text:
@article{satterthwaite_2013,
	title = {An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data},
	volume = {64},
	issn = {1053-8119},
	url = {http://www.sciencedirect.com/science/article/pii/S1053811912008609},
	doi = {10.1016/j.neuroimage.2012.08.052},
	abstract = {Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.},
	language = {en},
	urldate = {2020-08-27},
	journal = {NeuroImage},
	author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
	month = jan,
	year = {2013},
	keywords = {Adolescence, Adolescent, Algorithms, Artifact, Artifacts, Brain, Child, Connectivity, Connectome, Data Interpretation, Statistical, Development, Female, fMRI, Head Movements, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Motion, Network, Regression Analysis, Reproducibility of Results, Rest, Resting-state, Sensitivity and Specificity, Young Adult},
	pages = {240--256},
	file = {Accepted Version:/Users/adebimpe/Zotero/storage/CETLP2IT/Satterthwaite et al. - 2013 - An improved framework for confound regression and .pdf:application/pdf;ScienceDirect Full Text PDF:/Users/adebimpe/Zotero/storage/CLIZIABN/Satterthwaite et al. - 2013 - An improved framework for confound regression and .pdf:application/pdf;ScienceDirect Snapshot:/Users/adebimpe/Zotero/storage/SMSXEFDU/S1053811912008609.html:text/html},
}

@article{mitigating_2018,
	title = {Mitigating head motion artifact in functional connectivity {MRI}},
	volume = {13},
	copyright = {2018 Springer Nature Limited},
	issn = {1750-2799},
	url = {https://www.nature.com/articles/s41596-018-0065-y},
	doi = {10.1038/s41596-018-0065-y},
	abstract = {Ciric et al. describe a protocol for the removal of motion artifacts from functional MRI data. They introduce a software package that implements common denoising protocols and provides tools for assessing the efficacy of denoising.},
	language = {en},
	number = {12},
	urldate = {2020-05-08},
	journal = {Nature Protocols},
	author = {Ciric, Rastko and Rosen, Adon F. G. and Erus, Guray and Cieslak, Matthew and Adebimpe, Azeez and Cook, Philip A. and Bassett, Danielle S. and Davatzikos, Christos and Wolf, Daniel H. and Satterthwaite, Theodore D.},
	month = dec,
	year = {2018},
	note = {Number: 12
Publisher: Nature Publishing Group},
	pages = {2801--2826},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/MIQPCV9N/Ciric et al. - 2018 - Mitigating head motion artifact in functional conn.pdf:application/pdf;Full Text PDF:/Users/adebimpe/Zotero/storage/EG44AHWM/Ciric et al. - 2018 - Mitigating head motion artifact in functional conn.pdf:application/pdf;Full Text PDF:/Users/adebimpe/Zotero/storage/AYQ7MQL8/Ciric et al. - 2018 - Mitigating head motion artifact in functional conn.pdf:application/pdf;Snapshot:/Users/adebimpe/Zotero/storage/L9IBAGHF/s41596-018-0065-y.html:text/html;Snapshot:/Users/adebimpe/Zotero/storage/KRGYTD3R/s41596-018-0065-y.html:text/html;Snapshot:/Users/adebimpe/Zotero/storage/NCAA6VTV/s41596-018-0065-y.html:text/html},
}

@article{benchmarkp,
	title = {Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity},
	volume = {154},
	issn = {1053-8119},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5483393/},
	doi = {10.1016/j.neuroimage.2017.03.020},
	abstract = {dd.},
	urldate = {2020-05-08},
	journal = {NeuroImage},
	author = {Ciric, Rastko and Wolf, Daniel H. and Power, Jonathan D. and Roalf, David R. and Baum, Graham and Ruparel, Kosha and Shinohara, Russell T. and Elliott, Mark A. and Eickhoff, Simon B. and Davatzikos, Christos and Gur, Ruben C. and Gur, Raquel E. and Bassett, Danielle S. and Satterthwaite, Theodore D.},
	month = jul,
	year = {2017},
	pmid = {28302591},
	pmcid = {PMC5483393},
	keywords = {Adolescent, Adult, Artifact, Benchmarking, Child, Confound, Connectome, fMRI, Functional connectivity, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Motion, Noise, Young Adult},
	pages = {174--187},
	file = {Full Text:/Users/adebimpe/Zotero/storage/GMIUFRTS/Ciric et al. - 2017 - Benchmarking of participant-level confound regress.pdf:application/pdf;PubMed Central Full Text PDF:/Users/adebimpe/Zotero/storage/54IW5EVJ/Ciric et al. - 2017 - Benchmarking of participant-level confound regress.pdf:application/pdf},
}

@article{esteban2019fmriprep,
  title={fMRIPrep: a robust preprocessing pipeline for functional MRI},
  author={Esteban, Oscar and Markiewicz, Christopher J and Blair, Ross W and Moodie, Craig A and Isik, A Ilkay and Erramuzpe, Asier and Kent, James D and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and others},
  journal={Nature methods},
  volume={16},
  number={1},
  pages={111--116},
  year={2019},
  publisher={Nature Publishing Group},
  url={https://www.nature.com/articles/s41592-018-0235-4},
  doi={10.1038/s41592-018-0235-4},
}

