Multiple Imaging Sites Research Articles

Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain.

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

A whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry

Psychosis (including symptoms of delusions, hallucinations, and disorganized conduct/speech) is a main feature of schizophrenia and is frequently present in other major psychiatric illnesses. Studies in individuals with first-episode (FEP) and early psychosis (EP) have the potential to interpret aberrant connectivity associated with psychosis during a period with minimal influence from medication and other confounds. The current study uses a data-driven whole-brain approach to examine patterns of aberrant functional network connectivity (FNC) in a multi-site dataset comprising resting-state functional magnetic resonance images (rs-fMRI) from 117 individuals with FEP or EP and 130 individuals without a psychiatric disorder, as controls. Accounting for age, sex, race, head motion, and multiple imaging sites, differences in FNC were identified between psychosis and control participants in cortical (namely the inferior frontal gyrus, superior medial frontal gyrus, postcentral gyrus, supplementary motor area, posterior cingulate cortex, and superior and middle temporal gyri), subcortical (the caudate, thalamus, subthalamus, and hippocampus), and cerebellar regions. The prominent pattern of reduced cerebellar connectivity in psychosis is especially noteworthy, as most studies focus on cortical and subcortical regions, neglecting the cerebellum. The dysconnectivity reported here may indicate disruptions in cortical-subcortical-cerebellar circuitry involved in rudimentary cognitive functions which may serve as reliable correlates of psychosis.

Setting up regional diagnostic reference levels for pediatric computed tomography in Latin America: preliminary results, challenges and the work ahead.

We established a framework for collecting radiation doses for head, chest and abdomen-pelvis computed tomography (CT) in children scanned at multiple imaging sites across Latin America with an aim towards establishing diagnostic reference levels (DRLs) and achievable doses (ADs) in pediatric CT in Latin America. Our study included 12 Latin American sites (inArgentina, Bolivia,Brazil, Chile,Colombia, Ecuador, Hondurasand Panama) contributing data on the four most common pediatric CT examinations (non-contrast head, non-contrast chest, post-contrast chest and post-contrast abdomen-pelvis). Sites contributed data on patients' age, sexand weight, scan factors (tube current and potential), volumeCT dose index (CTDIvol) and dose lengthproduct (DLP). Data were verified, leading to the exclusion of two sites with missing or incorrect data entries. We estimated overall and site-specific 50th (AD) and 75th (diagnostic reference level [DRL]) percentile CTDIvol and DLP for each CT protocol. Non-normal data were compared using the Kruskal-Wallis test. Sites contributed data from 3,934 children (1,834 females) for different CT exams (head CT 1,568/3,934, 40%; non-contrast chest CT 945/3,934, 24%; post-contrast chest CT 581/3,934, 15%; abdomen-pelvis CT 840/3,934, 21%). There were significant statistical differences in 50th and 75th percentile CTDIvol and DLP values across the participating sites (P<0.001). The 50th and 75th percentile doses for most CT protocols were substantially higher than the corresponding doses reported from the United States of America. Our study demonstrates substantial disparities and variations in pediatric CT examinations performed in multiple sites in Latin America. We will use the collected data toimprove scan protocols and perform a follow-up CTstudy to establish DRLs and ADs based on clinical indications.

IBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction.

The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline ( http://www.ibeat.cloud ), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners.

Verification of the brain network marker of major depressive disorder: Test-retest reliability and anterograde generalization performance for newly acquired data

BackgroundRecently, we developed a generalizable brain network marker for the diagnosis of major depressive disorder (MDD) across multiple imaging sites using resting-state functional magnetic resonance imaging. Here, we applied this brain network marker to newly acquired data to verify its test-retest reliability and anterograde generalization performance for new patients. MethodsWe tested the sensitivity and specificity of our brain network marker of MDD using data acquired from 43 new patients with MDD as well as new data from 33 healthy controls (HCs) who participated in our previous study. To examine the test-retest reliability of our brain network marker, we evaluated the intraclass correlation coefficients (ICCs) between the brain network marker-based classifier's output (probability of MDD) in two sets of HC data obtained at an interval of approximately 1 year. ResultsTest-retest correlation between the two sets of the classifier's output (probability of MDD) from HCs exhibited moderate reliability with an ICC of 0.45 (95 % confidence interval,0.13–0.68). The classifier distinguished patients with MDD and HCs with an accuracy of 69.7 % (sensitivity, 72.1 %; specificity, 66.7 %). LimitationsThe data of patients with MDD in this study were cross-sectional, and the clinical significance of the marker, such as whether it is a state or trait marker of MDD and its association with treatment responsiveness, remains unclear. ConclusionsThe results of this study reaffirmed the test-retest reliability and generalization performance of our brain network marker for the diagnosis of MDD.

