Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup.

Multi-institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure-based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI-rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure-based analysis showed widespread DTI abnormalities in FEP and rs-fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof-of-concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub-groups.

Evaluation of group-specific, whole-brain atlas generation using Volume-based Template Estimation (VTE): application to normal and Alzheimer's populations.

MRI-based human brain atlases, which serve as a common coordinate system for image analysis, play an increasingly important role in our understanding of brain anatomy, image registration, and segmentation. Study-specific brain atlases are often obtained from one of the subjects in a study or by averaging the images of all participants after linear or non-linear registration. The latter approach has the advantage of providing an unbiased anatomical representation of the study population. But, the image contrast is influenced by both inherent MR contrasts and residual anatomical variability after the registration; in addition, the topology of the brain structures cannot reliably be preserved. In this study, we demonstrated a population-based template-creation approach, which is based on Bayesian template estimation on a diffeomorphic random orbit model. This approach attempts to define a population-representative template without the cross-subject intensity averaging; thus, the topology of the brain structures is preserved. It has been tested for segmented brain structures, such as the hippocampus, but its validity on whole-brain MR images has not been examined. This paper validates and evaluates this atlas generation approach, i.e., Volume-based Template Estimation (VTE). Using datasets from normal subjects and Alzheimer's patients, quantitative measurements of sub-cortical structural volumes, metric distance, displacement vector, and Jacobian were examined to validate the group-averaged shape features of the VTE. In addition to the volume-based quantitative analysis, the preserved brain topology of the VTE allows surface-based analysis within the same atlas framework. This property was demonstrated by analyzing the registration accuracy of the pre- and post-central gyri. The proposed method achieved registration accuracy within 1mm for these population-preserved cortical structures in an elderly population.

Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures.

The purpose of this paper is to extend the single-subject Eve atlas from Johns Hopkins University, which currently contains diffusion tensor and T1-weighted anatomical maps, by including contrast based on quantitative susceptibility mapping. The new atlas combines a "deep gray matter parcellation map" (DGMPM) derived from a single-subject quantitative susceptibility map with the previously established "white matter parcellation map" (WMPM) from the same subject's T1-weighted and diffusion tensor imaging data into an MNI coordinate map named the "Everything Parcellation Map in Eve Space," also known as the "EvePM." It allows automated segmentation of gray matter and white matter structures. Quantitative susceptibility maps from five healthy male volunteers (30 to 33 years of age) were coregistered to the Eve Atlas with AIR and Large Deformation Diffeomorphic Metric Mapping (LDDMM), and the transformation matrices were applied to the EvePM to produce automated parcellation in subject space. Parcellation accuracy was measured with a kappa analysis for the left and right structures of six deep gray matter regions. For multi-orientation QSM images, the Kappa statistic was 0.85 between automated and manual segmentation, with the inter-rater reproducibility Kappa being 0.89 for the human raters, suggesting "almost perfect" agreement between all segmentation methods. Segmentation seemed slightly more difficult for human raters on single-orientation QSM images, with the Kappa statistic being 0.88 between automated and manual segmentation, and 0.85 and 0.86 between human raters. Overall, this atlas provides a time-efficient tool for automated coregistration and segmentation of quantitative susceptibility data to analyze many regions of interest. These data were used to establish a baseline for normal magnetic susceptibility measurements for over 60 brain structures of 30- to 33-year-old males. Correlating the average susceptibility with age-based iron concentrations in gray matter structures measured by Hallgren and Sourander (1958) allowed interpolation of the average iron concentration of several deep gray matter regions delineated in the EvePM.

Digital 3D Brain MRI Arterial Territories Atlas.

The locus and extent of brain damage in the event of vascular insult can be quantitatively established quickly and easily with vascular atlases. Although highly anticipated by clinicians and clinical researchers, no digital MRI arterial atlas is readily available for automated data analyses. We created a digital arterial territory atlas based on lesion distributions in 1,298 patients with acute stroke. The lesions were manually traced in the diffusion-weighted MRIs, binary stroke masks were mapped to a common space, probability maps of lesions were generated and the boundaries for each arterial territory was defined based on the ratio between probabilistic maps. The atlas contains the definition of four major supra- and infra-tentorial arterial territories: Anterior, Middle, Posterior Cerebral Arteries and Vertebro-Basilar, and sub-territories (thalamoperforating, lenticulostriate, basilar and cerebellar arterial territories), in two hierarchical levels. This study provides the first publicly-available, digital, 3D deformable atlas of arterial brain territories, which may serve as a valuable resource for large-scale, reproducible processing and analysis of brain MRIs of patients with stroke and other conditions.

A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke.

To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. Large datasets are therefore imperative, as well as fully automated image post-processing tools to analyze them. The development of such tools, particularly with artificial intelligence, is highly dependent on the availability of large datasets to model training and testing. We present a public dataset of 2,888 multimodal clinical MRIs of patients with acute and early subacute stroke, with manual lesion segmentation, and metadata. The dataset provides high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating function to frequency lesion maps.

