Publications by authors named "Ani Eloyan"

Introduction: Early-onset and late-onset Alzheimer's disease (EOAD and LOAD, respectively) have distinct clinical manifestations, with prior work based on small samples suggesting unique patterns of neurodegeneration. The current study performed a head-to-head comparison of cortical atrophy in EOAD and LOAD, using two large and well-characterized cohorts (LEADS and ADNI).

Methods: We analyzed brain structural magnetic resonance imaging (MRI) data acquired from 377 sporadic EOAD patients and 317 sporadicLOAD patients who were amyloid positive and had mild cognitive impairment (MCI) or mild dementia (i.

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Background: As literature suggests that Early-Onset Alzheimer's Disease (EOAD) and late-onset AD may differ in important ways, need exists for randomized clinical trials for treatments tailored to EOAD. Accurately measuring reliable cognitive change in individual patients with EOAD will have great value for these trials.

Objectives: The current study sought to characterize and validate 12-month reliable change from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) neuropsychological battery.

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Background: The clinical presentations of early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease are distinct, with EOAD having a more aggressive disease course with greater heterogeneity. Recent publications from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) described EOAD as predominantly amnestic, though this phenotypic description was based solely on clinical judgment. To better understand the phenotypic range of EOAD presentation, we applied a neuropsychological data-driven method to subtype the LEADS cohort.

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Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gaussian process model parameters with ultra-high-dimensional likelihoods. We propose a shift in viewpoint from whole to local data perspectives that is rooted in distributed model building and integrated estimation and inference. The framework's backbone is a computationally and statistically efficient integration procedure that simultaneously incorporates dependence within and between spatial resolutions in a recursively partitioned spatial domain.

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Introduction: Early-onset Alzheimer's disease (EOAD) manifests prior to the age of 65, and affects 4%-8% of patients with Alzheimer's disease (AD). The current analyses sought to examine longitudinal cognitive trajectories of participants with early-onset dementia.

Methods: Data from 307 cognitively normal (CN) volunteer participants and those with amyloid-positive EOAD or amyloid-negative cognitive impairment (EOnonAD) were compared.

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Introduction: Early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD) share similar amyloid etiology, but evidence from smaller-scale studies suggests that they manifest differently clinically. Current analyses sought to contrast the cognitive profiles of EOAD and LOAD.

Methods: Z-score cognitive-domain composites for 311 amyloid-positive sporadic EOAD and 314 amyloid-positive LOAD participants were calculated from baseline data from age-appropriate control cohorts.

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Background: Delirium occurs frequently in patients with stroke, but the role of preexisting neural substrates in delirium pathogenesis remains unclear. We sought to explore associations between acute and chronic neural substrates of delirium in patients with intracerebral hemorrhage (ICH).

Methods: Using data from a single-center ICH registry, we identified consecutive patients with acute nontraumatic ICH and available magnetic resonance imaging scans.

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Functional magnetic resonance imaging (fMRI) is a noninvasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform tasks or during periods of rest. In this paper, we propose a rigorous definition of task-evoked functional connectivity at the population level (ptFC).

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Background: Thalamic volume loss is known to be associated with clinical and cognitive disability in progressive multiple sclerosis (PMS).

Objective: To investigate the treatment effect of ibudilast on thalamic atrophy more than 96 weeks in the phase 2 trial in progressive(MS Secondary and Primary Progressive Ibudilast NeuroNEXT Trial in Multiple Sclerosis [SPRINT-MS]).

Methods: A total of 231 participants were randomized to either ibudilast ( = 114) or placebo ( = 117).

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Article Synopsis
  • Researchers developed an MRI-based signature to identify brain atrophy linked to sporadic early-onset Alzheimer's disease (EOAD) using two patient samples, one small (n=25) and another larger (n=211).
  • The study found consistent atrophy patterns in specific brain regions, like the caudal lateral temporal cortex and inferior parietal lobule, while the medial temporal lobe was relatively spared.
  • The EOAD-signature atrophy correlates with cognitive impairment severity, suggesting it's a reliable biomarker for Alzheimer’s-related neurodegeneration in clinical trials.
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  • The LEADS study focuses on understanding the genetic causes of early-onset Alzheimer's Disease (EOAD), specifically in individuals aged 40-64, by screening for known pathogenic variants.
  • *Whole exome sequencing of 299 participants found pathogenic variants in 1.35% of EOAD cases and 6.58% of early-onset non-Alzheimer's disease cases, but no gene showed a significant enrichment for rare functional variants.
  • *The findings suggest that LEADS may include new genetic variants related to early-onset cognitive impairment, making it an important resource for ongoing Alzheimer's research.*
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  • The study focuses on analyzing baseline amyloid-beta and tau-PET scans in participants with early-onset Alzheimer's disease (EOAD) to improve diagnostic understanding.* -
  • Out of the 321 cognitively impaired participants, 75.7% were classified as having EOAD based on amyloid-PET, with 95.1% of them also showing elevated tau-PET signals, particularly in younger and female subjects.* -
  • The findings highlight the significance of using these biomarkers for more accurate EOAD diagnoses and suggest potential implications for treatment strategies based on tau-PET levels.*
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  • The study examines the Rey Auditory Verbal Learning Test (RAVLT) as a tool for assessing memory impairment in early-onset Alzheimer's disease (EOAD), focusing on the effects of amyloid presence and diagnostic syndrome.
  • RAVLT recordings from 303 participants were analyzed, revealing that amyloid-positive individuals showed significant differences in memory performance compared to amyloid-negative individuals, including effects on raw scores and timing measures.
  • The findings suggest that RAVLT is sensitive to variations in memory impairment linked to amyloid and syndrome types in EOAD, highlighting the need for further research to understand the predictive capabilities of these memory scores.
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Introduction: We used sex and apolipoprotein E ε4 (APOE ε4) carrier status as predictors of pathologic burden in early-onset Alzheimer's disease (EOAD).