@article{esteban2020analysis,
  title={Analysis of task-based functional MRI data preprocessed with fMRIPrep},
  author={Esteban, Oscar and Ciric, Rastko and Finc, Karolina and Blair, Ross W and Markiewicz, Christopher J and Moodie, Craig A and Kent, James D and Goncalves, Mathias and DuPre, Elizabeth and Gomez, Daniel EP and others},
  journal={Nature protocols},
  volume={15},
  number={7},
  pages={2186--2202},
  year={2020},
  publisher={Nature Publishing Group},
  url={https://doi.org/10.1038/s41596-020-0327-3},
  doi={10.1038/s41596-020-0327-3}
}

@article{nipype1,
	title = {Nipype: {A} {Flexible}, {Lightweight} and {Extensible} {Neuroimaging} {Data} {Processing} {Framework} in {Python}},
	volume = {5},
	issn = {1662-5196},
	shorttitle = {Nipype},
	url = {https://www.frontiersin.org/articles/10.3389/fninf.2011.00013/full},
	doi = {10.3389/fninf.2011.00013},
	abstract = {Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient and optimal use of neuroimaging analysis approaches: 1) No uniform access to neuroimaging analysis software and usage information; 2) No framework for comparative algorithm development and dissemination; 3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; 4) Neuroimaging software packages do not address computational efficiency; and 5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is BSD licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.},
	language = {English},
	urldate = {2020-12-15},
	journal = {Frontiers in Neuroinformatics},
	author = {Gorgolewski, Krzysztof and Burns, Christopher D. and Madison, Cindee and Clark, Dav and Halchenko, Yaroslav O. and Waskom, Michael L. and Ghosh, Satrajit S.},
	year = {2011},
	note = {Publisher: Frontiers},
	keywords = {data processing, neuroimaging, Neuroimaging, pipeline, python, Python, reproducible research, Reproducible Research, workflow},
	file = {Full Text:/Users/adebimpe/Zotero/storage/IAF7G66B/Gorgolewski et al. - 2011 - Nipype a flexible, lightweight and extensible neu.pdf:application/pdf;Full Text:/Users/adebimpe/Zotero/storage/HWIG35V6/Gorgolewski et al. - 2011 - Nipype A Flexible, Lightweight and Extensible Neu.pdf:application/pdf;Full Text PDF:/Users/adebimpe/Zotero/storage/UUK4BQPM/Gorgolewski et al. - 2011 - Nipype A Flexible, Lightweight and Extensible Neu.pdf:application/pdf},
}

@article{cox1996afni,
  title={AFNI: software for analysis and visualization of functional magnetic resonance neuroimages},
  author={Cox, Robert W},
  journal={Computers and Biomedical research},
  volume={29},
  number={3},
  pages={162--173},
  year={1996},
  publisher={Elsevier}
}

@article{cox1997software,
  title={Software tools for analysis and visualization of fMRI data},
  author={Cox, Robert W and Hyde, James S},
  journal={NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In Vivo},
  volume={10},
  number={4-5},
  pages={171--178},
  year={1997},
  publisher={Wiley Online Library}
}

@article{taylor2013fatcat,
  title={FATCAT:(an efficient) functional and tractographic connectivity analysis toolbox},
  author={Taylor, Paul A and Saad, Ziad S},
  journal={Brain connectivity},
  volume={3},
  number={5},
  pages={523--535},
  year={2013},
  publisher={Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA}
}

@article{surface_reho,
	title = {Surface-based regional homogeneity in bipolar disorder: {A} resting-state {fMRI} study},
	volume = {278},
	issn = {0165-1781},
	shorttitle = {Surface-based regional homogeneity in bipolar disorder},
	url = {http://www.sciencedirect.com/science/article/pii/S0165178119305384},
	doi = {10.1016/j.psychres.2019.05.045},
	abstract = {Surface-based, two-dimensional regional homogeneity (2dReHo) was used in the current study to compare local functional synchronization of spontaneous neuronal activity between patients with bipolar disorder (BD) and healthy controls (HC), rather than volume-based, three-dimensional regional homogeneity (3dReHo) methods that have been previously described. Seventy-one BD patients and 113 HC participated in structural and resting-state fMRI scans. Participants ranged in age from 12 to 54 years. All subjects were rated with the Young Mania Rating Scale and the Hamilton Depression Rating Scale. BD patients showed reduced surface-based ReHo across the cortical surface, both at the global level and in the left ventral visual stream (VVS). Additionally, ReHo value across the cortical surface showed a significant negative correlation with age in both groups at the global level. Abnormal activity in the left VVS cortex may contribute to the pathogenesis of BD. Therefore, surface-based ReHo may be a useful index to explore the pathophysiology of BD.},
	language = {en},
	urldate = {2021-01-15},
	journal = {Psychiatry Research},
	author = {Zhang, Bo and Wang, Fei and Dong, Hao-Ming and Jiang, Xiao-Wei and Wei, Sheng-Nan and Chang, Miao and Yin, Zhi-Yang and Yang, Ning and Zuo, Xi-Nian and Tang, Yan-Qing and Xu, Ke},
	month = aug,
	year = {2019},
	keywords = {Adolescent, Adult, Bipolar disorder, Bipolar Disorder, Cerebral Cortex, Child, Female, Functional magnetic resonance imaging, Functional Neuroimaging, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Regional homogeneity, Ventral visual stream, Visual Cortex, Young Adult},
	pages = {199--204},
	file = {ScienceDirect Full Text PDF:/Users/adebimpe/Zotero/storage/2BJ7UDSV/Zhang et al. - 2019 - Surface-based regional homogeneity in bipolar diso.pdf:application/pdf;ScienceDirect Snapshot:/Users/adebimpe/Zotero/storage/2USZM4J4/S0165178119305384.html:text/html},
}

