Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC)

By Craddock Cameron, S. Sharad, C. Brian, K. Ranjeet, Ghosh Satrajit, Chaogan Yan, Qingyang Li, Lurie Daniel, V. Joshua, Burns Randal and 6 more. 2013

352 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Health, Toxicology and Mutagenesis, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/90c0f411e47a0dd625bae3833638f8ef5dd8b543

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In Cognitive Neuroscience

In Health, Toxicology and Mutagenesis

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods (2016, background): “T1w, fMRI [28] Hyperalignment Computes hyperalignment transformations for functional alignment. fMRI [29] mindboggle Pipeline to improve the accuracy, precision, and consistency of automated labeling and shape analysis of human brain image data.”
Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, positive): “With this covariance structure, we study three different parametrizations of functional interactions: full correlation , partial correlation (Smith et al., 2011; Varoquaux and Craddock, 2013) and the tangent space of covariance matrices .”
Deriving reproducible biomarkers from multi‐site resting‐state data: An Autism‐based example (2016, methodology): “Data were selected based on the results of quality visual inspection by three human experts who inspected for largely incomplete brain coverage, high movement peaks, ghosting and other scanner artifacts.”
Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease (2018, methodology): “To ensure a fair comparison with the state of the art (Abraham et al., 2017) we use the same preprocessing pipeline, the Conﬁgurable Pipeline for the Analysis of Connectomes (C-PAC) (Craddock et al., 2013), which involves skull striping, slice timing correction, motion correction, global mean intens”
The Human Thalamus Is an Integrative Hub for Functional Brain Networks (2016, methodology): “Functional MRI Data Preprocessing Image preprocessing was performed the software Configurable Pipeline for the Analysis of Connectomics (Sikka et al., 2014).”
Spectral Graph Convolutions for Population-based Disease Prediction (2017, methodology): “To ensure a fair comparison with the state of the art [1], we use the same preprocessing pipeline [3], which involves skull striping, slice timing correction, motion correction, global mean intensity normalisation, nuisance signal regression, band-pass ﬁltering (0.01-0.1Hz) and registration of the…”
Quantitative Assessment of Structural Image Quality (2017, methodology): “Most of the measures we evaluated were produced using the Quality Assurance Pipeline (QAP) (Shehzad et al., 2015) included in the Configurable Pipeline for Analysis of Connectomes (C-PAC) (Sikka et al., 2013).”