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Orth L, Meeh J, Leiding D, Habel U, Neuner I, Sarkheil P. Aberrant Functional Connectivity of the Salience Network in Adult Patients with Tic Disorders: A Resting-State fMRI Study. eNeuro 2024; 11:ENEURO.0223-23.2024. [PMID: 38744491 PMCID: PMC11167695 DOI: 10.1523/eneuro.0223-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 12/27/2023] [Accepted: 02/26/2024] [Indexed: 05/16/2024] Open
Abstract
Tic disorders (TD) are characterized by the presence of motor and/or vocal tics. Common neurophysiological frameworks suggest dysregulations of the cortico-striatal-thalamo-cortical (CSTC) brain circuit that controls movement execution. Besides common tics, there are other "non-tic" symptoms that are primarily related to sensory perception, sensorimotor integration, attention, and social cognition. The existence of these symptoms, the sensory tic triggers, and the modifying effect of attention and cognitive control mechanisms on tics may indicate the salience network's (SN) involvement in the neurophysiology of TD. Resting-state functional MRI measurements were performed in 26 participants with TD and 25 healthy controls (HC). The group differences in resting-state functional connectivity patterns were measured based on seed-to-voxel connectivity analyses. Compared to HC, patients with TD exhibited altered connectivity between the core regions of the SN (insula, anterior cingulate cortex, and temporoparietal junction) and sensory, associative, and motor-related cortices. Furthermore, connectivity changes were observed in relation to the severity of tics in the TD group. The SN, particularly the insula, is likely to be an important site of dysregulation in TD. Our results provide evidence for large-scale neural deviations in TD beyond the CSTC pathologies. These findings may be relevant for developing treatment targets.
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Affiliation(s)
- Linda Orth
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
| | - Johanna Meeh
- Department of Psychiatry and Psychotherapy, University of Münster, 48149 Münster, Germany
| | - Delia Leiding
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Pegah Sarkheil
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
- Department of Psychiatry and Psychotherapy, University of Münster, 48149 Münster, Germany
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Waugh RE, Parker JA, Hallett M, Horovitz SG. Classification of Functional Movement Disorders with Resting-State Functional Magnetic Resonance Imaging. Brain Connect 2023; 13:4-14. [PMID: 35570651 PMCID: PMC9942186 DOI: 10.1089/brain.2022.0001] [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] [Indexed: 11/13/2022] Open
Abstract
Introduction: Functional movement disorder (FMD) is a type of functional neurological disorder characterized by abnormal movements that patients do not perceive as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier using a support vector machine to accurately distinguish between 61 FMD patients and 59 healthy controls using features derived from resting-state functional magnetic resonance imaging. Materials and Methods: First, we selected 66 seed regions based on prior related studies, then we calculated the full correlation matrix between them before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier. Results: We identified 29 features of interest that were highly predictive of the FMD condition, classifying patients and controls with 80% accuracy. Several key features included regions in the right sensorimotor cortex, left dorsolateral prefrontal cortex, left cerebellum, and left posterior insula. Conclusions: The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward, and sensorimotor integration, potentially mediating communication between regions associated with motor function, attention, and executive function. Exploratory machine learning was able to identify this distinctive abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients. Clinical-Trials.gov ID: NCT00500994 Impact statement Our research presents novel results that further elucidate the pathophysiology of functional movement disorder (FMD) with a machine learning model that classifies FMD and healthy controls correctly 80% of the time. Herein, we demonstrate how known differences in resting-state functional magnetic resonance imaging connectivity in FMD patients can be leveraged to better understand the complex pattern of neural changes in these patients. Knowing that there are measurable predictable differences in brain activity in patients with FMD may help both clinicians and patients conceptualize and better understand the illness at the point of diagnosis and during treatment. Our methods demonstrate how an effective combination of machine learning and qualitative approaches to analyzing functional brain connectivity can enhance our understanding of abnormal patterns of brain activity in FMD patients.
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Affiliation(s)
- Rebecca E. Waugh
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Jacob A. Parker
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Mark Hallett
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Silvina G. Horovitz
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
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Marapin RS, van der Horn HJ, van der Stouwe AMM, Dalenberg JR, de Jong BM, Tijssen MAJ. Altered brain connectivity in hyperkinetic movement disorders: A review of resting-state fMRI. Neuroimage Clin 2023; 37:103302. [PMID: 36669351 PMCID: PMC9868884 DOI: 10.1016/j.nicl.2022.103302] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 12/25/2022]
Abstract
BACKGROUND Hyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive. OBJECTIVES Providing a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures. METHODS A systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes. RESULTS Most articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described. CONCLUSION Key brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes.
