craddockGigascience2014.bib

@article{Assaf2013,
abstract = {In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome.},
author = {Assaf, Yaniv and Alexander, Daniel C and Jones, Derek K and Bizzi, Albero and Behrens, Tim E J and Clark, Chris a and Cohen, Yoram and Dyrby, Tim B and Huppi, Petra S and Knoesche, Thomas R and Lebihan, Denis and Parker, Geoff J M and Poupon, Cyril and Anaby, Debbie and Anwander, Alfred and Bar, Leah and Barazany, Daniel and Blumenfeld-Katzir, Tamar and De-Santis, Silvia and Duclap, Delphine and Figini, Matteo and Fischi, Elda and Guevara, Pamela and Hubbard, Penny and Hofstetter, Shir and Jbabdi, Saad and Kunz, Nicolas and Lazeyras, Francois and Lebois, Alice and Liptrot, Matthew G and Lundell, Henrik and Mangin, Jean-Fran\c{c}ois and Dominguez, David Moreno and Morozov, Darya and Schreiber, Jan and Seunarine, Kiran and Nava, Simone and Riffert, Till and Sasson, Efrat and Schmitt, Benoit and Shemesh, Noam and Sotiropoulos, Stam N and Tavor, Ido and Zhang, Hui Gary and Zhou, Feng-Lei},
doi = {10.1016/j.neuroimage.2013.05.055},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Assaf et al. - 2013 - The CONNECT project Combining macro- and micro-structure.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain: cytology,Brain: physiology,Connectome,Connectome: methods,Diffusion Tensor Imaging,Diffusion Tensor Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Models, Anatomic,Models, Neurological,Nerve Net,Nerve Net: cytology,Nerve Net: physiology},
month = oct,
pages = {273--82},
pmid = {23727318},
publisher = {Elsevier Inc.},
title = {{The CONNECT project: Combining macro- and micro-structure.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23727318},
volume = {80},
year = {2013}
}
@article{Biswal2010,
abstract = {Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon\_1000/.},
author = {Biswal, Bharat B and Mennes, Maarten and Zuo, Xi-Nian and Gohel, Suril and Kelly, Clare and Smith, Steve M and Beckmann, Christian F and Adelstein, Jonathan S and Buckner, Randy L and Colcombe, Stan and Dogonowski, Anne-Marie and Ernst, Monique and Fair, Damien and Hampson, Michelle and Hoptman, Matthew J and Hyde, James S and Kiviniemi, Vesa J and K\"{o}tter, Rolf and Li, Shi-Jiang and Lin, Ching-Po and Lowe, Mark J and Mackay, Clare and Madden, David J and Madsen, Kristoffer H and Margulies, Daniel S and Mayberg, Helen S and McMahon, Katie and Monk, Christopher S and Mostofsky, Stewart H and Nagel, Bonnie J and Pekar, James J and Peltier, Scott J and Petersen, Steven E and Riedl, Valentin and Rombouts, Serge a R B and Rypma, Bart and Schlaggar, Bradley L and Schmidt, Sein and Seidler, Rachael D and Siegle, Greg J and Sorg, Christian and Teng, Gao-Jun and Veijola, Juha and Villringer, Arno and Walter, Martin and Wang, Lihong and Weng, Xu-Chu and Whitfield-Gabrieli, Susan and Williamson, Peter and Windischberger, Christian and Zang, Yu-Feng and Zhang, Hong-Ying and Castellanos, F Xavier and Milham, Michael P},
doi = {10.1073/pnas.0911855107},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Biswal et al. - 2010 - Toward discovery science of human brain function(2).pdf:pdf},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Adolescent,Adult,Age Factors,Aged,Algorithms,Analysis of Variance,Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy \& histology,Brain: physiology,Female,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Middle Aged,Neural Pathways,Neural Pathways: anatomy \& histology,Neural Pathways: physiology,Sex Factors,Young Adult},
month = mar,
number = {10},
pages = {4734--9},
pmid = {20176931},
title = {{Toward discovery science of human brain function.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2842060\&tool=pmcentrez\&rendertype=abstract},
volume = {107},
year = {2010}
}
@article{Bogdanov2014,
archivePrefix = {arXiv},
arxivId = {arXiv:1407.5590v1},
author = {Bogdanov, Petko and Dereli, Nazli and Bassett, DS},
eprint = {arXiv:1407.5590v1},
file = {:home/rtungaraza/PapersBooks/LearningAboutLearningSugraphs2014.pdf:pdf},
journal = {arXiv preprint arXiv: \ldots},
title = {{Learning about Learning: Human Brain Sub-Network Biomarkers in fMRI Data}},
url = {http://arxiv.org/abs/1407.5590},
year = {2014}
}
@article{Bullmore2011,