@article{Schaefer_2017,
	title = {Local-{Global} {Parcellation} of the {Human} {Cerebral} {Cortex} from {Intrinsic} {Functional} {Connectivity} {MRI}},
	volume = {28},
	issn = {1460-2199},
	doi = {10.1093/cercor/bhx179},
	abstract = {A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable\_projects/brain\_parcellation/Schaefer2018\_LocalGlobal).},
	language = {eng},
	number = {9},
	journal = {Cerebral Cortex (New York, N.Y.: 1991)},
	author = {Schaefer, Alexander and Kong, Ru and Gordon, Evan M. and Laumann, Timothy O. and Zuo, Xi-Nian and Holmes, Avram J. and Eickhoff, Simon B. and Yeo, B. T. Thomas},
	month = sep,
	year = {2018},
	pmid = {28981612},
	pmcid = {PMC6095216},
	keywords = {Adolescent, Adult, Brain Mapping, Cerebral Cortex, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Young Adult},
	pages = {3095--3114},
	file = {Full Text:/Users/adebimpe/Zotero/storage/Y55YIC6E/Schaefer et al. - 2018 - Local-Global Parcellation of the Human Cerebral Co.pdf:application/pdf;Full Text:/Users/adebimpe/Zotero/storage/ZGCZD8II/Schaefer et al. - 2018 - Local-Global Parcellation of the Human Cerebral Co.pdf:application/pdf},
}

@article{mni152nlin2009casym,
	title = {Unbiased {Average} {Age}-{Appropriate} {Atlases} for {Pediatric} {Studies}},
	volume = {54},
	issn = {1053-8119},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2962759/},
	doi = {10.1016/j.neuroimage.2010.07.033},
	abstract = {Spatial normalization, registration, and segmentation techniques for Magnetic Resonance Imaging (MRI) often use a target or template volume to facilitate processing, take advantage of prior information, and define a common coordinate system for analysis. In the neuroimaging literature, the MNI305 Talairach-like coordinate system is often used as a standard template. However, when studying pediatric populations, variation from the adult brain makes the MNI305 suboptimal for processing brain images of children. Morphological changes occurring during development render the use of age-appropriate templates desirable to reduce potential errors and minimize bias during processing of pediatric data. This paper presents the methods used to create unbiased, age-appropriate MRI atlas templates for pediatric studies that represent the average anatomy for the age range of 4.5–18.5 years, while maintaining a high level of anatomical detail and contrast. The creation of anatomical T1-weighted, T2-weighted, and proton density-weighted templates for specific developmentally important age-ranges, used data derived from the largest epidemiological, representative (healthy and normal) sample of the U.S. population, where each subject was carefully screened for medical and psychiatric factors and characterized using established neuropsychological and behavioral assessments. . Use of these age-specific templates was evaluated by computing average tissue maps for gray matter, white matter, and cerebrospinal fluid for each specific age range, and by conducting an exemplar voxel-wise deformation-based morphometry study using 66 young (4.5–6.9 years) participants to demonstrate the benefits of using the age-appropriate templates. The public availability of these atlases/templates will facilitate analysis of pediatric MRI data and enable comparison of results between studies in a common standardized space specific to pediatric research.},
	number = {1},
	urldate = {2021-07-23},
	journal = {NeuroImage},
	author = {Fonov, Vladimir and Evans, Alan C. and Botteron, Kelly and Almli, C. Robert and McKinstry, Robert C. and Collins, D. Louis},
	month = jan,
	year = {2011},
	pmid = {20656036},
	pmcid = {PMC2962759},
	pages = {313--327},
	file = {PubMed Central Full Text PDF:/Users/adebimpe/Zotero/storage/G5IEYMP7/Fonov et al. - 2011 - Unbiased Average Age-Appropriate Atlases for Pedia.pdf:application/pdf},
}

@article{power_fd_dvars,
	title = {Methods to detect, characterize, and remove motion artifact in resting state {fMRI}},
	volume = {84},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.08.048},
	abstract = {Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.},
	language = {eng},
	journal = {NeuroImage},
	author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
	month = jan,
	year = {2014},
	pmid = {23994314},
	pmcid = {PMC3849338},
	keywords = {Adolescent, Adult, Algorithms, Artifact, Artifacts, Brain, Brain Mapping, Female, Functional connectivity, Head Movements, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Motion, Movement, MRI, Pattern Recognition, Automated, Reproducibility of Results, Rest, Resting state, Sensitivity and Specificity, Subtraction Technique, Young Adult},
	pages = {320--341},
	file = {Accepted Version:/Users/adebimpe/Zotero/storage/2GWXNAAS/Power et al. - 2014 - Methods to detect, characterize, and remove motion.pdf:application/pdf},
}

