text:
= {Accessed 12 13 2014}
}

@Article{Glasser2013,
   Author="Glasser, M. F.  and Sotiropoulos, S. N.  and Wilson, J. A.  and Coalson, T. S.  and Fischl, B.  and Andersson, J. L.  and Xu, J.  and Jbabdi, S.  and Webster, M.  and Polimeni, J. R.  and Van Essen, D. C.  and Jenkinson, M. ",
   Title="{{T}he minimal preprocessing pipelines for the {H}uman {C}onnectome {P}roject}",
   Journal="Neuroimage",
   Year="2013",
   Volume="80",
   Pages="105--124",
   Month="Oct",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3720813}{PMC3720813}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2013.04.127}{10.1016/j.neuroimage.2013.04.127}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/23668970}{23668970}] }
}

@Article{Fox2002,
   Author="Fox, P. T.  and Lancaster, J. L. ",
   Title="{{O}pinion: {M}apping context and content: the {B}rain{M}ap model}",
   Journal="Nat. Rev. Neurosci.",
   Year="2002",
   Volume="3",
   Number="4",
   Pages="319--321",
   Month="Apr",
   Note={[DOI:\href{http://dx.doi.org/10.1038/nrn789}{10.1038/nrn789}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/11967563}{11967563}] }
}

@Article{Yarkoni2011,
   Author="Yarkoni, T.  and Poldrack, R. A.  and Nichols, T. E.  and Van Essen, D. C.  and Wager, T. D. ",
   Title="{{L}arge-scale automated synthesis of human functional neuroimaging data}",
   Journal="Nat. Methods",
   Year="2011",
   Volume="8",
   Number="8",
   Pages="665--670",
   Month="Aug",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146590}{PMC3146590}] [DOI:\href{http://dx.doi.org/10.1038/nmeth.1635}{10.1038/nmeth.1635}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/21706013}{21706013}] }
}

@misc{Neurovault,
  author = {Chris Gorgolewski and Tal Yarkoni and Yannick Schwarz and Camille Maumet and Daniel Margulies},
  year = {2014},
  title = {{N}eurovault},
  language = {English},
  url = {http://www.neurovault.org},
  note = {Accessed 12 13 2014}
}

@misc{Brainspell,
  author = {Roberto Toro},
  year = {2014},
  title = {{B}rainspell},
  language = {English},
  url = {http://www.brainspell.org},
  note = {Accessed 12 13 2014}
}

@misc{openfmri,
  author = {Russ Poldrack},
  year = {2014},
  title = {{O}penf{MRI}},
  language = {English},
  url = {https://openfmri.org/},
  note = {Accessed 12 13 2014}
}

@misc{NITRC,
  author = {National Institutes of Health Blueprint for Neuroscience Research},
  year = {2006},
  title = {{N}euroimaging {I}nformatics {T}ools and {R}esources {C}learinghouse ({NITRC})},
  language = {English},
  url = {http://www.nitrc.org},
  note = {Accessed 12 13 2014}
}

@ARTICLE{Milham2012adhd,
    AUTHOR={Milham, Michael P.  and  Fair, Damien  and  Mennes, Maarten  and  Mostofsky, Stewart H.},
    TITLE={The adhd-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience},
    JOURNAL={Frontiers in Systems Neuroscience},
    VOLUME={6},
    YEAR={2012},
    NUMBER={62},
    URL={http://www.frontiersin.org/systems_neuroscience/10.3389/fnsys.2012.00062/full},
    DOI={10.3389/fnsys.2012.00062},
    ISSN={1662-5137}
}