abstract = {Brain graphs provide a relatively simple and increasingly popular way of modeling the human brain connectome, using graph theory to abstractly define a nervous system as a set of nodes (denoting anatomical regions or recording electrodes) and interconnecting edges (denoting structural or functional connections). Topological and geometrical properties of these graphs can be measured and compared to random graphs and to graphs derived from other neuroscience data or other (nonneural) complex systems. Both structural and functional human brain graphs have consistently demonstrated key topological properties such as small-worldness, modularity, and heterogeneous degree distributions. Brain graphs are also physically embedded so as to nearly minimize wiring cost, a key geometric property. Here we offer a conceptual review and methodological guide to graphical analysis of human neuroimaging data, with an emphasis on some of the key assumptions, issues, and trade-offs facing the investigator.},
author = {Bullmore, Edward T and Bassett, Danielle S},
doi = {10.1146/annurev-clinpsy-040510-143934},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bullmore, Bassett - 2011 - Brain graphs graphical models of the human brain connectome.pdf:pdf},
issn = {1548-5951},
journal = {Annual review of clinical psychology},
keywords = {Animals,Brain,Brain: anatomy \& histology,Brain: physiology,Humans,Magnetic Resonance Imaging,Models, Neurological,Nerve Net,Nerve Net: anatomy \& histology,Nerve Net: physiology,Neural Networks (Computer)},
month = jan,
pages = {113--40},
pmid = {21128784},
title = {{Brain graphs: graphical models of the human brain connectome.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21128784},
volume = {7},
year = {2011}
}
@article{Craddock2009,
abstract = {The application of multivoxel pattern analysis methods has attracted increasing attention, particularly for brain state prediction and real-time functional MRI applications. Support vector classification is the most popular of these techniques, owing to reports that it has better prediction accuracy and is less sensitive to noise. Support vector classification was applied to learn functional connectivity patterns that distinguish patients with depression from healthy volunteers. In addition, two feature selection algorithms were implemented (one filter method, one wrapper method) that incorporate reliability information into the feature selection process. These reliability feature selections methods were compared to two previously proposed feature selection methods. A support vector classifier was trained that reliably distinguishes healthy volunteers from clinically depressed patients. The reliability feature selection methods outperformed previously utilized methods. The proposed framework for applying support vector classification to functional connectivity data is applicable to other disease states beyond major depression.},
author = {Craddock, R Cameron and Holtzheimer, Paul E and Hu, Xiaoping P and Mayberg, Helen S},
doi = {10.1002/mrm.22159},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Craddock et al. - 2009 - Disease state prediction from resting state functional connectivity.pdf:pdf},
issn = {1522-2594},
journal = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
keywords = {Adult,Algorithms,Artificial Intelligence,Depressive Disorder, Major,Depressive Disorder, Major: diagnosis,Female,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity},
month = dec,
number = {6},
pages = {1619--28},
pmid = {19859933},
title = {{Disease state prediction from resting state functional connectivity.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19859933},
volume = {62},
year = {2009}
}
@article{Craddock2005,
author = {Craddock, R Cameron and James, G Andrew and Holtzheimer, Paul E and Hu, Xiaoping P and Mayberg, Helen S},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Craddock et al. - 2005 - ROI Atlas Generation from Whole Brain Parcellation of Resting State fMRI Data.pdf:pdf},
journal = {ISMRM},
pages = {77869--77869},
title = {{ROI Atlas Generation from Whole Brain Parcellation of Resting State fMRI Data}},
volume = {25},
year = {2005}
}
@article{Craddock2012,
author = {Craddock, R Cameron and James, G Andrew and Iii, Paul E Holtzheimer and Hu, Xiaoping P and Mayberg, Helen S},
doi = {10.1002/hbm.21333.A},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Craddock et al. - 2012 - A whole brain fMRI atlas generated via spatially constrained spectral clustering.pdf:pdf},
journal = {Human Brain Mapping},
keywords = {atlas,clustering,functional connectivity,regions of interest,resting state},
