To view a list of publications by Beau Ances, MD, PhD via PubMed, click the button below. A new tab will appear, and you will be taken to the PubMed site.
Publications by Topic
View a complete list of publications by topic by Beau Ances, MD, PhD via PubMed, click the links below. A new tab will appear, and you will be taken to the PubMed site. Under each topic are the most recent publications.
In total, 290 PWH (85% with undetectable VL) and 165 HIV-negative controls participated in neuroimaging and cognitive testing. BAG was measured using a Gaussian process regression model trained to predict age from diffusion magnetic resonance imaging in publicly available normative controls. To test for accelerated aging, BAG was modeled as an age × VL interaction. The relationship between BAG and global neuropsychological performance was examined. Other potential predictors of pathological aging were investigated in an exploratory analysis.
We compared structural (diffusion tensor imaging; DTI) and functional (cerebral blood flow; CBF) neuroimaging markers in PWH to frailty and cognitive performance. Virologically controlled PWH were dichotomized as either frail (≥3) or non-frail (<3) using the Fried criteria.
We used dual energy X-ray absorptiometry to quantify body composition parameters, and measured plasma proinflammatory/endocrine markers in 56 MLWH. We compared body composition to a publicly available dataset of 450 HIV-seronegative men of similar age. Within the MLWH group, body composition and plasma proinflammatory/endocrine markers were compared between individuals on INSTI and non-INSTI regimens, accounting for SES.
This cross-sectional study established the HIV Working Group within the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) consortium to pool and harmonize data from existing HIV neuroimaging studies
People living with HIV (PLWH) may be at higher risk for adverse outcomes indirectly associated with the severe acute respiratory syndrome coronavirus (SARS-CoV-2). When comparing responses to questionnaires administered when social distancing and quarantine guidelines were first implemented, we found that PLWH were more likely to have restricted access to medical care, increased financial stress, increased symptoms of anxiety and depression, and increased substance use compared to demographically-similar people without HIV.
This study examines relationships between anticholinergic (AC) medication burden and brain integrity in people living with HIV (PLWH) and people without HIV (HIV-).
Mindfulness-based stress reduction (MBSR) has potential to improve neurocognitive performance, psychosocial wellbeing, and quality of life, but empirical studies in this growing vulnerable population are lacking. In this trial, participants who are living with HIV infection and continue to experience behavioral and cognitive symptoms of HAND, are randomized to MBSR.
Virologically suppressed PLWH on combined antiretroviral therapy and HIV-negative (HIV-) controls completed TSPO-PET imaging using the radiotracer [C]PBR28. Because of tracer complexity and differing procedures used in previous studies, we employed an expansive methodological approach, using binding potential (BP) and standard uptake value ratio and multiple different reference regions to estimate [C]PBR28 binding.
Frailty is an important clinical concern for the aging population of people living with HIV (PLWH). The objective of this study was to identify the combination of risk features that distinguish frail from non-frail individuals. Machine learning analysis of highly dimensional risk features was performed on a clinical cohort of PLWH.
This study examined predictors of, and relationships between, objective (Medication Management Test-Revised (MMT-R)) and self-reported medication management ability in older (≥ 50 years) PLWH compared with HIV-uninfected (HIV-) individuals.
Deep learning algorithms of cerebral blood flow were used to classify cognitive impairment and frailty in people living with HIV (PLWH). Feature extraction techniques identified brain regions that were the strongest predictors.
A cross-sectional analysis of PLWH on combined antiretroviral therapy aged more than 18 years at a single institution.
Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls.
We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls.
Identification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death).
We sought to determine whether CSF NfL is more strongly associated with total gray matter, white matter, or white matter hyperintensity (WMH) volume, and to quantify the relative importance of brain tissue volume, age, and AD marker status (i.e., APOE genotype, brain amyloidosis, tauopathy, and cognitive status) in predicting CSF NfL.
Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer’s disease.
The factors that predispose to relapse in patients recovering with autoimmune encephalitis (AE) are largely unknown, complicating efforts to distinguish patients with resurgent symptoms who may benefit from additional immune-modulating therapies from those with other causes of impairment.
We assessed influence of obesity on neuroinflammation imaging that may mediate brain morphometric changes. Establishing the role of neuroinflammation in obesity will enhance understanding of this modifiable disorder as a risk factor for Alzheimer’s disease (AD) dementia.
