2016_brainhack_proceedings.bib

@article{10.3897/rio.3.e12095,
	author = {Horea-Ioan Ioanas and Bechara Saab and Markus Rudin},
	title = {Gentoo Linux for Neuroscience - a replicable, flexible, scalable, rolling-release environment that provides direct access to development software},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e12095},
	publisher = {Pensoft Publishers},
	abstract = {},,
	issn = {},
	pages = {e12095},
	URL = {https://doi.org/10.3897/rio.3.e12095},
	eprint = {https://doi.org/10.3897/rio.3.e12095},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e13395,
	author = {Kaori L Ito and Julia M Anglin and Hosung Kim and Sook-Lei Liew},
	title = {Semi-automated Robust Quantification of Lesions (SRQL) Toolbox},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e13395},
	publisher = {Pensoft Publishers},
	abstract = {Quantifying lesions in a reliable manner is fundamental for studying the effects of neuroanatomical changes related to recovery in the post-stroke brain. However, the wide variability in lesion characteristics across individuals makes manual lesion segmentation a challenging and often subjective process. This often makes it difficult to combine stroke lesion data across multiple research sites, due to subjective differences in how lesions may be defined. Thus, we developed the Semi-automated Robust Quantification of Lesions (SRQL; https://github.com/npnl/SRQL; DOI: 10.5281/zenodo.557114) Toolbox that performs several analysis steps: 1) a white matter intensity correction that removes healthy white matter voxels from the lesion mask, thereby making lesions slightly more robust to subjective errors; 2) an automated report of descriptive statistics on lesions for simplified comparison between or across groups, and 3) an option to perform analyses in both native and standard space to facilitate analyses in either space. Here, we describe the methods implemented in the toolbox.},
	issn = {},
	pages = {e13395},
	URL = {https://doi.org/10.3897/rio.3.e13395},
	eprint = {https://doi.org/10.3897/rio.3.e13395},
	journal = {Research Ideas and Outcomes}
}


@article{10.3897/rio.3.e13726,
	author = {Erin Dickie and Steven M Hodge and R. Cameron Craddock and Jean-Baptiste Poline and David N. Kennedy},
	title = {Tools Matter: Comparison of Two Surface Analysis Tools Applied to the ABIDE Dataset},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e13726},
	publisher = {Pensoft Publishers},
	abstract = {We examine the similarity of outputs from Freesurfer version 5.1, Freesurfer version 5.3 and ANTS for the ABIDEI dataset.},
	issn = {},
	pages = {e13726},
	URL = {https://doi.org/10.3897/rio.3.e13726},
	eprint = {https://doi.org/10.3897/rio.3.e13726},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e13394,
	author = {Daniel Peterson},
	title = {Streamlining the Process of 3D Printing a Brain From a Structural MRI},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e13394},
	publisher = {Pensoft Publishers},
	abstract = {Currently, the process of obtaining a 3D model from a structural MRI requires specialized knowlege and skills. This is not due to the fundamental difficulty and complexity of the process, but is a result of the fact that the neccessary tools were developed for and by neuroimaging researchers. This project describes a publically available utility implemented as a Docker image that takes a structural MRI as input, and gives files for 3D printing as output, along with a rendered image of the surface.},
	issn = {},
	pages = {e13394},
	URL = {https://doi.org/10.3897/rio.3.e13394},
	eprint = {https://doi.org/10.3897/rio.3.e13394},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e13391,
	author = {Richard AI Bethlehem and Marcel Falkiewicz and Jan Freyberg and Owen E Parsons and Seyedeh-Rezvan  Farahibozorg and Charlotte Pretzsch and Bjoern Soergel and Daniel S Margulies},
	title = {Gradients of cortical hierarchy in Autism},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e13391},
	publisher = {Pensoft Publishers},
	abstract = {Autism is a developmental condition associated with altered functional connectivity. We propose to re-frame the functional connectivity alterations in terms of gradients that capture the functional hierarchy of cortical processing from sensory to default-mode network regions. We hypothesized that this hierarchy will be altered in ASD. To test that, we compared the scale of gradients in people with autism and healthy controls. The present results do not support our hypothesis. There are two alternative implications: either the processing hierarchies are preserved in autism or the scale of the gradients does not capture them. In the future we will attempt to settle which alternative is more likely.},
	issn = {},
	pages = {e13391},
	URL = {https://doi.org/10.3897/rio.3.e13391},
