377 research outputs found

    Clinical Characteristics and Neuroanatomical Predictors of Acute Antidepressant Outcome for Patients with Comorbid Depression and Mild Cognitive Impairment

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    Background: Older adults presenting with both a depressive disorder (DEP) and cognitive impairment (CI) represent a unique, understudied population. The classification of cognitive impairment severity continues to be debated though it has recently been subtyped into late (LMCI) versus early (EMCI) stages. Previous studies have found associations between treatment outcome and both cortical thickness and white matter hyperintensities (WMH), though report inconsistent directionality and affected regions. In this study, we examined baseline clinical characteristics and neuroanatomical features as prognostic indicators for older adults with comorbid DEP and CI participating in an open antidepressant trial. EMCI is hypothesized to have greater cortical thickness and global cognition than LMCI. Antidepressant treatment remitters and responders are hypothesized to have greater cortical thickness and lower WMH burden than non-remitters and non-responders. Methods: Key inclusion criteria were diagnosis of major depression or dysthymic disorder with Hamilton Depression Rating Scale (HDRS) score \u3e14, and cognitive impairment defined by MMSE score โ‰ฅ21 and impaired performance on the WMS-R Logical Memory II test. Patients were classified as EMCI or LMCI based on the 1.5 SD cutoff on tests of verbal memory, and compared on baseline clinical, neuropsychological, and anatomical characteristics. All patients underwent a baseline MRI scan and received open antidepressant treatment for 8 weeks. Cortical thickness was extracted using an automated brain segmentation and reconstruction program (FreeSurfer). Vertex-wise analyses were conducted using general linear models to evaluate the relationships between cortical thickness and clinical variables. Results: 79 DEP-CI patients were recruited, of whom 39 met criteria for EMCI and 40 for LMCI. The mean age was 68.9 and mean HDRS was 23.0. LMCI patients had significantly worse global cognition and smaller right hippocampal volume compared to EMCI patients. EMCI patients had thicker right medial orbitofrontal cortex than LMCI. MRI indices of cerebrovascular disease did not differ between MCI subtypes. Remitters had greater deep WMH burden, left medial orbitofrontal gyrus thickness, and right superior frontal gyrus thickness than non-remitters. Greater HDRS depressive severity was positively correlated with right pars triangularis thickness. Stronger ADAS-Cog global cognitive performance was positively correlated with thickness in diffuse cortical areas. Conclusions: Cognitive and neuronal loss markers differed between EMCI and LMCI among patients with DEP-CI, with LMCI being more likely to have the clinical and neuronal loss markers known to be associated with Alzheimerโ€™s disease. Samples of DEP-CI exhibit unique patterns of cortical thickness and WMHs compared to their non-CI peers. Cortical thickness may serve as predictor of treatment remission and relates to both depressive severity and global cognition

    Effect of IV alteplase on the ischemic brain lesion at 24-48 hours after ischemic stroke

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    OBJECTIVE: To determine whether alteplase alters the development of ischemic lesions on brain imaging after stroke. METHODS: The Third International Stroke Trial (IST-3) was a randomized controlled trial of IV alteplase for ischemic stroke. We assessed CT or brain MRI at baseline (pretreatment) and 24 to 48 hours posttreatment for acute lesion visibility, extent, and swelling, masked to all other data. We analyzed associations between treatment allocation, change in brain tissue appearances between baseline and follow-up imaging, and 6-month functional outcome in IST-3. We performed a meta-analysis of randomized trials of alteplase vs control with pre- and postrandomization imaging. RESULTS: Of 3,035 patients recruited in IST-3, 2,916 had baseline and follow-up brain imaging. Progression in either lesion extent or swelling independently predicted poorer 6-month outcome (adjusted odds ratio [OR] = 0.92, 95% confidence interval [CI] 0.88-0.96, p < 0.001; OR = 0.73, 95% CI 0.66-0.79, p < 0.001, respectively). Patients allocated alteplase were less likely than controls to develop increased lesion visibility at follow-up (OR = 0.77, 95% CI 0.67-0.89, p < 0.001), but there was no evidence that alteplase reduced progression of lesion extent or swelling. In meta-analysis of 6 trials including IST-3 (n = 4,757), allocation to alteplase was associated with a reduction in ischemic lesion extent on follow-up imaging (OR = 0.85, 95% CI 0.76-0.95, p = 0.004). CONCLUSION: Alteplase was associated with reduced short-term progression in lesion visibility. In meta-analysis, alteplase reduced lesion extent. These findings may indicate that alteplase improves functional outcome by reducing tissue damage. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that IV alteplase impedes the progression of ischemic brain lesions on imaging after stroke

