Stepwise connectivity patterns along the gradients of brain organization in Alzheimer's disease

Poster No:

187 

Submission Type:

Abstract Submission 

Authors:

Jazlynn Tan1, Min Su Kang2, Yi-Hsuan Yeh2, Gleb Bezgin3, Nesrine Rahmouni4, Firoza Lussier4, Seok Jun Hong5, Jean-Paul Soucy6, Serge Gauthier4, Boris Bernhardt7, Sandra Black2,8, Pedro Rosa-Neto4,6, Maged Goubran2,1,9, Julie Ottoy2

Institutions:

1Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada, 2LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada, 3Neuroinformatics for Personalized Medicine lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada, 4Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, Montreal, Quebec, Canada, 5Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Gyeonggi-do, Republic of Korea, 6McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada, 7Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada, 8Department of Medicine (Division of Neurology), University of Toronto, Toronto, Ontario, Canada, 9Physical Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada

First Author:

Jazlynn Tan  
Department of Medical Biophysics, University of Toronto
Toronto, Ontario, Canada

Co-Author(s):

Min Su Kang  
LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute
Toronto, Ontario, Canada
Yi-Hsuan Yeh  
LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute
Toronto, Ontario, Canada
Gleb Bezgin  
Neuroinformatics for Personalized Medicine lab, Montreal Neurological Institute, McGill University
Montreal, Quebec, Canada
Nesrine Rahmouni  
Translational Neuroimaging laboratory, McGill Centre for Studies in Aging
Montreal, Quebec, Canada
Firoza Lussier  
Translational Neuroimaging laboratory, McGill Centre for Studies in Aging
Montreal, Quebec, Canada
Seok Jun Hong  
Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Gyeonggi-do, Republic of Korea
Jean-Paul Soucy  
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University
Montreal, Quebec, Canada
Serge Gauthier  
Translational Neuroimaging laboratory, McGill Centre for Studies in Aging
Montreal, Quebec, Canada
Boris Bernhardt  
Montreal Neurological Institute and Hospital
Montreal, Quebec, Canada
Sandra Black  
LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute|Department of Medicine (Division of Neurology), University of Toronto
Toronto, Ontario, Canada|Toronto, Ontario, Canada
Pedro Rosa-Neto  
Translational Neuroimaging laboratory, McGill Centre for Studies in Aging|McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
Maged Goubran  
LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute|Department of Medical Biophysics, University of Toronto|Physical Sciences Platform, Sunnybrook Research Institute, University of Toronto
Toronto, Ontario, Canada|Toronto, Ontario, Canada|Toronto, Ontario, Canada
Julie Ottoy, PhD  
LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute
Toronto, Ontario, Canada

Introduction:

In Alzheimer's Disease (AD), the entorhinal cortex (EC) is recognized as one of the earliest sites of tau tangle deposition. Existing studies have predominantly focused on tau propagation along direct (seed-to-target) neural connections between brain regions (Sepulcre et al. 2018). Here, we hypothesize that exploring indirect, multi-step connections adds new insights on the spread of AD in the brain. We first employ graph theory-based stepwise connectivity (Sepulcre et al. 2012) to elucidate multi-step functional and structural connections between the EC and the rest of the brain. We then implement a novel integration of stepwise connectivity with low-dimensional gradient space (Margulies et al. 2016) to elucidate connectivity trajectories along the major axes of functional and structural brain organization.

Methods:

We acquired resting-state functional MRI (rs-fMRI) and diffusion-weighted MRI (dMRI) in 213 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, including 103 cognitively normal Aβ-negative controls, 35 cognitively normal Aβ-positive (CN A+) and 75 cognitively impaired Aβ-positive (CI) participants. Subject-specific functional and structural connectomes were estimated using regional time series correlations (Esteban et al. 2019) and probabilistic fiber tractography (Tournier et al. 2019), respectively, with parcellations from a high-resolution atlas adapted from Glasser et al. (2016). We then employed functional or structural stepwise connectivity (SFC or SSC) analyses (Sepulcre et al. 2012) to unveil higher-order indirect connectivity patterns between the EC and the rest of the brain. The SFC or SSC value assigned to a region denotes the number of walks of a particular edge length (1 to 7 edges) to reach the EC from that region (Fig 1A). Groupwise (within-subject normalized) SFC/SSC values were compared via linear regression adjusted for age, sex, and APOE-ε4. Finally, we investigated these stepwise connectivity patterns within a coordinate system spanned by the principal components ('gradients') explaining the most variance in connectivity after non-linear dimensionality reduction (Margulies et al. 2016).

Results:

SFC was highest closest to the EC seed (step 1) and propagated to regions of the default-mode network at step 2 before shifting to sensorimotor regions at steps 3-7. SSC from the EC propagated from posterior (step 1-2) to anterior (step 3-7) regions (Fig 1B). Group comparisons revealed hypoconnectivity from the EC to temporal and posterior regions in CI compared to controls. Conversely, hyperconnectivity from the EC to frontoparietal and sensorimotor regions were observed in CI compared to controls (Fig 1C). In functional gradient space, CN A+ showed accelerated SFC propagation from the EC to the rest of the brain (Fig 2A, yellow pixels), which may be compensatory connectivity in preclinical stages. Later-stage CI subjects showed diminished SFC to the default-mode at the transmodal pole of gradient 1 (Fig 2A: green pixels; 2B: blue t-stats), with accelerated propagation to the sensorimotor regions at the unimodal pole of gradient 1 which were not revealed in the standard SFC analysis (Fig 2A: yellow pixels, 2B: red t-stats). Finally, in structural gradient space, propagation was restrained in the temporal-posterior pole of the structural gradient in CI compared to controls (Fig 2C: yellow pixels, 2D: blue t-stats).
Supporting Image: OHBM_abstractfig1.png
   ·Figure 1. Stepwise functional and structural connectivity patterns.
Supporting Image: OHBM_abstractfig2_nopval.png
   ·Figure 2. Stepwise connectivity trajectories in gradient space.
 

Conclusions:

Using a novel integrated stepwise connectivity and gradient approach, we demonstrated widespread network reorganization in AD affecting both short and long connections. Combining the stepwise connectivity and gradient space allows new insight previously inaccessible through conventional analyses in anatomical space. It unveils how AD affects connectivity strength along the major axes of brain organization.

Disorders of the Nervous System:

Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural) 2

Keywords:

Degenerative Disease
Modeling
MRI
Other - gradient; Alzheimer’s disease; stepwise connectivity; neuroimaging; multi-modal

1|2Indicates the priority used for review

Provide references using author date format

Esteban O, Markiewicz CJ, Blair RW, et al (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16:111–116
Glasser MF, Coalson TS, Robinson EC, et al (2016) A multi-modal parcellation of human cerebral cortex. Nature 536:171–178
Margulies DS, Ghosh SS, Goulas A, et al (2016) Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A 113:12574–12579
Sepulcre J, Grothe MJ, d’Oleire Uquillas F, et al (2018) Neurogenetic contributions to amyloid beta and tau spreading in the human cortex. Nat Med 24:1910–1918
Sepulcre J, Sabuncu MR, Yeo TB, et al (2012) Stepwise connectivity of the modal cortex reveals the multimodal organization of the human brain. J Neurosci 32:10649–10661
Tournier J-D, Smith R, Raffelt D, et al (2019) MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202:116137