1
|
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 2022; 37:103302. [PMID: 36669351 PMCID: PMC9868884 DOI: 10.1016/j.nicl.2022.103302] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [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.
Collapse
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.
| |
Collapse
|
2
|
Bolton TAW, Van De Ville D, Régis J, Witjas T, Girard N, Levivier M, Tuleasca C. Morphometric features of drug-resistant essential tremor and recovery after stereotactic radiosurgical thalamotomy. Netw Neurosci 2022; 6:850-869. [PMID: 36605417 PMCID: PMC9810368 DOI: 10.1162/netn_a_00253] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/02/2022] [Indexed: 01/09/2023] Open
Abstract
Essential tremor (ET) is the most common movement disorder. Its neural underpinnings remain unclear. Here, we quantified structural covariance between cortical thickness (CT), surface area (SA), and mean curvature (MC) estimates in patients with ET before and 1 year after ventro-intermediate nucleus stereotactic radiosurgical thalamotomy, and contrasted the observed patterns with those from matched healthy controls. For SA, complex rearrangements within a network of motion-related brain areas characterized patients with ET. This was complemented by MC alterations revolving around the left middle temporal cortex and the disappearance of positive-valued covariance across both modalities in the right fusiform gyrus. Recovery following thalamotomy involved MC readjustments in frontal brain centers, the amygdala, and the insula, capturing nonmotor characteristics of the disease. The appearance of negative-valued CT covariance between the left parahippocampal gyrus and hippocampus was another recovery mechanism involving high-level visual areas. This was complemented by the appearance of negative-valued CT/MC covariance, and positive-valued SA/MC covariance, in the right inferior temporal cortex and bilateral fusiform gyrus. Our results demonstrate that different morphometric properties provide complementary information to understand ET, and that their statistical cross-dependences are also valuable. They pinpoint several anatomical features of the disease and highlight routes of recovery following thalamotomy.
Collapse
Affiliation(s)
- Thomas A. W. Bolton
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland,Connectomics Laboratory, Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland,* Corresponding Author:
| | - Dimitri Van De Ville
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland,Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Jean Régis
- Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, Assistance Publique-Hôpitaux de Marseille, Centre Hospitalier Universitaire de la Timone, Marseille, France
| | - Tatiana Witjas
- Neurology Department, Assistance Publique-Hôpitaux de Marseille, Centre Hospitalier Universitaire de la Timone, Marseille, France
| | - Nadine Girard
- Department of Diagnostic and Interventional Neuroradiology, Centre de Résonance Magnétique Biologique et Médicale, Assistance Publique-Hôpitaux de Marseille, Centre Hospitalier Universitaire de la Timone, Marseille, France
| | - Marc Levivier
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland,Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Constantin Tuleasca
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland,Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland,Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| |
Collapse
|
3
|
Zhang X, Tao L, Chen H, Zhang X, Wang H, He W, Li Q, Lv F, Luo T, Luo J, Man Y, Xiao Z, Cao J, Fang W. Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor. Front Neurol 2022; 13:847650. [PMID: 35620789 PMCID: PMC9127760 DOI: 10.3389/fneur.2022.847650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients.
Collapse
Affiliation(s)
- Xueyan Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Tao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huiyue Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoyu Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hansheng Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wanlin He
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qin Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tianyou Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin Luo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yun Man
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jun Cao
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weidong Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Weidong Fang
| |
Collapse
|
4
|
van den Berg KRE, Helmich RC. The Role of the Cerebellum in Tremor - Evidence from Neuroimaging. Tremor Other Hyperkinet Mov (N Y) 2021; 11:49. [PMID: 34820148 PMCID: PMC8603856 DOI: 10.5334/tohm.660] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 10/28/2021] [Indexed: 01/04/2023] Open
Abstract
Background Neuroimaging research has played a key role in identifying which cerebral changes are associated with tremor. Here we will focus on the cerebellum, which may drive tremor oscillations, process tremor-related afferents, modulate activity in remote brain regions, or a combination. Methods On the 6th of October 2021, we conducted a PubMed search to select articles providing neuroimaging evidence for cerebellar involvement in essential tremor (ET), Parkinson's disease (PD) tremor, and dystonic tremor (DT). Results In ET, tremor-related activity is found in motor areas of the bilateral cerebellum, and altered functional connectivity within and outside the cerebellum correlates with tremor severity. Furthermore, ET is associated with cerebellar atrophy, but also with compensatory structural changes outside the cerebellum (e.g. supplementary motor area). In PD, tremor-related cerebellar activity and increased cerebello-thalamic coupling has been found. Emerging evidence suggests that the cerebellum plays a key role in dopamine-resistant rest tremor and in postural tremor. Cerebellar structural alterations have been identified in PD, but only some relate to tremor. DT is associated with more widespread cerebral networks than other tremor types. Discussion In ET, the cerebellum likely acts as an oscillator, potentially due to loss of inhibitory mechanisms. In contrast, in PD the cerebellum may be a modulator, which contributes to tremor oscillations by influencing the thalamo-cortical system. The precise role of the cerebellum in DT remains unclear. We recommend that future research measures tremor-related activity directly by combining electrophysiology with neuroimaging, while brain stimulation techniques may be used to establish causality. Highlights This review of neuroimaging studies has provided convincing evidence that the cerebellum plays a key role in the pathophysiology of ET, PD tremor, and dystonic tremor syndromes. This contribution may consist of driving tremor oscillations, processing tremor-related afferents, modulating activity in remote brain regions, or all the above.
Collapse
Affiliation(s)
- Kevin R. E. van den Berg
- Centre of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Rick C. Helmich
- Centre of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| |
Collapse
|
5
|
Li Y, Tao L, Chen H, Wang H, Zhang X, Zhang X, Duan X, Fang Z, Li Q, He W, Lv F, Luo J, Xiao Z, Cao J, Fang W. Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis. Front Hum Neurosci 2021; 15:736155. [PMID: 34712127 PMCID: PMC8545862 DOI: 10.3389/fnhum.2021.736155] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 09/07/2021] [Indexed: 12/30/2022] Open
Abstract
Background and Objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis and diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify patients with depressed ET at the individual level and help to reveal the brain network pathogenesis of depression in patients with ET. Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance. Results: While the total accuracy (40.45%) of four-class GPC was poor, the four-class GPC could discriminate depressed ET from non-depressed ET, primary depression, and HCs with a sensitivity of 70.73% (P < 0.001). At the same time, the sensitivity of using binary SVM to discriminate depressed ET from non-depressed ET, primary depression, and HCs was 73.17, 80.49, and 75.61%, respectively (P < 0.001). The significant discriminative features were mainly located in cerebellar-motor-prefrontal cortex circuits (P < 0.001), and a further correlation analysis showed that the GBC values of significant discriminative features in the right middle prefrontal gyrus, bilateral cerebellum VI, and Crus 1 were correlated with clinical depression severity in patients with depressed ET. Conclusion: Our findings demonstrated that GBC mapping combined with machine learning MVPA could be used to identify patients with depressed ET, and the GBC changes in cerebellar-prefrontal cortex circuits not only posed as the significant discriminative features but also helped to understand the network pathogenesis underlying depression in patients with ET.
Collapse
Affiliation(s)
- Yufen Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Tao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huiyue Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hansheng Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoyu Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xueyan Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiyue Duan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhou Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qin Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wanlin He
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin Luo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jun Cao
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weidong Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| |
Collapse
|