Biology subjects

ADNI,

Publications and source records attributed to ADNI,.

7 recordsLinked to original sources

bioRxiv2025

Biological versus Technical Reliability of Epigenetic Clocks and Implications for Disease Prognosis and Intervention Response

DNA methylation-based aging biomarkers, or epigenetic clocks, are increasingly used to estimate biological age and predict health outcomes. Their translational utility, however, depends not only on predictive accuracy but also on reliability, the ability to provide consistent results across technical replicates and repeated biological measures. Here, we leveraged the TranslAGE platform to comprehensively evaluate the technical and biological reliability of 18 Epigenetic clocks, including chronological predictors, mortality predictors, pace-of-aging measures, reliable variants, and newer explainable clocks. Technical reliability was quantified across four independent datasets. For standard replicate assays on EPIC and 450K arrays, nearly all clocks achieved excellent technical reproducibility. However, some clocks showed dramatic drops in technical reliability based on differences in slide position and DNA extraction protocol. PC-based clocks, especially PCGrimAge and SystemsAge remained technically reliable in all cases. In contrast, biological reliability, measured across repeated samples collected within hours, before and after meals, under acute stress, across environmental exposures, and over days, was markedly lower, with most clocks showing only moderate stability. PCGrimAge was the only clock with good ICC > 0.75 for biological reliability. Importantly, technical reproducibility did not predict biological reliability; clocks that were technically robust often proved biologically unreliable. We further demonstrated that reliability directly constrains downstream applications. Clocks with higher ICCs produced more stable prognostic associations with cognitive decline and more consistent responsiveness to a vegan diet intervention, whereas unreliable clocks yielded highly variable or spurious effects. Together, these findings reveal that technical reliability is not enough: biological reliability remains a critical limitation for many DNA methylation clocks that constrains their utility, and our work provides a roadmap for prioritizing next-generation clocks most suited for clinical translation.

bioinformatics↗

bioRxiv2022

Denoising Approach Affects Diagnostic Differences in Brain Connectivity across the Alzheimer's Disease Continuum

Graph theory provides a promising technique to investigate Alzheimers disease (AD)-related alterations in brain connectivity. However, discrepancies exist in the reported disruptions that occur to network topology across the AD continuum, which may be attributed to differences in the denoising approach used in fMRI processing to remove the effect of non-neuronal sources from signal. The current study aimed to determine if diagnostic differences in graph metrics were dependent on nuisance regression strategy. Sixty cognitively normal (CN), 60 MCI, and 40 AD matched for age, sex, and motion, were selected from the ADNI database for analysis. Resting state images were preprocessed using AFNI (v21.2.04) and 16 nuisance regression approaches were employed, which included the unique combination of four nuisance regressors (derivatives of the realignment parameters, motion censoring [euclidean norm > 0.3mm], outlier censoring [outlier fraction > .10], bandpass filtering [0.01 - 0.1 Hz]). Graph metrics representing network segregation (clustering coefficient, local efficiency, modularity), network integration (largest connected component, path length, local efficiency), and small-worldness (clustering coefficient/path length) were calculated. The results showed a significant interaction between diagnosis and nuisance approach on path length, such that diagnostic differences were only evident when motion derivatives and censoring of both motion and outlier volumes were applied. Further, regardless of the denoising approach, AD patients exhibited less segregated networks and lower small-worldness than CN and MCI. Finally, independent of diagnosis, denoising strategy significantly affected the magnitude of nearly all metrics (except local efficiency), such that models including bandpass filtering had higher graph metrics than those without. These findings suggest the relative robustness of network segregation and small-worldness properties to denoising strategy. However, caution should be taken when interpreting path length findings across studies, as subtle variations in regression approach may account for discrepancies. Continued efforts should be taken towards harmonizing preprocessing pipelines across studies to aid replication efforts and build consensus towards understanding the mechanisms underlying pathological aging.

neuroscience↗

bioRxiv2022

Functional Redundancy of the Posterior Hippocampi, but not Anterior Hippocampi or Left Frontal Cortex, is Disrupted in Pathological Brain Aging

As prevalence rates of Alzheimers disease (AD), the leading cause of dementia, are projected to more than double by 2050, emphasis has been placed on early intervention strategies that target resilience mechanisms to delay or prevent the onset of clinical symptoms. Several neural mechanisms underlying brain resilience to AD have been proposed, including redundant neural connections between the posterior hippocampi (HC) and all other brain regions, and global functional connectivity of the left frontal cortex (LFC). It remains unknown, however, if regional redundancy of the HC and LFC underscores neural resilience in the presence of AD pathologies. From the ADNI database, 363 cognitively normal older adults (CN) (N = 220; 36% A{beta}+) and patients with Mild Cognitive Impairment (MCI) (N = 143; 51% A{beta}+) were utilized. Regional redundancy was calculated from resting state fMRI data using a graph theoretical approach by summing the direct and indirect paths (path lengths=1-4) between each ROI and its 262 functional connections. The results showed that A{beta}-status significantly disrupted posterior HC, but not anterior HC or LFC, redundancy. A{beta}- groups showed higher redundancy of the bilateral posterior HC than A{beta}+. In regard to redundancy-cognition relationships, higher posterior HC redundancy was related to better episodic memory performance, an effect which was primarily driven by the A{beta}- group. Despite the positive relationship between posterior HC redundancy and cognition, we did not find compelling evidence that redundancy of the posterior HC serves in a resilience manner, as posterior HC redundancy did not moderate the potentially deleterious relationship between A{beta} deposition and cognition. No relationships were found between anterior HC or LFC redundancy and cognitive performance. Together, these findings suggest that redundancy of the LFC does not underpin its role in resilience and that posterior HC redundancy may capture disruptions to network connectivity that occur as a result of A{beta} deposition.