Dataset of relationship between longitudinal change in cognitive performance and functional connectivity in cognitively normal older individuals.

The data show an association between measured and predicted changes in cognitive performance in older adults who are cognitively normal. Changes in cognitive performance over two years were assessed using the Cognitive Composite Score. The prediction of change in cognitive function was based on changes in pairwise functional connectivity between 80 gray matter regions examined by resting-state functional magnetic resonance imaging. A feature extraction process based on the Variable Importance Testing Approach (VITA) identified changes in 11 pairs of functional connections associated with the default mode network as features related to changes in cognitive performance. Linear and elastic net regression models were applied to these 11 features to predict changes in cognitive performance over two years. A relationship between the 11 features and the geriatric depression score was also shown. The dataset supplements the research findings in the "Changes in pairwise functional connectivity associated with changes in cognitive performance in cognitively normal older individuals: a two-year observational study" published in Oishi et al. (2022). The raw rs-fMRI correlation matrix and associated clinical data can be accessed upon request from the BIOCARD website (www.biocard-se.org) and can be reused for predictive model building.

Changes in pairwise functional connectivity associated with changes in cognitive performance in cognitively normal older individuals: A two-year observational study.

Neurobiological substrates of cognitive decline in cognitively normal older individuals have been investigated by resting-state functional magnetic resonance imaging, but little is known about the relationship between longitudinal changes in the whole brain. In this study, we examined two-year changes in functional connectivity among 80 gray matter areas and investigated the relationship to two-year changes in cognitive performance. A cross-validated permutation variable importance measure was applied to select features related to a change in cognitive performance. Age-corrected changes in eleven pairs of functional connections were selected as important features, all related to brain areas that belong to the default mode network. A linear regression model with cross-validation demonstrated a mean correlation coefficient of 0.55 between measured and predicted changes in the cognitive composite score. These results suggest that intra- and inter-network connections in the default mode network are associated with cognitive changes over two years among cognitively normal individuals.

Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke.

Background: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research.

Relationship between neuropsychological behavior and brain white matter in first-episode psychosis.

We addressed the relationship between white matter architecture, represented by MRI fractional anisotropy (FA), and cognition in individuals with first-episode psychosis (FEP) by applying for a new methodology that allows whole brain parcellation of core and peripheral white matter in a biologically meaningful fashion. Regionally specific correlations were found in FEP between three specific domains of cognition (processing speed, attention/working memory, and executive functioning) and FA at the deep (cerebral peduncles, sagittal striatum, uncinate, internal/external capsule, cingulum) and peripheral white matter (adjacent to inferior temporal, angular, supramarginal, insula, occipital, rectus gyrus).

Extended multimodal whole-brain anatomical covariance analysis: detection of disrupted correlation networks related to amyloid deposition.

BACKGROUND: An anatomical covariance analysis (ACA) enables to elucidate inter-regional connections on a group basis, but little is known about the connections among white matter structures or among gray and white matter structures. Effect of including multiple magnetic resonance imaging (MRI) modalities into ACA framework in detecting white-to-white or gray-to-white connections is yet to be investigated. NEW METHOD: Proposed extended anatomical covariance analysis (eACA), analyzes correlations among gray and white matter structures (multi-structural) in various types of imaging modalities (T1-weighted images, T2 maps obtained from dual-echo sequences, and diffusion tensor images (DTI)). To demonstrate the capability to detect a disruption of the correlation network affected by pathology, we applied the eACA to two groups of cognitively-normal elderly individuals, one with (PiB+) and one without (PiB-) amyloid deposition in their brains. RESULTS: The volume of each anatomical structure was symmetric and functionally related structures formed a cluster. The pseudo-T2 value was highly homogeneous across the entire cortex in the PiB- group, while a number of physiological correlations were altered in the PiB + group. The DTI demonstrated unique correlation network among structures within the same phylogenetic portions of the brain that were altered in the PiB + group. COMPARISON WITH EXISTING METHOD: The proposed eACA expands the concept of existing ACA to the connections among the white matter structures. The extension to other image modalities expands the way in which connectivity may be detected. CONCLUSION: The eACA has potential to evaluate alterations of the anatomical network related to pathological processes.

Test-retest reproducibility of a multi-atlas automated segmentation tool on multimodality brain MRI.

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.

AtlasGuide: software for stereotaxic guidance using 3D CT/MRI hybrid atlases of developing mouse brains.

