Significant local aging of the brain maps proximity to Alzheimer’s-related cognitive impairment
Poster No:
132
Submission Type:
Abstract Submission
Authors:
Phoebe Imms1, Owen M Vega1, Tamara Jafar1, Kien Nguyen1, Sophie Martin2, Haoqing Wang1, Siyu Chen1, Nikhil Chaudhari1, Nahian Chowdhury1, Andrei Irimia1
Institutions:
1University of Southern California, Los Angeles, CA, 2University College London, London, London
First Author:
Co-Author(s):
Introduction:
Brain aging involves complex neuroanatomic deteriorations that are not well captured by basic morphometrics. Deep learning estimation of brain age (BA) offers a powerful alternative by detecting metrics that are unapparent to the human eye without extensive manual pre-processing (1). The discrepancy between BA and chronological age (CA), termed the age gap (AG), represents cumulative neuroanatomical aging (2). Local AG, the difference between BA and CA in specific brain regions, offers regionally interpretable insights into neuroanatomical aging.
Methods:
We validate local age gaps' (AGs) capacity to spatiotemporally map Alzheimer's disease (AD) neurodegeneration. Participants are 1,320 persons with ('converters', mean age = 75.72 ± 7.63 yrs) and without ('non-converters', mean age = 75.61 ± 7.51 yrs) CI in their future, and those already with CI at study inception ('pre-converters', mean age = 75.68 ± 7.56 yrs). Subjects are age and sex matched, with 440 subjects, 42.0% female, in each group. We compare group-level differences in global AG to differences in brain volume (BV). Additionally, we compare local AG across short-term (ST, 0.5 to 2.5 yrs), mid-term (MT, 2.5 to 6 yrs), and long-term (> 6 yrs) converters. Correlations between local AGs and time to conversion are compared to correlations between local brain volumes and time to conversion.
Results:
Global AG (but not BV) was significantly lower in non-converters compared to short-term (ST) converters (mean difference = -2.08 yrs, p = 0.02) and to mid-term MT converters (mean difference = -2.14 yrs, p = 0.02). Mean local AGs were significantly lower in long-term (LT) converters (mean ± SD = 0.05 ± 0.95) compared to MT (mean = 1.65 ± 1.02, p < 0.001) and ST converters (mean = 1.58 ± 0.99, p < 0.001). Cortical maps revealed that local AGs recapitulate known neuropathology propagation patterns throughout the cortex (Fig 1). Deviant aging begins in the temporal lobe (e.g., left pole = 1.9 yrs, right pole = 2.3 yrs) and orbitofrontal cortex (e.g., left subcallosal gyri= 2.02 yrs, right subcallosal gyri = 2.20 yrs) in LT converters and propagates to the anterior cingulate cortex, and to the lateral temporal and frontal lobes in MT and ST converters. Finally, almost all local AGs (but only few BVs) significantly correlate with time to conversion, with strongest correlations observed in the right putamen (r = -0.24, p < 0.001) and right orbital and insular regions.
Conclusions:
Our findings challenge the assumption that T1w MRIs cannot reveal temporal dynamics of neurodegeneration. Local AG maps align with amyloid and tau propagation proposed by Braak staging (3, 4) and positron emission tomography (PET) imaging (5), underscoring their utility in identifying individuals at risk and predicting the timing of cognitive decline. This study advances efforts to identify converters for early intervention and to estimate time to conversion using non-invasive neuroimaging biomarkers.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Other Methods 2
Keywords:
Aging
Machine Learning
MRI
Other - brain age
1|2Indicates the priority used for review
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(2) Yin, C., Imms, P., Cheng, M., Amgalan, A., Chowdhury, N. F., Massett, R. J., . . . Bogdan, P. (2023). Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment. Proceedings of the National Academy of Sciences, 120(2), e2214634120.
(3) Braak, H., & Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta neuropathologica, 82(4), 239-259.
(4) Braak, H., & Braak, E. (1996). Development of Alzheimer-related neurofibrillary changes in the neocortex inversely recapitulates cortical myelogenesis. Acta neuropathologica, 92, 197-201.
(5) Sepulcre, J., Grothe, M. J., d’Oleire Uquillas, F., Ortiz-Terán, L., Diez, I., Yang, H.-S., . . . El-Fakhri, G. (2018). Neurogenetic contributions to amyloid beta and tau spreading in the human cortex. Nature Medicine, 24(12), 1910-1918. https://pmc.ncbi.nlm.nih.gov/articles/PMC6518398/pdf/nihms-1027665.pdf