Investigation of fibre bundle alterations in early Alzheimer’s disease using fixel-based analysis
Poster No:
279
Submission Type:
Abstract Submission
Authors:
Aurélie Lebrun1, Yann Leprince1, Julien Lagarde2,3,4, Pauline Olivieri2, Marie Sarazin2,3,4, Michel Bottlaender1,4
Institutions:
1UNIACT, NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France, 2Service de Neurologie de la Mémoire et du Langage, GHU Paris Psychiatrie et Neurosciences, Paris, France, 3Université Paris-Cité, Paris, France, 4BioMaps, Service Hospitalier Frédéric Joliot, CEA, Université Paris-Saclay, Inserm, Orsay, France
First Author:
Co-Author(s):
Julien Lagarde
Service de Neurologie de la Mémoire et du Langage, GHU Paris Psychiatrie et Neurosciences|Université Paris-Cité|BioMaps, Service Hospitalier Frédéric Joliot, CEA, Université Paris-Saclay, Inserm
Paris, France|Paris, France|Orsay, France
Service de Neurologie de la Mémoire et du Langage, GHU Paris Psychiatrie et Neurosciences|Université Paris-Cité|BioMaps, Service Hospitalier Frédéric Joliot, CEA, Université Paris-Saclay, Inserm
Paris, France|Paris, France|Orsay, France
Pauline Olivieri
Service de Neurologie de la Mémoire et du Langage, GHU Paris Psychiatrie et Neurosciences
Paris, France
Service de Neurologie de la Mémoire et du Langage, GHU Paris Psychiatrie et Neurosciences
Paris, France
Marie Sarazin
Service de Neurologie de la Mémoire et du Langage, GHU Paris Psychiatrie et Neurosciences|Université Paris-Cité|BioMaps, Service Hospitalier Frédéric Joliot, CEA, Université Paris-Saclay, Inserm
Paris, France|Paris, France|Orsay, France
Service de Neurologie de la Mémoire et du Langage, GHU Paris Psychiatrie et Neurosciences|Université Paris-Cité|BioMaps, Service Hospitalier Frédéric Joliot, CEA, Université Paris-Saclay, Inserm
Paris, France|Paris, France|Orsay, France
Michel Bottlaender
UNIACT, NeuroSpin, CEA, Université Paris-Saclay|BioMaps, Service Hospitalier Frédéric Joliot, CEA, Université Paris-Saclay, Inserm
Gif-sur-Yvette, France|Orsay, France
UNIACT, NeuroSpin, CEA, Université Paris-Saclay|BioMaps, Service Hospitalier Frédéric Joliot, CEA, Université Paris-Saclay, Inserm
Gif-sur-Yvette, France|Orsay, France
Introduction:
Alzheimer's disease (AD) is primarily characterised by the aggregation and accumulation of specific misfolded proteins (β-amyloid and tau), which initiate focally in a subset of vulnerable neurons and consequently propagate along specific pathways throughout the brain [3],[6]. In this context, studying white matter (WM) could provide crucial information. We aimed to study fibre bundle WM alterations in early AD with a fixel-based analysis (FBA) and to perform linear regressions between these alterations and (i) a clinical marker of disease severity, and (ii) the accumulation of abnormal tau protein.
Methods:
This study includes 27 AD patients (14 F; mean age 70 yo; mean Mini Mental State Examination MMSE=23.4) and 19 healthy controls HC (13 F; mean age 68 yo) from the SHATAU7/IMATAU cohort [7]. All AD patients had positive AD CSF biomarkers, and amyloid and tau PET imaging when available. Participants underwent 3T MRI with a multi-shell diffusion protocol (b=200, 1700, 4200 s/mm², 60 directions per shell, voxel size: 1.3 mm³ iso). We first preprocessed the data (FSL eddy [1]) then we implemented the FBA (MRtrix3 [4],[10]). We used a common FOD template computed with data from 32 participants of the cohort. We then extracted the FD (Fibre Density), FC (Fibre bundle Cross-section) and FDC (Fibre Density and Cross-section) metrics. We first computed a whole-brain FBA to identify WM tracts that are altered in AD with respect to HC (statistical tests on all fixels in the template using a general linear model GLM including age, sex, MMSE, and intracranial volume ICV as covariates; significance of the results assessed with CFE [9]). We then reconstructed the identified tracts on the template (Figure 1A) using beginning and ending ROIs extracted from FreeSurfer parcellation [5] on the T1-weighted images before registration on the template, and we performed tract-based analyses. To do so, we calculated the mean FD, FC and FDC for each tract by taking the average of each metric over all fixels associated with the tract, normalised by tract density. We performed statistical tests for each metric using a GLM with age, sex, MMSE, and ICV as covariates to compare AD patients and HC. For each metric, significance of the results was assessed with one-sided T-tests, and Bonferroni correction for multiple testing over the 11 tracts tested. Finally, we explored linear regressions between FDC and MMSE among the 27 AD patients only, and FDC and tau accumulation in the ipsilateral entorhinal cortex (left cortex for left temporal tracts) among 17 AD patients for whom tau ([18F]-Flortaucipir) PET images were available. To do so, we performed statistical tests for each metric using a GLM with age, sex, and ICV as covariates. For each metric, significance of the results was assessed with one-sided T-tests, and Bonferroni correction for multiple testing over the 11 or 8 tracts tested.
