The rabbit model has become increasingly popular in neurodevelopmental studies as it is best suited to bridge the gap in translational research between small and large animals. In the context of ...preclinical studies, high-resolution magnetic resonance imaging (MRI) is often the best modality to investigate structural and functional variability of the brain, both in vivo and ex vivo. In most of the MRI-based studies, an important requirement to analyze the acquisitions is an accurate parcellation of the considered anatomical structures. Manual segmentation is time-consuming and typically poorly reproducible, while state-of-the-art automated segmentation algorithms rely on available atlases. In this work we introduce the first digital neonatal rabbit brain atlas consisting of 12 multi-modal acquisitions, parcellated into 89 areas according to a hierarchical taxonomy. Delineations were performed iteratively, alternating between segmentation propagation, label fusion and manual refinements, with the aim of controlling the quality while minimizing the bias introduced by the chosen sequence. Reliability and accuracy were assessed with cross-validation and intra- and inter-operator test-retests. Multi-atlas, versioned controlled segmentations repository and supplementary materials download links are available from the software repository documentation at https://github.com/gift-surg/SPOT-A-NeonatalRabbit.
•In magnetic resonance image analysis accurate automatic segmentations tools are critically important.•Multi-atlas based methods are valuable tools to obtain an automatic MRI segmentation.•The rabbit model has become increasingly popular in neuro developmental studies.•Currently there is no multi-atlas of the newborn rabbit available in literature.•In this manuscript we provide the first high resolution MRI multi atlas for the newborn rabbit brain.
A tool was developed to automatically segment several subcortical limbic structures (nucleus accumbens, basal forebrain, septal nuclei, hypothalamus without mammillary bodies, the mammillary bodies, ...and fornix) using only a T1-weighted MRI as input. This tool fills an unmet need as there are few, if any, publicly available tools to segment these clinically relevant structures. A U-Net with spatial, intensity, contrast, and noise augmentation was trained using 39 manually labeled MRI data sets. In general, the Dice scores, true positive rates, false discovery rates, and manual-automatic volume correlation were very good relative to comparable tools for other structures. A diverse data set of 698 subjects were segmented using the tool; evaluation of the resulting labelings showed that the tool failed in less than 1% of cases. Test-retest reliability of the tool was excellent. The automatically segmented volume of all structures except mammillary bodies showed effectiveness at detecting either clinical AD effects, age effects, or both. This tool will be publicly released with FreeSurfer (surfer.nmr.mgh.harvard.edu/fswiki/ScLimbic). Together with the other cortical and subcortical limbic segmentations, this tool will allow FreeSurfer to provide a comprehensive view of the limbic system in an automated way.
Qualitative visual assessment of MRI scans is a key mechanism by which inflammation is assessed in clinical practice. For example, in axial spondyloarthritis (axSpA), visual assessment focuses on the ...identification of regions with increased signal in the bone marrow, known as bone marrow oedema (BMO), on water-sensitive images. The identification of BMO has an important role in the diagnosis, quantification and monitoring of disease in axSpA. However, BMO evaluation depends heavily on the experience and expertise of the image reader, creating substantial imprecision. Deep learning-based segmentation is a natural approach to addressing this imprecision, but purely automated solutions require large training sets that are not currently available, and deep learning solutions with limited data may not be sufficiently trustworthy for use in clinical practice. To address this, we propose a workflow for inflammation segmentation incorporating both deep learning and human input. With this 'human-machine cooperation' workflow, a preliminary segmentation is generated automatically by deep learning; a human reader then 'cleans' the segmentation by removing extraneous segmented voxels. The final cleaned segmentation defines the volume of hyperintense inflammation (VHI), which is proposed as a quantitative imaging biomarker (QIB) of inflammation load in axSpA. We implemented and evaluated the proposed human-machine workflow in a cohort of 29 patients with axSpA who had undergone prospective MRI scans before and after starting biologic therapy. The performance of the workflow was compared against purely visual assessment in terms of inter-observer/inter-method segmentation overlap, inter-observer agreement and assessment of response to biologic therapy. The human-machine workflow showed superior inter-observer segmentation overlap than purely manual segmentation (Dice score 0.84 versus 0.56). VHI measurements produced by the workflow showed similar or better inter-observer agreement than visual scoring, with similar response assessments. We conclude that the proposed human-machine workflow offers a mechanism to improve the consistency of inflammation assessment, and that VHI could be a valuable QIB of inflammation load in axSpA, as well as offering an exemplar of human-machine cooperation more broadly.
•We reconstructed cubic centimeters of human cerebellar samples at micrometer resolution in five subjects.•Thickness of the granular layer varies greater than that of the molecular ...layer.•Cross-subject variability is higher in optical property than cortical morphology.•Our results suggest homogenous cell and myelin density in the cortical layers of human cerebellum despite the highly convoluted folding patterns.