Stereotaxic operations of the mouse brain are critically important for various types of neuroscience research studies, which include electrical recording of neural activities or site-targeted injection of stem cells, chemical tracers, and vectors, to name a few. To guide such operations, two-dimensional histology-based mouse brain atlases, such as the Paxinos and Franklin atlas, are widely used. Recently, computed tomography (CT) and magnetic resonance imaging (MRI) based hybrid three-dimensional (3D) atlases of developing mouse brains have been introduced. In this study, a new stereotaxic guidance software, called AtlasGuide, is introduced, which was developed to fully utilize the benefits of the 3D atlases for high-precision stereotaxic targeting. The AtlasGuide software provides functions to visualize oblique needle paths in 2D and 3D views, which allow investigators to simultaneously examine brain structures that could be damaged by the needle path and optimize the injection angles for high-precision trajectory selection through critical neural tissue. It allows reorientation and scaling of the atlases dynamically to match the orientation of the animal brain prepared for surgery, thereby eliminating the need to manually align the subject to the atlas, a procedure which is essential while using conventional 2D atlases. In addition, the software enables loading user-defined atlases when researchers need image-based guidance for different age groups, strains, or species. The software with integrated 3D stereotaxic mouse atlases is available for download at the http://lbam.med.jhmi.edu website.

Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

Molecular phylogeny of the carnivora (mammalia): assessing the impact of increased sampling on resolving enigmatic relationships.

This study analyzed 76 species of Carnivora using a concatenated sequence of 6243 bp from six genes (nuclear TR-i-I, TBG, and IRBP; mitochondrial ND2, CYTB, and 12S rRNA), representing the most comprehensive sampling yet undertaken for reconstructing the phylogeny of this clade. Maximum parsimony and Bayesian methods were remarkably congruent in topologies observed and in nodal support measures. We recovered all of the higher level carnivoran clades that had been robustly supported in previous analyses (by analyses of morphological and molecular data), including the monophyly of Caniformia, Feliformia, Arctoidea, Pinnipedia, Musteloidea, Procyonidae + Mustelidae sensu stricto, and a clade of (Hyaenidae + (Herpestidae + Malagasy carnivorans)). All of the traditional "families," with the exception of Viverridae and Mustelidae, were robustly supported as monophyletic groups. We further have determined the relative positions of the major lineages within the Caniformia, which previous studies could not resolve, including the first robust support for the phylogenetic position of marine carnivorans (Pinnipedia) within the Arctoidea (as the sister-group to musteloids [sensu lato], with ursids as their sister group). Within the pinnipeds, Odobenidae (walrus) was more closely allied with otariids (sea lions/fur seals) than with phocids ("true" seals). In addition, we recovered a monophyletic clade of skunks and stink badgers (Mephitidae) and resolved the topology of musteloid interrelationships as: Ailurus (Mephitidae (Procyonidae, Mustelidae [sensu stricto])). This pattern of interrelationships of living caniforms suggests a novel inference that large body size may have been the primitive condition for Arctoidea, with secondary size reduction evolving later in some musteloids. Within Mustelidae, Bayesian analyses are unambiguous in supporting otter monophyly (Lutrinae), and in both MP and Bayesian analyses Martes is paraphyletic with respect to Gulo and Eira, as has been observed in some previous molecular studies. Within Feliformia, we have confirmed that Nandinia is the outgroup to all other extant feliforms, and that the Malagasy Carnivora are a monophyletic clade closely allied with the mongooses (Herpestidae [sensu stricto]). Although the monophyly of each of the three major feliform clades (Viverridae sensu stricto, Felidae, and the clade of Hyaenidae + (Herpestidae + Malagasy carnivorans)) is robust in all of our analyses, the relative phylogenetic positions of these three lineages is not resolvable at present. Our analyses document the monophyly of the "social mongooses," strengthening evidence for a single origin of eusociality within the Herpestidae. For a single caniform node, the position of pinnipeds relative to Ursidae and Musteloidea, parsimony analyses of data for the entire Carnivora did not replicate the robust support observed for both parsimony and Bayesian analyses of the caniform ingroup alone. More detailed analyses and these results demonstrate that outgroup choice can have a considerable effect on the strength of support for a particular topology. Therefore, the use of exemplar taxa as proxies for entire clades with diverse evolutionary histories should be approached with caution. The Bayesian analysis likelihood functions generally were better able to reconstruct phylogenetic relationships (increased resolution and more robust support for various nodes) than parsimony analyses when incompletely sampled taxa were included. Bayesian analyses were not immune, however, to the effects of missing data; lower resolution and support in those analyses likely arise from non-overlap of gene sequence data among less well-sampled taxa. These issues are a concern for similar studies, in which different gene sequences are concatenated in an effort to increase resolving power.

Secondary aortoesophageal fistula after endoluminal exclusion because of thoracic aortic transection.

Secondary aortoesophageal fistula (AEF) is a rare but catastrophic complication that occurs after thoracic aortic reconstruction. Recently endoluminal stent grafts have been used in selected patients with a thoracic aortic aneurysm, dissection, or traumatic aortic transection. A 24-year-old woman had massive upper gastrointestinal tract bleeding 15 months after endoluminal stent graft placement because of traumatic descending thoracic aortic transection. Evaluation demonstrated an AEF from the mid-esophagus to the endoluminal stent graft. The endoluminal graft was explanted, with primary repair of the thoracic aortic defect and simultaneous primary repair of the esophageal injury. The patient is well 15 months after open repair of the AEF.