@article{glasser2013minimal,
	title = {The minimal preprocessing pipelines for the {Human} {Connectome} {Project}},
	volume = {80},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.04.127},
	abstract = {The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines.},
	language = {eng},
	journal = {NeuroImage},
	author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark and {WU-Minn HCP Consortium}},
	month = oct,
	year = {2013},
	pmid = {23668970},
	pmcid = {PMC3720813},
	keywords = {Algorithms, Brain, CIFTI, Connectome, Diffusion Tensor Imaging, Grayordinates, Human Connectome Project, Humans, Image analysis pipeline, Image Interpretation, Computer-Assisted, Models, Anatomic, Models, Neurological, Multi-modal data integration, Nerve Net, Surface-based analysis},
	pages = {105--124},
}

@article{2020SciPy-NMeth,
	title = {{SciPy} 1.0: fundamental algorithms for scientific computing in {Python}},
	volume = {17},
	copyright = {2020 The Author(s)},
	issn = {1548-7105},
	shorttitle = {{SciPy} 1.0},
	url = {https://www.nature.com/articles/s41592-019-0686-2},
	doi = {10.1038/s41592-019-0686-2},
	abstract = {SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.},
	language = {en},
	number = {3},
	urldate = {2021-07-23},
	journal = {Nature Methods},
	author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and van der Walt, Stéfan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C. J. and Polat, İlhan and Feng, Yu and Moore, Eric W. and VanderPlas, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Antônio H. and Pedregosa, Fabian and van Mulbregt, Paul},
	month = mar,
	year = {2020},
	note = {Bandiera\_abtest:},
	pages = {261--272},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/HBFK9K6T/Virtanen et al. - 2020 - SciPy 1.0 fundamental algorithms for scientific c.pdf:application/pdf;Snapshot:/Users/adebimpe/Zotero/storage/F87IPJ7C/s41592-019-0686-2.html:text/html},
}

@article{Gordon_2014,
	title = {Generation and {Evaluation} of a {Cortical} {Area} {Parcellation} from {Resting}-{State} {Correlations}},
	volume = {26},
	issn = {1047-3211},
	url = {https://doi.org/10.1093/cercor/bhu239},
	doi = {10.1093/cercor/bhu239},
	abstract = {The cortical surface is organized into a large number of cortical areas; however, these areas have not been comprehensively mapped in the human. Abrupt transitions in resting-state functional connectivity (RSFC) patterns can noninvasively identify locations of putative borders between cortical areas (RSFC-boundary mapping; Cohen et al. 2008). Here we describe a technique for using RSFC-boundary maps to define parcels that represent putative cortical areas. These parcels had highly homogenous RSFC patterns, indicating that they contained one unique RSFC signal; furthermore, the parcels were much more homogenous than a null model matched for parcel size when tested in two separate datasets. Several alternative parcellation schemes were tested this way, and no other parcellation was as homogenous as or had as large a difference compared with its null model. The boundary map-derived parcellation contained parcels that overlapped with architectonic mapping of areas 17, 2, 3, and 4. These parcels had a network structure similar to the known network structure of the brain, and their connectivity patterns were reliable across individual subjects. These observations suggest that RSFC-boundary map-derived parcels provide information about the location and extent of human cortical areas. A parcellation generated using this method is available at http://www.nil.wustl.edu/labs/petersen/Resources.html.},
	number = {1},
	urldate = {2021-07-23},
	journal = {Cerebral Cortex},
	author = {Gordon, Evan M. and Laumann, Timothy O. and Adeyemo, Babatunde and Huckins, Jeremy F. and Kelley, William M. and Petersen, Steven E.},
	month = jan,
	year = {2016},
	pages = {288--303},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/QLBJJPHA/Gordon et al. - 2016 - Generation and Evaluation of a Cortical Area Parce.pdf:application/pdf;Snapshot:/Users/adebimpe/Zotero/storage/P8BIYZIW/2367115.html:text/html},
}

@article{Glasser_2016,
	title = {A multi-modal parcellation of human cerebral cortex},
	volume = {536},
	copyright = {2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
	issn = {1476-4687},
	url = {https://www.nature.com/articles/nature18933},
	doi = {10.1038/nature18933},
	abstract = {Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6\% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.},
	language = {en},
	number = {7615},
	urldate = {2021-07-23},
	journal = {Nature},
	author = {Glasser, Matthew F. and Coalson, Timothy S. and Robinson, Emma C. and Hacker, Carl D. and Harwell, John and Yacoub, Essa and Ugurbil, Kamil and Andersson, Jesper and Beckmann, Christian F. and Jenkinson, Mark and Smith, Stephen M. and Van Essen, David C.},
	month = aug,
	year = {2016},
	note = {Bandiera\_abtest: },
	pages = {171--178},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/AKCDJSLB/Glasser et al. - 2016 - A multi-modal parcellation of human cerebral corte.pdf:application/pdf},
}

