human subjects Search Results


90
Partners HealthCare System Inc non–human subjects research
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OhioHealth Corporation policies and procedures for protection of human subjects
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ARTEC CO LTD 3d scan of a human subject
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Franz Steiner Verlag twentieth century ethics of human subjects research: historical perspectives on values, practices, and regulations
Twentieth Century Ethics Of Human Subjects Research: Historical Perspectives On Values, Practices, And Regulations, supplied by Franz Steiner Verlag, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Siemens AG connectome adult human phantom data (correlation) infant data (mean between-subject variability)
Flowchart depicting the data processing stream. (Right stream) The λ1 images are spatially coregistered to a neonatal T2-weighted template (step not shown) and FA images are transformed into standard space using those transformations in order to construct a study-specific FA neonatal template. The template is back-transformed into native space, along with the neonatal parcellation atlas in order to define gray matter regions in native space. (Left stream) A white matter segmentation is performed in SPM8 using the FA images and neonatal GM, WM, and CSF templates. Tractography is then performed starting from each white matter voxel, which avoids the use of a strict FA threshold. (Bottom) <t>Connectomes</t> are computed using either: adjacency (the matrix has 0 or 1 depending on whether there is at least one streamline connecting two regions), average FA (across all streamlines connecting two regions), or the total # of streamlines connecting two regions.
Connectome Adult Human Phantom Data (Correlation) Infant Data (Mean Between Subject Variability), supplied by Siemens AG, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Flowchart depicting the data processing stream. (Right stream) The λ1 images are spatially coregistered to a neonatal T2-weighted template (step not shown) and FA images are transformed into standard space using those transformations in order to construct a study-specific FA neonatal template. The template is back-transformed into native space, along with the neonatal parcellation atlas in order to define gray matter regions in native space. (Left stream) A white matter segmentation is performed in SPM8 using the FA images and neonatal GM, WM, and CSF templates. Tractography is then performed starting from each white matter voxel, which avoids the use of a strict FA threshold. (Bottom) Connectomes are computed using either: adjacency (the matrix has 0 or 1 depending on whether there is at least one streamline connecting two regions), average FA (across all streamlines connecting two regions), or the total # of streamlines connecting two regions.

Journal: Human brain mapping

Article Title: Structural Network Topology Correlates of Microstructural Brain Dysmaturation in Term Infants with Congenital Heart Disease

doi: 10.1002/hbm.24308

Figure Lengend Snippet: Flowchart depicting the data processing stream. (Right stream) The λ1 images are spatially coregistered to a neonatal T2-weighted template (step not shown) and FA images are transformed into standard space using those transformations in order to construct a study-specific FA neonatal template. The template is back-transformed into native space, along with the neonatal parcellation atlas in order to define gray matter regions in native space. (Left stream) A white matter segmentation is performed in SPM8 using the FA images and neonatal GM, WM, and CSF templates. Tractography is then performed starting from each white matter voxel, which avoids the use of a strict FA threshold. (Bottom) Connectomes are computed using either: adjacency (the matrix has 0 or 1 depending on whether there is at least one streamline connecting two regions), average FA (across all streamlines connecting two regions), or the total # of streamlines connecting two regions.

Article Snippet: Connectome Adult Human Phantom Data (Correlation) Infant Data (Mean Between-Subject Variability) Siemens (CHP) Philips (CHLA) GE (CHP) Adjacency 0.82 0.069 0.078 0.074 # of Tracts 0.91 0.86 0.82 0.73 Average FA 0.81 0.022 0.023 0.029 Open in a separate window Nodal Metrics Results are given for global metrics (cost/global efficiency); for the adult human phantom data correlation coefficients for cost and global efficiency are estimated from bootstrap resampling (1000 samples with replacement) the graph weights and nodal efficiency, respectively.

Techniques: Transformation Assay, Construct

Comparison between CHD neonates pre-operatively vs. normal healthy controls (average FA connectome): (A) comparison of network cost and global efficiency (values normalized to unity average graph weight); (B) Comparison of nodal efficiency (all regions significant at FDR-corrected q < 0.05); (C) comparison of DTI metrics FA, RD, MD, and AD (hot colors = CHD > controls, cold colors = CHD < controls, all regions significant at FWE-corrected p < 0.05).

Journal: Human brain mapping

Article Title: Structural Network Topology Correlates of Microstructural Brain Dysmaturation in Term Infants with Congenital Heart Disease

doi: 10.1002/hbm.24308

Figure Lengend Snippet: Comparison between CHD neonates pre-operatively vs. normal healthy controls (average FA connectome): (A) comparison of network cost and global efficiency (values normalized to unity average graph weight); (B) Comparison of nodal efficiency (all regions significant at FDR-corrected q < 0.05); (C) comparison of DTI metrics FA, RD, MD, and AD (hot colors = CHD > controls, cold colors = CHD < controls, all regions significant at FWE-corrected p < 0.05).

