| 1. |
- Velickaite, Vilma
(author)
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Analysis of regional atrophy on brain imaging compared with cognitive function in the elderly and in patients with dementia – cross-sectional and longitudinal evaluation
- 2019
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Doctoral thesis (other academic/artistic)abstract
- During aging, brain changes are not homogeneous throughout the entire brain, but are related to changes in the morphology of neurons, as well as to changes in the tissue density, and are specific to each region of the brain. Dementia is a broad category of brain disorders with a set of symptoms including memory, visual-spatial and language problems. Most types of dementia are slowly progressing, and by the time the person shows signs of the disorder, processes in the brain are already advanced. Dementia reduces not only the person’s ability to perform everyday activities, it also increases mortality rates significantly. Because of the increasing incidence of dementia, possible prevention and treatment of dementia as early as possible are essential.The aim of the PhD project is to compare a quantitative and qualitative image analysis of regional cerebral atrophy with cognitive function in the elderly persons.In paper I, 58 persons participated (84–88 years old) from the ULSAM (Uppsala Longitudinal Study of Adult Men) cohort. They underwent CT of the brain, cognitive testing and LP. This study showed that AD biomarkers seem to be less informative in subjects with an advanced age.In papers II–IV, the cohort included subjects from the PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study.Paper II showed that at age 75, gender and education are confounders for MTA rating. Subjects with abnormal right MTA, but normal MMSE scores had developed worse MMSE scores 5 years later.Paper III showed that automated rating of MTA could be used in clinical practice to support the radiological evaluation. Automated rating of PA and F-GCA should be tested in future studies.In paper IV, we found a mild age-associated decrease in regional brain volumes in this healthy cohort with well-preserved cognitive and executive functions.In conclusion, the included studies in this thesis compare regional atrophy grades in the brain on CT and MRI and clinical data and provide knowledge that may be used in future investigations that aim to detect dementia in its early stages.
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| 2. |
- Brusini, Irene
(author)
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Methods for the analysis and characterization of brain morphology from MRI images
- 2022
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Doctoral thesis (other academic/artistic)abstract
- Brain magnetic resonance imaging (MRI) is an imaging modality that produces detailed images of the brain without using any ionizing radiation. From a structural MRI scan, it is possible to extract morphological properties of different brain regions, such as their volume and shape. These measures can both allow a better understanding of how the brain changes due to multiple factors (e.g., environmental and pathological) and contribute to the identification of new imaging biomarkers of neurological and psychiatric diseases. The overall goal of the present thesis is to advance the knowledge on how brain MRI image processing can be effectively used to analyze and characterize brain structure.The first two works presented in this thesis are animal studies that primarily aim to use MRI data for analyzing differences between groups of interest. In Paper I, MRI scans from wild and domestic rabbits were processed to identify structural brain differences between these two groups. Domestication was found to significantly reshape brain structure in terms of both regional gray matter volume and white matter integrity. In Paper II, rat brain MRI scans were used to train a brain age prediction model. This model was then tested on both controls and a group of rats that underwent long-term environmental enrichment and dietary restriction. This healthy lifestyle intervention was shown to significantly affect the predicted brain age trajectories by slowing the rats' aging process compared to controls. Furthermore, brain age predicted on young adult rats was found to have a significant effect on survival.Papers III to V are human studies that propose deep learning-based methods for segmenting brain structures that can be severely affected by neurodegeneration. In particular, Papers III and IV focus on U-Net-based 2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS) patients. In both studies, good segmentation accuracy was obtained and a significant correlation was found between CC area and the patient's level of cognitive and physical disability. Additionally, in Paper IV, shape analysis of the segmented CC revealed a significant association between disability and both CC thickness and bending angle. Conversely, in Paper V, a novel method for automatic segmentation of the hippocampus is proposed, which consists of embedding a statistical shape prior as context information into a U-Net-based framework. The inclusion of shape information was shown to significantly improve segmentation accuracy when testing the method on a new unseen cohort (i.e., different from the one used for training). Furthermore, good performance was observed across three different diagnostic groups (healthy controls, subjects with mild cognitive impairment and Alzheimer's patients) that were characterized by different levels of hippocampal atrophy.In summary, the studies presented in this thesis support the great value of MRI image analysis for the advancement of neuroscientific knowledge, and their contribution is mostly two-fold. First, by applying well-established processing methods on datasets that had not yet been explored in the literature, it was possible to characterize specific brain changes and disentangle relevant problems of a clinical or biological nature. Second, a technical contribution is provided by modifying and extending already-existing brain image processing methods to achieve good performance on new datasets.
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| 3. |
- Brusini, Irene, et al.
(author)
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MRI-derived brain age as a biomarker of ageing in rats : validation using a healthy lifestyle intervention
- 2022
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In: Neurobiology of Aging. - : Elsevier BV. - 0197-4580 .- 1558-1497. ; 109, s. 204-215
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Journal article (peer-reviewed)abstract
- The difference between brain age predicted from MRI and chronological age (the so-called BrainAGE) has been proposed as an ageing biomarker. We analyse its cross-species potential by testing it on rats undergoing an ageing modulation intervention. Our rat brain age prediction model combined Gaussian process regression with a classifier and achieved a mean absolute error (MAE) of 4.87 weeks using cross-validation on a longitudinal dataset of 31 normal ageing rats. It was then tested on two groups of 24 rats (MAE = 9.89 weeks, correlation coefficient = 0.86): controls vs. a group under long-term environmental enrichment and dietary restriction (EEDR). Using a linear mixed-effects model, BrainAGE was found to increase more slowly with chronological age in EEDR rats ( p = 0 . 015 for the interaction term). Cox re-gression showed that older BrainAGE at 5 months was associated with higher mortality risk ( p = 0 . 03 ). Our findings suggest that lifestyle-related prevention approaches may help to slow down brain ageing in rodents and the potential of BrainAGE as a predictor of age-related health outcomes.
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| 4. |
- Poulakis, Konstantinos, et al.
(author)
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Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression
- 2020
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In: Aging. - : IMPACT JOURNALS LLC. - 1945-4589. ; 12:13, s. 12622-12647
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Journal article (peer-reviewed)abstract
- Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer's disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that simultaneously: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows for study confounding effects, 5) provides cluster visualization, 6) measures clustering uncertainty. We used amyloid-beta positive AD and negative healthy subjects, three longitudinal structural magnetic resonance imaging scans (cortical thickness and subcortical volume) over two years. We found three distinct longitudinal AD brain atrophy patterns: one typical diffuse pattern (n=34, 47.2%), and two atypical patterns: minimal atrophy (n=23 31.9%) and hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.
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