Cortical Myelination Correlates of Clinical Measures in Parkinson’s disease with Freezing of Gait
Poster No:
232
Submission Type:
Abstract Submission
Authors:
Gaurav Rathi1, Jessica Caldwell2, Jason Longhurst3, Zoltan Mari4, Virendra Mishra1
Institutions:
1University of Alabama at Birmingham, Birmingham, AL, 2Cleveland Clinic, Las Vegas, NV, 3Saint Louis University, St. Louis, MO, 4Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV
First Author:
Co-Author(s):
Introduction:
Freezing of gait (FOG) in Parkinson's Disease (PD) is a sudden inability to move, severely affecting mobility and quality of life (Nutt et al., 2011). Traditional neuropsychological assessments of FOG in PD focus on isolated cognitive domains, overlooking cognitive-motor interactions (Bluett et al., 2018). Thus, a more integrated approach is needed to address this gap. The role of cortical microstructural changes, particularly cortical myelination (CM), as assessed through T1-weighted (T1w) and T2-weighted (T2w) MRI, remains underexplored as a potential imaging biomarker for understanding PD individuals with FOG (PD-FOG) (Uddin et al., 2019). This study seeks to evaluate the mean differences and correlations between CM and neuropsychological test domains including attention and working memory, executive function, language skills, learning and memory, and visuospatial abilities to clarify the neural mechanisms underlying FOG and its effects on gait.
Methods:
Our cross-sectional study included three groups: PD-FOG (n=15), PD individuals without FOG (PD-nFOG) (n=16), and healthy controls (HC) (n=16). All participants underwent clinical, neuropsychological assessments, and the physical therapy scores were collected for all participants. FOG episodes were documented during clinical assessments. High-resolution T1w and T2w MRI scans were acquired using a Siemens 3T Skyra scanner (voxel size=1.0mm³, slice thickness=1.0mm). For the T1w scans, the repetition time (TR)=2300ms, and the echo time (TE)=2.96ms; for the T2w scans, TR=3200ms, and TE=412ms. MRI data were processed using the Human Connectome Project pipeline (https://www.humanconnectome.org/software/hcp-mr-pipelines), which included motion correction, spatial normalization, and T1w/T2w ratio calculations to generate CM maps for each hemisphere. Group-wise mean comparisons and the correlation analysis of T1w/T2w CM with neuropsychological variables were performed using PALM (Winkler et al., 2014) in FSL. Statistical significance thresholds were uncorrected permutation p<0.005 (mean comparison) and FWE-corrected p...corr<0.05 (correlation analysis).
Results:
Mean comparisons revealed significantly reduced CM in PD-FOG compared to both PD-nFOG and HC across bilateral hemispheres. Specifically, PD-nFOG exhibited higher CM levels than HC in the left hemisphere (Figure 1). Correlation analysis demonstrated significant associations between all neuropsychological test domains, while D-KEFS Letter Fluency (FAS) test from the execute function domain demonstrated the strongest correlation with CM. In the left hemisphere (Figure 2(A)), the PD-nFOG group exhibited a negative correlation. The slope of the PD-FOG and PD-nFOG correlation showed a stronger relationship, with a higher Pearson coefficient 'r' and effect size 'd' compared to the slope of the PD-nFOG and HC. However, in the right hemisphere (Figure 2(B)), a similar negative correlation was observed for the PD-nFOG. Similarly, the slope of the PD-FOG and PD-nFOG correlation, as well as the slope of the PD-nFOG and HC correlation, showed the exact same trend as in the left hemisphere, but with greater Pearson coefficients and effect sizes, respectively.
Conclusions:
This study highlights the critical role of CM in understanding the neural mechanisms underlying FOG in PD. The observed reductions in CM among individuals with FOG point to potential disruptions in cortical microstructure that may contribute to this debilitating motor symptom. Furthermore, the associations between CM and various cognitive domains, particularly executive function, suggest an intricate relationship between cognitive and motor networks in FOG. These findings emphasize the value of CM as an imaging biomarker, offering insights into the interplay of neural integrity and functional outcomes in PD and specifically in FOG. Future research combining neuroimaging and clinical assessments could improve FOG diagnosis and guide targeted therapies to enhance quality of life.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Motor Behavior:
Motor Planning and Execution 2
Keywords:
Data analysis
DISORDERS
Movement Disorder
MRI
STRUCTURAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Nutt, J. G., Bloem, B. R., Giladi, N., Hallett, M., Horak, F. B., & Nieuwboer, A. (2011). Freezing of gait: moving forward on a mysterious clinical phenomenon. Lancet Neurol, 10(8), 734-744. https://doi.org/10.1016/S1474-4422(11)70143-0
Uddin, M. N., Figley, T. D., Solar, K. G., Shatil, A. S., & Figley, C. R. (2019). Comparisons between multi-component myelin water fraction, T1w/T2w ratio, and diffusion tensor imaging measures in healthy human brain structures. Sci Rep, 9(1), 2500. https://doi.org/10.1038/s41598-019-39199-x
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. Neuroimage, 92(100), 381-397. https://doi.org/10.1016/j.neuroimage.2014.01.060