    REGISTRATION AND SEGMENTATION OF BRAIN MR IMAGES FROM ELDERLY INDIVIDUALS

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    Quantitative analysis of the MRI structural and functional images is a fundamental component in the assessment of brain anatomical abnormalities, in mapping functional activation onto human anatomy, in longitudinal evaluation of disease progression, and in computer-assisted neurosurgery or surgical planning. Image registration and segmentation is central in analyzing structural and functional MR brain images. However, due to increased variability in brain morphology and age-related atrophy, traditional methods for image registration and segmentation are not suitable for analyzing MR brain images from elderly individuals. The overall goal of this dissertation is to develop algorithms to improve the registration and segmentation accuracy in the geriatric population. The specific aims of this work includes 1) to implement a fully deformable registration pipeline to allow a higher degree of spatial deformation and produce more accurate deformation field, 2) to propose and validate an optimum template selection method for atlas-based segmentation, 3) to propose and validate a multi-template strategy for image normalization, which characterizes brain structural variations in the elderly, 4) to develop an automated segmentation and localization method to access white matter integrity (WMH) in the elderly population, and finally 5) to study the default-mode network (DMN) connectivity and white matter hyperintensity in late-life depression (LLD) with the developed registration and segmentation methods. Through a series of experiments, we have shown that the deformable registration pipeline and the template selection strategies lead to improved accuracy in the brain MR image registration and segmentation, and the automated WMH segmentation and localization method provides more specific and more accurate information about volume and spatial distribution of WMH than traditional visual grading methods. Using the developed methods, our clinical study provides evidence for altered DMN connectivity in LLD. The correlation between WMH volume and DMN connectivity emphasizes the role of vascular changes in LLD's etiopathogenesis

    Automated segmentation and characterisation of white matter hyperintensities

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    Neuroimaging has enabled the observation of damage to the white matter that occurs frequently in elderly population and is depicted as hyperintensities in specific magnetic resonance images. Since the pathophysiology underlying the existence of these signal abnormalities and the association with clinical risk factors and outcome is still investigated, a robust and accurate quantification and characterisation of these observations is necessary. In this thesis, I developed a data-driven split and merge model selection framework that results in the joint modelling of normal appearing and outlier observations in a hierarchical Gaussian mixture model. The resulting model can then be used to segment white matter hyperintensities (WMH) in a post-processing step. The validity of the method in terms of robustness to data quality, acquisition protocol and preprocessing and its comparison to the state of the art is evaluated in both simulated and clinical settings. To further characterise the lesions, a subject-specific coordinate frame that divides the WM region according to the relative distance between the ventricular surface and the cortical sheet and to the lobar location is introduced. This coordinate frame is used for the comparison of lesion distributions in a population of twin pairs and for the prediction and standardisation of visual rating scales. Lastly the cross-sectional method is extended into a longitudinal framework, in which a Gaussian Mixture model built on an average image is used to constrain the representation of the individual time points. The method is validated through a purpose-build longitudinal lesion simulator and applied to the investigation of the relationship between APOE genetic status and lesion load progression

