Quantitative MRI microstructural features of medial temporal lobe subfields relates to tauopathy
Poster No:
145
Submission Type:
Abstract Submission
Authors:
Alfie Wearn1, Christine Tardif2, Ilana Leppert2, Giulia Baracchini3, Colleen Hughes1, Elisabeth Sylvain4, Jennifer Tremblay-Mercier4, Judes Poirier4, Sylvia Villeneuve5, Gary Turner6, Nathan Spreng3
Institutions:
1McGill University, Montreal, Québec, 2McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, 3Montreal Neurological Institute and Hospital, Montreal, Quebec, 4Douglas Mental Health University Institute, Montreal, Québec, 5Brain Imaging Centre, Douglas Institute Research Centre, Montreal, Qc, 6York University, Toronto, Ontario
First Author:
Co-Author(s):
Christine Tardif
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Quebec
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Quebec
Ilana Leppert
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Quebec
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital
Montreal, Quebec
Introduction:
Characterizing early brain changes in Alzheimer's disease (AD) is essential to develop effective therapies. The medial temporal lobe (MTL) is one of the earliest affected brain areas in AD, however, macroscale atrophy is a relatively late-stage change [1]. Brain microstructure and composition can be assessed with quantitative MRI (qMRI) and may reveal signs of pathology earlier than volumetry [2]. qMRI measures are sensitive to features like myelin and iron but tend to be non-specific. By assessing multiple MR properties we can gain a more complete picture of underlying biological tissue composition.
We describe associations between qMRI measures of microstructure and macrostructure (volume) for different MTL subfields. We also test the hypothesis that these measures are sensitive to pathology in prodromal AD.
We describe associations between qMRI measures of microstructure and macrostructure (volume) for different MTL subfields. We also test the hypothesis that these measures are sensitive to pathology in prodromal AD.
Methods:
197 participants with family history of AD were included from the PResymptomatic EValuation of Experimental or Novel Treatments for AD (PREVENT-AD) cohort [3] (mean age 68.4y, 74% female).
3T MRI sequences:
- Anatomical scans:
o T1w MPRAGE: 1mm isotropic, TR/TE/TI=2300/2.96/900ms, FA=9°
o T2w SPACE: 0.6mm isotropic, TR/TE=2500/198ms
- Multiparametric Mapping: 3 multi-echo gradient-echo sequences (1mm isotropic, TA=17:30) with weighting for:
o T1: TR=18ms, 6 echoes, TE=2.16-14.81ms, FA 20°
o Magnetization transfer (MT): TR=27ms, 6 echoes, TE=2.04-14.89ms, FA 6°, MT pulse FA 540°, 2.2kHz off-resonance, 12.8ms
o Proton density (PD): TR=27ms, 8 echoes, TE=2.04-22.20ms, FA 6°
- B1+ field maps: 2 spin-echo echo-planar sequences: 2x2x4mm, TR/TE=4010/46 ms, FA [60,120]°
Image Processing:
Microstructure maps (R1, MT saturation (MTsat), R2* and PD) were computed using hMRI toolbox v0.5.0 [4].
MTL subfields were segmented using the Automatic Segmentation of Hippocampal Subfields (ASHS) software, using the T1w and T2w anatomical scans [5].
Brain tau was assessed using PET (18-F Flortaucipir) in the 'meta ROI', a collection of brain regions known to be affected by tau early in AD, primary in middle and inferior temporal lobe [6]. Tau positivity was defined as a standardized uptake value ratio >1.3.
Statistical Analysis:
Interparameter correlations were calculated within each subfield using Product-moment correlations.
We tested the relationship between Tau PET and MTL subfield structure using a linear regression model for each structural measure containing all five subfields:
Tau Load ~ Subfield : (Structure : Tau status) + Tau status + age + sex + education
3T MRI sequences:
- Anatomical scans:
o T1w MPRAGE: 1mm isotropic, TR/TE/TI=2300/2.96/900ms, FA=9°
o T2w SPACE: 0.6mm isotropic, TR/TE=2500/198ms
- Multiparametric Mapping: 3 multi-echo gradient-echo sequences (1mm isotropic, TA=17:30) with weighting for:
o T1: TR=18ms, 6 echoes, TE=2.16-14.81ms, FA 20°
o Magnetization transfer (MT): TR=27ms, 6 echoes, TE=2.04-14.89ms, FA 6°, MT pulse FA 540°, 2.2kHz off-resonance, 12.8ms
o Proton density (PD): TR=27ms, 8 echoes, TE=2.04-22.20ms, FA 6°
- B1+ field maps: 2 spin-echo echo-planar sequences: 2x2x4mm, TR/TE=4010/46 ms, FA [60,120]°
Image Processing:
Microstructure maps (R1, MT saturation (MTsat), R2* and PD) were computed using hMRI toolbox v0.5.0 [4].
