Showing papers by "Michael I. Miller published in 2019"
TL;DR: The use of n-3 FA (4 g/d) for improving atherosclerotic cardiovascular disease risk in patients with hypertriglyceridemia is supported by a 25% reduction in major adverse cardiovascular events in REDUCE-IT (Reduction of Cardiovascular Events With EPA Intervention Trial), a randomized placebo-controlled trial of EPA-only in high-risk patients treated with a statin.
Abstract: Hypertriglyceridemia (triglycerides 200–499 mg/dL) is relatively common in the United States, whereas more severe triglyceride elevations (very high triglycerides, ≥500 mg/dL) are far less frequent...
263 citations
TL;DR: The long period of time prior to symptom onset during which AD pathology is accumulating in the brain is highlighted, including the early changes in cognition and the laterality of the MRI findings.
Abstract: Objective: Several models have been proposed for the evolution of Alzheimer's disease (AD) biomarkers. The aim of this study was to identify changepoints in a range of biomarkers during the preclinical phase of AD. Methods: We examined nine measures based on cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) and cognitive testing, obtained from 306 cognitively normal individuals, a subset of whom subsequently progressed to the symptomatic phase of AD. A changepoint model was used to determine which of the measures had a significant change in slope in relation to clinical symptom onset. Results: All nine measures had significant changepoints, all of which preceded symptom onset, however, the timing of these changepoints varied considerably. A single measure, CSF t-tau, had an early changepoint (34 years prior to symptom onset). A group of measures, including the remaining CSF measures (CSF Abeta and phosphorylated tau) and all cognitive tests had changepoints 10-15 years prior to symptom onset. A second group is formed by medial temporal lobe shape composite measures, with a 6-year time difference between the right and left side (respectively nine and 3 years prior to symptom onset). Conclusion: These findings highlight the long period of time prior to symptom onset during which AD pathology is accumulating in the brain. There are several significant findings, including the early changes in cognition and the laterality of the MRI findings. Additional work is needed to clarify their significance.
58 citations
TL;DR: Experimental results show that Siamese networks perform better in certain metrics by explicitly encoding the asymmetry in brain volumes, compared to traditional prediction methods that do not use the asymmetric, on the ADNI and BIOCARD datasets.
54 citations
TL;DR: An integrated neuro-histological pipeline as well as a grid-based tracer injection strategy for systematic mesoscale connectivity mapping in the common marmoset will facilitate the systematic assembly of a mesoscales connectivity matrix together with unprecedented 3D reconstructions of brain-wide projection patterns in a primate brain.
Abstract: Understanding the connectivity architecture of entire vertebrate brains is a fundamental but difficult task. Here we present an integrated neuro-histological pipeline as well as a grid-based tracer injection strategy for systematic mesoscale connectivity mapping in the common marmoset (Callithrix jacchus). Individual brains are sectioned into ~1700 20 µm sections using the tape transfer technique, permitting high quality 3D reconstruction of a series of histochemical stains (Nissl, myelin) interleaved with tracer labeled sections. Systematic in-vivo MRI of the individual animals facilitates injection placement into reference-atlas defined anatomical compartments. Further, by combining the resulting 3D volumes, containing informative cytoarchitectonic markers, with in-vivo and ex-vivo MRI, and using an integrated computational pipeline, we are able to accurately map individual brains into a common reference atlas despite the significant individual variation. This approach will facilitate the systematic assembly of a mesoscale connectivity matrix together with unprecedented 3D reconstructions of brain-wide projection patterns in a primate brain.
51 citations
TL;DR: An automated process to segment brain nuclei and quantify tissue susceptibility in these regions based on a susceptibility multi‐atlas library, consisting of 10 atlases with T1‐weighted images, gradient echo magnitude images and QSM images of brains with different anatomic patterns is developed.
49 citations
TL;DR: It is suggested that the transentorhinal cortex (TEC) thickness could serve as a biomarker for Alzheimer's disease in the prodromal phase of the disease.