    Diseases of the Brain, Head and Neck, Spine 2020โ€“2023

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    This open access book offers an essential overview of brain, head and neck, and spine imaging. Over the last few years, there have been considerable advances in this area, driven by both clinical and technological developments. Written by leading international experts and teachers, the chapters are disease-oriented and cover all relevant imaging modalities, with a focus on magnetic resonance imaging and computed tomography. The book also includes a synopsis of pediatric imaging. IDKD books are rewritten (not merely updated) every four years, which means they offer a comprehensive review of the state-of-the-art in imaging. The book is clearly structured and features learning objectives, abstracts, subheadings, tables and take-home points, supported by design elements to help readers navigate the text. It will particularly appeal to general radiologists, radiology residents, and interventional radiologists who want to update their diagnostic expertise, as well as clinicians from other specialties who are interested in imaging for their patient care

    Brain Iron Accumulation in Atypical Parkinsonian Syndromes: in vivo MRI Evidences for Distinctive Patterns

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    Recent data suggest mechanistic links among perturbed iron homeostasis, oxidative stress, and misfolded protein aggregation in neurodegenerative diseases. Iron overload and toxicity toward dopaminergic neurons have been established as playing a role in the pathogenesis of Parkinson's disease (PD). Brain iron accumulation has also been documented in atypical parkinsonian syndromes (APS), mainly comprising multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). Iron-sensitive magnetic resonance imaging (MRI) has been applied to identify iron-related signal changes for the diagnosis and differentiation of these disorders. Topographic patterns of widespread iron deposition in deep brain nuclei have been described as differing between patients with MSA and PSP and those with PD. A disease-specific increase of iron occurs in the brain regions mainly affected by underlying disease pathologies. However, whether iron changes are a primary pathogenic factor or an epiphenomenon of neuronal degeneration has not been fully elucidated. Moreover, the clinical implications of iron-related pathology in APS remain unclear. In this review study, we collected data from qualitative and quantitative MRI studies on brain iron accumulation in APS to identify disease-related patterns and the potential role of iron-sensitive MRI

