Multimodal neuroimaging and plasma marker evidence of white matter loss in Parkinson's dementia
Poster No:
194
Submission Type:
Abstract Submission
Authors:
Angeliki Zarkali1, Naomi Hannaway2, Peter McColgan3, Amanda Heslegrave3, Elena Veleva3, Rhiannon Laban3, Henrik Zetterberg3, Andrew Lees3, Nick Fox3, Rimona Weil3
Institutions:
1University College London, London, NA, 2University College London, LONDON, United Kingdom, 3University College London, London, United Kingdom
First Author:
Co-Author(s):
Introduction:
Parkinson's (PD) is common and debilitating with over half of patients progressing to dementia or death within 10 years [1]. However, onset and rate of progression is highly variable, reflecting heterogeneity in underlying pathology. Biomarker studies to-date have been limited to a single modality or assessed patients with established cognitive impairment.
Methods:
We assessed multimodal neuroimaging and plasma markers in 98 PD patients and 28 controls followed-up over 3 years, to identify baseline markers predicting future poor outcomes. Participants underwent clinical and neuropsychological assessments at baseline, after 18- and 36-months. PD patients were classified as PD poor outcomes if they developed death, frailty, dementia [2] or mild cognitive impairment [3] during follow-up. Remaining PD patients were defined as PD good outcome. We assessed:
1) Gray matter imaging: cortical thickness and volume-based analyses
3D MPRAGE images were processed using FreeSurfer v6.0, default cross-sectional parameters. We used a general linear model to compare baseline cortical thickness between PD poor vs PD good outcomes, age, sex and total intracranial volume (TIV) as nuisance covariates, FDR-corrected over both hemispheres. We also performed a volume-based region-of-interest analysis over 360 cortical regions (Glasser parcellation4) and 19 subcortical regions (age, sex, TIV as covariates, FDR-corrected).
2) White matter imaging: fixel-based analysis
Diffusion weighted imaging (DWI, b=0-2000) was acquired; after preprocessing, multi-shell 3-tissue constrained spherical deconvolution was performed, each participant's fibre-orientation distribution image registered to a group template [5]. We derived: fibre density (microstructure), fibre cross-section (macrostructure) and combined fibre density and cross-section (FDC). Whole brain connectivity-based fixel enhancement and non-parametric permutation testing was performed to assess whole-brain changes (FWE-corrected, extent-based threshold:10 voxels). We confirmed findings on tract-of-interest analysis across 52 tracts reconstructed using TractSeg [6].
3) Structural and functional connectivity
Structural images were used to parcellate the brain into 360 cortical [4] and 19 subcortical regions (ROIs). For structural connectomes, these were warped in DWI-space and anatomically constrained tractography was performed with 10 million streamlines [7]. For functional connectomes (preprocessed via fmriprep [8]) Pearson correlation coefficient between ROIs was performed. Network-based statistics was used to identify structural and functional connectivity changes in PD poor outcomes (5000 permutations, t=3.0, FWE-correction, age and sex as covariates).
4) Plasma biomarker
Neurofilament light chain (NFL), a disease agnostic marker of axonal damage [9] and phosphorylated tau (p-tau) 181, a marker of brain tau and β-amyloid [10] were assessed, corrected for age, sex and batch effect.
1) Gray matter imaging: cortical thickness and volume-based analyses
3D MPRAGE images were processed using FreeSurfer v6.0, default cross-sectional parameters. We used a general linear model to compare baseline cortical thickness between PD poor vs PD good outcomes, age, sex and total intracranial volume (TIV) as nuisance covariates, FDR-corrected over both hemispheres. We also performed a volume-based region-of-interest analysis over 360 cortical regions (Glasser parcellation4) and 19 subcortical regions (age, sex, TIV as covariates, FDR-corrected).
2) White matter imaging: fixel-based analysis
Diffusion weighted imaging (DWI, b=0-2000) was acquired; after preprocessing, multi-shell 3-tissue constrained spherical deconvolution was performed, each participant's fibre-orientation distribution image registered to a group template [5]. We derived: fibre density (microstructure), fibre cross-section (macrostructure) and combined fibre density and cross-section (FDC). Whole brain connectivity-based fixel enhancement and non-parametric permutation testing was performed to assess whole-brain changes (FWE-corrected, extent-based threshold:10 voxels). We confirmed findings on tract-of-interest analysis across 52 tracts reconstructed using TractSeg [6].
3) Structural and functional connectivity
Structural images were used to parcellate the brain into 360 cortical [4] and 19 subcortical regions (ROIs). For structural connectomes, these were warped in DWI-space and anatomically constrained tractography was performed with 10 million streamlines [7]. For functional connectomes (preprocessed via fmriprep [8]) Pearson correlation coefficient between ROIs was performed. Network-based statistics was used to identify structural and functional connectivity changes in PD poor outcomes (5000 permutations, t=3.0, FWE-correction, age and sex as covariates).
4) Plasma biomarker
Neurofilament light chain (NFL), a disease agnostic marker of axonal damage [9] and phosphorylated tau (p-tau) 181, a marker of brain tau and β-amyloid [10] were assessed, corrected for age, sex and batch effect.
Results:
We found extensive baseline white matter macrostructural changes in PD who progress to poor outcomes (Figure 1): up to 19% reduction in fibre cross-section and a subnetwork of reduced structural connectivity strength (105 nodes, 215 edges, p=0.017). This subnetwork particularly involved connections between right fronto-parietal and left frontal, right fronto-parietal and left parietal and right temporo-occipital and left parietal modules. In contrast, grey matter and functional connectivity were preserved in PD with poor outcomes at baseline. NFL (β=4.378, p=0.016), but not p-tau181 levels (β=0.461, p=0.106) were increased in PD with poor outcomes and correlated with white matter loss (Figure 2).
·Figure 1
·Figure 2
Conclusions:
Our findings provide convergent evidence of white matter axonal loss in PD patients who progress to poor outcomes. Imaging of white matter macrostructure and plasma NFL may be useful biomarkers in PD. As new targeted treatments emerge, these may aid patient selection for treatments and improve stratification to clinical trials.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Diffusion MRI Modeling and Analysis 2
Keywords:
Movement Disorder
STRUCTURAL MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Parkinson's disease; Parkinson's dementia; markers of progression;
1|2Indicates the priority used for review
Provide references using author date format
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