@article{alff,
	title = {An improved approach to detection of amplitude of low-frequency fluctuation ({ALFF}) for resting-state {fMRI}: fractional {ALFF}},
	volume = {172},
	issn = {0165-0270},
	shorttitle = {An improved approach to detection of amplitude of low-frequency fluctuation ({ALFF}) for resting-state {fMRI}},
	doi = {10.1016/j.jneumeth.2008.04.012},
	abstract = {Most of the resting-state functional magnetic resonance imaging (fMRI) studies demonstrated the correlations between spatially distinct brain areas from the perspective of functional connectivity or functional integration. The functional connectivity approaches do not directly provide information of the amplitude of brain activity of each brain region within a network. Alternatively, an index named amplitude of low-frequency fluctuation (ALFF) of the resting-state fMRI signal has been suggested to reflect the intensity of regional spontaneous brain activity. However, it has been indicated that the ALFF is also sensitive to the physiological noise. The current study proposed a fractional ALFF (fALFF) approach, i.e., the ratio of power spectrum of low-frequency (0.01-0.08 Hz) to that of the entire frequency range and this approach was tested in two groups of resting-state fMRI data. The results showed that the brain areas within the default mode network including posterior cingulate cortex, precuneus, medial prefrontal cortex and bilateral inferior parietal lobule had significantly higher fALFF than the other brain areas. This pattern was consistent with previous neuroimaging results. The non-specific signal components in the cistern areas in resting-state fMRI were significantly suppressed, indicating that the fALFF approach improved the sensitivity and specificity in detecting spontaneous brain activities. Its mechanism and sensitivity to abnormal brain activity should be evaluated in the future studies.},
	language = {eng},
	number = {1},
	journal = {Journal of Neuroscience Methods},
	author = {Zou, Qi-Hong and Zhu, Chao-Zhe and Yang, Yihong and Zuo, Xi-Nian and Long, Xiang-Yu and Cao, Qing-Jiu and Wang, Yu-Feng and Zang, Yu-Feng},
	month = jul,
	year = {2008},
	pmid = {18501969},
	pmcid = {PMC3902859},
	keywords = {Adolescent, Adult, Age Factors, Brain, Brain Mapping, Child, Female, Functional Laterality, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Oxygen, Rest, Time Factors},
	pages = {137--141},
	file = {Accepted Version:/Users/adebimpe/Zotero/storage/DMI75RMF/Zou et al. - 2008 - An improved approach to detection of amplitude of .pdf:application/pdf},
}

@article{satterthwaite2,
	title = {Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth},
	volume = {83},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.06.045},
	abstract = {Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity. This confound is of particular importance for studies of neurodevelopment in youth because motion is strongly related to subject age during this period. Critically, the effects of motion on connectivity mimic major findings in neurodevelopmental research, specifically an age-related strengthening of distant connections and weakening of short-range connections. Here, in a sample of 780 subjects ages 8-22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion at both the subject and group levels. We find that motion artifact inflates both overall estimates of age-related change as well as specific distance-related changes in connectivity. When motion is more fully accounted for, the prevalence of age-related change as well as the strength of distance-related effects is substantially reduced. However, age-related changes remain highly significant. In contrast, motion artifact tends to obscure age-related changes in connectivity associated with segregation of functional brain modules; improved preprocessing techniques allow greater sensitivity to detect increased within-module connectivity occurring with development. Finally, we show that subject's age can still be accurately estimated from the multivariate pattern of functional connectivity even while controlling for motion. Taken together, these results indicate that while motion artifact has a marked and heterogeneous impact on estimates of connectivity change during adolescence, functional connectivity remains a valuable phenotype for the study of neurodevelopment.},
	language = {eng},
	journal = {NeuroImage},
	author = {Satterthwaite, Theodore D. and Wolf, Daniel H. and Ruparel, Kosha and Erus, Guray and Elliott, Mark A. and Eickhoff, Simon B. and Gennatas, Efstathios D. and Jackson, Chad and Prabhakaran, Karthik and Smith, Alex and Hakonarson, Hakon and Verma, Ragini and Davatzikos, Christos and Gur, Raquel E. and Gur, Ruben C.},
	month = dec,
	year = {2013},
	pmid = {23792981},
	pmcid = {PMC3874413},
	keywords = {Adolescence, Adolescent, Aging, Artifacts, Brain, Brain Mapping, Child, Connectivity, Connectome, Development, Female, fMRI, Humans, Male, Motion, Motion artifact, Nerve Net, Network, Reproducibility of Results, Resting-state, Sensitivity and Specificity, Young Adult},
	pages = {45--57},
	file = {Accepted Version:/Users/adebimpe/Zotero/storage/E5FGCFXX/Satterthwaite et al. - 2013 - Heterogeneous impact of motion on fundamental patt.pdf:application/pdf},
}

@article{harris2020array,
	title = {Array programming with {NumPy}},
	volume = {585},
	copyright = {2020 The Author(s)},
	issn = {1476-4687},
	url = {https://www.nature.com/articles/s41586-020-2649-2},
	doi = {10.1038/s41586-020-2649-2},
	abstract = {Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.},
	language = {en},
	number = {7825},
	urldate = {2021-07-23},
	journal = {Nature},
	author = {Harris, Charles R. and Millman, Jarrod K.  and van der Walt, Stéfan J. and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and Kern, Robert and Picus, Matti and Hoyer, Stephan and van Kerkwijk, Marten H. and Brett, Matthew and Haldane, Allan and del Río, Jaime Fernández and Wiebe, Mark and Peterson, Pearu and Gérard-Marchant, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E.},
	month = sep,
	year = {2020},
	note = {Bandiera\_abtest: a computer-science;software;solar-physics},
	pages = {357--362},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/ZLJBSN8B/Harris et al. - 2020 - Array programming with NumPy.pdf:application/pdf;Snapshot:/Users/adebimpe/Zotero/storage/2S9QHZBQ/s41586-020-2649-2.html:text/html},
}