    ๋…ธ์ธ์—์„œ ์กฐ์ ˆ ์ค‘์ธ ๊ณ ํ˜ˆ์••์ด ๋Œ€๋‡Œ๋ฐฑ์งˆ๊ณ ๊ฐ•๋„์‹ ํ˜ธ์™€ ์ธ์ง€๊ธฐ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋‡Œ์ธ์ง€๊ณผํ•™๊ณผ, 2021.8. ์ด์˜์ง€.์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ : ๊ณ ํ˜ˆ์••์€ ์ธ์ง€์žฅ์• ์˜ ์œ„ํ—˜์ธ์ž์ด๋‹ค. ๋˜ํ•œ, ๊ณ ํ˜ˆ์••์€ ๋Œ€๋‡Œ๋ฐฑ์งˆ๊ณ ๊ฐ•๋„์‹ ํ˜ธ (WMH)์˜ ์œ„ํ—˜์ธ์ž์ด๊ณ  WMH๋Š” ์ธ์ง€์žฅ์• ์˜ ์œ„ํ—˜์ธ์ž์ด์ง€๋งŒ, WMH์˜ ๊ณ ํ˜ˆ์••๊ณผ ์ธ์ง€๊ธฐ๋Šฅ๊ฐ„์˜ ๋งค๊ฐœํšจ๊ณผ๋Š” ์•„์ง ์ถฉ๋ถ„ํžˆ ๊ฒ€์ฆ๋œ ์ ์ด ์—†๋‹ค. ๊ณ ํ˜ˆ์••ํ™˜์ž์—์„œ WMH๊ฐ€ ์ธ์ง€์žฅ์• ๋ฅผ ๋งค๊ฐœํ•œ๋‹ค๋ฉด, WMH์˜ ์กด์žฌ๋‚˜ ํฌ๊ธฐ๋Š” ์ธ์ง€์žฅ์• ๋ฅผ ํŒ๋‹จํ•  ์ˆ˜ ์žˆ๋Š” ์ค‘์š”ํ•œ ์ง€ํ‘œ๊ฐ€ ๋  ๊ฒƒ์ด๋‹ค. ํ•˜์ง€๋งŒ ๊ฑด๊ฐ•ํ•œ ๋…ธ์ธ์˜ WMH ํ™•๋ฅ ์ง€๋„ (WMHPM)๋‚˜ WMHPM์„ ํ™œ์šฉํ•œ ์ธ์ง€์žฅ์• ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์•„์ง๊นŒ์ง€ ์ง„ํ–‰๋œ๋ฐ” ์—†๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ๊ฐœ์˜ ๊ฐ€์„ค์„ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ•œ๋‹ค. 1) ๋น„์น˜๋งค ๋…ธ์ธ์—์„œ์˜ WMH๊ฐ€ ์กฐ์ ˆ๋œ ๊ณ ํ˜ˆ์••์ด ์ธ์ง€๊ธฐ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์ •ํ•˜๋Š”๊ฐ€? 2) WMHPM๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ถ”์ •๋œ WMH ๋‚˜์ด๊ฐ€ ์กฐ์ ˆ๋œ ๊ณ ํ˜ˆ์•• ๋…ธ์ธ์˜ ํ˜„์žฌ์˜ ์ธ์ง€์žฅ์• ์™€ ๋ฏธ๋ž˜์˜ ์ธ์ง€์ €ํ•˜๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€? ์—ฐ๊ตฌ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฃผ์š” ์ •์‹ ํ•™์  ๋˜๋Š” ์‹ ๊ฒฝํ•™์  ์งˆํ™˜์ด ์—†๋Š” 890๋ช…์˜ ์ง€์—ญ์‚ฌํšŒ ๊ฑฐ์ฃผ 60์„ธ ์ด์ƒ์˜ ๋น„์น˜๋งค ๋…ธ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ ์ค‘ 368๋ช…์ด 2๋…„ํ›„ ์ถ”์ ๊ฒ€์‚ฌ๋ฅผ ํ•˜์˜€๋‹ค. WMHPM์€ 300๋ช…์˜ ์ฃผ์š” ์ •์‹ ํ•™์  ๋˜๋Š” ์‹ ๊ฒฝํ•™์  ์งˆํ™˜์ด ์—†๊ณ  ์ธ์ง€๊ธฐ๋Šฅ์ด ์ •์ƒ์ธ ๊ฑด๊ฐ•ํ•œ 60์„ธ ์ด์ƒ ์ง€์—ญ์‚ฌํšŒ ๊ฑฐ์ฃผ๋…ธ์ธ์œผ๋กœ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๋Œ€์ƒ์ž์˜ ํ˜ˆ์••์€ ์ขŒ์œ„ ์ž์„ธ์—์„œ ์ž๋™ํ˜ˆ์••์ธก์ •๊ธฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์„ธ ๋ฒˆ ์ธก์ •๊ฐ’์˜ ํ‰๊ท ๊ฐ’์„ ์ด์šฉํ•˜์˜€๋‹ค. ์กฐ์ ˆ๋œ ๊ณ ํ˜ˆ์•• (cHT)์€ ๊ณ ํ˜ˆ์••๋ณ‘๋ ฅ์ด ์žˆ๊ณ  ์ธก์ •๋œ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••์ด 140 mm Hg ๋ฏธ๋งŒ์ด๋ฉด์„œ ์ด์™„๊ธฐ ํ˜ˆ์••์€ 90 mm Hg ๋ฏธ๋งŒ์ธ ์ž๋กœ ์ •์˜ํ•˜์˜€๋‹ค. ๋‚ฎ์€ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์•• (LSBP)๋Š” ์ธก์ •๋œ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••์ด 110 mm Hg ์ดํ•˜์ธ ์ž๋กœ ์ •์˜ํ•˜์˜€๊ณ , ๋‚ฎ์€ ์ด์™„๊ธฐ ํ˜ˆ์•• (LDBP)๋Š” ์ธก์ •๋œ ์ด์™„๊ธฐ ํ˜ˆ์••์ด 60 mm Hg ์ดํ•˜์ธ ์ž๋กœ ์ •์˜ํ•˜์˜€๋‹ค. ์ธ์ง€๊ธฐ๋Šฅ์€ CERAD-K ์‹ ๊ฒฝ์‹ฌ๋ฆฌ๊ฒ€์‚ฌ, ์ „๋‘์—ฝ๊ธฐ๋Šฅํ‰๊ฐ€, ์ˆซ์ž์™ธ์šฐ๊ธฐ ๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. CERAD-TS ์ ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. WMH ์ถ”์ถœ์€ 3.0T ์•ก์ฒด๊ฐ์‡ ์—ญ์ „ํšŒ๋ณต ์ž๊ธฐ๊ณต๋ช…์˜์ƒ์„ ์ด์šฉํ•˜์˜€๋‹ค. ๊ฐœ์ธ์˜ WMH ์˜์ƒ๊ณผ 5๊ฐœ์˜ ์—ฐ๋ น๋Œ€์˜ WMHPM ์‚ฌ์ด์˜ ์ตœ์ € ํŽธ์ฐจ ๊ฐ’์„ ๊ณ„์‚ฐํ•˜์—ฌ ๊ฐœ์ธ์˜ WMH ์—ฐ๋ น์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. WMH์—ฐ๋ น์ด ์‹ค์ œ์—ฐ๋ น๊ณผ ๊ฐ™์„ ์‹œ normal WMH ๋‚˜์ด, ๋†’์„ ์‹œ older WMH ๋‚˜์ด, ๋‚ฎ์„ ์‹œ younger WMH ๋‚˜์ด๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. WMH๊ฐ€ ๊ณ ํ˜ˆ์••์ด ์ธ์ง€๊ธฐ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์ •ํ•˜๋Š”์ง€ Baron๊ณผ Kenny ๋ฐฉ๋ฒ•์œผ๋กœ ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋กœ์ง€์Šคํ‹ฑํšŒ๊ท€๋ถ„์„์„ ์ด์šฉํ•˜์—ฌ ๊ณ ํ˜ˆ์••๊ณผ WMH๋‚˜์ด๊ฐ€ ์ธ์ง€์žฅ์• ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ: cHT (p < .001), LSBP (p = .018)์™€ ์ƒํ˜ธ์ž‘์šฉ (p < .001)์€ WMH์šฉ์ ์˜ ์ปค์ง๊ณผ ๊ด€๋ จ์ด ์žˆ๋‹ค. WMH์šฉ์ ์€ ์ธ์ง€๊ธฐ๋Šฅ์˜ ๋‚ฎ์€ ์ˆ˜ํ–‰์ ์ˆ˜์™€ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค (๋ชจ๋“  ์ธ์ง€๊ฒ€์‚ฌ: p < .001). WMH๋Š” ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••์ด 1 mm Hg ๊ฐ์†Œํ•  ๋•Œ ์ธ์ง€๊ธฐ๋Šฅ์ ์ˆ˜๊ฐ€ 0.016 ~ 0.030 ํฌ์ธํŠธ ๊ฐ์†Œํ•˜๋Š” ๊ด€๊ณ„์— ๋งค๊ฐœํ•˜์˜€๋‹ค. Younger ํ˜น์€ normal WMH ๋‚˜์ด์— ๋น„ํ•ด older WMH ๋‚˜์ด ๊ตฐ์ด ๋ชจ๋“  ์ธ์ง€๊ธฐ๋Šฅ ๊ฒ€์‚ฌ์—์„œ ๋‚ฎ์€ ์ˆ˜ํ–‰๋Šฅ๋ ฅ์„ ๋ณด์˜€๋‹ค. (๋ชจ๋“  ์ธ์ง€๊ฒ€์‚ฌ: p < .001; DST: p = .002 for DST). cHT (p = .002), LSBP (p = .003), LDBP (p = .013), ์ƒํ˜ธ์ž‘์šฉ (p = .010)์ด older WMH์™€ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค. cHT๊ตฐ ์ค‘ older WMH ๋‚˜์ด์ธ ์‚ฌ๋žŒ๋“ค์€ ์ •์ƒํ˜ˆ์••์„ ๊ฐ€์ง„ normal ํ˜น์€ younger WMH ๋‚˜์ด์ธ ์‚ฌ๋žŒ๋“ค์— ๋น„ํ•ด 2๋…„ํ›„ ์ธ์ง€๊ธฐ๋Šฅ์ €ํ•˜๊ฐ€ ๋น ๋ฅด๊ณ  ๊ฒฝ๋„์ธ์ง€์žฅ์• ๊ฐ€ ๋ฐœ๋ณ‘ํ•  ํ™•๋ฅ ์ด 8๋ฐฐ ๋†’์•˜๋‹ค. ๊ฒฐ๋ก : cHT ํ™˜์ž์—์„œ LSBP๋Š” WMH ์šฉ์ ์„ ์ฆ๊ฐ€์‹œํ‚ด์œผ๋กœ์จ ์ธ์ง€๊ธฐ๋Šฅ์ €ํ•˜์™€ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค. ๊ฑด๊ฐ•ํ•œ ๋…ธ์ธ์˜ WMHPM์„ ์‚ฌ์šฉํ•˜๋ฉด ์ž„์ƒํ™˜๊ฒฝ์—์„œ WMH ์—ฐ๋ น์„ ์ถ”์ •ํ•˜์—ฌ ์ธ์ง€์ €ํ•˜ ์œ„ํ—˜์ด ์žˆ๋Š” ๊ณ ํ˜ˆ์••ํ™˜์ž๋ฅผ ๊ตฌ๋ณ„ํ•ด๋‚ผ ์ˆ˜ ์žˆ๋‹ค.Background and Objectives: Hypertension, even when controlled, is associated with cognitive impairment. Although hypertension is also associated with white matter hyperintensity (WMH) and WMH is associated with cognitive impairments, the mediation role of WMH in the association of hypertension with cognitive impairments has never been directly investigated. If WMH shows to mediate the cognitive impairments in participants with controlled hypertension, presence or volume of WMH may be a good biomarker of those who are at risk of cognitive impairments. However, neither the WMH probability map (WMHPM) of healthy older adults nor the predictive validity of WMH age estimated by WMHPM for cognitive impairments has been investigated. This study examined two main hypotheses; 1) Does cerebral WMH mediate the effect of controlled hypertension on cognitive function in nondemented older adults?; 2) Does WMH age estimated using the WMHPM predict current cognitive impairment and future cognitive decline in older adults with controlled hypertension? Methods: We recruited 890 community-dwelling nondemented Koreans aged 60 years or older; 505 from the participants of the Korean Longitudinal