Results:
FDC, which provides a measure sensitive to the number of fibres within the fibre bundle, was decreased in AD compared to HC in all tested tracts, which are mainly tracts of the temporal and limbic lobes (Figure 1B). Moreover, these alterations are driven more by a reduction in FC (atrophy) than in FD (fibre density loss). The temporopulvinar bundle of Arnold was particularly altered. Figure 2 further sheds light on this bundle as its alteration was significantly associated with a clinical marker of disease severity (MMSE), and with tau accumulation in the entorhinal cortex.
·Figure 1
·Figure 2
Conclusions:
These results are consistent with previous results on WM alterations in AD and confirms that bundles of the temporal and limbic poles are the primary deteriorated bundles in AD [8]. Moreover, we highlight the alteration of the temporopulvinar bundle of Arnold, a tract that, to our knowledge, has not been described in the AD diffusion MRI literature to date. The involvement of this tract at the early stage of AD and its association with abnormal tau accumulation are congruent with neuropathological data [2].
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Novel Imaging Acquisition Methods:
Diffusion MRI
PET
Keywords:
Amnesia
Degenerative Disease
Limbic Systems
MRI
Positron Emission Tomography (PET)
Thalamus
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Fixel-based-analysis
1|2Indicates the priority used for review
Provide references using author date format
[2] Braak, H., et al. 1991. ‘Alzheimer’s Disease Affects Limbic Nuclei of the Thalamus’. Acta Neuropathologica 81(3):261–68. doi: 10.1007/BF00305867.
[3] Brettschneider, Johannes, et al. 2015. ‘Spreading of Pathology in Neurodegenerative Diseases: A Focus on Human Studies’. Nature Reviews Neuroscience 16(2):109–20. doi: 10.1038/nrn3887.
[4] Dhollander, Thijs, et al. 2021. ‘Fixel-Based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities’. NeuroImage 241:118417. doi: 10.1016/j.neuroimage.2021.118417.
[5] Fischl, Bruce, et al. 2004. ‘Automatically Parcellating the Human Cerebral Cortex’. Cerebral Cortex 14(1):11–22. doi: 10.1093/cercor/bhg087.
[6] Goedert, Michel. 2015. ‘Alzheimer’s and Parkinson’s Diseases: The Prion Concept in Relation to Assembled Aβ, Tau, and α-Synuclein’. Science 349(6248):1255555. doi: 10.1126/science.1255555.
[7] Lagarde, Julien, et al. 2021. ‘Distinct Amyloid and Tau PET Signatures Are Associated with Diverging Clinical and Imaging Trajectories in Patients with Amnestic Syndrome of the Hippocampal Type’. Translational Psychiatry 11(1):1–10. doi: 10.1038/s41398-021-01628-9.
[8] Mito, Remika, et al. 2018. ‘Fibre-Specific White Matter Reductions in Alzheimer’s Disease and Mild Cognitive Impairment’. Brain 141(3):888–902. doi: 10.1093/brain/awx355.
[9] Raffelt, David A., et al. 2015. ‘Connectivity-Based Fixel Enhancement: Whole-Brain Statistical Analysis of Diffusion MRI Measures in the Presence of Crossing Fibres’. NeuroImage 117:40–55. doi: 10.1016/j.neuroimage.2015.05.039.
[10] Tournier, J.-Donald, et al. 2019. ‘MRtrix3: A Fast, Flexible and Open Software Framework for Medical Image Processing and Visualisation’. NeuroImage 202:116137. doi: 10.1016/j.neuroimage.2019.116137.