neuroscience↗

bioRxiv2021

DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans. In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans and 64476 registrations from several publicly available datasets and applied seven linear registration tools to them. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200). In terms of agreement with a manual rater, our automated QC method was able to achieve 89%accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%). The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets.

neuroscience↗

bioRxiv2020

Predicting future regional tau accumulation in asymptomatic and early Alzheimer's disease

The earliest stages of Alzheimers disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future tau accumulation, translating predictive information from deep phenotyping cohorts at early stages of AD to cognitively normal individuals. In particular, we use machine learning to quantify interactions between key pathological markers ({beta}-amyloid, medial temporal atrophy, tau and APOE 4) at early and asymptomatic stages of AD. We next derive a predictive index that stratifies individuals based on future pathological tau accumulation, highlighting two critical features for optimal clinical trial design. First, future tau accumulation provides a better outcome measure compared to changes in cognition. Second, stratification based on multimodal data compared to {beta}-amyloid alone reduces the sample size required to detect a clinically meaningful change in tau accumulation. Further, we extend our machine learning approach to derive individualised trajectories of future pathological tau accumulation in early AD patients and accurately predict regional future rate of tau accumulation in an independent sample of cognitively unimpaired individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design at asymptomatic and early stages of AD. One Sentence SummaryOur machine learning approach combines baseline multimodal data to make individualised predictions of future pathological tau accumulation at prodromal and asymptomatic stages of Alzheimers disease with high accuracy and regional specificity.

neuroscience↗

bioRxiv2019

Aging-related hypometabolism in the anterior cingulate cortex mediates the relationship between age vs. executive function but not vs. memory in cognitively intact elders

Elucidating the pathophysiology of cognitive decline during aging in those without overt neurodegeneration is a prerequisite to improved diagnosis, prevention, and treatment of cognitive aging. We showed previously the anterior cingulate cortex (ACC) and adjacent medial prefrontal cortex (mPFC) are centers for aging-related metabolic dysfunction that correlate with age-associated cognitive decline in healthy volunteers. Here, we examine using the extensive and well-characterized ADNI dataset the hypothesis that ACC metabolism in healthy seniors functions as a mediator in the relationship between age and executive function. In agreement with our previous findings, highly significant correlations arose between age and metabolism; metabolism and fluency; and age and fluency. These observations motivated a mediation model in which ACC metabolism mediates the relationship between age and fluency score. Significance of the indirect effect was examined by Sobel testing and bootstrapping. In these cognitively intact seniors with \"typical aging,\" there was neither a correlation between age and memory scores nor between ACC metabolism and memory scores. The metabolism in a control region, the primary motor cortex, showed no correlation with age or ACC metabolism. These findings motivate further research into aging-related ACC dysfunction to prevent, diagnose, and treat the decline in executive function associated with aging in the absence of known neurodegenerative diseases.\n\nSIGNIFICANCE STATEMENTThe pathophysiology of aging-related cognitive decline remains unclear but the anterior cingulate cortex (ACC), a major component of the anterior human attention system, shows decreasing metabolism that correlates with declining executive function despite otherwise intact cognition. Here, the relationships between ACC metabolism, age, executive function, and memory were examined using the large, public, ADNI database. Earlier findings were confirmed. In addition, ACC metabolism was found a mediator between age and executive function. In contrast, no correlation arose between memory and age or between memory and ACC metabolism. No correlations surfaced when using the metabolism of the right primary motor cortex as a control region. Development of preventive medicine and novel treatments will require elucidation of aging-related ACC pathophysiology requiring further research.

neuroscience↗

bioRxiv2018

Generalization of the minimum covariance determinant algorithm for categorical and mixed data types

The minimum covariance determinant (MCD) algorithm is one of the most common techniques to detect anomalous or outlying observations. The MCD algorithm depends on two features of multivariate data: the determinant of a matrix (i.e., geometric mean of the eigenvalues) and Mahalanobis distances (MD). While the MCD algorithm is commonly used, and has many extensions, the MCD is limited to analyses of quantitative data and more specifically data assumed to be continuous. One reason why the MCD does not extend to other data types such as categorical or ordinal data is because there is not a well-defined MD for data types other than continuous data. To address the lack of MCD-like techniques for categorical or mixed data we present a generalization of the MCD. To do so, we rely on a multivariate technique called correspondence analysis (CA). Through CA we can define MD via singular vectors and also compute the determinant from CAs eigenvalues. Here we define and illustrate a generalized MCD on categorical data and then show how our generalized MCD extends beyond categorical data to accommodate mixed data types (e.g., categorical, ordinal, and continuous). We illustrate this generalized MCD on data from two large scale projects: the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Alzheimers Disease Neuroimaging Initiative (ADNI), with genetics (categorical), clinical instruments and surveys (categorical or ordinal), and neuroimaging (continuous) data. We also make R code and toy data available in order to illustrate our generalized MCD.

bioinformatics↗