35 citations
TL;DR: The ASL‐MRICloud tool was implemented to be compatible with data acquired by scanners from all major MRI manufacturers, is capable of processing several common forms of AsL, including pseudo‐continuous ASL and pulsed ASL, and can process single‐delay and multi‐delay ASL data.
Abstract: Arterial spin labeling (ASL) MRI is increasingly used in research and clinical settings. The purpose of this work is to develop a cloud-based tool for ASL data processing, referred to as ASL-MRICloud, which may be useful to the MRI community. In contrast to existing ASL toolboxes, which are based on software installation on the user's local computer, ASL-MRICloud uses a web browser for data upload and results download, and the computation is performed on the remote server. As such, this tool is independent of the user's operating system, software version, and CPU speed. The ASL-MRICloud tool was implemented to be compatible with data acquired by scanners from all major MRI manufacturers, is capable of processing several common forms of ASL, including pseudo-continuous ASL and pulsed ASL, and can process single-delay and multi-delay ASL data. The outputs of ASL-MRICloud include absolute and relative values of cerebral blood flow, arterial transit time, voxel-wise masks indicating regions with potential hyper-perfusion and hypo-perfusion, and an image quality index. The ASL tool is also integrated with a T1 -based brain segmentation and normalization tool in MRICloud to allow generation of parametric maps in standard brain space as well as region-of-interest values. The tool was tested on a large data set containing 309 ASL scans as well as on publicly available ASL data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.
32 citations
TL;DR: The increasing use of large sample sizes for population and personalized medicine requires high‐throughput tools for imaging processing that can handle large amounts of data with diverse image modalities, perform a biologically meaningful information reduction, and result in comprehensive quantification.
Abstract: INTRODUCTION The increasing use of large sample sizes for population and personalized medicine requires high-throughput tools for imaging processing that can handle large amounts of data with diverse image modalities, perform a biologically meaningful information reduction, and result in comprehensive quantification. Exploring the reproducibility of these tools reveals the specific strengths and weaknesses that heavily influence the interpretation of results, contributing to transparence in science. METHODS We tested-retested the reproducibility of MRICloud, a free automated method for whole-brain, multimodal MRI segmentation and quantification, on two public, independent datasets of healthy adults. RESULTS The reproducibility was extremely high for T1-volumetric analysis, high for diffusion tensor images (DTI) (however, regionally variable), and low for resting-state fMRI. CONCLUSION In general, the reproducibility of the different modalities was slightly superior to that of widely used software. This analysis serves as a normative reference for planning samples and for the interpretation of structure-based MRI studies.
22 citations
TL;DR: In this paper, the energy harvesting performance of a flapping hydrofoil with wall confinement was investigated in a circulating water flume at a Reynolds number of 50,000, and the heave component of efficiency was the primary contributor to the performance improvement.
21 citations
TL;DR: The regional shape analyses conducted in this study provide useful insights into the effects of HD pathology in subcortical structures, including significant region‐specific atrophies in all subcortsical structures studied.
Abstract: Huntington's disease (HD) involves preferential and progressive degeneration of striatum and other subcortical regions as well as regional cortical atrophy. It is caused by a CAG repeat expansion in the Huntingtin gene, and the longer the expansion the earlier the age of onset. Atrophy begins prior to manifest clinical signs and symptoms, and brain atrophy in premanifest expansion carriers can be studied. We employed a diffeomorphometric pipeline to contrast subcortical structures' morphological properties in a control group with three disease groups representing different phases of premanifest HD (far, intermediate, and near to onset) as defined by the length of the CAG expansion and the participant's age (CAG-Age-Product). A total of 1,428 magnetic resonance image scans from 694 participants from the PREDICT-HD cohort were used. We found significant region-specific atrophies in all subcortical structures studied, with the estimated abnormality onset time varying from structure to structure. Heterogeneous shape abnormalities of caudate nuclei were present in premanifest HD participants estimated furthest from onset and putaminal shape abnormalities were present in participants intermediate to onset. Thalamic, hippocampal, and amygdalar shape abnormalities were present in participants nearest to onset. We assessed whether the estimated progression of subcortical pathology in premanifest HD tracked specific pathways. This is plausible for changes in basal ganglia circuits but probably not for changes in hippocampus and amygdala. The regional shape analyses conducted in this study provide useful insights into the effects of HD pathology in subcortical structures.
