A connectivity-based test-retest dataset of multi-modal magnetic resonance imaging in young healthy adults.
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ABSTRACT:
SUBMITTER: Lin Q
PROVIDER: S-EPMC4623457 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Functional connectivity (FC) has become a leading method for resting-state functional magnetic resonance imaging (rs-fMRI) analysis. However, the majority of the previous studies utilized pairwise, temporal synchronization-based FC. Recently, high-order FC (HOFC) methods were proposed with the idea of computing "correlation of correlations" to capture high-level, more complex associations among the brain regions. There are two types of HOFC. The first type is topographical profile similarity-based HOFC (tHOFC) and its variant, associated HOFC (aHOFC), for capturing different levels of HOFC. Instead of measuring the similarity of the original rs-fMRI signals with the traditional FC (low-order FC, or LOFC), tHOFC measures the similarity of LOFC profiles (i.e., a set of LOFC values between a region and all other regions) between each pair of brain regions. The second type is dynamics-based HOFC (dHOFC) which defines the quadruple relationship among every four brain regions by first calculating two pairwise dynamic LOFC "time series" and then measuring their temporal synchronization (i.e., temporal correlation of the LOFC fluctuations, not the BOLD fluctuations). Applications have shown the superiority of HOFC in both disease biomarker detection and individualized diagnosis than LOFC. However, no study has been carried out for the assessment of test-retest reliability of different HOFC metrics. In this paper, we systematically evaluate the reliability of the two types of HOFC methods using test-retest rs-fMRI data from 25 (12 females, age 24.48 ± 2.55 years) young healthy adults with seven repeated scans (with interval = 3-8 days). We found that all HOFC metrics have satisfactory reliability, specifically (1) fair-to-good for tHOFC and aHOFC, and (2) fair-to-moderate for dHOFC with relatively strong connectivity strength. We further give an in-depth analysis of the biological meanings of each HOFC metric and highlight their differences compared to the LOFC from the aspects of cross-level information exchanges, within-/between-network connectivity, and modulatory connectivity. In addition, how the dynamic analysis parameter (i.e., sliding window length) affects dHOFC reliability is also investigated. Our study reveals unique functional associations characterized by the HOFC metrics. Guidance and recommendations for future applications and clinical research using HOFC are provided. This study has made a further step toward unveiling more complex human brain connectome.
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Project description:Aims: To evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix. Methods and Results: The sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Images were segmented according to a pre-defined protocol to select three regions of interest (ROI) in end-diastole and end-systole: right ventricle, left ventricle (LV), and LV myocardium. We extracted radiomics shape features from all three ROIs and, additionally, first-order and texture features from the LV myocardium. Overall, 280 features were derived per study. For each feature, we calculated intra-class correlation coefficient (ICC), within-subject coefficient of variation, and mean relative difference. We ranked robustness of features according to mean ICC stratified by feature category, ROI, and cardiac phase, demonstrating a wide range of repeatability. There were features with good and excellent repeatability (ICC ≥ 0.75) within all feature categories and ROIs. A high proportion of first-order and texture features had excellent repeatability (ICC ≥ 0.90), however, these categories also contained features with the poorest repeatability (ICC < 0.50). Conclusion: CMR radiomic features have a wide range of repeatability. This paper is intended as a reference for future researchers to guide selection of the most robust features for clinical CMR radiomics models. Further work in larger and richer datasets is needed to further define the technical performance and clinical utility of CMR radiomics.