@Article{DiMartino2014,
   Author="Di Martino, A.  and Yan, C. G.  and Li, Q.  and Denio, E.  and Castellanos, F. X.  and Alaerts, K.  and Anderson, J. S.  and Assaf, M.  and Bookheimer, S. Y.  and Dapretto, M.  and Deen, B.  and Delmonte, S.  and Dinstein, I.  and Ertl-Wagner, B.  and Fair, D. A.  and Gallagher, L.  and Kennedy, D. P.  and Keown, C. L.  and Keysers, C.  and Lainhart, J. E.  and Lord, C.  and Luna, B.  and Menon, V.  and Minshew, N. J.  and Monk, C. S.  and Mueller, S.  and Muller, R. A.  and Nebel, M. B.  and Nigg, J. T.  and O'Hearn, K.  and Pelphrey, K. A.  and Peltier, S. J.  and Rudie, J. D.  and Sunaert, S.  and Thioux, M.  and Tyszka, J. M.  and Uddin, L. Q.  and Verhoeven, J. S.  and Wenderoth, N.  and Wiggins, J. L.  and Mostofsky, S. H.  and Milham, M. P. ",
   Title="{{T}he autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism}",
   Journal="Mol. Psychiatry",
   Year="2014",
   Volume="19",
   Number="6",
   Pages="659--667",
   Month="Jun",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4162310}{PMC4162310}] [DOI:\href{http://dx.doi.org/10.1038/mp.2013.78}{10.1038/mp.2013.78}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/23774715}{23774715}] }
}

@ARTICLE{Zuo2014,
  author = {Xi-Nian Zuo and Jeffrey S. Anderson and Pierre Bellec and Rasmus M. Birn and Bharat B. Biswal and Janusch Blautzik and John C.S. Breitner and Randy L. Buckner and Vince D. Calhoun and F. Xavier Castellanos and Antao Chen and Bing Chen and Jiangtao Chen and Xu Chen and Stanley J. Colcombe and William Courtney and RCameron Craddock and Adriana Di Martino and Hao-Ming Dong and Xiaolan Fu and Qiyong Gong and Krzyszotof J. Gorgolewski and Ying Han and Ye He and Yong He and Erica Ho and Avram Holmes and Xiao-Hui Hou and Jeremy Huckins and Tianzi Jiang and Yi Jiang and William Kelley and Clare Kelly and Margaret King and Stephen M. LaConte and Janet E. Lainhart and Xu Lei and Hui-Jie Li and Kaiming Li and Kuncheng Li and Qixiang Lin and Dongqiang Liu and Jia Liu and Xun Liu and Yijun Liu and Guangming Lu and Jie Lu and Beatriz Luna and Jing Luo and Daniel Lurie and Ying Mao and Daniel S. Margulies and Andrew R. Mayer and Thomas Meindl and Mary E. Meyerand and Weizhi Nan and Jared A. Nielsen and David O’Connor and David Paulsen and Vivek Prabhakaran and Zhigang Qi and Jiang Qiu and Chunhong Shao and Zarrar Shehzad and Weijun Tang and Arno Villringer and Huiling Wang and Kai Wang and Dongtao Wei and Gao-Xia Wei and Xu-Chu Weng and Xuehai Wu and Ting Xu and Ning Yang and Zhi Yang and Yu-Feng Zang and Lei Zhang and Qinglin Zhang and Zhe Zhang and Zhiqiang Zhang and Ke Zhao and Zonglei Zhen and Yuan Zhou and Xing-Ting Zhu and Michael P. Milham},
  title = {An open science resource for establishing reliability and reproducibility in functional connectomics.},
  journal = {Scientific Data},
  year = {in press}
}

@ARTICLE{Nooner2012,
  author = {Nooner, K. B. and Colcombe, S. J. and Tobe, R. H. and Mennes, M.
	and Benedict, M. M. and Moreno, A. L. and Panek, L. J. and Brown,
	S. and Zavitz, S. T. and Li, Q. and Sikka, S. and Gutman, D. and
	Bangaru, S. and Schlachter, R. T. and Kamiel, S. M. and Anwar, A.
	R. and Hinz, C. M. and Kaplan, M. S. and Rachlin, A. B. and Adelsberg,
	S. and Cheung, B. and Khanuja, R. and Yan, C. and Craddock, C. C.
	and Calhoun, V. and Courtney, W. and King, M. and Wood, D. and Cox,
	C. L. and Kelly, A. M. and Di Martino, A. and Petkova, E. and Reiss,
	P. T. and Duan, N. and Thomsen, D. and Biswal, B. and Coffey, B.
	and Hoptman, M. J. and Javitt, D. C. and Pomara, N. and Sidtis, J.
	J. and Koplewicz, H. S. and Castellanos, F. X. and Leventhal, B.
	L. and Milham, M. P.},
  title = {The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery
	Science in Psychiatry},
  journal = {Frontiers in neuroscience},
  year = {2012},
  volume = {6},
  pages = {152},
  doi = {10.3389/fnins.2012.00152},
  issn = {1662-453X (Electronic) 1662-453X (Linking)},
  type = {Journal Article},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/23087608}
}