21 citations
TL;DR: The results suggest that NPTX2 mechanisms may play a central role among older individuals in connectivity within the salience/ventral attention network and for cognitive tasks that require modulation of attention and response selection.
Abstract: Intrinsic functional connectivity of large-scale brain networks has been shown to change with aging and Alzheimer's disease (AD). These alterations are thought to reflect changes in synaptic function, but the underlying biological mechanisms are poorly understood. This study examined whether Neuronal Pentraxin 2 (NPTX2), a synaptic protein that mediates homeostatic strengthening of inhibitory circuits to control cortical excitability, is associated with functional connectivity as measured by resting-state functional magnetic resonance imaging (rsfMRI) in five large-scale cognitive brain networks. In this cross-sectional study, rsfMRI scans were obtained from 130 older individuals (mean age = 69 years) with normal cognition (N = 113) and Mild Cognitive Impairment (N = 17); NPTX2 was measured in the same individuals in cerebrospinal fluid (CSF). Higher levels of NPTX2 in CSF were associated with greater functional connectivity in the salience/ventral attention network, based on linear regression analysis. Moreover, this association was stronger among individuals with lower levels of cognitive reserve, as measured by a composite score (comprised of years of education, reading, and vocabulary measures). Additionally, higher connectivity in the salience/ventral attention network was related to better performance on a composite measure of executive function. Levels of NPTX2 were not associated with connectivity in other networks (executive control, limbic, dorsal attention, and default-mode). Findings also confirmed prior reports that individuals with MCI have lower levels of NPTX2 compared to those with normal cognition. Taken together, the results suggest that NPTX2 mechanisms may play a central role among older individuals in connectivity within the salience/ventral attention network and for cognitive tasks that require modulation of attention and response selection.
TL;DR: In vivo and ex vivo analyses identified plasticity in circuits relevant to selecting actions in a sensory-motor context, through exploitation of learned association and decision making in skilled motor learning.
Abstract: We do not have a full understanding of the mechanisms underlying plasticity in the human brain. Mouse models have well controlled environments and genetics, and provide tools to help dissect the mechanisms underlying the observed responses to therapies devised for humans recovering from injury of ischemic nature or trauma. We aimed to detect plasticity following learning of a unilateral reaching movement, and relied on MRI performed with a rapid structural protocol suitable for in vivo brain imaging, and a longer diffusion tensor imaging (DTI) protocol executed ex vivo. In vivo MRI detected contralateral volume increases in trained animals (reachers), in circuits involved in motor control, sensory processing, and importantly, learning and memory. The temporal association area, parafascicular and mediodorsal thalamic nuclei were also enlarged. In vivo MRI allowed us to detect longitudinal effects over the ~25 days training period. The interaction between time and group (trained versus not trained) supported a role for the contralateral, but also the ipsilateral hemisphere. While ex vivo imaging was affected by shrinkage due to the fixation, it allowed for superior resolution and improved contrast to noise ratios, especially for subcortical structures. We examined microstructural changes based on DTI, and identified increased fractional anisotropy and decreased apparent diffusion coefficient, predominantly in the cerebellum and its connections. Cortical thickness differences did not survive multiple corrections, but uncorrected statistics supported the contralateral effects seen with voxel based volumetric analysis, showing thickening in the somatosensory, motor and visual cortices. In vivo and ex vivo analyses identified plasticity in circuits relevant to selecting actions in a sensory-motor context, through exploitation of learned association and decision making. By mapping a connectivity atlas into our ex vivo template we revealed that changes due to skilled motor learning occurred in a network of 35 regions, including the primary and secondary motor (M1, M2) and sensory cortices (S1, S2), the caudate putamen (CPu), visual (V1) and temporal association cortex. The significant clusters intersected tractography based networks seeded in M1, M2, S1, V1 and CPu at levels > 80%. We found that 89% of the significant cluster belonged to a network seeded in the contralateral M1, and 85% to one seeded in the contralateral M2. Moreover, 40% of the M1 and S1 cluster by network intersections were in the top 80th percentile of the tract densities for their respective networks. Our investigation may be relevant to studies of rehabilitation and recovery, and points to widespread network changes that accompany motor learning that may have potential applications to designing recovery strategies following brain injury.
