Author name cluster

Carlos Cruchaga

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

YNICL Journal 2024 Journal Article

Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change?

Gengsheng Chen
Nicole S. McKay
Brian A. Gordon
Jingxia Liu
Nelly Joseph-Mathurin
Suzanne E. Schindler
Jason Hassenstab
Andrew J. Aschenbrenner

C-Pittsburgh compound B (PiB) Aβ-PET to predict cognitive decline. A cohort of 153 participants who previously underwent PiB-PET scans and comprehensive clinical assessments were used in this study. Our analyses revealed that baseline Aβ is significantly associated with the rate of change in cognitive composite scores, with cognition declining more rapidly when baseline PiB Aβ levels were higher. In contrast, no signification association was identified between the rate of change in PiB-PET Aβ and cognitive decline. Additionally, the ability of the rate of change in the PiB-PET measures to predict cognitive decline was significantly influenced by APOE ε4 carrier status. These results suggest that a single PiB-PET scan is sufficient to predict cognitive decline and that longitudinal measures of Aβ accumulation do not improve the prediction of cognitive decline once someone is amyloid positive.

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ICLR Conference 2024 Conference Paper

Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability

Zehao Dong
Muhan Zhang
Philip R. O. Payne
Michael A. Province
Carlos Cruchaga
Tianyu Zhao
Fuhai Li 0001
Yixin Chen 0001

The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a typical approach to distinguish non-isomorphic graphs, yet rarely adopted when developing expressive GNNs. This paper proposes to maximize the expressivity of GNNs by graph canonization, then the power of such GNNs is studies from the perspective of model stability. A stable GNN will map similar graphs to close graph representations in the vectorial space, and the stability of GNNs is critical to generalize their performance to unseen graphs. We theoretically reveal the trade-off of expressivity and stability in graph-canonization-enhanced GNNs. Then we introduce a notion of universal graph canonization as the general solution to address the trade-off and characterize a widely applicable sufficient condition to solve the universal graph canonization. A comprehensive set of experiments demonstrates the effectiveness of the proposed method. In many popular graph benchmark datasets, graph canonization successfully enhances GNNs and provides highly competitive performance, indicating the capability and great potential of proposed method in general graph representation learning. In graph datasets where the sufficient condition holds, GNNs enhanced by universal graph canonization consistently outperform GNN baselines and successfully improve the SOTA performance up to $31$%, providing the optimal solution to numerous challenging real-world graph analytical tasks like gene network representation learning in bioinformatics.

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YNIMG Journal 2023 Journal Article

Associations between age, sex, APOE genotype, and regional vascular physiology in typically aging adults

Nikou L. Damestani
John Jacoby
Shrikanth M. Yadav
Allison E. Lovely
Aurea Michael
Melissa Terpstra
Marziye Eshghi
Barnaly Rashid

Altered blood flow in the human brain is characteristic of typical aging. However, numerous factors contribute to inter-individual variation in patterns of blood flow throughout the lifespan. To better understand the mechanisms behind such variation, we studied how sex and APOE genotype, a primary genetic risk factor for Alzheimer's disease (AD), influence associations between age and brain perfusion measures. We conducted a cross-sectional study of 562 participants from the Human Connectome Project - Aging (36 to >90 years of age). We found widespread associations between age and vascular parameters, where increasing age was associated with regional decreases in cerebral blood flow (CBF) and increases in arterial transit time (ATT). When grouped by sex and APOE genotype, interactions between group and age demonstrated that females had relatively greater CBF and lower ATT compared to males. Females carrying the APOEε4 allele showed the strongest association between CBF decline and ATT incline with age. This demonstrates that sex and genetic risk for AD modulate age-associated patterns of cerebral perfusion measures.

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YNIMG Journal 2022 Journal Article

Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

Peter R. Millar
Patrick H. Luckett
Brian A. Gordon
Tammie L.S. Benzinger
Suzanne E. Schindler
Anne M. Fagan
Randall J. Bateman
Jae-Hong Lee

"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

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YNICL Journal 2019 Journal Article

Quantification of white matter cellularity and damage in preclinical and early symptomatic Alzheimer's disease

Qing Wang
Yong Wang
Jingxia Liu
Courtney L. Sutphen
Carlos Cruchaga
Tyler Blazey
Brian A. Gordon
Yi Su

Interest in understanding the roles of white matter (WM) inflammation and damage in the pathophysiology of Alzheimer disease (AD) has been growing significantly in recent years. However, in vivo magnetic resonance imaging (MRI) techniques for imaging inflammation are still lacking. An advanced diffusion-based MRI method, neuro-inflammation imaging (NII), has been developed to clinically image and quantify WM inflammation and damage in AD. Here, we employed NII measures in conjunction with cerebrospinal fluid (CSF) biomarker classification (for β-amyloid (Aβ) and neurodegeneration) to evaluate 200 participants in an ongoing study of memory and aging. Elevated NII-derived cellular diffusivity was observed in both preclinical and early symptomatic phases of AD, while disruption of WM integrity, as detected by decreased fractional anisotropy (FA) and increased radial diffusivity (RD), was only observed in the symptomatic phase of AD. This may suggest that WM inflammation occurs earlier than WM damage following abnormal Aβ accumulation in AD. The negative correlation between NII-derived cellular diffusivity and CSF Aβ42 level (a marker of amyloidosis) may indicate that WM inflammation is associated with increasing Aβ burden. NII-derived FA also negatively correlated with CSF t-tau level (a marker of neurodegeneration), suggesting that disruption of WM integrity is associated with increasing neurodegeneration. Our findings demonstrated the capability of NII to simultaneously image and quantify WM cellularity changes and damage in preclinical and early symptomatic AD. NII may serve as a clinically feasible imaging tool to study the individual and composite roles of WM inflammation and damage in AD.

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