Publications by authors named "Maria Ly"

Background: Recent studies have focused on improving our understanding of gut microbiome dysbiosis and its impact on cognitive function. However, the relationship between gut microbiome composition, accelerated brain atrophy, and cognitive function has not yet been fully explored.

Methods: We recruited 292 participants from South Korean memory clinics to undergo brain magnetic resonance imaging, clinical assessments, and collected stool samples.

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Objective: This study investigated how obesity, BMI ≥ 30 kg/m, abdominal adiposity, and systemic inflammation relate to neuroinflammation using diffusion basis spectrum imaging.

Methods: We analyzed data from 98 cognitively normal midlife participants (mean age: 49.4 [SD 6.

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Introduction: Brain age is a machine learning-derived estimate that captures lower brain volume. Previous studies have found that brain age is significantly higher in mild cognitive impairment and Alzheimer's disease (AD) compared to healthy controls. Few studies have investigated changes in brain age longitudinally in MCI and AD.

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Background: Given the advent of large-scale neuroimaging data-driven endeavors for Alzheimer's disease, there is a burgeoning need for well-characterized neuroimaging databases of healthy individuals. With the rise of initiatives around the globe for the rapid and unrestricted sharing of data resources, there is now an abundance of open-source neuroimaging datasets available to the research community. However, there is not yet a systematic review that fully details the demographic information and modalities actually available in all open access neuroimaging databases around the globe.

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Introduction: Subjective cognitive decline (SCD) may represent the earliest preclinical stage of Alzheimer's Disease (AD) for some older adults. However, the underlying neurobiology of SCD is not completely understood. Since executive function may be affected earlier than memory function in the progression of AD, we aimed to characterize SCD symptoms in terms of fMRI brain activity during the computerized digit-symbol substitution task (DSST), an executive function task.

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Obesity and excess adiposity at midlife are risk factors for Alzheimer disease (AD). Visceral fat is known to be associated with insulin resistance and a pro-inflammatory state, the two mechanisms involved in AD pathology. We assessed the association of obesity, MRI-determined abdominal adipose tissue volumes, and insulin resistance with PET-determined amyloid and tau uptake in default mode network areas, and MRI-determined brain volume and cortical thickness in AD cortical signature in the cognitively normal midlife population.

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Obesity, depression and Alzheimer's disease (AD) are three major interrelated modern health conditions with complex relationships. Early-life depression may serve as a risk factor for AD, while late-life depression may be a prodrome of AD. Depression affects approximately 23% of obese individuals, and depression itself raises the risk of obesity by 37%.

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Article Synopsis
  • Researchers studied how aging in the brain looks different in people with multiple sclerosis (MS) compared to healthy people.
  • They used a special method with brain scans to see how brain age can show how bad the disease is and how much it might get worse over time.
  • They found that when a person's brain looks older than it is, it usually means they will have more problems with disability later on.
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Brain PET adds value in diagnosing neurodegenerative disorders, especially frontotemporal dementia (FTD) due to its syndromic presentation that overlaps with a variety of other neurodegenerative and psychiatric disorders. 18F-FDG-PET has improved sensitivity and specificity compared with structural MR imaging, with optimal diagnostic results achieved when both techniques are utilized. PET demonstrates superior sensitivity compared with SPECT for FTD diagnosis that is primarily a supplement to other imaging and clinical evaluations.

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Article Synopsis
  • A new brain age model based on machine learning was developed to detect amyloid and validate its accuracy using clinical data from 650 participants in South Korea, showing it can estimate brain age with a mean error of about 5.68 years.
  • The study found that an increased brain age correlates with higher amyloid levels and worse cognitive function, indicating its potential as a predictor of cognitive decline.
  • The model replicated earlier findings, successfully distinguishing between different stages of dementia and amyloid status, suggesting it could be useful for monitoring cognitive impairment in older adults.
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Exposure to tobacco smoke (TS) has been considered a risk factor for osteonecrosis of the femoral head (ONFH). Soluble epoxide hydrolase inhibitors (sEHIs) have been found to reduce inflammation and oxidative stress in a variety of pathologies. This study was designed to assess the effect of sEHI on the development of ONFH phenotypes induced by TS exposure in spontaneously hypertensive (SH) rats.

