Essential Tremor: The Relationship Between Hand Dominance and Tremor Severity by MVPA
Poster No:
301
Submission Type:
Abstract Submission
Authors:
Alma Torres-Torres1, Jelle Dalenberg1, A.M. Van Der Stouwe1, Marina Tijssen1
Institutions:
1University Medical Center Groningen, Groningen, Groningen
First Author:
Co-Author(s):
Introduction:
Essential Tremor (ET) is a prevalent movement disorder, affecting approximately 1.3% of the global population [1]. Characterized by involuntary oscillatory postural tremors of the upper limbs, ET impacts daily activities and diminishes the quality of life. Despite its prevalence, the precise etiology of ET remains unclear. This study analyses task-based functional Magnetic Resonance Imaging (task-fMRI) to explore neural activity in individuals with ET compared to healthy controls (HC). The research also delves into the intricate relationship between hand dominance and tremor severity, aiming to contribute to the understanding of the brain networks related to this heterogeneous disorder.
Methods:
Eighteen ET right-handed subjects were included for this study from the Next Move in Movement Disorders (NEMO) [2]. 20 HC right-handed subjects were age-matched with the ET group. For ET the severity of each hand was assessed using the Fahn Tolosa Marin Essential Tremor Scale [3]. Participants performed a finger tapping task that alternated between left and right in a block design, as illustrated in Figure 1, this block were repeated five times.
MRI data were collected on a 3T Siemens Prisma scanner at the UMCG using a Siemens 64-channel head coil. Task-based fMRI scans were acquired using a multi-band, multi-echo T2*-weighted echo-planar sequence with the following scanning parameters: TR=1.101 ms; TE=12, 36.1, 60.2 ms; voxel size=3.5 mm isotropic. The fMRI data were preprocessed using a custom pipeline, incorporating fMRIprep v22.0.2, TE-dependence analysis v0.0.12 [4], and Advanced Normalization Tools v2.3.5 [5]. Task-fMRI data were analyzed using searchlight multi-voxel pattern analysis (MVPA) to identify differences between ET and HC. First, BrainIAK v0.11 [6] was used, classifying right hand vs. rest and left hand vs. rest for each subject using four post-stimulus time-lags. Spheres with a 5 mm radius were applied, employing radial basis function (RBF) SVM in a stratified 5-fold cross-validation from scikit-learn (v1.1.0), resulting in an accuracy map for each subject and lag. Secondly, group-level statistical analysis was performed per lag with a non-parametric permutation method, contrasting ET > HC. Age and tremor severity were added as covariates.. Statistical thresholding was set at p < 0.001 and FDR corrected (α=0.05). Additionally, we investigated which brain areas correlated with tremor severity
MRI data were collected on a 3T Siemens Prisma scanner at the UMCG using a Siemens 64-channel head coil. Task-based fMRI scans were acquired using a multi-band, multi-echo T2*-weighted echo-planar sequence with the following scanning parameters: TR=1.101 ms; TE=12, 36.1, 60.2 ms; voxel size=3.5 mm isotropic. The fMRI data were preprocessed using a custom pipeline, incorporating fMRIprep v22.0.2, TE-dependence analysis v0.0.12 [4], and Advanced Normalization Tools v2.3.5 [5]. Task-fMRI data were analyzed using searchlight multi-voxel pattern analysis (MVPA) to identify differences between ET and HC. First, BrainIAK v0.11 [6] was used, classifying right hand vs. rest and left hand vs. rest for each subject using four post-stimulus time-lags. Spheres with a 5 mm radius were applied, employing radial basis function (RBF) SVM in a stratified 5-fold cross-validation from scikit-learn (v1.1.0), resulting in an accuracy map for each subject and lag. Secondly, group-level statistical analysis was performed per lag with a non-parametric permutation method, contrasting ET > HC. Age and tremor severity were added as covariates.. Statistical thresholding was set at p < 0.001 and FDR corrected (α=0.05). Additionally, we investigated which brain areas correlated with tremor severity
·Figure 1. Experimental paradigm of one block, this block was repeated five times.
Results:
In the HC, the experimental paradigm activated the expected motor networks, revealing significant differences between the dominant and non-dominant hand tasks. ET exhibited similar activation patterns within motor networks. However, ET displayed a distinctive activation pattern in the frontal lobe in both tasks, areas that were not prominent in the HC. Specifically, HC revealed lower MVPA accuracy during the non-dominant task in the cerebellum and left thalamus compared to ET. Tremor severity analysis in ET revealed a positive correlation between tremor severity and MVPA accuracy in the inferior olive region during the dominant hand task, consistent with previous reports [7]. Additionally, in the non-dominant hand task, tremor severity correlated positively with cerebellar MVPA accuracy.
Conclusions:
In summary, our study aims to improve the comprehension of the brain networks in ET by leveraging this integration of MVPA analysis. Through MVPA analysis, we identified regions of interest associated with intentional movement in ET, some of which are novel and include frontal areas not previously reported in the literature. The inferior olive seems to play a modulating role ET in dominant hand, and cerebellum in the non-dominant hand. Further research on these identified regions holds promise for a deeper understanding of ET pathophysiology.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Keywords:
Cerebellum
FUNCTIONAL MRI
Movement Disorder
Multivariate
Thalamus
1|2Indicates the priority used for review
Provide references using author date format
2. Esteban, Oscar, Christopher J. Markiewicz, Ross W. Blair, Craig A. Moodie, A. Ilkay Isik, Asier Erramuzpe, James D. Kent, et al. 2018. “FMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” Nature Methods 2018 16:1 16 (1): 111–16. https://doi.org/10.1038/s41592-018-0235-4.
3. Fahn S, Tolosa E, Conceppcion M. 1993. “Clinical Rating Scale for Tremor In: Jankovic J, Tolosa E, Eds.” In Parkinson’s Disease and Movement Disorders., 271–280.
4. Kumar, Manoj, Michael J. Anderson, James W. Antony, Christopher Baldassano, Paula P. Brooks, Ming Bo Cai, Po-Hsuan Cameron Chen, et al. 2022. “BrainIAK: The Brain Imaging Analysis Kit.” Aperture Neuro 2021 (4). https://doi.org/10.52294/31BB5B68-2184-411B-8C00-A1DACB61E1DA.
5. Louis, Elan D., and Morgan McCreary. 2021. “How Common Is Essential Tremor? Update on the Worldwide Prevalence of Essential Tremor.” Tremor and Other Hyperkinetic Movements 11 (1). https://doi.org/10.5334/TOHM.632.
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7. Stouwe, A.M.M. van der, I Tuitert, Ioannis Giotis, J Calon, R Gannamani, J.R. Dalenberg, S. van der Veen, M.R. Klamer, A C Telea, and M.A.J. Tijssen. 2021. “The next Move in Movement Disorders (NEMO): Developing a Computer Aided Classification Tool for Hyperkinetic Movement Disorders.” BMJ Open.