Biology subjects

Saykin, A.

Publications and source records attributed to Saykin, A..

6 recordsLinked to original sources

bioRxiv2026

Divergent amyloid trajectories distinguish normal aging from Alzheimer's disease progression

Aging is the greatest risk factor for Alzheimer's disease (AD), yet how and when AD-related pathological progression diverges from aging remains poorly understood. This distinction is particularly difficult at early stages, when clinically and biomarker-defined populations contain individuals following fundamentally different trajectories. Here, we model AD progression as a deviation from aging using longitudinal amyloid PET and a self-supervised trajectory-learning framework. In ADNI, the learned trajectory revealed a shared early path that bifurcated into an aging branch and an AD-related branch. The two branches showed distinct profiles in amyloid burden, cognitive decline, risk of progression to AD dementia, and genetic risk. Unseen participants from the independent NACC cohort were projected onto the trajectory without retraining, further reproducing key branch-specific biological and genetic patterns. Together, the bifurcating trajectory provides a biologically grounded framework for resolving early-stage heterogeneity and enabling risk stratification beyond binary amyloid status.

neuroscience↗

bioRxiv2026

Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning

Accurate in vivo prediction of neuropathology is critical for advancing diagnosis and treatment of Alzheimers disease and related dementias (ADRDs). As many individuals with ADRDs have mixed pathologies ({beta}-amyloid, pathologic tau, cerebrovascular disease, vascular brain injury, pathologic TDP-43, hippocampal sclerosis, Lewy bodies), there is interest in determining how accurately we can infer these pathologic changes from clinical data, biofluid assays (e.g., CSF), and neuroimaging. Here we evaluated automated machine learning models trained on data curated by the AD Sequencing Project Phenotype Harmonization Consortium (N=7,894 individuals), to predict 26 autopsy-confirmed neuropathological outcomes. Predictors included in vivo clinical and cognitive composite scores, brain measures from 3D structural MRI and diffusion tensor imaging, image-derived measures of white matter hyperintensities (WMH), and CSF biomarkers. Predictive models were trained using ensemble learning with stratified cross-validation. We assessed performance using Spearmans rank correlation and Matthews correlation coefficient, to accommodate co-occurring pathologic changes. The added value of neuroimaging and CSF versus clinical features alone was quantified. Braak stage was among the most consistently predicted outcomes. CSF biomarkers best predicted {beta}-amyloid and tau pathology, but diffusion MRI metrics best captured vascular brain injury and white matter injury, and outperformed clinical and cognitive measures and anatomical MRI in predicting Lewy body disease. Anatomical measures from structural MRI outperformed standard clinical assessments in assessing neurodegeneration and hippocampal sclerosis, and WMH complemented cognitive measures in predicting TDP-43 pathology. These results establish a baseline for comparing modalities for inferring neuropathology.

neuroscience↗

bioRxiv2026

Individualized Mapping of Functional Brain Networks in Older Adulthood

The functional network architecture of the aging brain undergoes significant systematic and idiosyncratic changes. Emergent individualized network mapping approaches may yield better or more sensitive explanatory insight about age-related neural and behavioral variability, although most applications have focused on young adults. In the current study, we tested the validity and impact of mapping individual-specific topography in two fMRI datasets comprising 112 young (18-35 years) and 176 older adults (60-92 years). Older adults had more idiosyncratic network topography than young adults. Individualized maps from resting-state fMRI improved network homogeneity and fidelity to task fMRI activations, while also exhibiting intra-individual reliability and inter-individual discriminability over a 2-year interval. Last, traditional group-averaged (vs. individualized) network mapping had a moderate-to-large impact on individual-level estimates of network segregation, a widely-studied measure of functional brain aging. Therefore, individualized network mapping captures important heterogeneity in older adulthood and may yield more precise characterization of neurocognitive aging.

neuroscience↗

bioRxiv2018

Neuropathological correlates and genetic architecture of microglial activation in elderly human brain