The surface of the human cerebellar cortex is much more tightly folded than the cerebral cortex. Volumetric analysis of cerebellar morphometry in magnetic resonance imaging studies suffers from insufficient resolution, and therefore has had limited impact on disease assessment. Automatic serial polarization-sensitive optical coherence tomography (as-PSOCT) is an emerging technique that offers the advantages of microscopic resolution and volumetric reconstruction of large-scale samples. In this study, we reconstructed multiple cubic centimeters of ex vivo human cerebellum tissue using as-PSOCT. The morphometric and optical properties of the cerebellar cortex across five subjects were quantified. While the molecular and granular layers exhibited similar mean thickness in the five subjects, the thickness varied greatly in the granular layer within subjects. Layer-specific optical property remained homogenous within individual subjects but showed higher cross-subject variability than layer thickness. High-resolution volumetric morphometry and optical property maps of human cerebellar cortex revealed by as-PSOCT have great potential to advance our understanding of cerebellar function and diseases.
Accurate and reliable whole-brain segmentation is critical to longitudinal neuroimaging studies. We undertake a comparative analysis of two subcortical segmentation methods, Automatic Segmentation ...(ASEG) and Sequence Adaptive Multimodal Segmentation (SAMSEG), recently provided in the open-source neuroimaging package FreeSurfer 7.1, with regard to reliability, bias, sensitivity to detect longitudinal change, and diagnostic sensitivity to Alzheimer’s disease. First, we assess intra- and inter-scanner reliability for eight bilateral subcortical structures: amygdala, caudate, hippocampus, lateral ventricles, nucleus accumbens, pallidum, putamen and thalamus. For intra-scanner analysis we use a large sample of participants (n = 1629) distributed across the lifespan (age range = 4–93 years) and acquired on a 1.5T Siemens Avanto (n = 774) and a 3T Siemens Skyra (n = 855) scanners. For inter-scanner analysis we use a sample of 24 participants scanned on the day with three models of Siemens scanners: 1.5T Avanto, 3T Skyra and 3T Prisma. Second, we test how each method detects volumetric age change using longitudinal follow up scans (n = 491 for Avanto and n = 245 for Skyra; interscan interval = 1–10 years). Finally, we test sensitivity to clinically relevant change. We compare annual rate of hippocampal atrophy in cognitively normal older adults (n = 20), patients with mild cognitive impairment (n = 20) and Alzheimer’s disease (n = 20). We find that both ASEG and SAMSEG are reliable and lead to the detection of within-person longitudinal change, although with notable differences between age-trajectories for most structures, including hippocampus and amygdala. In summary, SAMSEG yields significantly lower differences between repeated measures for intra- and inter-scanner analysis without compromising sensitivity to changes and demonstrating ability to detect clinically relevant longitudinal changes.
•We add diffusion MRI to Bayesian thalamic nuclei segmentation with structural MRI.•Adding fiber tracts to probabilistic atlases enables orientation modelling.•Thalamus segmentation from joint ...structural and diffusion MRI improves accuracy.•Atlas and companion segmentation code are freely distributed with FreeSurfer.
The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer’s disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).
Abstract
Fluorinert (perfluorocarbon) represents an inexpensive option for minimizing susceptibility artifacts in ex vivo brain MRI scanning, and provides an alternative to Fomblin. However, its ...impact on fixed tissue and histological analysis has not been rigorously and quantitatively validated. In this study, we excised tissue blocks from 2 brain regions (frontal pole and cerebellum) of 5 formalin-fixed specimens (2 progressive supranuclear palsy cases, 3 controls). We excised 2 blocks per region per case (20 blocks in total), one of which was subsequently immersed in Fluorinert for a week and then returned to a container with formalin. The other block from each region was kept in formalin for use as control. The tissue blocks were then sectioned and histological analysis was performed on each, including routine stains and immunohistochemistry. Visual inspection of the stained histological sections by an experienced neuropathologist through the microscope did not reveal any discernible differences between any of the samples. Moreover, quantitative analysis based on automated image patch classification showed that the samples were almost indistinguishable for a state-of-the-art classifier based on a deep convolutional neural network. The results showed that Fluorinert has no effect on subsequent histological analysis of the tissue even after a long (1 week) period of immersion, which is sufficient for even the lengthiest scanning protocols.
Background
Smaller hippocampal volume in patients with posttraumatic stress disorder (PTSD) represents the most consistently reported structural alteration in the brain. Subfields of the hippocampus ...play distinct roles in encoding and processing of memories, which are disrupted in PTSD. We examined PTSD‐associated alterations in 12 hippocampal subfields in relation to global hippocampal shape, and clinical features.
Methods
Case‐control cross‐sectional studies of U.S. military veterans (n = 282) from the Iraq and Afghanistan era were grouped into PTSD (n = 142) and trauma‐exposed controls (n = 140). Participants underwent clinical evaluation for PTSD and associated clinical parameters followed by MRI at 3 T. Segmentation with FreeSurfer v6.0 produced hippocampal subfield volumes for the left and right CA1, CA3, CA4, DG, fimbria, fissure, hippocampus‐amygdala transition area, molecular layer, parasubiculum, presubiculum, subiculum, and tail, as well as hippocampal meshes. Covariates included age, gender, trauma exposure, alcohol use, depressive symptoms, antidepressant medication use, total hippocampal volume, and MRI scanner model.
