Biology subjects

Andreescu, C.

Publications and source records attributed to Andreescu, C..

3 recordsLinked to original sources

bioRxiv2026

Network Localization of Anxiety as an Early Manifestation of Alzheimer's Disease-related Neurodegeneration

Objective Anxiety is a prominent neuropsychiatric symptom in preclinical Alzheimer's disease (AD), but the neural circuitry linking anxiety and AD remains uncharacterized. We assessed whether anxiety in preclinical AD is independently associated with AD-related neurodegeneration and localizes to a common anxiety network. Methods In cognitively unimpaired (CU) older adults with 3T MRI and amyloid-beta PET (N=1,775), we examined whether an established AD-related atrophy network is associated with anxiety (STAI-6). Then, using Atrophy Network Mapping (ANM), which maps individual cortical atrophy patterns onto a normative functional connectome (N=1000), we derived an anxiety network. We tested its 1) spatial convergence with the AD network and with a causal anxiety network derived from lesion and neuromodulation data, 2) specificity relative to depression, 3) generalizability in samples from independent cohorts with AD and Parkinson's disease (total n=878), and 4) association with longitudinal outcomes. Results In CU older adults, greater AD-network atrophy was associated with higher anxiety, independent of depression, cognition, cortical atrophy, and amyloid burden. Anxiety localized to a network closely resembling the AD network (r=0.74, p=0.04) and the causal anxiety network (r=0.72, p=0.02). This convergence extended across the AD continuum and to the Parkinson's disease cohort and was specific to anxiety relative to depression. Greater anxiety-network atrophy was associated with cognitive decline over 4.6 years. Conclusion Anxiety localizes to a brain circuit resembling the AD-related atrophy network, is consistent across the AD continuum and may extend beyond AD. These findings support anxiety as an independent marker of early AD-related neurodegeneration.

neuroscience↗

bioRxiv2026

Evaluating Clustering and Imputation Approaches for Multivariate Longitudinal Clinical Data Using a Real-World eCDF-Copula Resampling Framework

The rapid growth of technologies such as wearable devices and high-throughput sequencing has generated unprecedented volumes of longitudinal data, often with multiple correlated features. This trend highlights the need for robust clustering methods tailored to multi-feature longitudinal datasets. Although previous reviews have benchmarked clustering methods in this context, most evaluations relied heavily on simulated and overly simplified datasets, which substantially limit their interpretability and applicability to real-world data. Missing data is also ubiquitous in longitudinal data, yet understudied in the context of clustering. To address these gaps, we systematically evaluate clustering methods across three distinct types of real-world longitudinal datasets: (1) a high-dimensional, low-timepoint metagenomic study, (2) a low-dimensional, high-timepoint multi-site repeated-measure study, and (3) a high-dimensional, high-timepoint repeated-measure study. We further investigate how missing data and different imputation strategies influence clustering performance. In addition, we introduce a novel resampling framework based on the empirical cumulative distribution function (eCDF) and a copula. This framework enables us to simulate multi-feature longitudinal data that preserves the correlation structure of real datasets, while also allowing for controlled variations such as sample size. Our simulation framework naturally accommodates missing data, thereby providing a flexible tool for benchmarking and comparing clustering methods in more realistic longitudinal settings.

bioinformatics↗

bioRxiv2026

CREB: Consistent Reference External Batch Harmonization

AO_SCPLOWBSTRACTC_SCPLOWMachine learning models using functional magnetic resonance imaging (fMRI) are becoming increasingly popular - these models often rely on training data from multiple, large, and publicly available datasets. It is often necessary to harmonize these data across sites and sequences, and algorithms like ComBat are frequently applied to correct for these differences. This has been shown to improve model performance and generalizability. However, applying traditional ComBat necessitates harmonizing all data (train, validation, test, and other unseen external test sets) simultaneously, which leads to potential data leakage and limits application to new unseen data. We introduce Consistent Reference External Batch (CREB) harmonization, a novel extension of ComBat that learns the prior distribution of site effects exclusively from a designated training set. This learned prior serves as a consistent, easily deployable reference point that employs the empirical Bayes framework to update the site effect for any new, external unseen data. This approach enables training, validation, and test sets to be harmonized separately, thereby preventing data leakage, ensuring the integrity of downstream analyses, and application to new unseen data. CREB is different from traditional ComBat in which each sites prior distribution is estimated at once, but this cannot be applied to unseen data or data from sites not included in the original set of data. We tested CREB with train data from 2846 participants (ages 18-97 years) across 9 different studies and test data from 1113 participants (ages 18-88 years) from 3 studies. We evaluated the performance of harmonization with functional connectivity and gray matter volume. We show that CREB can effectively harmonize the test data to the train data, and have comparable performance to ComBat. CREB is able to conduct this harmonization in a two-step procedure that prevents leakage and is deployable to new unseen data. Finally, we tested whether CREB could similarly preserve biological variance (e.g., whether age associations were preserved after harmonization). We found that CREB, like ComBat could preserve age associations with both functional connectivity and gray matter volume measures. CREB provides an easily deployable, robust harmonization method to standardize data to a common reference distribution, making it uniquely suitable for training generalizable machine learning models.

bioengineering↗