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| 001 | 874484 | ||
| 005 | 20220930130232.0 | ||
| 024 | 7 | _ | |a 10.1007/s00429-020-02046-1 |2 doi |
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| 037 | _ | _ | |a FZJ-2020-01463 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Reuter, Niels |0 P:(DE-Juel1)170074 |b 0 |
| 245 | _ | _ | |a CBPtools: a Python package for regional connectivity-based parcellation |
| 260 | _ | _ | |a Heidelberg |c 2020 |b Springer |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 500 | _ | _ | |a This study was supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 785907 (HBP SGA2) and Grant Agreement no. 7202070 (HBP SGA1). SG is supported by the Deutsche Forschungsgemeinschaft (DFG) under Grant Agreement GE 2835/1-1. |
| 520 | _ | _ | |a Regional connectivity-based parcellation (rCBP) is a widely used procedure for investigating the structural and functional differentiation within a region of interest (ROI) based on its long-range connectivity. No standardized software or guidelines currently exist for applying rCBP, making the method only accessible to those who develop their own tools. As such, there exists a discrepancy between the laboratories applying the procedure each with their own software solutions, making it difficult to compare and interpret the results. Here, we outline an rCBP procedure accompanied by an open source software package called CBPtools. CBPtools is a Python (version 3.5+) package that allows users to run an extensively evaluated rCBP analysis workflow on a given ROI. It currently supports two modalities: resting-state functional connectivity and structural connectivity based on diffusion-weighted imaging, along with support for custom connectivity matrices. Analysis parameters are customizable and the workflow can be scaled to a large number of subjects using a parallel processing environment. Parcellation results with corresponding validity metrics are provided as textual and graphical output. Thus, CBPtools provides a simple plug-and-play, yet customizable way to conduct rCBP analyses. By providing an open-source software we hope to promote reproducible and comparable rCBP analyses and, importantly, make the rCBP procedure readily available. Here, we demonstrate the utility of CBPtools using a voluminous data set on an average compute-cluster infrastructure by performing rCBP on three ROIs prominently featured in parcellation literature. |
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| 536 | _ | _ | |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) |0 G:(EU-Grant)720270 |c 720270 |f H2020-Adhoc-2014-20 |x 2 |
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| 700 | 1 | _ | |a Kharabian Masouleh, Shahrzad |0 P:(DE-Juel1)171719 |b 2 |
| 700 | 1 | _ | |a Hoffstaedter, Felix |0 P:(DE-Juel1)131684 |b 3 |
| 700 | 1 | _ | |a Liu, Xiaojin |0 P:(DE-Juel1)171422 |b 4 |u fzj |
| 700 | 1 | _ | |a Kalenscher, Tobias |0 0000-0002-0358-9020 |b 5 |
| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 6 |
| 700 | 1 | _ | |a Patil, Kaustubh R. |0 P:(DE-Juel1)172843 |b 7 |e Corresponding author |
| 773 | _ | _ | |a 10.1007/s00429-020-02046-1 |0 PERI:(DE-600)2303775-1 |p 1261–1275 |t Brain structure & function |v 225 |y 2020 |x 1863-2661 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/874484/files/Reuter2020_Article_CBPtoolsAPythonPackageForRegio-1.pdf |
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