number = {8},
title = {{A whole brain fMRI atlas generated via spatially constrained spectral clustering}},
volume = {33},
year = {2012}
}
@article{Craddock2013,
abstract = {At macroscopic scales, the human connectome comprises anatomically distinct brain areas, the structural pathways connecting them and their functional interactions. Annotation of phenotypic associations with variation in the connectome and cataloging of neurophenotypes promise to transform our understanding of the human brain. In this Review, we provide a survey of magnetic resonance imaging–based measurements of functional and structural connectivity. We highlight emerging areas of development and inquiry and emphasize the importance of integrating structural and functional perspectives on brain architecture.},
author = {Craddock, R Cameron and Jbabdi, Saad and Yan, Chao-Gan and Vogelstein, Joshua T and Castellanos, F Xavier and {Di Martino}, Adriana and Kelly, Clare and Heberlein, Keith and Colcombe, Stan and Milham, Michael P},
doi = {10.1038/nmeth.2482},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Craddock et al. - 2013 - Imaging human connectomes at the macroscale.pdf:pdf},
issn = {1548-7105},
journal = {Nature methods},
keywords = {Brain,Brain: cytology,Brain: physiology,Connectome,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Phenotype},
month = jun,
number = {6},
pages = {524--39},
pmid = {23722212},
title = {{Imaging human connectomes at the macroscale.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23722212},
volume = {10},
year = {2013}
}
@article{DagliMSIngeholmJE1999,
author = {{Dagli MS, Ingeholm JE}, and Haxby JV},
journal = {NeuroImage},
pages = {407--415},
title = {{Localization of cardiac-in- duced signal change in fMRI}},
volume = {9},
year = {1999}
}
@article{Dosenbach2010,
abstract = {Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals' brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55\% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain's major functional networks.},
author = {Dosenbach, Nico U F and Nardos, Binyam and Cohen, Alexander L and Fair, Damien a and Power, Jonathan D and Church, Jessica a and Nelson, Steven M and Wig, Gagan S and Vogel, Alecia C and Lessov-Schlaggar, Christina N and Barnes, Kelly Anne and Dubis, Joseph W and Feczko, Eric and Coalson, Rebecca S and Pruett, John R and Barch, Deanna M and Petersen, Steven E and Schlaggar, Bradley L},
doi = {10.1126/science.1194144},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dosenbach et al. - 2010 - Prediction of individual brain maturity using fMRI.pdf:pdf},
issn = {1095-9203},
journal = {Science (New York, N.Y.)},
keywords = {Adolescent,Adult,Aging,Algorithms,Artificial Intelligence,Brain,Brain Mapping,Brain: growth \& development,Brain: physiology,Cerebellum,Cerebellum: growth \& development,Cerebellum: physiology,Child,Female,Frontal Lobe,Frontal Lobe: growth \& development,Frontal Lobe: physiology,Humans,Magnetic Resonance Imaging,Male,Multivariate Analysis,Neural Pathways,Occipital Lobe,Occipital Lobe: growth \& development,Occipital Lobe: physiology,Young Adult},
month = sep,
number = {5997},
pages = {1358--61},
pmid = {20829489},
title = {{Prediction of individual brain maturity using fMRI.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3135376\&tool=pmcentrez\&rendertype=abstract},
volume = {329},
year = {2010}
}
@article{Eloyan2014,
abstract = {Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.},
author = {Eloyan, Ani and Li, Shanshan and Muschelli, John and Pekar, Jim J and Mostofsky, Stewart H and Caffo, Brian S},
doi = {10.1371/journal.pone.0089470},
file = {:home/rtungaraza/PapersBooks/caffoFMRIpaper.pdf:pdf},
issn = {1932-6203},
journal = {PloS one},
month = jan,
number = {2},
pages = {e89470},
pmid = {24586801},
title = {{Analytic programming with FMRI data: a quick-start guide for statisticians using R.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3938835\&tool=pmcentrez\&rendertype=abstract},
volume = {9},
year = {2014}
}
@article{Fornito2014,