TL;DR: MRI-based brain oxygen extraction shows that cognitively healthy carriers of the apolipoprotein E4 gene manifest diminishedbrain oxygen extraction capacity independent of amyloid burden, suggesting that the effect of APOE4 on OEF is not mediated by amyloids.
Abstract: Background Apolipoprotein E4 (APOE4) is a major genetic risk factor for late-onset Alzheimer disease. However, the mechanisms by which APOE4 affects the brain, underpinning this risk, have not been fully elucidated. Purpose To investigate the influence of APOE4 on global cerebral oxygen extraction fraction (OEF) and possible mediation through amyloid burden by using MRI-based brain oxygen extraction technique. Materials and Methods Participants were enrolled from a longitudinal prospective study, the Biomarkers for Older Controls at Risk for Dementia study (data collected from January 2015 to December 2017), of whom 35% (50 of 143 participants) were APOE4 carriers. OEF was measured by using a T2-relaxation-under-spin-tagging MRI technique with a 3.0-T MRI system. PET acquired with carbon 11-labeled Pittsburgh compound B tracer was available in 119 participants to measure amyloid burden. Cognitive performance was assessed by using domain-specific composite scores including executive function, episodic memory, visual-spatial processing, and language. Linear regression analysis was performed to investigate the relationship between APOE4, OEF, and amyloid burden. The association between OEF and cognitive function was studied for the entire study cohort and separately for the APOE4 carriers and noncarriers. Results A total of 143 cognitively healthy individuals (mean age 6 standard deviation, 69.1 years 6 8.2; 57 men and 86 women) were studied. APOE4 genetic status was associated with lower OEF (noncarriers, 41.1% 6 5.8; one E4 allele, 40.1% 6 4.9; two E4 alleles, 36.7% 6 4.5; P = .03). Furthermore, among APOE4 carriers, lower OEF correlated with lower executive function scores (b = 0.079 z score for each percent change in OEF; P = .03). Amyloid burden and OEF were independently associated with APOE4 but were not associated with one another, suggesting that the effect of APOE4 on OEF is not mediated by amyloid. Conclusion MRI-based brain oxygen extraction shows that cognitively healthy carriers of the apolipoprotein E4 gene manifest diminished brain oxygen extraction capacity independent of amyloid burden. ©RSNA, 2019 Online supplemental material is available for this article.
TL;DR: Mediation analyses revealed that putamen and globus pallidus volumes mediated the relationship between diagnosis and commission error rate and suggest that anomalous basal ganglia morphology is related to impaired motor response control among boys with ADHD.
TL;DR: In this article, the authors confirmed the continuity between calbindin-rich cells in LGN K layers and the central lateral division of IPul (IPulCL) in marmoset monkeys by employing a high-throughput neuronal tracing method, and they found that both the koniocellular (K) layers and IPulCL form comparable patterns of connections with striate and extrastriate cortices; these connections are largely different to those of the parvocellular and magnocellular laminae of LGN.