Examining the usefulness of the brain network marker program using fMRI for the diagnosis and stratification of major depressive disorder: a non-randomized study protocol

BackgroundAlthough many studies have reported the biological basis of major depressive disorder (MDD), none have been put into practical use. Recently, we developed a generalizable brain network marker for MDD diagnoses (diagnostic marker) across multiple imaging sites using resting-state functional magnetic resonance imaging (rs-fMRI). We have planned this clinical trial to establish evidence for the practical applicability of this diagnostic marker as a medical device. In addition, we have developed generalizable brain network markers for MDD stratification (stratification markers), and the verification of these brain network markers is a secondary endpoint of this study.MethodsThis is a non-randomized, open-label study involving patients with MDD and healthy controls (HCs). We will prospectively acquire rs-fMRI data from 50 patients with MDD and 50 HCs and anterogradely verify whether our diagnostic marker can distinguish between patients with MDD and HCs. Furthermore, we will longitudinally obtain rs-fMRI and clinical data at baseline and 6 weeks later in 80 patients with MDD treated with escitalopram and verify whether it is possible to prospectively distinguish MDD subtypes that are expected to be effectively responsive to escitalopram using our stratification markers.DiscussionIn this study, we will confirm that sufficient accuracy of the diagnostic marker could be reproduced for data from a prospective clinical study. Using longitudinally obtained data, we will also examine whether the “brain network marker for MDD diagnosis” reflects treatment effects in patients with MDD and whether treatment effects can be predicted by “brain network markers for MDD stratification”. Data collected in this study will be extremely important for the clinical application of the brain network markers for MDD diagnosis and stratification.Trial registrationJapan Registry of Clinical Trials (jRCTs062220063). Registered 12/10/2022.

Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites.

SummaryBackgroundEstablishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based on small sample size and did not account for the brain connectome that is associated with the pathophysiology of MDD. Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset.MethodsResting-state functional MRI scans of 1586 participants (821 MDD vs. 765 controls) across 16 sites of Rest-meta-MDD consortium were collected. GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient regions contributing to classification, and explore the relationship between topological characteristics of salient regions and clinical measures.FindingsGCN achieved an accuracy of 81·5% (95%CI: 80·5–82·5%, AUC: 0·865), which was higher than other common machine learning classifiers. The most salient regions contributing to classification were primarily identified within the default mode, fronto-parietal, and cingulo-opercular networks. Nodal topologies of the left inferior parietal lobule and left dorsolateral prefrontal cortex were associated with depressive severity and illness duration, respectively.InterpretationThese findings based on a large, multi-site dataset support the feasibility and effectiveness of GCN in characterizing MDD, and also illustrate the potential utility of GCN for enhancing understanding of the neurobiology of MDD by detecting clinically-relevant disruption in functional network topology.FundingThis study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 82027808, 81820108018).

Generalizable brain network markers of major depressive disorder across multiple imaging sites.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

A standardized protocol to reliably visualize the left atrial appendage with intracardiac echocardiography: Importance of multiple imaging sites.