abstract = {In recent years, pathophysiological models of brain disorders have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disturbances of interconnected neural systems. This shift has paralleled rapid advances in connectomics, a field concerned with comprehensively mapping the neural elements and inter-connections that constitute the brain. Magnetic resonance imaging (MRI) has played a central role in these efforts, as it allows relatively cost-effective in vivo assessment of the macro-scale architecture of brain network connectivity. In this paper, we provide a brief introduction to some of the basic concepts in the field and review how recent developments in imaging connectomics are yielding new insights into brain disease, with a particular focus on Alzheimer's disease and schizophrenia. Specifically, we consider how research into circuit-level, connectome-wide and topological changes is stimulating the development of new aetiopathological theories and biomarkers with potential for clinical translation. The findings highlight the advantage of conceptualizing brain disease as a result of disturbances in an interconnected complex system, rather than discrete pathology in isolated sub-sets of brain regions.},
author = {Fornito, Alex and Bullmore, Edward T},
doi = {10.1016/j.euroneuro.2014.02.011},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Fornito, Bullmore - 2014 - Connectomics A new paradigm for understanding brain disease.pdf:pdf},
issn = {1873-7862},
journal = {European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology},
month = mar,
pmid = {24726580},
title = {{Connectomics: A new paradigm for understanding brain disease.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24726580},
year = {2014}
}
@inproceedings{Fu2013,
author = {Fu, Zenigh and Di, Xiing and Chan, Shing-Chow and Hung, Yeung-Sam and Biswal, B.B. and Zhang, Zhiguo},
booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE},
pages = {2944--2947},
title = {{Time-varying correlation coefficients estimation and its application to dynamic connectivity analysis of fMRI}},
year = {2013}
}
@article{Gudbjartsson1995,
author = {Gudbjartsson, H and Patz, S},
file = {:home/rtungaraza/PapersBooks/RicianNoiseMRI.pdf:pdf},
journal = {Magnetic resonance in medicine},
keywords = {gaussian,noise,rayleigh,rician},
number = {6},
pages = {910--914},
title = {{The Rician distribution of noisy MRI data}},
url = {http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910340618/full},
volume = {34},
year = {1995}
}
@phdthesis{Hagmann2005,
author = {Hagmann, Patric},
title = {{From diffusion MRI to brain connectomics}},
year = {2005}
}
@article{Iakovidou2013,
abstract = {Complex networks constitute a recurring issue in the analysis of neuroimaging data. Recently, network motifs have been identified as patterns of interconnections since they appear in a significantly higher number than in randomized networks, in a given ensemble of anatomical or functional connectivity graphs. The current approach for detecting and enumerating motifs in brain networks requires a predetermined motif repertoire and can operate only with motifs of small size (consisting of few nodes). There is a growing interest in methodologies for frequent graph-based pattern mining in large graph datasets that can facilitate adaptive design of motifs. The results presented in this paper are based on the graph-based Substructure pattern mining (gSpan) algorithm and introduce a manifold of ways to exploit it for data-driven motif extraction in connectomics research. Functional connectivity graphs from electroencephalographic (EEG) recordings during resting state and mental calculations are used to demonstrate our approach. Relying on either time-invariant or time-evolving graphs, characteristic motifs associated with various frequency bands were derived and compared. With a suitable manipulation, the gSpan discovers motifs which are specific to performing mental arithmetics. Finally, the subject-dependent temporal signatures of motifs' appearance revealed the transient nature of the evolving functional connectivity (math-related motifs "come and go").},
author = {Iakovidou, Nantia D and Dimitriadis, Stavros I and Laskaris, Nikolaos a and Tsichlas, Kostas and Manolopoulos, Yannis},
doi = {10.1016/j.jneumeth.2012.12.018},
file = {:home/rtungaraza/Desktop/Neuro2013idltm.pdf:pdf},
issn = {1872-678X},
journal = {Journal of neuroscience methods},
keywords = {Adult,Algorithms,Brain,Brain: physiology,Computational Biology,Computational Biology: methods,Humans,Models, Neurological,Nerve Net,Nerve Net: physiology},
month = mar,
number = {2},
pages = {204--13},
pmid = {23274947},
publisher = {Elsevier B.V.},
title = {{On the discovery of group-consistent graph substructure patterns from brain networks.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23274947},