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Project description:BackgroundQuantification and visualisation of left ventricular (LV) blood flow is afforded by three-dimensional, time resolved phase contrast cardiovascular magnetic resonance (CMR 4D flow). However, few data exist upon the repeatability and variability of these parameters in a healthy population. We aimed to assess the repeatability and variability over time of LV 4D CMR flow measurements.MethodsForty five controls underwent CMR 4D flow data acquisition. Of these, 10 underwent a second scan within the same visit (scan-rescan), 25 returned for a second visit (interval scan; median interval 52 days, IQR 28-57 days). The LV-end diastolic volume (EDV) was divided into four flow components: 1) Direct flow: inflow that passes directly to ejection; 2) Retained inflow: inflow that enters and resides within the LV; 3) Delayed ejection flow: starts within the LV and is ejected and 4) Residual volume: blood that resides within the LV for > 2 cardiac cycles. Each flow components' volume was related to the EDV (volume-ratio). The kinetic energy at end-diastole (ED) was measured and divided by the components' volume.ResultsThe dominant flow component in all 45 controls was the direct flow (volume ratio 38 ± 4%) followed by the residual volume (30 ± 4%), then delayed ejection flow (16 ± 3%) and retained inflow (16 ± 4%). The kinetic energy at ED for each component was direct flow (7.8 ± 3.0 microJ/ml), retained inflow (4.1 ± 2.0 microJ/ml), delayed ejection flow (6.3 ± 2.3 microJ/ml) and the residual volume (1.2 ± 0.5 microJ/ml). The coefficients of variation for the scan-rescan ranged from 2.5%-9.2% for the flow components' volume ratio and between 13.5%-17.7% for the kinetic energy. The interval scan results showed higher coefficients of variation with values from 6.2-16.1% for the flow components' volume ratio and 16.9-29.0% for the kinetic energy of the flow components.ConclusionLV flow components' volume and their associated kinetic energy values are repeatable and stable within a population over time. However, the variability of these measurements in individuals over time is greater than can be attributed to sources of error in the data acquisition and analysis, suggesting that additional physiological factors may influence LV flow measurements.
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Project description:Neural and behavioral mechanisms during approach-avoidance conflict decision-making are relevant across various psychiatric disorders, particularly anxiety disorders. Studies using approach-avoidance conflict paradigms in healthy adults have identified preliminary neural mechanisms, but findings must be replicated and demonstrated as reliable before further application. This study sought to replicate previous findings and examine test-retest reliability of behavioral (approach behavior, reaction time) and neural (regions of interest [ROIs]) responses during an approach-avoidance conflict task conducted during functional magnetic resonance imaging (fMRI). Thirty healthy adults completed an approach-avoidance conflict task during fMRI on two occasions (mean interval: 17 days; range: 11-32). Effects of task condition during three task phases (decision-making, affective outcome and monetary reward) and intraclass correlation coefficients (ICCs) were calculated across time points. Results replicated that approach behavior was modulated by conflict during decision-making. ROI activations were replicated such that dorsal anterior cingulate cortex (dACC) was modulated by conflict during decision-making, and dACC, striatum, and anterior insula were modulated by valence during affective outcomes (p's <.0083). Approach behavior during conflict demonstrated excellent reliability (ICCs ≥.77). Activation of dACC during conflict decision-making and anterior insula during negative outcomes demonstrated fair reliability (ICCs = .51 and .54), and dACC and striatum activation demonstrated good reliability during negative outcomes (ICCs = .63 and .69). Two additional ROIs (amygdala, left dorsolateral prefrontal cortex) showed good reliability during negative outcomes (ICCs ≥.60). These results characterize several specific behavioral and neuroimaging responses that are replicable and sufficiently reliable during approach-avoidance conflict decision-making to support future utility.
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Project description:Multimodal magnetic resonance imaging (mMRI) has been widely used to map the structure and function of the human brain, as well as its behavioral associations. However, to date, a large sample with a long-term longitudinal design and a narrow age-span has been lacking for the assessment of test-retest reliability and reproducibility of brain-behavior correlations, as well as the development of novel causal insights into these correlational findings. Here we describe the SLIM dataset, which includes brain and behavioral data across a long-term retest-duration within three and a half years, mMRI scans provided a set of structural, diffusion and resting-state functional MRI images, along with rich samples of behavioral assessments addressed-demographic, cognitive and emotional information. Together with the Consortium for Reliability and Reproducibility (CoRR), the SLIM is expected to accelerate the reproducible sciences of the human brain by providing an open resource for brain-behavior discovery sciences with big-data approaches.
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Project description:This data collection contains Magnetic Resonance Imaging (MRI) data, including structural, diffusion, stimulus-evoked, and resting-state functional MRI and behavioural assessment results, including acute post-impact Loss-of-Righting Reflex time and acute, subacute, and longer-term Neural Severity Score, and Open Field Behaviour obtained from a mouse model of concussion. Four cohorts with 43 3-4 months old male mice in total were used: Sham (n = 14, n = 6 day 2, n = 3 day 7, n = 5 day 14), concussion day 2 (CON 2; n = 9), concussion day 7 (CON 7; n = 10), concussion day 14 (CON 14; n = 10). The data collection contains the aforementioned MRI data in compressed NIFTI format, data sheets on animal's backgrounds and behavioural outcomes and is made publicly available from a data repository. The available data are intended to facility cross-study comparisons, meta-analysis, and science reproducibility.
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