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  1. 1881
    “…In addition, both TBSS and separate ROI analyses revealed significant sex-by-diagnosis interactions for the corticospinal tract (CST), inferior longitudinal fasciculus (ILF) and SLF. …”
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  2. 1882
    “…There was a trend towards decreasing myocardial velocities with increasing ROI length. However, the cardiac cycle time intervals were similar irrespective of which ROI size was used. …”
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  3. 1883
    “…Resting state Functional Connectivity (RSFC) was assessed using the seed-ROI based analysis. Seed ROIs were positioned on basal ganglia, on primary sensory-motor cortices, on the supplementary motor areas and on the cerebellum. …”
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  4. 1884
  5. 1885
    “…By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses.…”
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  6. 1886
    “…Ipsilesional and contralesional FA, mean (MD), axial (AD), and radial (RD) diffusivities values were calculated in different Regions-of-Interest (ROIs): (1) the white matter underlying the precentral gyrus (M1), (2) the corona radiata (CoRad), (3) the posterior limb of the internal capsule (PLIC) and (4) the cerebral peduncles (CP). …”
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  7. 1887
    “…Time activity curves (TACs) were created from regions of interest (ROIs) that encompassed the entire small organs or samples with the highest activity within large organs. …”
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  8. 1888
    “…Within both anatomical and functional regions of interest (ROIs), a multi-voxel pattern classifier was used to quantitatively assess the fidelity of activity patterns representing stimulus category: whether a face or a scene was presented on each trial, and goal relevance, whether the presented image was task relevant (i.e., a face is relevant in a “Remember Faces” block, but irrelevant in a “Remember Scenes” block). …”
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  9. 1889
    “…Mean homogeneity index for orientation of local binary patterns (HI(angle,mean)) and fractal dimension of vertical structures (FD(Ver)) were related (p < 0.01) with connectivity density (HI(angle,mean): r = −0.73, FD(Ver): r = 0.69) and trabecular separation (HI(angle,mean): r = 0.73, FD(Ver): r = −0.70) when all ROIs were pooled together (n = 44). Bone density and structure in tibia from standard clinically available 2D radiographs are significantly correlated with true 3D microstructure of bone. …”
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  10. 1890
    “…We enrolled 82 PD patients (PD-all) with varying disease durations (≤5 years: PD≤5, n = 43; >5 years: PD>5, n = 39) and 38 matched healthy controls (HC), receiving diffusion tensor imaging as well as R2 and R2* relaxometry calculated from multi-echo T2*-weighted and dual-echo TSE imaging, respectively. ROIs were drawn to delineate caudate nucleus (CN), putamen (PU), globus pallidus (GP) and substantia nigra (SN) on the co-registered maps. …”
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  11. 1891
    “…Existing histological assessments of these two neuronal migration pathways have limitations inherent to microscopic studies and are confined to small anatomic regions of interest (ROIs). Thus, little evidence is available about their three-dimensional (3-D) fiber pathways and development throughout the entire brain. …”
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  12. 1892
    “…The decrease was noted in ipsilateral, contralateral, and interhemispheric regions for left and right precentral ROIs. Additionally, the left postcentral ROI demonstrated increased connectivity with the thalamus bilaterally in SCI patients. …”
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  13. 1893
    “…HCs, we found a significant (p≤0.05) difference of the MWF in all measured ROIs except of GCC and SCC. The mean reduction of MWF in the NAWM of MS-patients compared to HCs was 37%. …”
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  14. 1894
    “…The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. …”
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  15. 1895
    “…Using a cluster analysis technique based on fuzzy c-means to group cells according to their isotope ((13)C/(12)C, (15)N/(14)N, and (33)S/(32)S) and elemental ratio (C/CN and S/CN) profiles, our analysis partitioned ~2200 cellular regions of interest (ROIs) into five distinct groups. These isotope phenotype groupings are reflective of the variation in labeled substrate uptake by cells in a multispecies metabolic network dominated by Gamma- and Deltaproteobacteria. …”
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  16. 1896
    “…Here we used diffusion magnetic resonance imaging (MRI) and tractography with newly developed semi-automatic methods of defining seeds and selecting regions of interest (ROIs). We calculated mean thickness and fractional anisotropy (FA) in each ROI for the arcuate fasciculus (Arcuate) of the dorsal pathway, as well as for the inferior fronto-occipital fasciculus (IFOF) of the ventral pathway. …”
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  17. 1897
    “…Generalized estimating equation (GEE) analysis—comparing longitudinal changes in cortical thickness and subcortical volume between CP groups for several regions of interest (ROIs)—showed that these CP groups had differential trajectories of cortical thickness in the dorsolateral prefrontal cortex (dl-PFC), and the anterior cingulate cortex (ACC), and volume of the hippocampus. …”
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  18. 1898
    “…We set the regions of interest (ROIs) on the full-thickness cartilage of the medial femoral condyle (MFC) and medial tibial plateau (MTP) of the knee and measured the cartilage thickness (mm) and T1ρ and T2 relaxation times (ms). …”
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  19. 1899
    “…Additionally, sFC between 90 regions of interest (ROIs) was compared between the groups. RESULTS: CVR was significantly reduced in subjects with ipsilateral fPCA, most prominently in the temporal lobe. …”
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  20. 1900
    “…We performed meta‐analyses to localize regions‐of‐interest (ROIs), that is, the bilateral dorsolateral prefrontal cortex (DLPFC), inferior parietal cortex, and caudate nucleus, and performed group‐by‐task analyses and within‐group regression analyses of planning‐related neural activation. …”
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