1,028 research outputs found

    Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

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    Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

    APOE ?4 status is associated with white matter hyperintensities volume accumulation rate independent of AD diagnosis.

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    To assess the relationship between carriage of APOE ?4 allele and evolution of white matter hyperintensities (WMHs) volume, we longitudinally studied 339 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort with diagnoses ranging from normal controls to probable Alzheimer's disease (AD). A purpose-built longitudinal automatic method was used to segment WMH using constraints derived from an atlas-based model selection applied to a time-averaged image. Linear mixed models were used to evaluate the differences in rate of change across diagnosis and genetic groups. After adjustment for covariates (age, sex, and total intracranial volume), homozygous APOE ?4?4 subjects had a significantly higher rate of WMH accumulation (22.5% per year 95% CI [14.4, 31.2] for a standardized population having typical values of covariates) compared with the heterozygous (?4?3) subjects (10.0% per year [6.7, 13.4]) and homozygous ?3?3 (6.6% per year [4.1, 9.3]) subjects. Rates of accumulation increased with diagnostic severity; controls accumulated 5.8% per year 95% CI: [2.2, 9.6] for the standardized population, early mild cognitive impairment 6.6% per year [3.9, 9.4], late mild cognitive impairment 12.5% per year [8.2, 17.0] and AD subjects 14.7% per year [6.0, 24.0]. Following adjustment for APOE status, these differences became nonstatistically significant suggesting that APOE ?4 genotype is the major driver of accumulation of WMH volume rather than diagnosis of AD

    Computational modelling of imaging markers to support the diagnosis and monitoring of multiple sclerosis

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    Multiple sclerosis is a leading cause of neurological disability in young adults which affects more than 2.5 million people worldwide. An important substrate of disability accrual is the loss of neurons and connections between them (neurodegeneration) which can be captured by serial brain imaging, especially in the cerebral grey matter. In this thesis in four separate subprojects, I aimed to assess the strength of imaging-derived grey matter volume as a biomarker in the diagnosis, predicting the evolution of multiple sclerosis, and developing a staging system to stratify patients. In total, I retrospectively studied 1701 subjects, of whom 1548 had longitudinal brain imaging data. I used advanced computational models to investigate cross-sectional and longitudinal datasets. In the cross-sectional study, I demonstrated that grey matter volumes could distinguish multiple sclerosis from another demyelinating disorder (neuromyelitis optica) with an accuracy of 74%. In longitudinal studies, I showed that over time the deep grey matter nuclei had the fastest rate of volume loss (up to 1.66% annual loss) across the brain regions in multiple sclerosis. The volume of the deep grey matter was the strongest predictor of disability progression. I found that multiple sclerosis affects different brain areas with a specific temporal order (or sequence) that starts with the deep grey matter nuclei, posterior cingulate cortex, precuneus, and cerebellum. Finally, with multivariate mechanistic and causal modelling, I showed that brain volume loss causes disability and cognitive worsening which can be delayed with a potential neuroprotective treatment (simvastatin). This work provides conclusive evidence that grey matter volume loss affects some brain regions more severely, can predict future disability progression, can be used as an outcome measure in phase II clinical trials, and causes clinical and cognitive worsening. This thesis also provides a subject staging system based on which patients can be scored during multiple sclerosis

    APOE ε4 status is associated with white matter hyperintensities volume accumulation rate independent of AD diagnosis.

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    To assess the relationship between carriage of APOE ε4 allele and evolution of white matter hyperintensities (WMHs) volume, we longitudinally studied 339 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort with diagnoses ranging from normal controls to probable Alzheimer's disease (AD). A purpose-built longitudinal automatic method was used to segment WMH using constraints derived from an atlas-based model selection applied to a time-averaged image. Linear mixed models were used to evaluate the differences in rate of change across diagnosis and genetic groups. After adjustment for covariates (age, sex, and total intracranial volume), homozygous APOE ε4ε4 subjects had a significantly higher rate of WMH accumulation (22.5% per year 95% CI [14.4, 31.2] for a standardized population having typical values of covariates) compared with the heterozygous (ε4ε3) subjects (10.0% per year [6.7, 13.4]) and homozygous ε3ε3 (6.6% per year [4.1, 9.3]) subjects. Rates of accumulation increased with diagnostic severity; controls accumulated 5.8% per year 95% CI: [2.2, 9.6] for the standardized population, early mild cognitive impairment 6.6% per year [3.9, 9.4], late mild cognitive impairment 12.5% per year [8.2, 17.0] and AD subjects 14.7% per year [6.0, 24.0]. Following adjustment for APOE status, these differences became nonstatistically significant suggesting that APOE ε4 genotype is the major driver of accumulation of WMH volume rather than diagnosis of AD

    The age-dependent associations of white matter hyperintensities and neurofilament light in early- and late-stage Alzheimer's disease

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    Neurofilament light (NFL) is an emerging marker of axonal degeneration. This study investigated the relationship between white matter hyperintensities (WMHs) and plasma NFL in a large elderly cohort with, and without, cognitive impairment. We used the Alzheimer's Disease Neuroimaging Initiative and included 163 controls, 103 participants with a significant memory concern, 279 with early mild cognitive impairment (EMCI), 152 with late mild cognitive impairment (LMCI), and 130 with Alzheimer's disease, with 3T MRI and plasma NFL data. Multiple linear regression models examined the relationship between WMHs and NFL, with and without age adjustment. We used smoking status, history of hypertension, history of diabetes, and BMI as additional covariates to examine the effect of vascular risk. We found increases of between 20% and 41% in WMH volume per 1SD increase in NFL in significant memory concern, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer's disease groups (p < 0.02). Marked attenuation of the positive associations between WMHs and NFL were seen after age adjustment, suggesting that a significant proportion of the association between NFL and WMHs is age-related. No effect of vascular risk was observed. These results are supportive of a link between WMH and axonal degeneration in early to late disease stages, in an age-dependent, but vascular risk-independent manner

    SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry

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    Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain
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