Microglia, the resident immune cells of the brain, have important roles in brain health. However, little is known about the regulation and consequences of microglial activation in the aging human brain. We assessed the effect of microglial activation in the aging human brain by calculating the proportion of activated microglia (PAM), based on morphologically defined stages of activation in four regions sampled postmortem from up to 225 elderly individuals. We found that cortical and not subcortical PAM measures were strongly associated with {beta}-amyloid, tau-related neuropathology, and rates of cognitive decline. Effect sizes for PAM measures are substantial, comparable to that of APOE {varepsilon}4, the strongest genetic risk factor for Alzheimers disease. Mediation modeling suggests that PAM accelerates accumulation of tau pathology leading to cognitive decline, supporting an upstream role for microglial activation in Alzheimers disease. Genome-wide analyses identified a common variant (rs2997325) influencing cortical PAM that also affected in vivo microglial activation measured by positron emission tomography using [11C]-PBR28 in an independent cohort. Finally, we identify overlaps of PAMs genetic architecture with those of Alzheimers disease, educational attainment, and several other traits.

genomics↗

bioRxiv2018

Altered Bile Acid Profile Associates with Cognitive Impairment in Alzheimer’s Disease – An Emerging Role for Gut Microbiome

IntroductionIncreasing evidence suggests a role for the gut microbiome in central nervous system disorders and specific role for the gut-brain axis in neurodegeneration. Bile acids (BA), products of cholesterol metabolism and clearance, are produced in the liver and are further metabolized by gut bacteria. They have major regulatory and signaling functions and seem dysregulated in Alzheimer disease (AD).\n\nMethodsSerum levels of 15 primary and secondary BAs and their conjugated forms were measured in 1,464 subjects including 370 cognitively normal older adults (CN), 284 with early mild cognitive impairment (MCI), 505 with late MCI, and 305 AD cases enrolled in the AD Neuroimaging Initiative. We assessed associations of BA profiles including selected ratios with diagnosis, cognition, and AD-related genetic variants, adjusting for cofounders and multiple testing.\n\nResultsIn AD compared to CN, we observed significantly lower serum concentrations of a primary BA (cholic acid CA) and increased levels of the bacterially produced, secondary BA, deoxycholic acid (DCA), and its glycine and taurine conjugated forms. An increased ratio of DCA:CA, which reflects 7-dehydroxylation of CA by gut bacteria, strongly associated with cognitive decline, a finding replicated in serum and brain samples in the Rush Religious Orders and Memory and Aging Project. Several genetic variants in immune response related genes implicated in AD showed associations with BA profiles.\n\nConclusionWe report for the first time an association between altered BA profile, genetic variants implicated in AD and cognitive changes in disease using a large multicenter study. These findings warrant further investigation of gut dysbiosis and possible role of gut liver brain axis in the pathogenesis of AD.

neuroscience↗

bioRxiv2017

Do Candidate Genes Affect the Brain’s White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans

AbstractSusceptibility genes for psychiatric and neurological disorders - including APOE, BDNF, CLU,CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, and ZNF804A - have been reported to affect white matter (WM) microstructure in the healthy human brain, as assessed through diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain only a small fraction of the overall variance and are challenging to detect reliably in single cohort studies. To date, few studies have evaluated the reproducibility of these results. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects (CEU ancestry N=4,458) from 11 cohorts worldwide to evaluate effects of 15 candidate SNPs by examining their associations with WM microstructure. Additive association tests were conducted for each SNP. We used several meta-analytic and mega-analytic designs, and we evaluated regions of interest at multiple granularity levels. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Even so, in this very large sample, no significant associations remained after multiple-testing correction for the 15 SNPs investigated. Suggestive associations (1.3x10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta-and mega-analyses revealed similar findings. Regardless of the approach, the previously reported candidate SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure.

neuroscience↗