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Affiliation(s)
- Ramesh S Marapin
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands
| | - Harm J van der Horn
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands
| | - A M Madelein van der Stouwe
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands
| | - Jelle R Dalenberg
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands
| | - Bauke M de Jong
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands
| | - Marina A J Tijssen
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands.
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Hartmann A, Andrén P, Atkinson-Clement C, Czernecki V, Delorme C, Debes NM, Szejko N, Ueda K, Black K. Tourette syndrome research highlights from 2021. F1000Res 2022; 11:716. [PMID: 35923292 PMCID: PMC9315233 DOI: 10.12688/f1000research.122708.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/05/2022] [Indexed: 11/05/2022] Open
Abstract
We summarize selected research reports from 2021 relevant to Tourette syndrome that the authors consider most important or interesting. The authors welcome article suggestions and thoughtful feedback from readers.
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Affiliation(s)
- Andreas Hartmann
- Department of Neurology, APHP, Sorbonne University, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France,
| | - Per Andrén
- Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
| | - Cyril Atkinson-Clement
- Paris Brain Institute (ICM), Sorbonne Université, Inserm, CNRS, APHP, Paris, 75013, France
| | - Virginie Czernecki
- Department of Neurology, APHP, Sorbonne University, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France
| | - Cécile Delorme
- Department of Neurology, APHP, Sorbonne University, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France
| | | | - Natalia Szejko
- Department of Neurology, Medical University of Warsaw, Warsaw, Poland
| | - Keisuke Ueda
- Department of Psychiatry, Neurology, Radiology and Neuroscience, Washington University in St. Louis, Saint Louis, Missouri, USA
| | - Kevin Black
- Department of Psychiatry, Neurology, Radiology and Neuroscience, Washington University in St. Louis, Saint Louis, Missouri, USA
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Lin H, Xiang X, Huang J, Xiong S, Ren H, Gao Y. Abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: A resting-state functional magnetic resonance imaging study and support vector machine analysis. Front Psychiatry 2022; 13:960294. [PMID: 36147977 PMCID: PMC9486164 DOI: 10.3389/fpsyt.2022.960294] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Previous studies have revealed abnormal degree centrality (DC) in the structural and functional networks in the brains of patients with major depressive disorder (MDD). There are no existing reports on the DC analysis method combined with the support vector machine (SVM) to distinguish patients with MDD from healthy controls (HCs). Here, the researchers elucidated the variations in DC values in brain regions of MDD patients and provided imaging bases for clinical diagnosis. METHODS Patients with MDD (N = 198) and HCs (n = 234) were scanned using resting-state functional magnetic resonance imaging (rs-fMRI). DC and SVM were applied to analyze imaging data. RESULTS Compared with HCs, MDD patients displayed elevated DC values in the vermis, left anterior cerebellar lobe, hippocampus, and caudate, and depreciated DC values in the left posterior cerebellar lobe, left insula, and right caudate. As per the results of the SVM analysis, DC values in the left anterior cerebellar lobe and right caudate could distinguish MDD from HCs with accuracy, sensitivity, and specificity of 87.71% (353/432), 84.85% (168/198), and 79.06% (185/234), respectively. Our analysis did not reveal any significant correlation among the DC value and the disease duration or symptom severity in patients with MDD. CONCLUSION Our study demonstrated abnormal DC patterns in patients with MDD. Aberrant DC values in the left anterior cerebellar lobe and right caudate could be presented as potential imaging biomarkers for the diagnosis of MDD.
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Affiliation(s)
- Hang Lin
- Department of Psychiatry, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China.,Key Laboratory of Occupational Hazards and Identification, Wuhan University of Science and Technology, Wuhan, China
| | - Xi Xiang
- Department of Spine and Orthopedics, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Junli Huang
- Department of Medical Imaging, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Shihong Xiong
- Department of Nephrology, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Hongwei Ren
- Department of Medical Imaging, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yujun Gao
- Department of Psychiatry, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China.,Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
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