@Article{Bottger2014,
   Author="Bottger, J.  and Schurade, R.  and Jakobsen, E.  and Schaefer, A.  and Margulies, D. S. ",
   Title="{{C}onnexel visualization: a software implementation of glyphs and edge-bundling for dense connectivity data using brain{G}{L}}",
   Journal="Front Neurosci",
   Year="2014",
   Volume="8",
   Pages="15",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3941704}{PMC3941704}] [DOI:\href{http://dx.doi.org/10.3389/fnins.2014.00015}{10.3389/fnins.2014.00015}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/24624052}{24624052}] }
}

@book{Vapnik1998,
  title={Statistical learning theory},
  author={Vapnik, Vladimir Naumovich and Vapnik, Vlamimir},
  volume={2},
  year={1998},
  publisher={Wiley New York}
}

@misc{Aguirre2014,
  author = {Geoffrey Aguirre},
  year = {2014},
  title = {Number of neurons in a voxel},
  language = {English},
  url = {https://cfn.upenn.edu/aguirre/wiki/public:neurons_in_a_voxel},
  note = {Accessed 12 13 2014}
}

@Article{Cole2014,
   Author="Cole, M. W.  and Bassett, D. S.  and Power, J. D.  and Braver, T. S.  and Petersen, S. E. ",
   Title="{{I}ntrinsic and task-evoked network architectures of the human brain}",
   Journal="Neuron",
   Year="2014",
   Volume="83",
   Number="1",
   Pages="238--251",
   Month="Jul",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4082806}{PMC4082806}] [DOI:\href{http://dx.doi.org/10.1016/j.neuron.2014.05.014}{10.1016/j.neuron.2014.05.014}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/24991964}{24991964}] }
}

@Article{Krienen2014,
   Author="Krienen, F. M.  and Yeo, B. T.  and Buckner, R. L. ",
   Title="{{R}econfigurable task-dependent functional coupling modes cluster around a core functional architecture}",
   Journal="Philos. Trans. R. Soc. Lond., B, Biol. Sci.",
   Year="2014",
   Volume="369",
   Number="1653",
   Pages=" ",
   Month="Oct",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150301}{PMC4150301}] [DOI:\href{http://dx.doi.org/10.1098/rstb.2013.0526}{10.1098/rstb.2013.0526}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/25180304}{25180304}] }
}

@Article{Bellec2012,
   Author="Bellec, P.  and Lavoie-Courchesne, S.  and Dickinson, P.  and Lerch, J. P.  and Zijdenbos, A. P.  and Evans, A. C. ",
   Title="{{T}he pipeline system for {O}ctave and {M}atlab ({P}{S}{O}{M}): a lightweight scripting framework and execution engine for scientific workflows}",
   Journal="Front Neuroinform",
   Year="2012",
   Volume="6",
   Pages="7"
}

@Article{Gorgolewski2011,
   Author="Gorgolewski, K.  and Burns, C. D.  and Madison, C.  and Clark, D.  and Halchenko, Y. O.  and Waskom, M. L.  and Ghosh, S. S. ",
   Title="{{N}ipype: a flexible, lightweight and extensible neuroimaging data processing framework in python}",
   Journal="Front Neuroinform",
   Year="2011",
   Volume="5",
   Pages="13"
}

@Article{Yan2010,
   Author="Yan, Chao-Gan  and Zang, Yu-Feng ",
   Title="{{D}{P}{A}{R}{S}{F}: {A} {M}{A}{T}{L}{A}{B} {T}oolbox for "{P}ipeline" {D}ata {A}nalysis of {R}esting-{S}tate f{M}{R}{I}}",
   Journal="Front Syst Neurosci",
   Year="2010",
   Volume="4",
   Pages="13"
}