@article{tian2020topographic,
  title={Topographic organization of the human subcortex unveiled with functional connectivity gradients},
  author={Tian, Ye and Margulies, Daniel S and Breakspear, Michael and Zalesky, Andrew},
  journal={Nature neuroscience},
  volume={23},
  number={11},
  pages={1421--1432},
  year={2020},
  publisher={Nature Publishing Group},
  doi={10.1038/s41593-020-00711-6},
  url={https://doi.org/10.1038/s41593-020-00711-6}
}

@article{power2012spurious,
  title={Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion},
  author={Power, Jonathan D and Barnes, Kelly A and Snyder, Abraham Z and Schlaggar, Bradley L and Petersen, Steven E},
  journal={Neuroimage},
  volume={59},
  number={3},
  pages={2142--2154},
  year={2012},
  publisher={Elsevier},
  doi={10.1016/j.neuroimage.2011.10.018},
  url={https://doi.org/10.1016/j.neuroimage.2011.10.018}
}

@article{fair2020correction,
  title={Correction of respiratory artifacts in MRI head motion estimates},
  author={Fair, Damien A and Miranda-Dominguez, Oscar and Snyder, Abraham Z and Perrone, Anders and Earl, Eric A and Van, Andrew N and Koller, Jonathan M and Feczko, Eric and Tisdall, M Dylan and van der Kouwe, Andre and others},
  journal={Neuroimage},
  volume={208},
  pages={116400},
  year={2020},
  publisher={Elsevier},
  doi={10.1016/j.neuroimage.2019.116400},
  url={https://doi.org/10.1016/j.neuroimage.2019.116400}
}

@article{ciric2022templateflow,
  title={TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models},
  author={Ciric, Rastko and Thompson, William H and Lorenz, Romy and Goncalves, Mathias and MacNicol, Eilidh and Markiewicz, Christopher J and Halchenko, Yaroslav O and Ghosh, Satrajit S and Gorgolewski, Krzysztof J and Poldrack, Russell A and others},
  journal={bioRxiv},
  pages={2021--02},
  year={2022},
  publisher={Cold Spring Harbor Laboratory},
  doi={10.1101/2021.02.10.430678},
  url={https://doi.org/10.1101/2021.02.10.430678}
}

@software{brett_matthew_2022_6658382,
  author       = {Brett, Matthew and
                  Markiewicz, Christopher J. and
                  Hanke, Michael and
                  Côté, Marc-Alexandre and
                  Cipollini, Ben and
                  McCarthy, Paul and
                  Jarecka, Dorota and
                  Cheng, Christopher P. and
                  Halchenko, Yaroslav O. and
                  Cottaar, Michiel and
                  Larson, Eric and
                  Ghosh, Satrajit and
                  Wassermann, Demian and
                  Gerhard, Stephan and
                  Lee, Gregory R. and
                  Wang, Hao-Ting and
                  Kastman, Erik and
                  Kaczmarzyk, Jakub and
                  Guidotti, Roberto and
                  Daniel, Jonathan and
                  Duek, Or and
                  Rokem, Ariel and
                  Madison, Cindee and
                  Moloney, Brendan and
                  Morency, Félix C. and
                  Goncalves, Mathias and
                  Markello, Ross and
                  Riddell, Cameron and
                  Sólon, Anibal and
                  Burns, Christopher and
                  Millman, Jarrod and
                  Gramfort, Alexandre and
                  Leppäkangas, Jaakko and
                  van den Bosch, Jasper J.F. and
                  Vincent, Robert D. and
                  Braun, Henry and
                  Subramaniam, Krish and
                  Papadopoulos Orfanos, Dimitri and
                  Van, Andrew and
                  Gorgolewski, Krzysztof J. and
                  Raamana, Pradeep Reddy and
                  Klug, Julian and
                  Nichols, B. Nolan and
                  Baker, Eric M. and
                  Hayashi, Soichi and
                  Pinsard, Basile and
                  Haselgrove, Christian and
                  Hymers, Mark and
                  Esteban, Oscar and
                  Koudoro, Serge and
                  Pérez-García, Fernando and
                  Dockès, Jérôme and
                  Oosterhof, Nikolaas N. and
                  Amirbekian, Bago and
                  Nimmo-Smith, Ian and
                  Nguyen, Ly and
                  Reddigari, Samir and
                  St-Jean, Samuel and
                  Panfilov, Egor and
                  Garyfallidis, Eleftherios and
                  Varoquaux, Gael and
                  Legarreta, Jon Haitz and
                  Hahn, Kevin S. and
                  Waller, Lea and
                  Hinds, Oliver P. and
                  Fauber, Bennet and
                  Roberts, Jacob and
                  Poline, Jean-Baptiste and
                  Stutters, Jon and
                  Jordan, Kesshi and
                  Cieslak, Matthew and
                  Moreno, Miguel Estevan and
                  Hrnčiar, Tomáš and
                  Haenel, Valentin and
                  Schwartz, Yannick and
                  Baratz, Zvi and
                  Darwin, Benjamin C and
                  Thirion, Bertrand and
                  Gauthier, Carl and
                  Solovey, Igor and
                  Gonzalez, Ivan and
                  Palasubramaniam, Jath and
                  Lecher, Justin and
                  Leinweber, Katrin and
                  Raktivan, Konstantinos and
                  Calábková, Markéta and
                  Fischer, Peter and
                  Gervais, Philippe and
                  Gadde, Syam and
                  Ballinger, Thomas and
                  Roos, Thomas and
                  Reddam, Venkateswara Reddy and
                  freec84},
  title        = {nipy/nibabel:},
  month        = jun,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {4.0.0},
  doi          = {10.5281/zenodo.591597},
  url          = {https://doi.org/10.5281/zenodo.591597}
}