Currently, there is no accepted protocol for left atrial appendage (LAA) imaging with intracardiac echocardiography (ICE). This study aimed to assess the utility of ICE to reliably visualize the entire cavity of the LAA and propose a specific procedural protocol to achieve the above objective. We created a three-dimensional reconstruction of the LAA, using two-dimensional ICE sections obtained from three different location (the right atrium [RA], right ventricle inflow [RVI], and right ventricular outflow [RVOT]). We then compared the three-dimensional LAA reconstruction by ICE with one obtained by cardiac computed tomography angiography (CCTA) for morphological and volume differences. Three-dimensional reconstruction with ICE could reliably reproduce the LAA as visualized with CCTA but only when ICE sampling was performed from at least two catheter positions. There was no statistically significant difference between LAA volumes obtained with ICE and CCTA (P=0.33). The contribution of each anatomical location to the total volume was 17%±16.6%, 74%±13.3%, and 33%±26% for RA, RVI, and RVOT, respectively. In comparison with CCTA, the LAA can be reliably visualized in its entity by ICE, but only if multiple imaging positions (RA, RV inflow, and RVOT) are used.

Vertebral corners detection on sagittal X-rays based on shape modelling, random forest classifiers and dedicated visual features

Quantitative measurements of spine shape parameters on planar X-rays is critical for clinical applications, but remains tedious and with no fully automated solution demonstrated on the whole spine. This study aims to limit manual input, while demonstrating precise vertebrae corners positioning and shape parameter measurements from sagittal radiographs of the cervical and lumbar regions, exploiting novel dedicated visual features and specialised classifiers. A database of manually annotated X-rays is used to train specialised Random Forest classifiers for each spine region and corner type. An original combination of local gradient characteristics, Haar-like features and contextual features based on patch intensity and contrast is used as visual features. The proposed method is trained and evaluated on 109 sagittal X-rays of asymptomatic and pathological subjects, from multiple imaging sites, and with a large age range (6–69 years old). Performance is first evaluated for positioning a 2D spine shape model, where precisely detected corners enable to adjust the model via an original multilinear statistical regression. Root-mean-square errors of corners localisation and vertebra orientations are reported, demonstrating state-of-the-art precision compared to existing methods, but with minimal manual input. The method is then evaluated for the extraction of additional vertebrae shape characteristics, such as vertebral centre positioning, endplate centres positioning and endplate length measures, rarely studied in previous literature. The proposed method enables, with minimal initialisation, fast and precise individual vertebrae delineations on sagittal radiographs of normal and pathological cases, with a precision and robustness level required for objective support for diagnosis and therapy decision-making.

Evaluation of normal findings using a detailed and focused technique for transcutaneous abdominal ultrasonography in the horse.

BackgroundUltrasonography is an important diagnostic tool in the investigation of abdominal disease in the horse. Several factors may affect the ability to image different structures within the abdomen. The aim of the study was to describe the repeatability of identification of abdominal structures in normal horses using a detailed ultrasonographic examination technique and using a focused, limited preparation technique.MethodsA detailed abdominal ultrasound examination was performed in five normal horses, repeated on five occasions (total of 25 examinations). The abdomen was divided into ten different imaging sites, and structures identified in each site were recorded. Five imaging sites were then selected for a single focused ultrasound examination in 20 normal horses. Limited patient preparation was performed. Structures were recorded as ‘identified’ if ultrasonographic features could be distinguished. The location of organs and their frequency of identification were recorded. Data from both phases were analysed to determine repeatability of identification of structures in each examination (irrespective of imaging site), and for each imaging site.ResultsCaecum, colon, spleen, liver and right kidney were repeatably identified using the detailed technique, and had defined locations. Large colon and right kidney were identified in 100% of examinations with both techniques. Liver, spleen, caecum, duodenum and other small intestine were identified more frequently with the detailed examination. Small intestine was most frequently identified in the ventral abdomen, its identification varied markedly within and between horses, and required repeated examinations in some horses. Left kidney could not be identified in every horse using either technique. Sacculated colon was identified in all ventral sites, and was infrequently identified in dorsal sites.ConclusionsCaecum, sacculated large intestine, spleen, liver and right kidney were consistently identified with both techniques. There were some normal variations which should be considered when interpreting ultrasonographic findings in clinical cases: left kidney was not always identified, sacculated colon was occasionally identified in dorsal flank sites. Multiple imaging sites and repeated examinations may be required to identify small intestine. A focused examination identified most key structures, but has some limitations compared to a detailed examination.

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).