Methods: We included baseline data from 77 cognitively normal (CN), 230 EOAD, and 70 EO non-Alzheimer's disease (EOnonAD) participants from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS). We stratified each diagnostic group by males and females, then further subdivided each sex by APOE ε4 carrier status and compared imaging biomarkers in each stratification.

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Article Synopsis
  • The Longitudinal Early Onset Alzheimer's Disease Study (LEADS) aims to identify fluid biomarker characteristics specific to early-onset Alzheimer's disease (EOAD).
  • The study measured various cerebrospinal fluid (CSF) biomarkers in 165 participants, finding significant differences in certain biomarker levels between EOAD, cognitively normal individuals, and those with early-onset non-AD dementia.
  • The results highlight the correlation between biomarkers and cognitive performance, particularly within the EOAD group, providing valuable insights for future clinical trials targeting sporadic EOAD.
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Introduction: We compared white matter hyperintensities (WMHs) in early-onset Alzheimer's disease (EOAD) with cognitively normal (CN) and early-onset amyloid-negative cognitively impaired (EOnonAD) groups in the Longitudinal Early-Onset Alzheimer's Disease Study.

Methods: We investigated the role of increased WMH in cognition and amyloid and tau burden. We compared WMH burden of 205 EOAD, 68 EOnonAD, and 89 CN participants in lobar regions using t-tests and analyses of covariance.

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There is widespread interest in using deep learning to build prediction models for medical imaging data. These deep learning methods capture the local structure of the image and require no manual feature extraction. Despite the importance of modeling survival in the context of medical data analysis, research on deep learning methods for modeling the relationship of imaging and time-to-event data is still under-developed.

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Introduction: We examined neuropsychiatric symptoms (NPS) and psychotropic medication use in a large sample of individuals with early-onset Alzheimer's disease (EOAD; onset 40-64 years) at the midway point of data collection for the Longitudinal Early-onset Alzheimer's Disease Study (LEADS).

Methods: Baseline NPS (Neuropsychiatric Inventory - Questionnaire; Geriatric Depression Scale) and psychotropic medication use from 282 participants enrolled in LEADS were compared across diagnostic groups - amyloid-positive EOAD (n = 212) and amyloid negative early-onset non-Alzheimer's disease (EOnonAD; n = 70).

Results: Affective behaviors were the most common NPS in EOAD at similar frequencies to EOnonAD.

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Objective: The Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) seeks to provide comprehensive understanding of early-onset Alzheimer's disease (EOAD; onset <65 years), with the current study profiling baseline clinical, cognitive, biomarker, and genetic characteristics of the cohort nearing the data-collection mid-point.

Methods: Data from 371 LEADS participants were compared based on diagnostic group classification (cognitively normal [n = 89], amyloid-positive EOAD [n = 212], and amyloid-negative early-onset non-Alzheimer's disease [EOnonAD; n = 70]).

Results: Cognitive performance was worse for EOAD than other groups, and EOAD participants were apolipoprotein E (APOE) ε4 homozygotes at higher rates.

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Objective: Investigation of learning slopes in early-onset dementias has been limited. The current study aimed to highlight the sensitivity of learning slopes to discriminate disease severity in cognitively normal participants and those diagnosed with early-onset dementia with and without β-amyloid positivity METHOD: Data from 310 participants in the Longitudinal Early-Onset Alzheimer's Disease Study (aged 41 to 65) were used to calculate learning slope metrics. Learning slopes among diagnostic groups were compared, and the relationships of slopes with standard memory measures were determined RESULTS: Worse learning slopes were associated with more severe disease states, even after controlling for demographics, total learning, and cognitive severity.

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Current outcomes used to evaluate adrenomyeloneuropathy are limited by rater bias, not sensitive to preclinical changes, and require years to decades to detect disease progression. Quantitative outcomes are needed that detect meaningful change in a short time period over a broad range of disability. The study aim was to track sensorimotor outcomes in adults with adrenomyeloneuropathy and evaluate differences in progression between men and women.

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We consider an extension of Leo Breiman's thesis from "Statistical Modeling: The Two Cultures" to include a bifurcation of algorithmic modeling, focusing on parametric regressions, interpretable algorithms, and complex (possibly explainable) algorithms.

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Patients with early-onset Alzheimer's disease (EOAD) are commonly excluded from large-scale observational and therapeutic studies due to their young age, atypical presentation, or absence of pathogenic mutations. The goals of the Longitudinal EOAD Study (LEADS) are to (1) define the clinical, imaging, and fluid biomarker characteristics of EOAD; (2) develop sensitive cognitive and biomarker measures for future clinical and research use; and (3) establish a trial-ready network. LEADS will follow 400 amyloid beta (Aβ)-positive EOAD, 200 Aβ-negative EOnonAD that meet National Institute on Aging-Alzheimer's Association (NIA-AA) criteria for mild cognitive impairment (MCI) or AD dementia, and 100 age-matched controls.

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We propose a framework of principal manifolds to model high-dimensional data. This framework is based on Sobolev spaces and designed to model data of any intrinsic dimension. It includes principal component analysis and principal curve algorithm as special cases.

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Multiple sclerosis (MS) impacts balance and walking function, resulting in accidental falls. History of falls and clinical assessment are commonly used for fall prediction, yet these measures have limited predictive validity. Falls are multifactorial; consideration of disease-specific pathology may be critical for improving fall prediction in MS.

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