Abstract: Traditionally, the dorsal lateral geniculate nucleus (LGN) and the inferior pulvinar (IPul) nucleus are considered as anatomically and functionally distinct thalamic nuclei. However, in several primate species it has also been established that the koniocellular (K) layers of LGN and parts of the IPul have a shared pattern of immunoreactivity for the calcium-binding protein calbindin. These calbindin-rich cells constitute a thalamic matrix system which is implicated in thalamocortical synchronisation. Further, the K layers and IPul are both involved in visual processing and have similar connections with retina and superior colliculus. Here, we confirmed the continuity between calbindin-rich cells in LGN K layers and the central lateral division of IPul (IPulCL) in marmoset monkeys. By employing a high-throughput neuronal tracing method, we found that both the K layers and IPulCL form comparable patterns of connections with striate and extrastriate cortices; these connections are largely different to those of the parvocellular and magnocellular laminae of LGN. Retrograde tracer-labelled cells and anterograde tracer-labelled axon terminals merged seamlessly from IPulCL into LGN K layers. These results support continuity between LGN K layers and IPulCL, providing an anatomical basis for functional congruity of this region of the dorsal thalamic matrix and calling into question the traditional segregation between LGN and the inferior pulvinar nucleus.
TL;DR: A new method, known as the Multi-atlas based Detection and Localization (MADL), is introduced to evaluate WMH on fluid-attenuated inversion recovery (FLAIR) data, which revealed a significant association between age and WMH load in deep WM but not subcortical WM.
TL;DR: A T1‐weighted, neonatal, multi‐atlas repository is developed and integrated into the MALF‐based brain segmentation tools in the cloud‐based platform, MRICloud.org, which will make the latest MALf tools readily available to users, with minimum barriers, and will expedite and accelerate advancements in developmental neuroscience research, neonatology, and pediatric neuroradiology.
Abstract: Structure-by-structure analysis, in which the brain magnetic resonance imaging (MRI) is parcellated based on its anatomical units, is widely used to investigate chronological changes in morphology or signal intensity during normal development, as well as to identify the alterations seen in various diseases or conditions. The multi-atlas label fusion (MALF) method is considered a highly accurate parcellation approach, and anticipated for clinical application to quantitatively evaluate early developmental processes. However, the current MALF methods, which are designed for neonatal brain segmentations, are not widely available. In this study, we developed a T1-weighted, neonatal, multi-atlas repository and integrated it into the MALF-based brain segmentation tools in the cloud-based platform, MRICloud. The cloud platform ensures users instant access to the advanced MALF tool for neonatal brains, with no software or installation requirements for the client. The Web platform by braingps.mricloud.org will eliminate the dependence on a particular operating system (eg, Windows, Macintosh, or Linux) and the requirement for high computational performance of the user's computers. The MALF-based, fully automated, image parcellation could achieve excellent agreement with manual parcellation, and the whole and regional brain volumes quantified through this method demonstrated developmental trajectories comparable to those from a previous publication. This solution will make the latest MALF tools readily available to users, with minimum barriers, and will expedite and accelerate advancements in developmental neuroscience research, neonatology, and pediatric neuroradiology.
17 Oct 2019
TL;DR: The neuroimaging field is moving toward micron scale and molecular features in digital pathology and animal models, which require mapping to common coordinates for annotation, statistical analysis, and collaboration.
Abstract: The neuroimaging field is moving toward micron scale and molecular features in digital pathology and animal models. These require mapping to common coordinates for annotation, statistical analysis, and collaboration. An important example, the BRAIN Initiative Cell Census Network, is generating 3D brain cell atlases in mouse, and ultimately primate and human.
TL;DR: The eACA has potential to evaluate alterations of the anatomical network related to pathological processes and is applied to two groups of cognitively-normal elderly individuals to detect a disruption of the correlation network affected by pathology.
TL;DR: The feasibility and potential difficulties of automatically generating radiologic reports (RRs) to articulate the clinically important features of brain magnetic resonance (MR) images and the mechanisms of disagreement were investigated to understand where machine–human interface succeeded or failed.