@ARTICLE{Craddock2013c,
	AUTHOR={Craddock, Cameron  and  Sikka, Sharad  and  Cheung, Brian  and  Khanuja, Ranjeet  and  Ghosh, Satrajit S  and  Yan, Chaogan  and  Li, Qingyang  and  Lurie, Daniel  and  Vogelstein, Joshua  and  Burns, Randal  and  Colcombe, Stanley  and  Mennes, Maarten  and  Kelly, Clare  and  Di Martino, Adriana  and  Castellanos, Francisco Xavier  and  Milham, Michael},
	TITLE={Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC)},
	JOURNAL={Frontiers in Neuroinformatics},
	VOLUME={},
	YEAR={2013},
	NUMBER={42},
	URL={http://www.frontiersin.org/neuroinformatics/10.3389/conf.fninf.2013.09.00042/full},
	DOI={10.3389/conf.fninf.2013.09.00042},
	ISSN={1662-5196}
}

@ARTICLE{SLV2012,
  author = {S. Lavoie-Courchesne and P. Rioux and F. Chouinard-Decorte and T.
	Sherif and M.-E. Rousseau and S. Das and R. Adalat and J. Doyon and
	{\textbf{R. C.}} {\textbf{Craddock}} and D. Margulies and C. Chu,
	O. Lyttelton and A. C. Evans and P. Bellec},
  title = {Integration of a neuroimaging processing pipeline into a pan-canadian
	computing grid.},
  journal = {Journal of Physics: Conference Series},
  year = {2012},
  volume = {341(1)},
  doi = {doi:10.1088/1742-6596/341/1/012032},
  owner = {cameron},
  timestamp = {2011.11.22}
}

@Article{Dinov2010,
   Author="Dinov, I.  and Lozev, K.  and Petrosyan, P.  and Liu, Z.  and Eggert, P.  and Pierce, J.  and Zamanyan, A.  and Chakrapani, S.  and Van Horn, J.  and Parker, D. S.  and Magsipoc, R.  and Leung, K.  and Gutman, B.  and Woods, R.  and Toga, A. ",
   Title="{{N}euroimaging study designs, computational analyses and data provenance using the {L}{O}{N}{I} pipeline}",
   Journal="PLoS ONE",
   Year="2010",
   Volume="5",
   Number="9",
   Pages=" "
}

@article{Cox1996,
abstract = {A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described. The software can color overlay neural activation maps onto higher resolution anatomical scans. Slices in each cardinal plane can be viewed simultaneously. Manual placement of markers on anatomical landmarks allows transformation of anatomical and functional scans into stereotaxic (Talairach-Tournoux) coordinates. The techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described. Facilities are provided for several types of statistical analyses of multiple 3D functional data sets. The programs are written in ANSI C and Motif 1.2 to run on Unix workstations.},
author = {Cox, Robert W},
issn = {0010-4809},
journal = {Computers and biomedical research, an international journal},
keywords = {Brain,Brain: anatomy \& histology,Brain: physiology,Computer Systems,Computer-Assisted,Data Display,Humans,Image Processing,Magnetic Resonance Imaging,Programming Languages,Software,Stereotaxic Techniques,User-Computer Interface},
month = jun,
number = {3},
pages = {162--73},
pmid = {8812068},
title = {{AFNI: software for analysis and visualization of functional magnetic resonance neuroimages.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/8812068},
volume = {29},
year = {1996}
}

@article{Smith2004,
abstract = {The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).},
author = {Smith, Stephen M and Jenkinson, Mark and Woolrich, Mark W and Beckmann, Christian F and Behrens, Timothy E J and Johansen-Berg, Heidi and Bannister, Peter R and {De Luca}, Marilena and Drobnjak, Ivana and Flitney, David E and Niazy, Rami K and Saunders, James and Vickers, John and Zhang, Yongyue and {De Stefano}, Nicola and Brady, J Michael and Matthews, Paul M},
doi = {10.1016/j.neuroimage.2004.07.051},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Bayes Theorem,Brain,Brain: anatomy \& histology,Brain: physiology,Databases, Factual,Humans,Image Processing, Computer-Assisted,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics \& numerical,Models, Neurological,Models, Statistical,Software},
month = jan,
pages = {S208--19},
pmid = {15501092},
title = {{Advances in functional and structural MR image analysis and implementation as FSL.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15501092},
volume = {23 Suppl 1},
year = {2004}
}