	eprint = {https://doi.org/10.3897/rio.3.e13391},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e12733,
	author = {Swati Rane and Eshin Jolly and Anne Park and Hojin Jang and Cameron Craddock},
	title = {Developing predictive imaging biomarkers using whole-brain classifiers: Application to the ABIDE I dataset},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e12733},
	publisher = {Pensoft Publishers},
	abstract = {We designed a modular machine learning program that uses functional magnetic resonance imaging (fMRI) data in order to distinguish individuals with autism spectrum disorders from neurodevelopmentally normal individuals. Data was selected from the Autism Brain Imaging Dataset Exchange (ABIDE) I Preprocessed Dataset.},
	issn = {},
	pages = {e12733},
	URL = {https://doi.org/10.3897/rio.3.e12733},
	eprint = {https://doi.org/10.3897/rio.3.e12733},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e12641,
	author = {Jingyuan Chen and Deepika Bagga},
	title = {Noise paradoxically increases reliability metrics},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e12641},
	publisher = {Pensoft Publishers},
	abstract = {Lower signal to noise ratio (SNR) of the scanning environment is generally considered to exert a negative impact on the inter-/intra-subject consistency of resting state functional connectivity (RSFC) metrics. Here, we show through simulations that this assumption is not always true - poor SNR may paradoxically increase reliability metrics of RSFC under certain circumstances, due to the reduced senstivity to dynamic changes in brain connectivity.},
	issn = {},
	pages = {e12641},
	URL = {https://doi.org/10.3897/rio.3.e12641},
	eprint = {https://doi.org/10.3897/rio.3.e12641},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e12569,
	author = {Julia Leonard and John Flournoy and Christine Paula Lewis-de los Angeles and Kirstie Whitaker},
	title = {How much motion is too much motion? Determining motion thresholds by sample size for reproducibility in developmental resting-state MRI},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e12569},
	publisher = {Pensoft Publishers},
	abstract = {A constant problem developmental neuroimagers face is in-scanner head motion. Children move more than adults and this has led to concerns that developmental changes in resting-state connectivity measures may be artefactual. Furthermore, children are challenging to recruit into studies and therefore researchers have tended to take a permissive stance when setting exclusion criteria on head motion. The literature is not clear regarding our central question: How much motion is too much? Here, we systematically examine the effects of multiple motion exclusion criteria at different sample sizes and age ranges in a large openly available developmental cohort (ABIDE; http://preprocessed-connectomes-project.org/abide). We checked 1) the reliability of resting-state functional magnetic resonance imaging (rs-fMRI) pairwise connectivity measures across the brain and 2) the accuracy with which we can separate participants with autism spectrum disorder from typically developing controls based on their rs-fMRI scans using machine learning. We find that reliability on average is primarily sensitive to the number of participants considered, but that increasingly permissive motion thresholds lower case-control prediction accuracy for all sample sizes.},
	issn = {},
	pages = {e12569},
	URL = {https://doi.org/10.3897/rio.3.e12569},
	eprint = {https://doi.org/10.3897/rio.3.e12569},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e12394,
	author = {Kesshi M Jordan and Anisha Keshavan and Maria Luisa Mandelli and Roland G Henry},
	title = {Cluster-viz: A Tractography QC Tool},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e12394},
	publisher = {Pensoft Publishers},
	abstract = {Cluster-viz is a web application that provides a platform for cluster-based interactive quality-control of tractography algorithm outputs. This tool facilitates the creation of white matter fascicle models by employing a cluster-based approach to allow the user to select streamline bundles for inclusion/exclusion in the final fascicle model. This project was started at the 2016 Neurohackweek and BrainHack events and is still under development. We welcome contributions to the Cluster-viz github repository (https://github.com/kesshijordan/Cluster-viz).},
	issn = {},
	pages = {e12394},
	URL = {https://doi.org/10.3897/rio.3.e12394},
	eprint = {https://doi.org/10.3897/rio.3.e12394},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e12368,
	author = {Camille Maumet and Thomas E. Nichols},
	title = {Generating and reporting peak and cluster tables for voxel-wise inference in FSL},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e12368},
	publisher = {Pensoft Publishers},