Identified a global resting state functional connectivity (gFC) signature in mutation carriers (MC) from the Dominantly Inherited Alzheimer Network (DIAN). Assessed the gFC with regards to amyloid (A), tau (T), and neurodegeneration (N) biomarkers and estimated years to symptom onset (EYO).
The contributors to persistent cognitive impairment and hippocampal atrophy in leucine-rich glioma-inactivated 1 antibody encephalitis (LGI1) patients are unknown. We evaluated whether tau neuropathology measured with [18 F]flortaucipir PET neuroimaging associated with persistent cognitive impairment and hippocampal atrophy in four recovering LGI1 patients.
Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals.
As Alzheimer’s disease (AD) pathology accumulates, resting-state functional connectivity (rs-fc) within and between brain networks decreases, and fluctuations in cognitive performance known as intraindividual variability (IIV) increase. Here, we assessed the relationship between IIV and anticorrelations in rs-fc between the default mode network (DMN)-dorsal attention network (DAN) in cognitively normal older adults and symptomatic AD participants.
In this study we tested this model fitting linear versus quadratic trajectories and computed the timing of the inflection points vertexwise of cortical thickness and cortical diffusivity-a novel marker of cortical microstructure-changes in participants from the Dominantly Inherited Alzheimer Network.
We tested relationships between resting-state BOLD variability and biomarkers of amyloidosis, tauopathy, and neurodegeneration in a large, well-characterized sample of cognitively normal adults, using multivariate machine learning techniques.
The goal of the study was to understand how the COVID-19 pandemic has altered daily life (including residence, employment, and participation in adult disability day programs) and influenced the mood and behavior of adults with Down syndrome.
Adults with Down syndrome (DS) are predisposed to Alzheimer’s disease (AD) and reveal early amyloid beta (Aβ) pathology in the brain. Positron emission tomography (PET) provides an in vivo measure of Aβ throughout the AD continuum. Due to the high prevalence of AD in DS, there is need for longitudinal imaging studies of Aβ to better characterize the natural history of Aβ accumulation, which will aid in the staging of this population for clinical trials aimed at AD treatment and prevention.
This study aimed to investigate the cross-sectional diagnostic performance of plasma neurofilament light chain (Nf-L) and total-tau, individually and in combination among a cohort of DS adults.
The goal of the present study was to assess the presence of brain tau using [18F]AV-1451 positron emission tomography (PET) in DS and to assess the relationship of brain tau pathology to Aβ using Pittsburgh Compound B (PiB)-PET.
The National Institute on Aging in conjunction with the Alzheimer’s Association (NIA-AA) recently proposed a biological framework for defining the Alzheimer’s disease (AD) continuum. This new framework is based upon the key AD biomarkers (amyloid, tau, neurodegeneration, AT[N]) instead of clinical symptoms and represents the latest understanding that the pathological processes underlying AD begin decades before the manifestation of symptoms. By using these same biomarkers, individuals with Down syndrome (DS), who are genetically predisposed to developing AD, can also be placed more precisely along the AD continuum.
We describe the development of a multi-center, longitudinal study of biomarkers of AD in DS. The protocol includes longitudinal examination of clinical, cognitive, blood and cerebrospinal fluid-based biomarkers, magnetic resonance imaging and positron emission tomography measures (at 16-month intervals), as well as genetic modifiers of AD risk and progression.
Previously generated serum and plasma proteomic profiles were examined among adults with Down syndrome (DS) to determine whether these profiles could discriminate those with mild cognitive impairment (MCI-DS) and Alzheimer’s disease (DS-AD) from those cognitively stable (CS).
We adapted a widely used instrument, the Clinical Dementia Rating (CDR) Scale, which is a component of the Uniform Data Set used by all federally funded Alzheimer Disease Centers for use in adults with DS, and tested the instrument among 34 DS patients recruited from the community. The participants were assessed using two versions of the modified CDR-a caregiver questionnaire and an in-person interview involving both the caregiver and the DS adult. Assessment also included the Dementia Scale for Down Syndrome (DSDS) and the Raven’s Progressive Matrices to estimate IQ.
Publications by year
View a complete list of publications by year by Beau Ances, MD, PhD via PubMed, click the links below. A new tab will appear, and you will be taken to the PubMed site.