Abstract: Purpose: To examine the feasibility and potential difficulties of automatically generating radiologic reports (RRs) to articulate the clinically important features of brain magnetic resonance (MR) images. Materials and Methods: We focused on examining brain atrophy by using magnetization-prepared rapid gradient-echo (MPRAGE) images. The technology was based on multi-atlas whole-brain segmentation that identified 283 structures, from which larger superstructures were created to represent the anatomic units most frequently used in RRs. Through two layers of data-reduction filters, based on anatomic and clinical knowledge, raw images (~10 MB) were converted to a few kilobytes of human-readable sentences. The tool was applied to images from 92 patients with memory problems, and the results were compared to RRs independently produced by three experienced radiologists. The mechanisms of disagreement were investigated to understand where machine-human interface succeeded or failed. Results: The automatically generated sentences had low sensitivity (mean: 24.5%) and precision (mean: 24.9%) values; these were significantly lower than the inter-rater sensitivity (mean: 32.7%) and precision (mean: 32.2%) of the radiologists. The causes of disagreement were divided into six error categories: mismatch of anatomic definitions (7.2 ± 9.3%), data-reduction errors (11.4 ± 3.9%), translator errors (3.1 ± 3.1%), difference in the spatial extent of used anatomic terms (8.3 ± 6.7%), segmentation quality (9.8 ± 2.0%), and threshold for sentence-triggering (60.2 ± 16.3%). Conclusion: These error mechanisms raise interesting questions about the potential of automated report generation and the quality of image reading by humans. The most significant discrepancy between the human and automatically generated RRs was caused by the sentence-triggering threshold (the degree of abnormality), which was fixed to z-score >2.0 for the automated generation, while the thresholds by radiologists varied among different anatomical structures.
TL;DR: A prediction model built in a training dataset from Alzheimer's Disease Neuroimaging Initiative and applied to a test dataset of clinically acquired MRIs from the Johns Hopkins Memory and Alzheimer's Treatment Center, demonstrating promise as a platform on which computational MRI findings can easily be extended to clinical use.
Abstract: For patients with cognitive disorders and dementia, accurate prognosis of cognitive worsening is critical to their ability to prepare for the future, in collaboration with health-care providers. Despite multiple efforts to apply computational brain magnetic resonance image (MRI) analysis in predicting cognitive worsening, with several successes, brain MRI is not routinely quantified in clinical settings to guide prognosis and clinical decision-making. To encourage the clinical use of a cutting-edge image segmentation method, we developed a prediction model as part of an established web-based cloud platform, MRICloud. The model was built in a training dataset from Alzheimer’s Disease Neuroimaging Initiative (ADNI) where baseline MRI scans were combined with clinical data over time. Each MRI was parcellated into 265 anatomical units based on the MRICloud fully automated image segmentation function, to measure the volume of each parcel. The Mini Mental State Examination (MMSE) was used as a measure of cognitive function. The normalized volume of 265 parcels, combined with baseline MMSE score, age, and sex were input variables for a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with MMSE change in the subsequent two years as the target for prediction. A leave-one-out analysis performed on the training dataset estimated a correlation coefficient of 0.64 between true and predicted MMSE change. A receiver operating characteristic (ROC) analysis estimated a sensitivity of 0.88 and a specificity of 0.76 in predicting substantial cognitive worsening after two years, defined as MMSE decline of ≥4 points. This MRICloud prediction model was then applied to a test dataset of clinically acquired MRIs from the Johns Hopkins Memory and Alzheimer’s Treatment Center (MATC), a clinical care setting. In the latter setting, the model had both sensitivity and specificity of 1.0 in predicting substantial cognitive worsening. While the MRICloud prediction model demonstrated promise as a platform on which computational MRI findings can easily be extended to clinical use, further study with a larger number of patients is needed for validation.
01 Jul 2019
TL;DR: This paper proposes a new technique for interpolating shapes in order to upsample a sparsely acquired serial-section image stack based on a maximum a posteriori estimation strategy which models neighboring sections as observations of random deformations of an image to be estimated.