@Article{Avants2008,
   Author="Avants, B. B.  and Epstein, C. L.  and Grossman, M.  and Gee, J. C. ",
   Title="{{S}ymmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain}",
   Journal="Med Image Anal",
   Year="2008",
   Volume="12",
   Number="1",
   Pages="26--41",
   Month="Feb"
}

@article {Friston1994b,
author = {Friston, K. J. and Holmes, A. P. and Worsley, K. J. and Poline, J.-P. and Frith, C. D. and Frackowiak, R. S. J.},
title = {Statistical parametric maps in functional imaging: A general linear approach},
journal = {Human Brain Mapping},
volume = {2},
number = {4},
publisher = {Wiley Subscription Services, Inc., A Wiley Company},
issn = {1097-0193},
url = {http://dx.doi.org/10.1002/hbm.460020402},
doi = {10.1002/hbm.460020402},
pages = {189--210},
keywords = {statistical parametric maps, analysis of variance, general linear model, statistics, functional imaging, Gaussian fields, functional anatomy},
year = {1994}
}

@INPROCEEDINGS{Cox2004,
  author = {Cox, R. W. and Ashburner, J and Breman, H. and Fissell, K. and Haselgrove, C. and Holmes, C.J. and Lancaster, J. L. and Rex, D. E. and Smith, S. M. and Woodward, J. B. and Strother, S. C. },
  title = {A (sort of) new image data format standard: {N}if{T}{I}-1.},
  booktitle = {Proceedings Organization of Human Brain Mapping 10th Annual Meeting, Budapest, Hungary},
  year = {2004},
  address = {Budapest, Hungary}
}

@inproceedings{Amdahl1967,
 author = {Amdahl, Gene M.},
 title = {Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities},
 booktitle = {Proceedings of the April 18-20, 1967, Spring Joint Computer Conference},
 series = {AFIPS '67 (Spring)},
 year = {1967},
 location = {Atlantic City, New Jersey},
 pages = {483--485},
 numpages = {3},
 url = {http://doi.acm.org/10.1145/1465482.1465560},
 doi = {10.1145/1465482.1465560},
 acmid = {1465560},
 publisher = {ACM},
 address = {New York, NY, USA},
}

@article{ODriscoll2013,
title = "‘Big data’, Hadoop and cloud computing in genomics ",
journal = "Journal of Biomedical Informatics ",
volume = "46",
number = "5",
pages = "774 - 781",
year = "2013",
note = "",
issn = "1532-0464",
doi = "http://dx.doi.org/10.1016/j.jbi.2013.07.001",
url = "http://www.sciencedirect.com/science/article/pii/S1532046413001007",
author = "Aisling O’Driscoll and Jurate Daugelaite and Roy D. Sleator",
keywords = "Cloud computing",
keywords = "Bioinformatics",
keywords = "Big data",
keywords = "Genomics",
keywords = "Hadoop "
}

@misc{NITRC_CE2014,
  author = {NITRC},
  year = {2014},
  title = {NITRC Computational Environment},
  language = {English},
  url = {https://aws.amazon.com/marketplace/pp/B00AW0MBLO/ref=mkt_ste_l2_hls_f1?nc2=h_l3_hl},
  note = {Accessed 01 14 2015}
}

@misc{CPAC_AMI2014,
  author = {CPAC},
  year = {2014},
  title = {Configurable Pipeline for the Analysis of Connectomes Amazon Machine Instance},
  language = {English},
  url = {https://github.com/FCP-INDI/ndar-dev/blob/master/aws_walkthrough.md},
  note = {Accessed 12 13 2014}
}

@article{Eklund2012,
abstract = {The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data. Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study, 1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of 5\%, significant activity was found in 1\%-70\% of the 1484 rest datasets, depending on repetition time, paradigm and parameter settings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason for the high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra of the residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametric fMRI analysis in general, other software packages may give different results. By using the computational power of the graphics processing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was then found in 1\%-19\% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.},
author = {Eklund, Anders and Andersson, Mats and Josephson, Camilla and Johannesson, Magnus and Knutsson, Hans},
doi = {10.1016/j.neuroimage.2012.03.093},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Adolescent,Adult,Aged,Aged, 80 and over,Brain Mapping,Brain Mapping: methods,Databases, Factual,Evoked Potentials,Evoked Potentials: physiology,Female,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Logistic Models,Magnetic Resonance Imaging,Male,Middle Aged,Movement,Movement: physiology,Normal Distribution,Reproducibility of Results,Rest,Rest: physiology,Software,Young Adult},
month = jul,
number = {3},
pages = {565--78},
pmid = {22507229},
publisher = {Elsevier Inc.},
title = {{Does parametric fMRI analysis with SPM yield valid results? An empirical study of 1484 rest datasets.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22507229},
volume = {61},
year = {2012}
}