Article Snippet: Connectome Adult Human Phantom Data (Correlation) Infant Data (Mean Between-Subject Variability) Siemens (CHP) Philips (CHLA) GE (CHP) Adjacency 0.82 0.069 0.078 0.074 # of Tracts 0.91 0.86 0.82 0.73 Average FA 0.81 0.022 0.023 0.029 Open in a separate window Nodal Metrics Results are given for global metrics (cost/global efficiency); for the adult human phantom data correlation coefficients for cost and global efficiency are estimated from bootstrap resampling (1000 samples with replacement) the graph weights and nodal efficiency, respectively.

Techniques: Comparison

Comparison between CHD neonates post-operatively vs. normal healthy controls (average FA connectome): (A) comparison of network cost and global efficiency (values normalized to unity average graph weight); (B) Comparison of nodal efficiency (all regions significant at FDR-corrected q < 0.05); (C) comparison of DTI metrics FA, RD, MD, and AD (hot colors = CHD > controls, cold colors = CHD < controls, all regions significant at FWE-corrected p < 0.05).

Journal: Human brain mapping

Article Title: Structural Network Topology Correlates of Microstructural Brain Dysmaturation in Term Infants with Congenital Heart Disease

doi: 10.1002/hbm.24308

Figure Lengend Snippet: Comparison between CHD neonates post-operatively vs. normal healthy controls (average FA connectome): (A) comparison of network cost and global efficiency (values normalized to unity average graph weight); (B) Comparison of nodal efficiency (all regions significant at FDR-corrected q < 0.05); (C) comparison of DTI metrics FA, RD, MD, and AD (hot colors = CHD > controls, cold colors = CHD < controls, all regions significant at FWE-corrected p < 0.05).

Article Snippet: Connectome Adult Human Phantom Data (Correlation) Infant Data (Mean Between-Subject Variability) Siemens (CHP) Philips (CHLA) GE (CHP) Adjacency 0.82 0.069 0.078 0.074 # of Tracts 0.91 0.86 0.82 0.73 Average FA 0.81 0.022 0.023 0.029 Open in a separate window Nodal Metrics Results are given for global metrics (cost/global efficiency); for the adult human phantom data correlation coefficients for cost and global efficiency are estimated from bootstrap resampling (1000 samples with replacement) the graph weights and nodal efficiency, respectively.

Techniques: Comparison

Comparisons between CHD neonates pre- and post-operatively vs. normal healthy controls: comparison of network cost and global efficiency (values normalized to unity average graph weight) for # tracts connectome (A, C) and adjacency connectome (E, G); Comparison of nodal efficiency (all regions significant at FDR-corrected q < 0.05) for # tracts connectome (B, D) and adjacency connectome (F, H).

Journal: Human brain mapping

Article Title: Structural Network Topology Correlates of Microstructural Brain Dysmaturation in Term Infants with Congenital Heart Disease

doi: 10.1002/hbm.24308

Figure Lengend Snippet: Comparisons between CHD neonates pre- and post-operatively vs. normal healthy controls: comparison of network cost and global efficiency (values normalized to unity average graph weight) for # tracts connectome (A, C) and adjacency connectome (E, G); Comparison of nodal efficiency (all regions significant at FDR-corrected q < 0.05) for # tracts connectome (B, D) and adjacency connectome (F, H).

Article Snippet: Connectome Adult Human Phantom Data (Correlation) Infant Data (Mean Between-Subject Variability) Siemens (CHP) Philips (CHLA) GE (CHP) Adjacency 0.82 0.069 0.078 0.074 # of Tracts 0.91 0.86 0.82 0.73 Average FA 0.81 0.022 0.023 0.029 Open in a separate window Nodal Metrics Results are given for global metrics (cost/global efficiency); for the adult human phantom data correlation coefficients for cost and global efficiency are estimated from bootstrap resampling (1000 samples with replacement) the graph weights and nodal efficiency, respectively.

Techniques: Comparison

Correlation of DTI metrics (FA, RD, MD, AD) with global efficiency for pre-operative CHD (A, C, E) and post-operative CHD (B, D, F) for average FA connectome (A, B), # of tracts connectome (C, D) and adjacency connectome (E, F). (Hot colors = positive correlation, cold colors = negative correlation, all regions significant at FWE-corrected p < 0.05).

Journal: Human brain mapping

Article Title: Structural Network Topology Correlates of Microstructural Brain Dysmaturation in Term Infants with Congenital Heart Disease

doi: 10.1002/hbm.24308

Figure Lengend Snippet: Correlation of DTI metrics (FA, RD, MD, AD) with global efficiency for pre-operative CHD (A, C, E) and post-operative CHD (B, D, F) for average FA connectome (A, B), # of tracts connectome (C, D) and adjacency connectome (E, F). (Hot colors = positive correlation, cold colors = negative correlation, all regions significant at FWE-corrected p < 0.05).

Article Snippet: Connectome Adult Human Phantom Data (Correlation) Infant Data (Mean Between-Subject Variability) Siemens (CHP) Philips (CHLA) GE (CHP) Adjacency 0.82 0.069 0.078 0.074 # of Tracts 0.91 0.86 0.82 0.73 Average FA 0.81 0.022 0.023 0.029 Open in a separate window Nodal Metrics Results are given for global metrics (cost/global efficiency); for the adult human phantom data correlation coefficients for cost and global efficiency are estimated from bootstrap resampling (1000 samples with replacement) the graph weights and nodal efficiency, respectively.

Techniques: