Publications by authors named "Fischl B"

Background: The ARTS biomarker is a fully automated software container that predicts the presence of arteriolosclerosis based on in-vivo MRI data and demographic features. The present study describes findings from the instrumental and clinical validation of ARTS conducted by the MarkVCID consortium.

Method: Instrumental validation of ARTS involved assessment of inter-rater reliability, test-retest repeatability, and inter-scanner reproducibility.

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Background: The ARTS biomarker is a fully automated software container that predicts the presence of arteriolosclerosis based on in-vivo MRI data and demographic features. The present study describes findings from the instrumental and clinical validation of ARTS conducted by the MarkVCID consortium.

Method: Instrumental validation of ARTS involved assessment of inter-rater reliability, test-retest repeatability, and inter-scanner reproducibility.

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Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion correction techniques jointly optimize estimates of the image and the motion parameters. In this paper, we use a deep network to reduce the joint image-motion parameter search to a search over rigid motion parameters alone.

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Article Synopsis
  • Significant advancements have been made in understanding cortical networks related to conscious awareness, but research on subcortical arousal networks is still underdeveloped due to challenges in accurately defining brainstem arousal nuclei.
  • Researchers created a probabilistic atlas of brainstem arousal nuclei using high-resolution diffusion MRI scans of five ex vivo human brain samples, with annotations based on specific immunostaining.
  • A Bayesian segmentation algorithm was developed to automatically identify these nuclei across different MRI techniques, showing high accuracy and reliability, with applications in detecting changes related to disorders like Alzheimer's disease and traumatic coma.
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Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing.

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The increasing prevalence of multi-site diffusion-weighted magnetic resonance imaging (dMRI) studies potentially offers enhanced statistical power for investigating brain structure. However, these studies face challenges due to variations in scanner hardware and acquisition protocols. While several methods exist for dMRI data harmonization, few specifically address structural brain connectivity.

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Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Building on recent advancements in ultra-high-resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 $\mu $m, we propose a Multi-resolution U-Nets framework that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.

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Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functional variability of anatomically corresponding areas across subjects has been widely reported, particularly in higher-order cognitive areas.

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The accurate measurement of three-dimensional (3D) fiber orientation in the brain is crucial for reconstructing fiber pathways and studying their involvement in neurological diseases. Comprehensive reconstruction of axonal tracts and small fascicles requires high-resolution technology beyond the ability of current imaging (e.g.

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Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform.

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Article Synopsis
  • The text discusses open-source tools designed for 3D analysis of photographs from dissected human brain slices, which are often underutilized for quantitative studies.
  • These tools can reconstruct a 3D volume and segment brain images into 11 regions per hemisphere, serving as a cost-effective alternative to traditional MRI imaging.
  • Testing shows that the methodology provides accurate 3D reconstructions and can differentiate between Alzheimer's disease cases and healthy controls, with tools available in the FreeSurfer suite.
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Introduction: The entorhinal cortex (EC) and perirhinal cortex (PC) are vulnerable to Alzheimer's disease. A triggering factor may be the interaction of vascular dysfunction and tau pathology.

Methods: We imaged post mortem human tissue at 100 μm with 7 T magnetic resonance imaging and manually labeled individual blood vessels (mean = 270 slices/case).

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The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.

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Article Synopsis
  • Consciousness has two main parts: being awake (arousal) and being aware of things.
  • Scientists are trying to learn more about how the brain keeps us awake and alert, focusing on parts below the brain's surface.
  • They found important brain areas that help with wakefulness and connected them to parts that help with awareness, showing how these different brain networks work together.
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We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain-connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, thoroughly capturing representations from the input data.

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  • The effects of repeated blast exposure (RBE) on the brain health of US Special Operations Forces (SOF) are not fully understood, and currently, there is no test to diagnose injury from such exposures.
  • A study involving 30 active-duty US SOF found that higher blast exposure correlates with changes in brain structure and cognitive performance, particularly affecting the rostral anterior cingulate cortex (rACC).
  • These findings indicate that increased blast exposure can lead to health-related issues and suggest that a comprehensive, network-based diagnostic method may be beneficial for identifying brain injuries in SOF personnel.
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Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing.

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The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.

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Introduction: Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers.

Methods: We used four diffusion-MRI databases, three related to Alzheimer's disease (AD), to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways.

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Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Leveraging recent advancements in ultra-high resolution MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere scans at 120 m, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.

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The accurate measurement of three-dimensional (3D) fiber orientation in the brain is crucial for reconstructing fiber pathways and studying their involvement in neurological diseases. Optical imaging methods such as polarization-sensitive optical coherence tomography (PS-OCT) provide important tools to directly quantify fiber orientation at micrometer resolution. However, brain imaging based on the optic axis by PS-OCT so far has been limited to two-dimensional in-plane orientation, preventing the comprehensive study of connectivity in 3D.

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The study of aging and neurodegenerative processes in the human brain requires a comprehensive understanding of cytoarchitectonic, myeloarchitectonic, and vascular structures. Recent computational advances have enabled volumetric reconstruction of the human brain using thousands of stained slices, however, tissue distortions and loss resulting from standard histological processing have hindered deformation-free reconstruction. Here, the authors describe an integrated serial sectioning polarization-sensitive optical coherence tomography (PSOCT) and two photon microscopy (2PM) system to provide label-free multi-contrast imaging of intact brain structures, including scattering, birefringence, and autofluorescence of human brain tissue.

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Article Synopsis
  • United States Special Operations Forces (SOF) often experience explosive blasts during training and combat, which can affect their brain health.
  • The understanding of how repeated blast exposure impacts the brain is still lacking, and there is no existing diagnostic test for repeated blast brain injury (rBBI).
  • Developing a reliable test for rBBI could enhance SOF brain health, improve combat readiness, and enhance their overall quality of life.
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Brain cells are arranged in laminar, nuclear, or columnar structures, spanning a range of scales. Here, we construct a reliable cell census in the frontal lobe of human cerebral cortex at micrometer resolution in a magnetic resonance imaging (MRI)-referenced system using innovative imaging and analysis methodologies. MRI establishes a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries.

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Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learning-based approaches focus on accurate registration of folding patterns of the cerebral cortex, and assume that geometry predicts function and thus functional areas will also be well aligned. However, structure/functional variability of anatomically corresponding areas across subjects has been widely reported.

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