@article{Eklund2011,
abstract = {Parametric statistical methods, such as Z-, t-, and F-values, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single-subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation-based approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level.},
author = {Eklund, Anders and Andersson, Mats and Knutsson, Hans},
doi = {10.1155/2011/627947},
issn = {1687-4196},
journal = {International journal of biomedical imaging},
month = jan,
pages = {627947},
pmid = {22046176},
title = {{Fast random permutation tests enable objective evaluation of methods for single-subject FMRI analysis.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3199190\&tool=pmcentrez\&rendertype=abstract},
volume = {2011},
year = {2011}
}

@article{Eklund2011a,
abstract = {The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose (noisy) computed tomography (CT) data. While 3D image denoising previously has been applied to several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considers several volumes at the same time. The problem with 4D image denoising, compared to 2D and 3D denoising, is that the computational complexity increases exponentially. In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implement it on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512  × 512  × 445  × 20. The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFT-based filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutes for FFT-based filtering. The short processing time increases the clinical value of true 4D image denoising significantly.},
author = {Eklund, Anders and Andersson, Mats and Knutsson, Hans},
doi = {10.1155/2011/952819},
issn = {1687-4196},
journal = {International journal of biomedical imaging},
month = jan,
pages = {952819},
pmid = {21977020},
title = {{True 4D Image Denoising on the GPU.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3184419\&tool=pmcentrez\&rendertype=abstract},
volume = {2011},
year = {2011}
}

@article{Eklund2012a,
abstract = {Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution. There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needs to be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motion compensation, are normally applied. The high computational power of modern graphic cards has already successfully been used for MRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing steps and two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implemented on a GPU. For an fMRI dataset of typical size (80 volumes with 64×64×22voxels), all the preprocessing takes about 0.5s on the GPU, compared to 5s with an optimized CPU implementation and 120s with the commonly used statistical parametric mapping (SPM) software. A random permutation test with 10,000 permutations, with smoothing in each permutation, takes about 50s if three GPUs are used, compared to 0.5-2.5h with an optimized CPU implementation. The presented work will save time for researchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, both for conventional fMRI and for real-time fMRI.},
author = {Eklund, Anders and Andersson, Mats and Knutsson, Hans},
doi = {10.1016/j.cmpb.2011.07.007},
issn = {1872-7565},
journal = {Computer methods and programs in biomedicine},
keywords = {Brain,Brain: anatomy \& histology,Brain: physiology,Computer Graphics,Computer Graphics: statistics \& numerical data,Computer Systems,Computer Systems: statistics \& numerical data,Computers,Databases, Factual,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: statistics \& ,Linear Models,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics \& numerical,Software,Statistics, Nonparametric,methods},
mendeley-tags = {methods},
month = feb,
number = {2},
pages = {145--61},
pmid = {21862169},
title = {{fMRI analysis on the GPU-possibilities and challenges.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21862169},
volume = {105},
year = {2012}
}

@article{Eklund2013,
abstract = {Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.},
author = {Eklund, Anders and Dufort, Paul and Forsberg, Daniel and Laconte, Stephen M},
doi = {10.1016/j.media.2013.05.008},
issn = {1361-8423},
journal = {Medical image analysis},
month = jun,
number = {8},
pages = {1073--1094},
pmid = {23906631},
publisher = {Elsevier B.V.},
title = {{Medical image processing on the GPU - Past, present and future.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23906631},
volume = {17},
year = {2013}
}

