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Disambiguation of "Jahanshad, Neda"

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Workflow Reuse in Practice: A Study of Neuroimaging Pipeline Users

, , , , , , , , and . e-Science (e-Science), 2014 IEEE 10th International Conference on, 1, page 239--246. IEEE, (2014)

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Viktoriia Nedashkivska

Neda Daryab

Neda Dadashi

Neda Nabavi

Neda Lotfiomran

 

Other publications of authors with the same name

The heritability of the functional connectome is robust to common nonlinear registration methods., , , , , , , , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 97841R. SPIE, (2016)Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease., , , , , , , , , and . ISBI, page 1088-1092. IEEE, (2016)Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data., , , , , , , , , and 11 other author(s). Frontiers Neuroinformatics, (2019)Power Estimates for Voxel-Based Genetic Association Studies Using Diffusion Imaging., , , , , , , , , and 4 other author(s). CDMRI/MMBC@MICCAI, page 229-238. Springer, (2013)Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling., , , , , , , , , and 18 other author(s). NeuroImage, (2014)7T multi-shell hybrid diffusion imaging (HYDI) for mapping brain connectivity in mice., , , , , , , , and . Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 941309. SPIE, (2015)Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression., , , , , , , , and . CoRR, (2017)Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model., , , , , , , , , and 5 other author(s). CoRR, (2017)Automatic Generation of Portions of Scientific Papers for Large Multi-Institutional Collaborations Based on Semantic Metadata., , , , , , , , , and . SemSci@ISWC, volume 1931 of CEUR Workshop Proceedings, page 63-70. CEUR-WS.org, (2017)Changes in anatomical brain connectivity between ages 12 and 30: A HARDI study of 467 adolescents and adults., , , , , , , , , and . ISBI, page 904-907. IEEE, (2012)