabc1304
                Affiliations
                [1 ]Department of Psychology, University of Oslo, Oslo, Norway.
                [2 ]Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
                [3 ]Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
                [4 ]KM Fundamental Research Division, Korea Institute of Oriental Medicine, Daejeon, The Republic of Korea.
                [5 ]Department of Radiology, Logan University, Chesterfield, MO, USA.
                [6 ]Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
                [7 ]Department of Anesthesiology, Brigham and Women’s Hospital, Boston, MA, USA.
                [8 ]Endicott College, Beverly, MA, USA.
                [9 ]Program in Placebo Studies and Therapeutic Encounter (PiPS), Harvard Medical School, Boston, MA, USA.
                Author notes
                [* ]Corresponding author. Email: d.m.ellingsen@ 123456psykologi.uio.no
                Author information
                http://orcid.org/0000-0002-4832-3832
                http://orcid.org/0000-0002-4424-175X
                http://orcid.org/0000-0001-8837-5218
                http://orcid.org/0000-0001-5673-1775
                http://orcid.org/0000-0002-2253-1940
                Article
                abc1304
                10.1126/sciadv.abc1304
                7577722
                33087365
                30e6c69f-af01-4c81-9b32-bddbd3075e38
                Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 08 April 2020
                : 25 August 2020
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: S10RR023401
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: S10RR019307
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: S10RR019254
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: S10RR023043
                Funded by: doi http://dx.doi.org/10.13039/100000097, National Center for Research Resources;
                Award ID: P41RR14075
                Funded by: doi http://dx.doi.org/10.13039/100000097, National Center for Research Resources;
                Award ID: CRC 1 UL1 RR025758
                Funded by: Harvard Clinical and Translational Science Center;
                Funded by: Martinos Computing facilities;
                Funded by: Foundation for the Science of the Therapeutic Encounter;
                Funded by: doi http://dx.doi.org/10.13039/100008460, National Center for Complementary and Integrative Health;
                Award ID: R61-AT009306
                Funded by: doi http://dx.doi.org/10.13039/501100003718, Korea Institute of Oriental Medicine;
                Award ID: K18051
                Funded by: Neuroimaging Pilot Funding Initiative at the Martinos Center for Biomedical imaging, MGH;
                Award ID: R90DA023427
                Funded by: doi http://dx.doi.org/10.13039/501100005416, Norges Forskningsråd;
                Award ID: FRICON/COFUND-240553/F20
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Cognitive Neuroscience
                Health and Medicine
                Cognitive Neuroscience
                Custom metadata
                Karla Peñamante

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