Results
Significantly lower subfield volumes were associated with PTSD in left CA1 (P = 0.01; d = 0.21; uncorrected), CA3 (P = 0.04; d = 0.08; uncorrected), and right CA3 (P = 0.02; d = 0.07; uncorrected) only if ipsilateral whole hippocampal volume was included as a covariate. A trend level association of L‐CA1 with PTSD (F4, 221 = 3.32, P = 0.07) is present and the other subfield findings are nonsignificant if ipsilateral whole hippocampal volume is not included as a covariate. PTSD‐associated differences in global hippocampal shape were nonsignificant.
Conclusions
The present finding of smaller hippocampal CA1 in PTSD is consistent with model systems in rodents that exhibit increased anxiety‐like behavior from repeated exposure to acute stress. Behavioral correlations with hippocampal subfield volume differences in PTSD will elucidate their relevance to PTSD, particularly behaviors of associative fear learning, extinction training, and formation of false memories.
Subfield-specific measurements provide superior information in the early stages of neurodegenerative diseases compared to global hippocampal measurements. The overall goal was to systematically ...compare the performance of five representative manual and automated T1 and T2 based subfield labeling techniques in a sub-set of the ADNI2 population.
The high resolution T2 weighted hippocampal images (T2-HighRes) and the corresponding T1 images from 106 ADNI2 subjects (41 controls, 57 MCI, 8 AD) were processed as follows. A. T1-based: 1. Freesurfer+Large-Diffeomorphic-Metric-Mapping in combination with shape analysis. 2. FreeSurfer 5.1 subfields using in-vivo atlas. B. T2-HighRes: 1. Model-based subfield segmentation using ex-vivo atlas (FreeSurfer 6.0). 2. T2-based automated multi-atlas segmentation combined with similarity-weighted voting (ASHS). 3. Manual subfield parcellation. Multiple regression analyses were used to calculate effect sizes (ES) for group, amyloid positivity in controls, and associations with cognitive/memory performance for each approach.
Subfield volumetry was better than whole hippocampal volumetry for the detection of the mild atrophy differences between controls and MCI (ES: 0.27 vs 0.11). T2-HighRes approaches outperformed T1 approaches for the detection of early stage atrophy (ES: 0.27 vs.0.10), amyloid positivity (ES: 0.11 vs 0.04), and cognitive associations (ES: 0.22 vs 0.19).
T2-HighRes subfield approaches outperformed whole hippocampus and T1 subfield approaches. None of the different T2-HghRes methods tested had a clear advantage over the other methods. Each has strengths and weaknesses that need to be taken into account when deciding which one to use to get the best results from subfield volumetry.
•Comparison of 4 automated and 1 manual subfield labeling technique in common data set.•Subfield labeling approaches perform better than whole hippocampal approaches.•High resolution T2 based approaches perform better than T1 based approaches.•Different high res T2 approaches have different strengths/weaknesses.
Abstract
Study Objectives
To use relatively noisy routinely collected clinical data (brain magnetic resonance imaging (MRI) data, clinical polysomnography (PSG) recordings, and neuropsychological ...testing), to investigate hypothesis-driven and data-driven relationships between brain physiology, structure, and cognition.
Methods
We analyzed data from patients with clinical PSG, brain MRI, and neuropsychological evaluations. SynthSeg, a neural network-based tool, provided high-quality segmentations despite noise. A priori hypotheses explored associations between brain function (measured by PSG) and brain structure (measured by MRI). Associations with cognitive scores and dementia status were studied. An exploratory data-driven approach investigated age-structure-physiology-cognition links.
Results
Six hundred and twenty-three patients with sleep PSG and brain MRI data were included in this study; 160 with cognitive evaluations. Three hundred and forty-two participants (55%) were female, and age interquartile range was 52 to 69 years. Thirty-six individuals were diagnosed with dementia, 71 with mild cognitive impairment, and 326 with major depression. One hundred and fifteen individuals were evaluated for insomnia and 138 participants had an apnea–hypopnea index equal to or greater than 15. Total PSG delta power correlated positively with frontal lobe/thalamic volumes, and sleep spindle density with thalamic volume. rapid eye movement (REM) duration and amygdala volume were positively associated with cognition. Patients with dementia showed significant differences in five brain structure volumes. REM duration, spindle, and slow-oscillation features had strong associations with cognition and brain structure volumes. PSG and MRI features in combination predicted chronological age (R2 = 0.67) and cognition (R2 = 0.40).
Conclusions
Routine clinical data holds extended value in understanding and even clinically using brain-sleep-cognition relationships.
Graphical Abstract
Graphical Abstract
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