@software{goncalves_mathias_2022_7072346,
  author       = {Goncalves, Mathias and
                  Markiewicz, Christopher J. and
                  Esteban, Oscar and
                  Feczko, Eric and
                  Poldrack, Russell A. and
                  Fair, Damien A.},
  title        = {{NiBabies: a robust preprocessing pipeline for
                   infant functional MRI}},
  month        = sep,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {22.1.3},
  doi          = {10.5281/zenodo.4917621},
  url          = {https://doi.org/10.5281/zenodo.4917621}
}

@misc{Feczko_Earl_perrone_Fair_2021,
  title={ABCD-BIDS Community Collection (ABCC)},
  url={osf.io/psv5m},
  DOI={10.17605/OSF.IO/PSV5M},
  publisher={OSF},
  author={Feczko, Eric and Earl, Eric and perrone, anders and Fair, Damien},
  year={2021},
  month={Jan}
}

@article{feczko2021adolescent,
  title={Adolescent Brain Cognitive Development (ABCD) community MRI collection and utilities},
  author={Feczko, Eric and Conan, Greg and Marek, Scott and Tervo-Clemmens, Brenden and Cordova, Michaela and Doyle, Olivia and Earl, Eric and Perrone, Anders and Sturgeon, Darrick and Klein, Rachel and others},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory},
  url={https://doi.org/10.1101/2021.07.09.451638},
  doi={10.1101/2021.07.09.451638}
}

@article{gratton2020removal,
  title={Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity},
  author={Gratton, Caterina and Dworetsky, Ally and Coalson, Rebecca S and Adeyemo, Babatunde and Laumann, Timothy O and Wig, Gagan S and Kong, Tania S and Gratton, Gabriele and Fabiani, Monica and Barch, Deanna M and others},
  journal={Neuroimage},
  volume={217},
  pages={116866},
  year={2020},
  publisher={Elsevier},
  doi={10.1016/j.neuroimage.2020.116866},
  url={https://doi.org/10.1016/j.neuroimage.2020.116866}
}

@article{pruim2015ica,
  title={ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data},
  author={Pruim, Raimon HR and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K and Beckmann, Christian F},
  journal={Neuroimage},
  volume={112},
  pages={267--277},
  year={2015},
  publisher={Elsevier},
  doi={10.1016/j.neuroimage.2015.02.064},
  url={https://doi.org/10.1016/j.neuroimage.2015.02.064}
}

@article{behzadi2007component,
  title={A component based noise correction method (CompCor) for BOLD and perfusion based fMRI},
  author={Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T},
  journal={Neuroimage},
  volume={37},
  number={1},
  pages={90--101},
  year={2007},
  publisher={Elsevier}
}

@article{dice1945measures,
  title={Measures of the amount of ecologic association between species},
  author={Dice, Lee R},
  journal={Ecology},
  volume={26},
  number={3},
  pages={297--302},
  year={1945},
  publisher={JSTOR},
  url={https://doi.org/10.2307/1932409},
  doi={10.2307/1932409}
}

@article{sorensen1948method,
  title={A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons},
  author={Sorensen, Th A},
  journal={Biol. Skar.},
  volume={5},
  pages={1--34},
  year={1948}
}

@article{power2019distinctions,
  title={Distinctions among real and apparent respiratory motions in human fMRI data},
  author={Power, Jonathan D and Lynch, Charles J and Silver, Benjamin M and Dubin, Marc J and Martin, Alex and Jones, Rebecca M},
  journal={NeuroImage},
  volume={201},
  pages={116041},
  year={2019},
  publisher={Elsevier},
  doi={10.1016/j.neuroimage.2019.116041},
  url={https://doi.org/10.1016/j.neuroimage.2019.116041}
}

@article{marcus2011informatics,
  title={Informatics and data mining tools and strategies for the human connectome project},
  author={Marcus, Daniel S and Harwell, John and Olsen, Timothy and Hodge, Michael and Glasser, Matthew F and Prior, Fred and Jenkinson, Mark and Laumann, Timothy and Curtiss, Sandra W and Van Essen, David C},
  journal={Frontiers in neuroinformatics},
  volume={5},
  pages={4},
  year={2011},
  publisher={Frontiers Research Foundation}
}

@article{avants2009advanced,
  title={Advanced normalization tools (ANTS)},
  author={Avants, Brian B and Tustison, Nick and Song, Gang and others},
  journal={Insight j},
  volume={2},
  number={365},
  pages={1--35},
  year={2009}
}

@article{yarkoni2019pybids,
  title={PyBIDS: Python tools for BIDS datasets},
  author={Yarkoni, Tal and Markiewicz, Christopher J and de la Vega, Alejandro and Gorgolewski, Krzysztof J and Salo, Taylor and Halchenko, Yaroslav O and McNamara, Quinten and DeStasio, Krista and Poline, Jean-Baptiste and Petrov, Dmitry and others},
  journal={Journal of open source software},
  volume={4},
  number={40},
  year={2019},
  publisher={NIH Public Access}
}