Study on Cognitive Aging and Dementia and 385 from the visitors to the Dementia Clinic of the Seoul National University Bundang Hospital. Among them, 368 participants completed 2-year follow-up assessment. We constructed WMHPM using 300 community-dwelling cognitively and physically healthy Koreans aged 60 years or older; 228 from the KLOSCAD and 72 from the Gwangju Alzheimerโ€™s & Related Dementias Study. We defined controlled hypertension (cHT) as having history of hypertension, however, office-measured systolic blood pressure (SBP) less than 140 mm Hg and office-measured diastolic blood pressure (DBP) less than 90 mm Hg; low systolic blood pressure (LSBP) as having office-measured SBP of 110 mm Hg or below; low diastolic blood pressure (LDBP) as having office-measured DBP of 60 mm Hg or below. We measured blood pressure three times in a sitting position using an automated blood pressure monitoring device. We evaluated cognitive performance using the CERAD-K Neuropsychological Assessment Battery, Frontal Assessment Battery and Digit Span Test. We calculated Consortium to Establish a Registry for Alzheimer Disease neuropsychological battery total score (CERAD-TS). We segmented and quantified WMH from 3.0 Tesla fluid attenuated inversion recovery magnetic resonance images. We estimated WMH age using the WMHPM by calculating the lowest deviance between individualโ€™s WMH and each of the 5 age-banded WMHPMs. We classified the participants into three WMH age group; normal WMH age group whose WMH age is equal to their chronological age, younger WMH age group whose WMH age is younger than their chronological age, and the older WMH age group whose WMH age is older than their chronological age. We analyzed the mediation role of WMH on the effect of controlled hypertension on cognitive function using Baron and Kenny method of mediation analysis. We examined the effect of controlled hypertension and WMH age on the risk of incident mild cognitive impairment (MCI) using logistic regression analysis. Results: cHT (p < .001), LSBP (p = .018), and their interaction (p < .001) were associated with WMH volume, and WMH volume was associated with negative cognitive performance (p < .001 for all cognitive performance). WMH mediated the association of LSBP on the performance of neuropsychological tests with 1 mm Hg decrease of SBP affect 0.016 to 0.030 points decrease in various cognitive tests. Compared to the younger or normal WMH age groups, the older WMH age group performed worse in all neuropsychological tests (p = .002 for DST; p < .001 for other tests). cHT (p = .002), LSBP (p = .003), LDBP (p = .013) and their interaction (p = .010) were associated with older WMH age. The cHT with the older WMH age group showed the faster cognitive decline and 8 times higher risk of incident MCI after two years