Polypathologic Associations with Gray Matter Atrophy in Neurodegenerative Disease
Related Experiment Video
Updated: Jul 9, 2025

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains
Published on: June 3, 2020
Jeffrey S Phillips1, John L Robinson2, Katheryn A Q Cousins3
1Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 jeffrey.phillips@pennmedicine.upenn.edu.
Abstract:
Mixed pathologies are common in neurodegenerative disease; however, antemortem imaging rarely captures copathologic effects on brain atrophy due to a lack of validated biomarkers for non-Alzheimer's pathologies. We leveraged a dataset comprising antemortem MRI and postmortem histopathology to assess polypathologic associations with atrophy in a clinically heterogeneous sample of 125 human dementia patients (41 female, 84 male) with T1-weighted MRI ≤ 5 years before death and postmortem ordinal ratings of amyloid-[Formula: see text], tau, TDP-43, and [Formula: see text]-synuclein. Regional volumes were related to pathology using linear mixed-effects models; approximately 25% of data were held out for testing. We contrasted a polypathologic model comprising independent factors for each proteinopathy with two alternatives: a model that attributed atrophy entirely to the protein(s) associated with the patient's primary diagnosis and a protein-agnostic model based on the sum of ordinal scores for all pathology types. Model fits were evaluated using log-likelihood and correlations between observed and fitted volume scores. Additionally, we performed exploratory analyses relating atrophy to gliosis, neuronal loss, and angiopathy. The polypathologic model provided superior fits in the training and testing datasets. Tau, TDP-43, and [Formula: see text]-synuclein burden were inversely associated with regional volumes, but amyloid-[Formula: see text] was not. Gliosis and neuronal loss explained residual variance in and mediated the effects of tau, TDP-43, and [Formula: see text]-synuclein on atrophy. Regional brain atrophy reflects not only the primary molecular pathology but also co-occurring proteinopathies; inflammatory immune responses may independently contribute to degeneration. Our findings underscore the importance of antemortem biomarkers for detecting mixed pathology.
Insights
Brain atrophy in dementia is influenced by multiple protein pathologies, not just the primary diagnosis. Identifying co-occurring proteinopathies using biomarkers is crucial for understanding neurodegeneration.
Area of Science:
- Neuroscience
- Neuropathology
- Radiology
Background:
- Mixed pathologies are common in neurodegenerative diseases.
- Antemortem imaging often fails to capture co-pathologic effects on brain atrophy due to a lack of validated biomarkers for non-Alzheimer's pathologies.
Purpose of the Study:
- To assess polypathologic associations with brain atrophy using antemortem MRI and postmortem histopathology.
- To develop and validate a model that accounts for multiple proteinopathies in predicting brain atrophy.
Main Methods:
- Utilized a dataset of 125 dementia patients with T1-weighted MRI and postmortem histopathology.
- Employed linear mixed-effects models to relate regional volumes to ratings of amyloid, tau, TDP-43, and alpha-synuclein.
- Contrasted a polypathologic model with primary diagnosis and protein-agnostic models, evaluating fits with log-likelihood and correlation analyses.
Main Results:
- The polypathologic model demonstrated superior fits in both training and testing datasets.
- Tau, TDP-43, and alpha-synuclein burden were inversely associated with regional brain volumes, while amyloid was not.
- Gliosis and neuronal loss explained residual variance and mediated the effects of tau, TDP-43, and alpha-synuclein on atrophy.
Conclusions:
- Regional brain atrophy reflects primary molecular pathology and co-occurring proteinopathies.
- Inflammatory responses, such as gliosis, may independently contribute to neurodegeneration.
- Antemortem biomarkers are essential for detecting mixed pathologies in neurodegenerative diseases.
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