@article{abraham2014machine,
  title={Machine learning for neuroimaging with scikit-learn},
  author={Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Ga{\"e}l},
  journal={Frontiers in neuroinformatics},
  pages={14},
  year={2014},
  publisher={Frontiers}
}

@article{hunter2007matplotlib,
  title={Matplotlib: A 2D graphics environment},
  author={Hunter, John D},
  journal={Computing in science \& engineering},
  volume={9},
  number={03},
  pages={90--95},
  year={2007},
  publisher={IEEE Computer Society}
}

@article{jiang2016regional,
  title={Regional homogeneity: a multimodal, multiscale neuroimaging marker of the human connectome},
  author={Jiang, Lili and Zuo, Xi-Nian},
  journal={The Neuroscientist},
  volume={22},
  number={5},
  pages={486--505},
  year={2016},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}

@article{vijaymeena2016survey,
  title={A survey on similarity measures in text mining},
  author={Vijaymeena, MK and Kavitha, K},
  journal={Machine Learning and Applications: An International Journal},
  volume={3},
  number={2},
  pages={19--28},
  year={2016},
  url={https://doi.org/10.5121/mlaij.2016.3103},
  doi={10.5121/mlaij.2016.3103}
}

@article{eggebrecht2017joint,
  title={Joint attention and brain functional connectivity in infants and toddlers},
  author={Eggebrecht, Adam T and Elison, Jed T and Feczko, Eric and Todorov, Alexandre and Wolff, Jason J and Kandala, Sridhar and Adams, Chloe M and Snyder, Abraham Z and Lewis, John D and Estes, Annette M and others},
  journal={Cerebral Cortex},
  volume={27},
  number={3},
  pages={1709--1720},
  year={2017},
  publisher={Oxford University Press},
  url={https://doi.org/10.1093/cercor/bhw403},
  doi={10.1093/cercor/bhw403}
}

gha_language: Python


text:
package handy

import (
	"reflect"
	"runtime"

	"github.com/valyala/fastrand"
)

func GetFuncName(f interface{}) string {
	return runtime.FuncForPC(reflect.ValueOf(f).Pointer()).Name()
}

func Choose(weights []uint32) (index int, ok bool) {
	if len(weights) == 0 {
		return
	}

	total := uint32(0)
	for _, weight := range weights {
		total += weight
	}

	n := fastrand.Uint32n(total) + 1
	for i, w := range weights {
		if n <= w {
			return i, true
		}
		n -= w
	}
	return
}

license: MIT

repository: popeyeio/handy

license_type: permissive

repo_name: popeyeio/handy

branch_name: refs/heads/master

extension: go


text:
SUMMARY = "OpenBMC hwmon poller"
DESCRIPTION = "OpenBMC hwmon poller."
PR = "r1"

inherit skeleton-python

VIRTUAL-RUNTIME_skeleton_workbook ?= ""

RDEPENDS_${PN} += "\
        python-dbus \
        python-json \
        python-shell \
        python-pygobject \
        pyphosphor \
        ${VIRTUAL-RUNTIME_skeleton_workbook} \
        "

SKELETON_DIR = "pyhwmon"

repository: tpilukechen/openbmc

repo_name: tpilukechen/openbmc

extension: bb


text:
package multiserver

import "time"

// DefaultShutdownTimeout is the default amount of time that a graceful
// server shutdown will be allotted in the case of an unexpected failure.
const DefaultShutdownTimeout = time.Second * 3

// Option is a group option.
type Option func(s *Group)

// ShutdownTimeout defines the amount of time alloted for a group of servers
// to gracefully shutdown when one of their peers stops unexpectedly or the
// context is cancelled in ListenAndServeContext.
//
// If group.Shutdown() is called directly this value will be ignored.
func ShutdownTimeout(timeout time.Duration) Option {
	return func(g *Group) {
		g.shutdownTimeout = timeout
	}
}

repository: gentlemanautomaton/multiserver

repo_name: gentlemanautomaton/multiserver


text:
// Copyright 2014, Truveris Inc. All Rights Reserved.
// Use of this source code is governed by the ISC license in the LICENSE file.

package main

import (
	"encoding/json"
	"errors"
	"os"

	"github.com/jessevdk/go-flags"
)

type Cmd struct {
	ConfigFile string `short:"c" description:"Configuration file" default:"/etc/sayd.conf"`
}

type Cfg struct {
	// If defined, start a web server to list the aliases (e.g. :8989)
	HTTPServerAddress string
}

var (
	cfg = Cfg{}
	cmd = Cmd{}
)

// Look in the current directory for an config.json file.
func ParseConfigFile() error {
	file, err := os.Open(cmd.ConfigFile)
	if err != nil {
		return err
	}
	decoder := json.NewDecoder(file)
	err = decoder.Decode(&cfg)
	if err != nil {
		return err
	}

	if cfg.HTTPServerAddress == "" {
		return errors.New("'HTTPServerAddress' is not defined")
	}

	return nil
}

// Parse the command line arguments and populate the global cmd struct.
func ParseCommandLine() {
	flagParser := flags.NewParser(&cmd, flags.PassDoubleDash)
	_, err := flagParser.Parse()
	if err != nil {
		println("command line error: " + err.Error())
		flagParser.WriteHelp(os.Stderr)
		os.Exit(1)
	}
}

license: ISC

repository: truveris/sayd

repo_name: truveris/sayd