Biology subjects

Cohen, A. D.

Publications and source records attributed to Cohen, A. D..

3 recordsLinked to original sources

bioRxiv2026

Evaluation of harmonization methods to mitigate assay and cohort effects in plasma p-tau217

INTRODUCTIONThe growing number of assay platforms measuring blood-based biomarkers (BBMs) for Alzheimers disease (AD) has introduced challenges in interpretability and comparability across assays. Differences across studies also limit comparability of data. To address these challenges, a systematic evaluation of harmonization methods is needed to support BBM data integration within or across studies. METHODSTwo multisite studies, Alzheimers Disease Neuroimaging Initiative (ADNI, n = 219) and Human Connectome Project (HCP, n = 111), were used to evaluate harmonization methods for mitigating assay and cohort effects in plasma p-tau217 measurements. Methods includes various normalization, regression, and standardization approaches, including the recently developed CentiMarker. Assay effects were evaluated using repeated-measures data across assay platforms within each cohort, whereas cohort effects were assessed using pooled ADNI and HCP data. Harmonization performance was evaluated using distributional statistics and downstream modeling of p-tau217. RESULTSQuantile normalization and quantile mapping methods were most effective for mitigating assay effects, whereas conditional quantile mapping performed best for pooled multi-cohort data. These methods also preserved biological variability. In contrast, simple means adjustment and reference-based z-score standardization were least effective for mitigating assay effects, while simple means adjustment, z-score standardization, and quantile normalization were least effective for mitigating cohort effects. CentiMarker had minimal impact on assay or cohort effects. DISCUSSIONBased on our evaluation, we recommend (conditional) quantile mapping for p-tau217 studies integrating data across multiple assays or cohorts. In contrast, we caution against using CentiMarker and z-score-based methods, as they limit comparability and do not effectively mitigate technical variability.

neuroscience↗

bioRxiv2026

Latent brain state dynamics predict early amyloid accumulation and cognitive impairment

Amyloid-{beta} (A{beta}) accumulation is a continuous process central to pathological aging that begins decades before cognitive impairment emerges. While subthreshold A{beta} levels have been linked to future decline in cognitive control, the neural mechanisms connecting this early accumulation to its neurocognitive impact are poorly understood. Brain circuit dynamics, which are essential for cognitive function, may offer a sensitive lens into these initial pathological changes. Here, we tested whether brain state dynamics could serve as sensitive markers for cognitive impairment at an early stage of A{beta} burden. Using the Bayesian Switching Dynamic System (BSDS) model, we identified 4 distinct latent brain states from high-temporal-resolution (800 ms) fMRI data acquired from 116 older adults, including 72 cognitively normal (CN) individuals and 44 with mild cognitive impairment (MCI), during an N-back working-memory task. Adopting a dimensional approach, we examined how latent brain state dynamics relate to early amyloid burden, cognitive performance, and clinical symptoms. While A{beta} levels failed to differentiate clinical groups or predict clinical symptoms and task performance, the dynamics of latent brain states proved highly sensitive to both early A{beta} accumulation and cognition. Canonical correlation analysis revealed a significant relationship between brain state dynamics and early A{beta} burden. Furthermore, the temporal properties of brain states were significantly predictive of working memory performance in CN individuals, a relationship that was selectively disrupted in the MCI group. The features of brain dynamics can also successfully predict cognitive impairment. Our findings establish brain state dynamics as sensitive neural markers of initial A{beta} accumulation and early cognitive impairment, offering a new framework for developing predictive models to identify individuals at risk for future cognitive decline.

neuroscience↗

bioRxiv2024

Human IL-6 fosters long-term engraftment of patient derived disease-driving myeloma cells in immunodeficient mice

Multiple myeloma is a largely incurable and life-threatening malignancy of antibody-secreting plasma cells. An effective and widely available animal model that recapitulates human myeloma and related plasma cell disorders is lacking. We show that busulfan-conditioned hIL-6 transgenic NSG mice (NSG+hIL6) reliably support the engraftment of malignant and pre-malignant human plasma cells including from patients diagnosed with monoclonal gammopathy of undetermined significance, pre- and post-relapse myeloma, plasma cell leukemia, and AL amyloidosis. Consistent with human disease, NSG+hIL6 mice engrafted with patient-derived myeloma cells, developed serum M spikes, and a majority developed anemia, hypercalcemia, and/or bone lesions. Single cell RNA sequencing showed non-malignant and malignant cell engraftment, the latter expressing a wide array of mRNAs associated with myeloma cell survival and proliferation. Myeloma engrafted mice given CAR T-cells targeting plasma cells or bortezomib experienced reduced tumor burden. Our results establish NSG+hIL6 mice as an effective patient derived xenograft model for study and preclinical drug development of multiple myeloma and related plasma cell disorders.

cancer biology↗