118 research outputs found
Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging
Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator
Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI
The neuroimage analysis community has neglected the automated segmentation of
the olfactory bulb (OB) despite its crucial role in olfactory function. The
lack of an automatic processing method for the OB can be explained by its
challenging properties. Nonetheless, recent advances in MRI acquisition
techniques and resolution have allowed raters to generate more reliable manual
annotations. Furthermore, the high accuracy of deep learning methods for
solving semantic segmentation problems provides us with an option to reliably
assess even small structures. In this work, we introduce a novel, fast, and
fully automated deep learning pipeline to accurately segment OB tissue on
sub-millimeter T2-weighted (T2w) whole-brain MR images. To this end, we
designed a three-stage pipeline: (1) Localization of a region containing both
OBs using FastSurferCNN, (2) Segmentation of OB tissue within the localized
region through four independent AttFastSurferCNN - a novel deep learning
architecture with a self-attention mechanism to improve modeling of contextual
information, and (3) Ensemble of the predicted label maps. The OB pipeline
exhibits high performance in terms of boundary delineation, OB localization,
and volume estimation across a wide range of ages in 203 participants of the
Rhineland Study. Moreover, it also generalizes to scans of an independent
dataset never encountered during training, the Human Connectome Project (HCP),
with different acquisition parameters and demographics, evaluated in 30 cases
at the native 0.7mm HCP resolution, and the default 0.8mm pipeline resolution.
We extensively validated our pipeline not only with respect to segmentation
accuracy but also to known OB volume effects, where it can sensitively
replicate age effects
FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI
The hypothalamus plays a crucial role in the regulation of a broad range of
physiological, behavioural, and cognitive functions. However, despite its
importance, only a few small-scale neuroimaging studies have investigated its
substructures, likely due to the lack of fully automated segmentation tools to
address scalability and reproducibility issues of manual segmentation. While
the only previous attempt to automatically sub-segment the hypothalamus with a
neural network showed promise for 1.0 mm isotropic T1-weighted (T1w) MRI, there
is a need for an automated tool to sub-segment also high-resolutional (HiRes)
MR scans, as they are becoming widely available, and include structural detail
also from multi-modal MRI. We, therefore, introduce a novel, fast, and fully
automated deep learning method named HypVINN for sub-segmentation of the
hypothalamus and adjacent structures on 0.8 mm isotropic T1w and T2w brain MR
images that is robust to missing modalities. We extensively validate our model
with respect to segmentation accuracy, generalizability, in-session test-retest
reliability, and sensitivity to replicate hypothalamic volume effects (e.g.
sex-differences). The proposed method exhibits high segmentation performance
both for standalone T1w images as well as for T1w/T2w image pairs. Even with
the additional capability to accept flexible inputs, our model matches or
exceeds the performance of state-of-the-art methods with fixed inputs. We,
further, demonstrate the generalizability of our method in experiments with 1.0
mm MR scans from both the Rhineland Study and the UK Biobank. Finally, HypVINN
can perform the segmentation in less than a minute (GPU) and will be available
in the open source FastSurfer neuroimaging software suite, offering a
validated, efficient, and scalable solution for evaluating imaging-derived
phenotypes of the hypothalamus.Comment: Submitted to Imaging Neuroscienc
The Rotterdam Study: objectives and design update
The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in the Netherlands. The study targets cardiovascular, neurological, ophthalmological and endocrine diseases. As of 2008 about 15,000 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in some 600 research articles and reports (see http://www.epib.nl/rotterdamstudy). This article gives the reasons for the study and its design. It also presents a summary of the major findings and an update of the objectives and methods
Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI
Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at approximately 450,000 CpG sites in 9,732 middle-aged to older adults from 14 community-based studies. Single-CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single-CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5), and colocalized with FOLH1 expression in brain (posterior probability =0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single-CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis, and multi-omics colocalization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug repositioning analysis indicated antihyperlipidemic agents, more specifically peroxisome proliferator-activated receptor alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood brain barrier disruption
Common variation in PHACTR1 is associated with susceptibility to cervical artery dissection
Cervical artery dissection (CeAD), a mural hematoma in a carotid or vertebral artery, is a major cause of ischemic stroke in young adults although relatively uncommon in the general population (incidence of 2.6/100,000 per year). Minor cervical traumas, infection, migraine and hypertension are putative risk factors, and inverse associations with obesity and hypercholesterolemia are described. No confirmed genetic susceptibility factors have been identified using candidate gene approaches. We performed genome-wide association studies (GWAS) in 1,393 CeAD cases and 14,416 controls. The rs9349379[G] allele (PHACTR1) was associated with lower CeAD risk (odds ratio (OR) = 0.75, 95% confidence interval (CI) = 0.69-0.82; P = 4.46 × 10(-10)), with confirmation in independent follow-up samples (659 CeAD cases and 2,648 controls; P = 3.91 × 10(-3); combined P = 1.00 × 10(-11)). The rs9349379[G] allele was previously shown to be associated with lower risk of migraine and increased risk of myocardial infarction. Deciphering the mechanisms underlying this pleiotropy might provide important information on the biological underpinnings of these disabling conditions
The Rotterdam Study: 2010 objectives and design update
The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in close to a 1,000 research articles and reports (see www.epib.nl/rotterdamstudy). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods
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