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Background: Subjective cognitive decline (SCD) may be an early manifestation of pre-clinical Alzheimer's disease. Elevated amyloid-β (Aβ) is a correlate of SCD symptoms in some individuals. The underlying neural correlates of SCD symptoms and their association with Aβ is unknown.

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Older adults with anxiety have lower gray matter brain volume-a component of accelerated aging. We have previously validated a machine learning model to predict brain age, an estimate of an individual's age based on voxel-wise gray matter images. We investigated associations between brain age and anxiety, depression, stress, and emotion regulation.

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Background: Obesity is related to quantitative neuroimaging abnormalities including reduced gray matter volumes and impaired white matter microstructural integrity, although the underlying mechanisms are not well understood.

Objective: 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.

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Chronic low back pain (CLBP) is a leading cause of disability and is associated with neurodegenerative changes in brain structure. These changes lead to impairments in cognitive function and are consistent with those seen in aging, suggesting an accelerated aging pattern. In this study we assessed this using machine-learning estimated brain age (BA) as a holistic metric of morphometric changes associated with aging.

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Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e.

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Background: Pathological processes contributing to Alzheimer's disease begin decades prior to the onset of clinical symptoms. There is significant variation in cognitive changes in the presence of pathology, functional connectivity may be a marker of compensation to amyloid; however, this is not well understood.

Methods: We recruited 64 cognitively normal older adults who underwent neuropsychological testing and biannual magnetic resonance imaging (MRI), amyloid imaging with Pittsburgh compound B (PiB)-PET, and glucose metabolism (FDG)-PET imaging for up to 6 years.

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Brain age prediction is a machine learning method that estimates an individual's chronological age from their neuroimaging scans. Brain age indicates whether an individual's brain appears "older" than age-matched healthy peers, suggesting that they may have experienced a higher cumulative exposure to brain insults or were more impacted by those pathological insults. However, contemporary brain age models include older participants with amyloid pathology in their training sets and thus may be confounded when studying Alzheimer's disease (AD).

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This review article provides a general overview on the various methodologies for quantifying brain structure on magnetic resonance images of the human brain. This overview is followed by examples of applications in Alzheimer dementia and mild cognitive impairment. Other examples will include traumatic brain injury and other neurodegenerative dementias.

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Subjective Cognitive Decline (SCD) is possibly one of the earliest detectable signs of dementia, but we do not know which mental processes lead to elevated concern. In this narrative review, we will summarize the previous literature on the biomarkers and functional neuroanatomy of SCD. In order to extend upon the prevailing theory of SCD, compensatory hyperactivation, we will introduce a new model: the breakdown of homeostasis in the prediction error minimization system.

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Purpose Of Review: Mood and anxiety disorders are very commonly experienced by older adults and are becoming a growing concern due to the rapidly aging global population. Recent advances in neuroimaging may help in improving outcomes in late-life mood and anxiety disorders. The elucidation of mechanisms contributing to late-life mental health disorders may ultimately lead to the identification of novel therapeutic interventions.

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Young-onset dementia is a broad category of diseases that affect adults before the age of 65, with devastating effects on individuals and families. Neuroimaging plays a clear and ever-expanding role in the workup of these diseases. MRI demonstrates classic patterns of atrophy that help to confirm the clinical diagnosis and may predict the underlying disease.

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A wealth of evidence has implicated the hippocampus and surrounding medial temporal lobe cortices in support of recognition memory. However, the roles of the various subfields of the hippocampus are poorly understood. In this study, we concurrently varied stimulus familiarization and repetition to engage different facets of recognition memory.

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A vital component of episodic memory is the ability to determine the temporal order of remembered events. Although it has been demonstrated that the hippocampus plays a crucial role in this ability, the details of its contributions are not yet fully understood. One proposed contribution of the hippocampus is the reduction of mnemonic interference through pattern separation.

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