Abstract: In this paper, we propose a new technique for interpolating shapes in order to upsample a sparsely acquired serial-section image stack. The method is based on a maximum a posteriori estimation strategy which models neighboring sections as observations of random deformations of an image to be estimated. We show the computation of diffeomorphic trajectories between observed sections and define estimated upsampled image sections as a Jacobian-weighted sum of flowing images at corresponding distances along those trajectories. We apply this methodology to upsample stacks of sparse 2D magnetic resonance cross-sections through live mouse hearts. We show that the proposed method results in smoother and more accurate reconstructions over linear interpolation, and report a Dice coefficient of 0.8727 against ground truth segmentations in our dataset and statistically significant improvements in both left ventricular segmentation accuracy and image intensity estimates.
TL;DR: A new class of large deformation diffeomorphic metric mapping (LD-DMM) algorithms for generating dense atlas correspondences onto sparse 2D samples by introducing a field of hidden variables which must be estimated representing a large class of target image uncertainties.
Abstract: We examine the problem of mapping dense 3D atlases onto censored, sparsely sampled 2D target sections at micron and meso scales. We introduce a new class of large deformation diffeomorphic metric mapping (LD-DMM) algorithms for generating dense atlas correspondences onto sparse 2D samples by introducing a field of hidden variables which must be estimated representing a large class of target image uncertainties including (i) unknown parameters representing cross stain contrasts, (ii) censoring of tissue due to localized measurements of target subvolumes and (iii) sparse sampling of target tissue sections. For prediction of the hidden fields we introduce the generalized expectation-maximization algorithm (EM) for which the E-step calculates the conditional mean of the hidden variates simultaneously combined with the diffeomorphic correspondences between atlas and target coordinate systems. The algorithm is run to fixed points guaranteeing estimators satisfy the necessary maximizer conditions when interpreted as likelihood estimators. The dense mapping is an injective correspondence to the sparse targets implying all of the 3D variations are performed only on the atlas side with variation in the targets only 2D manipulations.
28 Jul 2019
TL;DR: A new approach to multimodality registration is developed that can replace ad hoc image similarity metrics (such as mutual information or normalized cross correlation) with a log likelihood under a noise model, enabling the community to benefit from atlas based computational image analysis techniques.
Abstract: The Computational Anatomy Gateway, powered largely by the Comet (San Diego Super-computer Center) and Stampede (Texas Advanced Computing Center) clusters through XSEDE, provides software as a service tools for atlas based analysis of human brain magnetic resonance images. This includes deformable registration, automatic labeling of tissue types, and morphometric analysis. Our goal is to extend these services to the broader neuroscience community, accommodating multiple model organisms and imaging modalities, as well as low quality or missing data. We developed a new approach to multimodality registration: by predicting one modality from another, we can replace ad hoc image similarity metrics (such as mutual information or normalized cross correlation) with a log likelihood under a noise model. This statistical approach enables us to account for missing data using the Expectation Maximization algorithm. For portability and scalability we have implemented this algorithm in tensorflow. For accessibility we have compiled and many working examples for multiple model organisms, imaging systems, and missing tissue or image anomaly situations. These examples are made easily usable in the form of Jupyter notebooks, and made publicly available through github. This framework will significantly reduce the barrier to entry for basic neuroscientists, enabling the community to benefit from atlas based computational image analysis techniques.
TL;DR: To investigate the volumetric growth trajectories, structural magnetic resonance imaging scans obtained from 618 healthy children were parcellated into five regions for the volume quantification and the most active growth was seen in the mesencephalon for both boys and girls.
TL;DR: A web-based interactive resource was created that included a 3D animation and manipulatable 3D models of hippocampal regions to help graduate students and scientists better understand and visualize the human hippocampus.
Abstract: The hippocampus is a critical region of the brain involved in memory and learning and is composed of layered, interconnected regions. Despite the importance of understanding normal hippocampal anatomy for studying its functions and disease processes that affect it, didactic educational visualizations are severely limited. To help graduate students and scientists better understand and visualize the human hippocampus, a web-based interactive resource was created that included a 3D animation and manipulatable 3D models of hippocampal regions. These resources allow users to expand their comprehension of this complex anatomy.