Tract-specific white matter microstructure analysis for differential diagnosis of dementia subtypes
Poster No:
1303
Submission Type:
Abstract Submission
Authors:
Tatjana Schmidt1, Marcella Montagnese1, Timothy Rittman2
Institutions:
1University of Cambridge, Cambridge, Cambridgeshire, 2University of Cambridge, Cambridge, United Kingdom
First Author:
Co-Author(s):
Introduction:
Dementia affects 55 million people globally and is a complex group of syndromes caused by various disease subtypes. Differential diagnosis is essential for prognosis and stratification in clinical trials, but remains a challenge (Braaten et al., 2006). Biomarkers can support differential diagnosis and MRI is ideal for this (Young et al., 2020), however the interpretation of clinical neuroimaging relies mostly on visual inspection of T1w/FLAIR MRI.
A key barrier preventing use of advanced MRI modalities is lack of clarity about which metrics can be implemented and made useful in clinics, in part due to the fact that studies are mostly conducted using high-quality research scans in unrepresentative cohorts of patients with Alzheimer's disease with the exclusion of other subtypes (Ritchie et al., 2015). We address this gap by analysing diffusion MRI data from a large real-world memory clinic cohort. We use the framework of fixel-based analysis (FBA, Raffelt et al., 2017) to investigate a) its feasibility in clinical MRI and b) whether fibre density (FD), fibre cross-section (FC) or a combination of both (FDC) in specific tracts differentiates between dementia subtypes.
A key barrier preventing use of advanced MRI modalities is lack of clarity about which metrics can be implemented and made useful in clinics, in part due to the fact that studies are mostly conducted using high-quality research scans in unrepresentative cohorts of patients with Alzheimer's disease with the exclusion of other subtypes (Ritchie et al., 2015). We address this gap by analysing diffusion MRI data from a large real-world memory clinic cohort. We use the framework of fixel-based analysis (FBA, Raffelt et al., 2017) to investigate a) its feasibility in clinical MRI and b) whether fibre density (FD), fibre cross-section (FC) or a combination of both (FDC) in specific tracts differentiates between dementia subtypes.
Methods:
Participants. Participants (n = 400, mean age = 72y) were recruited from NHS memory clinics as part of Quantitative MRI in NHS Memory Clinics (QMIN-MC) study. Participants' diagnoses included, among others, Alzheimer's disease, frontotemporal dementia, corticobasal syndrome.
MRI acquisition. Participants underwent diffusion MRI with a 3T Magnetom Prisma. Images were acquired in a SE-EPI sequence with b-values of 0, 300, 1000 and 2000 s/mm2 in 6, 8, 30 and 60 gradient directions. Other parameters were: isotropic resolution 1.75 mm3; phase-encoding direction AP; FOV = 192 x 192 x 133 mm3; TR = 2433 ms; TE = 75.60 ms; TA = 4.28 min.
Preprocessing. Preprocessing and FBA were conducted with the MRtrix3 package (Tournier et al., 2019) and included the steps recommended in the documentation. A unique set of tissue response functions was calculated with the Dhollander algorithm (Dhollander et al., 2016) from a subset of 40 representative participants; an FOD population template was calculated based on these FODs. Finally, fixels were segmented for each participant and fixel-based metrics were derived (FD, FC, FDC).
Tract of interest analysis. 72 tracts were segmented individually for each subject with TractSeg (Wasserthal et al., 2018), equivalent tracts in the left and right hemisphere were summed, the three FBA metric maps were masked with the resulting tract masks and mean values were extracted per tract per metric for each subject.
Statistical analysis. A one-way ANOVA was performed for each tract and each FBA metric to test whether there was a difference between the different diagnoses. P-values were FDR corrected.
MRI acquisition. Participants underwent diffusion MRI with a 3T Magnetom Prisma. Images were acquired in a SE-EPI sequence with b-values of 0, 300, 1000 and 2000 s/mm2 in 6, 8, 30 and 60 gradient directions. Other parameters were: isotropic resolution 1.75 mm3; phase-encoding direction AP; FOV = 192 x 192 x 133 mm3; TR = 2433 ms; TE = 75.60 ms; TA = 4.28 min.
Preprocessing. Preprocessing and FBA were conducted with the MRtrix3 package (Tournier et al., 2019) and included the steps recommended in the documentation. A unique set of tissue response functions was calculated with the Dhollander algorithm (Dhollander et al., 2016) from a subset of 40 representative participants; an FOD population template was calculated based on these FODs. Finally, fixels were segmented for each participant and fixel-based metrics were derived (FD, FC, FDC).
Tract of interest analysis. 72 tracts were segmented individually for each subject with TractSeg (Wasserthal et al., 2018), equivalent tracts in the left and right hemisphere were summed, the three FBA metric maps were masked with the resulting tract masks and mean values were extracted per tract per metric for each subject.
Statistical analysis. A one-way ANOVA was performed for each tract and each FBA metric to test whether there was a difference between the different diagnoses. P-values were FDR corrected.
Results:
The ANOVA identified 16 tracts consistent with previous evidence (Dhollander et al., 2021) in which FD differed between diagnoses: corpus callosum, arcuate fasciculus, anterior thalamic radiation, cingulum, middle longitudinal fasc., fornix, inferior occipito-frontal fasc., inferior longitudinal fasc., optic radiation, superior longitudinal fasc., uncinate fasc., thalamo-parietal, thalamo-occipital, striato-fronto-orbital, striato-parietal and striato-occipital. Similarly, the ANOVA for FDC returned the same significant tracts and additionally thalamo-prefrontal and striato-prefrontal. FC did not differ between diagnoses in any tract.
·Figure 1. Tracts in which mean FD and FDC significantly differed between diagnostic groups.
Conclusions:
We present a tract-specific fixel-based analysis in a real-world memory clinic cohort with diverse dementia diagnoses. We demonstrate the feasibility of this advanced MRI analysis in a clinical dataset and identified multiple tracts in which fibre density and a combined metric of fibre density/cross-section differed between diagnostic groups. The findings suggest that microstructural abnormality is specific to certain tracts and its spatial distribution can distinguish between dementia subtypes.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 1
Keywords:
Cognition
Computational Neuroscience
DISORDERS
Memory
MRI
Neurological
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Fixel-Based Analysis
1|2Indicates the priority used for review
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2. Dhollander, T, et al. Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. ISMRM Workshop on Breaking the Barriers of Diffusion MRI. 2016;5.
3. Dhollander, T, et al. Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities. Neuroimage. 2021;241:118417.
4. Raffelt, D A, et al. Connectivity-Based Fixel Enhancement: Whole-Brain Statistical Analysis of Diffusion MRI Measures in the Presence of Crossing Fibres. NeuroImage. 2015;117:40-55.
5. Ritchie, C W, et al. Dementia Trials and Dementia Tribulations: Methodological and Analytical Challenges in Dementia Research. Alzheimer’s Research & Therapy. 2015;7(1):31.
6. Tournier, J-D, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage. 2019;202:116-37.
7. Wasserthal, J, et al. TractSeg - Fast and Accurate White Matter Tract Segmentation. NeuroImage. 2018;183:239-253.
8. Young, P N E, et al. Imaging Biomarkers in Neurodegeneration: Current and Future Practices. Alzheimer’s Research & Therapy. 2020;12(1).