MTL subfields were segmented using the Automatic Segmentation of Hippocampal Subfields (ASHS) software, using the T1w and T2w anatomical scans [5].
Brain tau was assessed using PET (18-F Flortaucipir) in the 'meta ROI', a collection of brain regions known to be affected by tau early in AD, primary in middle and inferior temporal lobe [6]. Tau positivity was defined as a standardized uptake value ratio >1.3.
Statistical Analysis:
Interparameter correlations were calculated within each subfield using Product-moment correlations.
We tested the relationship between Tau PET and MTL subfield structure using a linear regression model for each structural measure containing all five subfields:
Tau Load ~ Subfield : (Structure : Tau status) + Tau status + age + sex + education
Results:
Across all ROIs R1 and MTsat correlated positively with each other and negatively with PD (Fig 1). R1 and MTsat correlated positively with R2* in hippocampal subfields (CA1, dentate gyrus, subiculum), but negatively with R2* in MTL cortices. Greater volume was associated with greater R1 and MTsat in CA1 and subiculum.
In Tau+ individuals, greater tau load was associated with smaller volume of CA1, dentate gyrus and entorhinal cortex (Fig 2). Greater tau load was also associated with lower R1, MTsat and R2* and greater PD throughout the hippocampus. In contrast, greater tau load positively correlated with R1 and MTsat in MTL cortices.
In Tau+ individuals, greater tau load was associated with smaller volume of CA1, dentate gyrus and entorhinal cortex (Fig 2). Greater tau load was also associated with lower R1, MTsat and R2* and greater PD throughout the hippocampus. In contrast, greater tau load positively correlated with R1 and MTsat in MTL cortices.
·Fig. 1
·Fig. 2
Conclusions:
In hippocampal subfields we identify a pattern of covariance between qMRI measures of microstructure that reflects myelination (R1, MTsat and R2* covarying negatively with PD). Consistent with a model of AD-related demyelination, we see that in tau+ individuals (likely prodromal AD), greater tau load was associated with lower R1, MTsat and R2* and greater PD, as well as smaller volume.
Negative covariance between MTsat and R2* in the MTL cortices indicates driving factors other than myelin. Here, greater tau load associated with higher MTsat in the tau+ group. MTsat may be directly sensitive to the presence of neurofibrillary tangles.
We highlight regional differences in qMRI measures of microstructure. This study takes steps toward a more complete understanding of the biological driving factors of these measures.
Negative covariance between MTsat and R2* in the MTL cortices indicates driving factors other than myelin. Here, greater tau load associated with higher MTsat in the tau+ group. MTsat may be directly sensitive to the presence of neurofibrillary tangles.
We highlight regional differences in qMRI measures of microstructure. This study takes steps toward a more complete understanding of the biological driving factors of these measures.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging
Modeling and Analysis Methods:
Multivariate Approaches
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Aging
Degenerative Disease
MRI
STRUCTURAL MRI
Other - Hippocampus; Quantitative MRI; Microstructure; Alzheimer's disease
1|2Indicates the priority used for review
Provide references using author date format
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[3] Tremblay-Mercier, J. et al. (2021). Open science datasets from PREVENT-AD, a longitudinal cohort of pre-symptomatic Alzheimer’s disease. NeuroImage: Clinical. DOI: 10.1016/j.nicl.2021.102733.
[4] Tabelow, K. et al. (2019). hMRI - A toolbox for quantitative MRI in neuroscience and clinical research. NeuroImage. DOI: 10.1016/j.neuroimage.2019.01.029.
[5] Yushkevich, P.A. et al. (2015). Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Human Brain Mapping. DOI: 10.1002/hbm.22627.
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