Power and discordance of exercise intervention on Parkinson’s Disease fMRI functional connectivity
Poster No:
1377
Submission Type:
Abstract Submission
Authors:
Erik Reimers1, Connor Bevington1, Sahib Dhaliwal1, Jess McKenzie1, Jon Stoessl1, Vesna Sossi1
Institutions:
1University of British Columbia, Vancouver, BC
First Author:
Co-Author(s):
Introduction:
The functional connectivity of the brain is made up of a complex network of connections between regions. Changes to the strengths of these connections due to disease and intervention are studied often as they highlight underlying disease mechanisms (Johansson, 2022). However, understanding the impact of disease-altering interventions across the whole brain can be difficult to untangle. To address this, we propose a simple model to analyze the change in connection strengths due to intervention as a function of disease. Inspired by methods such as (Shokri-Kojori, 2019), using an axis rotation on the data, changes in connectivity strength can be described in terms of 'power' and 'discordance', where connections with high relative 'power' and low relative 'discordance' are highly modulated by both disease and intervention. This novel application allows for a quantification of which brain regions are targeted by disease specific intervention. Here, we apply the model to investigate the effects of exercise on resting state (rs) fMRI functional connectivity in Parkinson's Disease (PD).
Methods:
22 subjects with PD and 20 age-matched healthy controls (HC) were recruited as part of a study investigating brain energetics. A subset of PD participants (N=7) were enrolled in a six-month exercise study which completed thrice weekly, supervised, 60-minute stationary cycling classes.
rs-fMRI (echo-planar imaging sequence, repetition time = 3 seconds, 164 volumes, voxel size = 2.5 mm isotropic, field of view = 240x240x155 mm) and anatomical MRI (T1-MPRAGE sequence, voxel size = 1 mm isotropic, field of view = 256x256x206 mm) data were collected on the GE SIGNA PET/MR for all subjects and included both pre-exercise and post-exercise program scans.
Preprocessing and statistical analysis of the MRI data were done using FreeSurfer, FSL, and CONN/SPM. Steps included brain segmentation of the anatomical image, distortion correction, slice timing correction, outlier detection, denoising, and bandpass filtering (0.008 Hz to 0.09 Hz).
First level analysis was performed using region of interest (ROI) to ROI connectivity matrices across 93 selected ROIs. Using the rs-fMRI time series, averaged across each subject-specific segmented ROI, functional connectivity strength between regions was represented by the Fisher-transformed correlation coefficient. Second level analysis was performed comparing HC and PD, and separately comparing pre-exercise and post-exercise.
The changes in connection strengths due to intervention were plotted as a function of the changes in connection strengths due to disease allowing for visualization of the effect of intervention on disease related connections. The data were then z-scored (separately across each axis) and rotated -45 degrees to describe changes in connectivity strengths in terms of 'power' and 'discordance'. To limit the scope of the analysis, connections with |power| > 2 and |discordance| < 0.5 were investigated as potential regions targeted by exercise intervention on PD.
rs-fMRI (echo-planar imaging sequence, repetition time = 3 seconds, 164 volumes, voxel size = 2.5 mm isotropic, field of view = 240x240x155 mm) and anatomical MRI (T1-MPRAGE sequence, voxel size = 1 mm isotropic, field of view = 256x256x206 mm) data were collected on the GE SIGNA PET/MR for all subjects and included both pre-exercise and post-exercise program scans.
Preprocessing and statistical analysis of the MRI data were done using FreeSurfer, FSL, and CONN/SPM. Steps included brain segmentation of the anatomical image, distortion correction, slice timing correction, outlier detection, denoising, and bandpass filtering (0.008 Hz to 0.09 Hz).
First level analysis was performed using region of interest (ROI) to ROI connectivity matrices across 93 selected ROIs. Using the rs-fMRI time series, averaged across each subject-specific segmented ROI, functional connectivity strength between regions was represented by the Fisher-transformed correlation coefficient. Second level analysis was performed comparing HC and PD, and separately comparing pre-exercise and post-exercise.
The changes in connection strengths due to intervention were plotted as a function of the changes in connection strengths due to disease allowing for visualization of the effect of intervention on disease related connections. The data were then z-scored (separately across each axis) and rotated -45 degrees to describe changes in connectivity strengths in terms of 'power' and 'discordance'. To limit the scope of the analysis, connections with |power| > 2 and |discordance| < 0.5 were investigated as potential regions targeted by exercise intervention on PD.
Results:
Changes in connectivity strength are shown in Fig. 1 (left). 'Power' and 'discordance' are shown in Fig. 1 (right). Connections with high positive power (> 2) were found to primarily include the cerebellum (16% of the connections which passed the described criteria) and thalamus (14%); highlighting regions found to be hyper-connected in PD in which exercise reduced connectivity strength. Connections with high negative power (< -2) were found to primarily include the putamen (21%) and caudate (5%); highlighting regions found to be hypo-connected in PD in which exercise increased connectivity strength.
Conclusions:
The proposed model allows for simple quantification of the impact of intervention on disease related connection strengths. When applied to exercise and PD, the method highlights potential exercise targeted regions providing new insights consistent with the broader literature (Johansson, 2022).
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Keywords:
Data analysis
Degenerative Disease
FUNCTIONAL MRI
Movement Disorder
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Shokri-Kojori, E. (2019). Correspondence between cerebral glucose metabolism and BOLD reveals relative power and cost in human brain. Nature Communications, 10, 690.