@article{Eklund2013a,
abstract = {Simple models and algorithms based on restrictive assumptions are often used in the field of neuroimaging for studies involving functional magnetic resonance imaging, voxel based morphometry, and diffusion tensor imaging. Nonparametric statistical methods or flexible Bayesian models can be applied rather easily to yield more trustworthy results. The spatial normalization step required for multisubject studies can also be improved by taking advantage of more robust algorithms for image registration. A common drawback of algorithms based on weaker assumptions, however, is the increase in computational complexity. In this short overview, we will therefore present some examples of how inexpensive PC graphics hardware, normally used for demanding computer games, can be used to enable practical use of more realistic models and accurate algorithms, such that the outcome of neuroimaging studies really can be trusted.},
author = {Eklund, Anders and Villani, Mattias and Laconte, Stephen M},
doi = {10.3758/s13415-013-0165-7},
issn = {1531-135X},
journal = {Cognitive, affective \& behavioral neuroscience},
keywords = {bayesian statistics,dti,fmri,graphics processing units,neuroimaging,non-parametric statistics,normalization,spatial,vbm},
month = sep,
number = {3},
pages = {587--97},
pmid = {23625719},
title = {{Harnessing graphics processing units for improved neuroimaging statistics.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23625719},
volume = {13},
year = {2013}
}

@article{eklund2014broccoli,
  title={BROCCOLI: Software for Fast fMRI Analysis on Many-Core CPUs and GPUs},
  author={Eklund, Anders and Dufort, Paul and Villani, Mattias and LaConte, Stephen},
  journal={Frontiers in Neuroinformatics},
  volume={8},
  pages={24},
  year={2014}
}

@Article{delgado2014,
   Author="Delgado, J.  and Moure, J. C.  and Vives-Gilabert, Y.  and Delfino, M.  and Espinosa, A.  and Gomez-Anson, B. ",
   Title="{{I}mproving the execution performance of {F}ree{S}urfer : a new scheduled pipeline scheme for optimizing the use of {C}{P}{U} and {G}{P}{U} resources}",
   Journal="Neuroinformatics",
   Year="2014",
   Volume="12",
   Number="3",
   Pages="413--421",
   Month="Jul"
}

@Article{Hernandez2013,
   Author="Daniela Hernandez",
   Title="{{N}ow {Y}ou {C}an {B}uild {G}oogle’s \$1{M} {A}rtificial {B}rain on the {C}heap}",
   Journal="Wired",
   Year="2013",
   Volume="6",
   Number="3",
   Pages="413--421",
   Month="Jul",
   url="http://www.wired.com/2013/06/andrew_ng/"
}

@article{Scheinost2013,
abstract = {Real-time functional magnetic resonance imaging (rt-fMRI) has recently gained interest as a possible means to facilitate the learning of certain behaviors. However, rt-fMRI is limited by processing speed and available software, and continued development is needed for rt-fMRI to progress further and become feasible for clinical use. In this work, we present an open-source rt-fMRI system for biofeedback powered by a novel Graphics Processing Unit (GPU) accelerated motion correction strategy as part of the BioImage Suite project ( www.bioimagesuite.org ). Our system contributes to the development of rt-fMRI by presenting a motion correction algorithm that provides an estimate of motion with essentially no processing delay as well as a modular rt-fMRI system design. Using empirical data from rt-fMRI scans, we assessed the quality of motion correction in this new system. The present algorithm performed comparably to standard (non real-time) offline methods and outperformed other real-time methods based on zero order interpolation of motion parameters. The modular approach to the rt-fMRI system allows the system to be flexible to the experiment and feedback design, a valuable feature for many applications. We illustrate the flexibility of the system by describing several of our ongoing studies. Our hope is that continuing development of open-source rt-fMRI algorithms and software will make this new technology more accessible and adaptable, and will thereby accelerate its application in the clinical and cognitive neurosciences.},
author = {Scheinost, Dustin and Hampson, Michelle and Qiu, Maolin and Bhawnani, Jitendra and Constable, R Todd and Papademetris, Xenophon},
doi = {10.1007/s12021-013-9176-3},
issn = {1559-0089},
journal = {Neuroinformatics},
keywords = {GPU,denoising,method,motion correction},
mendeley-tags = {GPU,denoising,method,motion correction},
month = jul,
number = {3},
pages = {291--300},
pmid = {23319241},
title = {{A graphics processing unit accelerated motion correction algorithm and modular system for real-time fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23319241},
volume = {11},
year = {2013}
}