than normotensive participants with the normal or younger WMH age. Conclusion: In the cHT patients, LSBP was associated with worse cognitive performance by increasing WMH volume. If we use WMHPM of healthy older adults, we can identify older adults with controlled hypertension who are at risk of cognitive decline by estimating their WMH age in clinical settings.1. Introduction 1 1.1. Study Background 1 1.2. Purpose of Research 3 2. Methods 4 2.1. Study population 4 2.1.1. Hypothesis 1. Does cerebral WMH mediate the effect of controlled hypertension on cognitive function in nondemented older adults 4 2.1.2. Hypothesis 2. Does WMH age estimated using the WMH probability map (WMHPM) predict current cognitive impairment and future cognitive decline in older adults with controlled hypertension 4 2.2. Research ethics 5 2.3. Assessments 6 2.4. Diagnoses 6 2.5. Acquisition of brain MRI 7 2.6. Processing of brain MRI 7 2.7. Segmentation of WMH. 8 2.8. Visual rating of WMH 8 2.9. Construction of WMHPM 9 2.10. Estimation of WMH age 9 2.11. Statistical analyses 10 3. Results 12 3.1. Hypothesis 1. Does cerebral WMH mediate the effect of controlled hypertension on cognitive function in nondemented older adults 12 3.2. Hypothesis 2. Does WMH age estimated using the WMH probability map (WMHPM) predict current cognitive impairment and future cognitive decline in older adults with controlled hypertension 14 4. Discussions 17 5. Conclusions 24 Bibliography 49 ๊ตญ๋ฌธ์ดˆ๋ก 57๋ฐ•

    Quantitative MRI in the diagnosis and monitoring of human prion diseases

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    This thesis examines the application of cerebral diffusion weighted imaging (DWI) and short echo time (TE) proton magnetic resonance spectroscopy (1H-MRS) for the evaluation of patients with different forms of human prion disease. Human prion diseases are progressive, uniformly fatal neurodegenerative diseases and as treatments are developed, early diagnosis is essential. Particularly important is the diagnosis of presymptomatic cases and prediction of disease onset in these individuals. In this thesis I demonstrate that MRI measures of Apparent Diffusion Coefficient (ADC) at low and high b-value and short TE 1H-MRS are potential neuroimaging biomarkers of prion disease activity. I show that ex-vivo MRI at high field provides important insights into the microstructural changes underlying the sensitivity of some of these quantitative MRI methods to prion disease pathology. The findings presented here exemplify the potential of quantitative MRI in both increasing our understanding of the pathophysiology of prion diseases and in providing neuroimaging biomarkers which will be of great importance for the future evaluation of treatment efficacy
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