Benzinger, T. | Search bioRxiv

bioRxiv2022

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study

BackgroundEstimates of "brain-predicted age" quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD), but has not been well explored in preclinical AD. Prior studies have typically modeled BAG with structural magnetic resonance imaging (MRI), but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. MethodsWe trained three models to predict age from FC, volumetric (Vol), or multimodal MRI (Vol+FC) in 390 control participants (18-89 years old). In independent samples of 144 older adult controls, 154 preclinical AD participants, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid, tau, and neurodegeneration, as well as a global cognitive composite. ResultsAll models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG and Vol+FC-BAG were marginally reduced in preclinical AD participants compared to controls. In CI participants only, elevated Vol-BAG and Vol+FC-BAG were associated with more advanced AD pathology and lower cognitive performance. ConclusionsBoth FC-BAG and Vol-BAG are elevated in CI participants. However, FC and volumetric MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to preclinical AD pathology, while Vol-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model captures these modality-specific patterns, and further, improves sensitivity to healthy age differences. FundingThis work was supported by the National Institutes of Health (P01-AG026276, P01-AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimers Association (SG-20-690363-DIAN).

neuroscience↗

bioRxiv2021

Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer's disease

Amyloid-beta (A{beta}) deposition is one of the hallmark pathologies in both sporadic Alzheimers disease (sAD) and autosomal dominant Alzheimers disease (ADAD), the latter of which is caused by mutations in genes involved in A{beta} processing. Despite A{beta} deposition being a centerpiece to both sAD and ADAD, some differences between these AD subtypes have been observed with respect to the spatial pattern of A{beta}. Previous work has shown that the spatial pattern of A{beta} in individuals spanning the sAD spectrum can be reproduced with high accuracy using an epidemic spreading model (ESM), which simulates the diffusion of A{beta} across neuronal connections and is constrained by individual rates of A{beta} production and clearance. However, it has not been investigated whether A{beta} deposition in the rarer ADAD can be modeled in the same way, and if so, how congruent the spreading patterns of A{beta} across sAD and ADAD are. We leverage the ESM as a data-driven approach to probe individual-level variation in the spreading patterns of A{beta} across three different large-scale imaging datasets (2 SAD, 1 ADAD). We applied the ESM separately to the Alzheimers Disease Neuroimaging initiative (N=737), the Open Access Series of Imaging Studies (N=510), and the Dominantly Inherited Alzheimers Network (N=249), the latter two of which were processed using an identical pipeline. We assessed inter- and intra-individual model performance in each dataset separately, and further identified the most likely epicenter of A{beta} spread for each individual. Using epicenters defined in previous work in sAD, the ESM provided moderate prediction of the regional pattern of A{beta} deposition across all three datasets. We further find that, while the most likely epicenter for most A{beta}-positive subjects overlaps with the default mode network, 13% of ADAD individuals were best characterized by a striatal origin of A{beta} spread. These subjects were also distinguished by being younger than ADAD subjects with a DMN A{beta} origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most ADAD patients express A{beta} spreading patters similar to those of sAD, but that there may be a subset of ADAD patients with a separate, striatal phenotype.

neuroscience↗