@Article{Hernandez2013dti,
   Author="Hernandez, M.  and Guerrero, G. D.  and Cecilia, J. M.  and Garcia, J. M.  and Inuggi, A.  and Jbabdi, S.  and Behrens, T. E.  and Sotiropoulos, S. N. ",
   Title="{{A}ccelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using {G}{P}{U}s}",
   Journal="PLoS ONE",
   Year="2013",
   Volume="8",
   Number="4",
   Pages="e61892"
}

@INPROCEEDINGS{Eklund2011b,
author={Eklund, A. and Friman, O. and Andersson, M. and Knutsson, H.},
booktitle={Image Processing (ICIP), 2011 18th IEEE International Conference on},
title={A GPU accelerated interactive interface for exploratory functional connectivity analysis of FMRI data},
year={2011},
month={Sept},
pages={1589-1592},
keywords={biomedical MRI;brain;coprocessors;correlation methods;medical image processing;rendering (computer graphics);time series;3D volume rendering;FMRI data;GPU accelerated interactive interface;GPU-accelerated interactive tool;brain;correlation analysis;correlation map;exploratory functional connectivity analysis;functional MRI data set;graphics processing unit;reference voxel time series;temporal correlation;Conferences;Correlation;Data visualization;Graphics processing unit;Real time systems;Three dimensional displays;Time series analysis;GPU;MeVisLab;OpenCL;fMRI;functional connectivity},
doi={10.1109/ICIP.2011.6115753},
ISSN={1522-4880}
}

@book{Munshi2011,
 author = {Munshi, Aaftab and Gaster, Benedict and Mattson, Timothy G. and Fung, James and Ginsburg, Dan},
 title = {OpenCL Programming Guide},
 year = {2011},
 isbn = {0321749642, 9780321749642},
 edition = {1st},
 publisher = {Addison-Wesley Professional}
}

license_type: permissive

branch_name: refs/heads/master

extension: bib


text:
require fontsproto_${PV}.bb

PR = "r1"

XORG_PN = "fontsproto"

S = "${WORKDIR}/fontsproto-${PV}"

inherit native

SRC_URI[archive.md5sum] = "f3a857deadca3144fba041af1dbf7603"
SRC_URI[archive.sha256sum] = "5a9af61dc9142488c9ba6e4ae30a9d970ea0f889a1ab7f59de1c1898c83aeb35"

license: MIT

extension: bb


text:
@article{Ruprecht2018,
  author = {Ruprecht, D.},
  title = {Wave propagation characteristics of Parareal},
  journal = {Computing and Visualization in Science},
  url = {https://doi.org/10.1007/s00791-018-0296-z},
  doi = {10.1007/s00791-018-0296-z},
  volume = {19},
  number = {1},
  pages = {1--17},
  year = {2018}
}

repository: comp-maths/website

repo_name: comp-maths/website


text:
const sortedUnion = require('./sortedUnion');

describe('Sorted Union - Write a function that takes two or more arrays and returns a new array of unique values in the order of the original provided arrays.', () => {
  it('shoud return an array', () => {
    expect(sortedUnion([1, 3, 2], [5, 2, 1, 4], [2, 1])).toEqual(
      expect.any(Array)
    );
  });
  it('shoud return [1, 3, 2, 5, 4]', () => {
    expect(sortedUnion([1, 3, 2], [5, 2, 1, 4], [2, 1])).toEqual([
      1,
      3,
      2,
      5,
      4,
    ]);
  });
  it('shoud return [1, 3, 2, [5], [4]]', () => {
    expect(sortedUnion([1, 3, 2], [1, [5]], [2, [4]])).toEqual([
      1,
      3,
      2,
      [5],
      [4],
    ]);
  });
  it('shoud return [1, 2, 3, 5]', () => {
    expect(sortedUnion([1, 2, 3], [5, 2, 1])).toEqual([1, 2, 3, 5]);
  });
  it('shoud return [1, 2, 3, 5, 4, 6, 7, 8]', () => {
    expect(sortedUnion([1, 2, 3], [5, 2, 1, 4], [2, 1], [6, 7, 8])).toEqual([
      1,
      2,
      3,
      5,
      4,
      6,
      7,
      8,
    ]);
  });
});

gha_license_id: MIT

gha_language: JavaScript

extension: js