	abstract = {Mass universities analyses, in which a statistical test is performed at each voxel in the brain, is the most widespread approach to analyzing task-evoked functional Magnetic Resonance Imaging (fMRI) data. Such analyses identify the brain areas that are significantly activated in response to a given stimulus. In the literature, the significant areas are usually summarised by providing a table, listing, for each significant region, the 3D positions of the local maxima along with corresponding statistical values. This tabular output is provided by all the major as dsa dneuroimaging software packages including SPM, FSL and AFNI. Yet, in the HTML report generated by FSL, peak and cluster tables are only provided for one type of inference (cluster-wise inference) but not when a voxel-wise threshold is specified. In this project, we proposed an update for FSL to generate and report peak and cluster tables for voxel-wise inferences.},
	issn = {},
	pages = {e12368},
	URL = {https://doi.org/10.3897/rio.3.e12368},
	eprint = {https://doi.org/10.3897/rio.3.e12368},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e12358,
	author = {Anisha Keshavan and Arno Klein and Ben Cipollini},
	title = {Interactive online brain shape visualization},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e12358},
	publisher = {Pensoft Publishers},
	abstract = {The open-source Mindboggle package improves the labeling and morphometry estimates of brain imaging data. At the 2015 Brainhack event, we developed a web-based, interactive, brain shape visualization of Mindboggle outputs. The application links a 3D brain visualization with boxplots that describe shape measures across a selected cortical label. The code is freely available at http://www.github.com/akeshavan/roygbiv and a demo is online at http://roygbiv.mindboggle.info.},
	issn = {},
	pages = {e12358},
	URL = {https://doi.org/10.3897/rio.3.e12358},
	eprint = {https://doi.org/10.3897/rio.3.e12358},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e12346,
	author = {Julia M Huntenburg and Konrad Wagstyl and Christopher J Steele and Thomas Funck and Richard A.I. Bethlehem and Ophélie Foubet and Benoit Larrat and Victor Borrell and Pierre-Louis Bazin},
	title = {Laminar Python: tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e12346},
	publisher = {Pensoft Publishers},
	abstract = {Increasingly available high-resolution brain imaging data require specialized processing tools that can leverage their anatomical detail and handle their size. Here, we present user-friendly Python tools for cortical depth resolved analysis in such data. Our implementation is based on the CBS High-Res Brain Processing framework, and aims to make high-resolution data processing tools available to the broader community.},
	issn = {},
	pages = {e12346},
	URL = {https://doi.org/10.3897/rio.3.e12346},
	eprint = {https://doi.org/10.3897/rio.3.e12346},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e12342,
	author = {Julia M Huntenburg and Alexandre Abraham and João Loula and Franziskus Liem and Kamalaker Dadi and Gaël Varoquaux},
	title = {Loading and plotting of cortical surface representations in Nilearn},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e12342},
	publisher = {Pensoft Publishers},
	abstract = {Processing neuroimaging data on the cortical surface traditionally requires dedicated heavy-weight software suites. Here, we present an initial support of cortical surfaces in Python within the neuroimaging data processing toolbox Nilearn. We provide loading and plotting functions for different surface data formats with minimal dependencies, along with examples of their application. Limitations of the current implementation and potential next steps are discussed.},
	issn = {},
	pages = {e12342},
	URL = {https://doi.org/10.3897/rio.3.e12342},
	eprint = {https://doi.org/10.3897/rio.3.e12342},
	journal = {Research Ideas and Outcomes}
}

@article{10.3897/rio.3.e12276,
	author = {Anisha Keshavan and Christopher R Madan and Esha Datta and Ian M McDonough},
	title = {Mindcontrol: Organize, quality control, annotate, edit, and collaborate on neuroimaging processing results},
	volume = {3},
	number = {},
	year = {2017},
	doi = {10.3897/rio.3.e12276},
	publisher = {Pensoft Publishers},
	abstract = {Mindcontrol is an open-source web-based dashboard to quality control and curate neuroimaging data. At Neurohackweek 2016, a group assembled to add new features to the Mindcontrol interface. Contributors used Python, Javascript, and Git to configure Mindcontrol for the ABIDE and CoRR open datasets, and add new types of plots to the interface. All contributions are freely available online, and the code is being actively maintained at http://www.github.com/akeshavan/mindcontrol.},
	issn = {},
	pages = {e12276},
	URL = {https://doi.org/10.3897/rio.3.e12276},
	eprint = {https://doi.org/10.3897/rio.3.e12276},
	journal = {Research Ideas and Outcomes}
}