volume = {213},
year = {2013}
}
@article{Jiang2013,
abstract = {The human brain can be studied as a hierarchy of complex networks on different temporal and spatial scales. On each scale, from gene, protein, synapse, neuron and microcircuit, to area, pathway and the whole brain, many advances have been made with the development of related techniques. Brain network studies on different temporal and spatial scales are booming. However, such studies have focused on single levels, and can only reflect limited aspects of how the brain is formed and how it works. Therefore, it is increasingly urgent to integrate a variety of techniques, methods and models, and to merge fragmented findings into a uniform research framework or platform. To this end, we have proposed the concept of the brainnetome and several related programs/projects have been launched in China. In this paper, we offer a brief review on the methodologies of the brainnetome, which include techniques on different scales, the brainnetome atlas, and methods of brain network analysis. We then take Alzheimer's disease and schizophrenia as examples to show how the brainnetome can be studied in neurological and psychiatric disorders. We also review the studies of how risk genes for brain diseases affect the brain networks. Finally, we summarize the challenges for the brainnetome, and what actions and measures have been taken to address these challenges in China. It is envisioned that the brainnetome will open new avenues and some long-standing issues may be solved by combining the brainnetome with other "omes".},
author = {Jiang, Tianzi},
doi = {10.1016/j.neuroimage.2013.04.002},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Jiang - 2013 - Brainnetome a new -ome to understand the brain and its disorders.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain Diseases,Brain Diseases: physiopathology,Brain: pathology,Brain: physiopathology,Connectome,Connectome: methods,Humans,Metabolome,Models, Anatomic,Models, Neurological,Nerve Net,Nerve Net: pathology,Nerve Net: physiopathology},
month = oct,
pages = {263--72},
pmid = {23571422},
publisher = {Elsevier Inc.},
title = {{Brainnetome: a new -ome to understand the brain and its disorders.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23571422},
volume = {80},
year = {2013}
}
@article{Johansen-Berg2013,
abstract = {Significant resources are now being devoted to large-scale international studies attempting to map the connectome - the brain's wiring diagram. This review will focus on the use of human neuroimaging approaches to map the connectome at a macroscopic level. This emerging field of human connectomics brings both opportunities and challenges. Opportunities arise from the ability to apply a powerful toolkit of mathematical and computational approaches to interrogate these rich datasets, many of which are being freely shared with the scientific community. Challenges arise in methodology, interpretability and biological or clinical validity. This review discusses these challenges and opportunities and highlights potential future directions.},
author = {Johansen-Berg, Heidi},
doi = {10.1016/j.neuroimage.2013.05.082},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Johansen-Berg - 2013 - Human connectomics - what will the future demand.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Animals,Brain,Brain: anatomy \& histology,Brain: physiology,Connectome,Connectome: trends,Forecasting,Humans,Models, Anatomic,Models, Neurological,Nerve Net,Nerve Net: anatomy \& histology,Nerve Net: physiology},
month = oct,
pages = {541--4},
pmid = {23727322},
publisher = {Elsevier B.V.},
title = {{Human connectomics - what will the future demand?}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23727322},
volume = {80},
year = {2013}
}
@article{Lee2013,
abstract = {SUMMARY: Resting-state fMRI measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. Application of this technique has allowed the identification of various RSNs, or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. Various methods exist for analyzing resting-state data, including seed-based approaches, independent component analysis, graph methods, clustering algorithms, neural networks, and pattern classifiers. Clinical applications of resting-state fMRI are at an early stage of development. However, its use in presurgical planning for patients with brain tumor and epilepsy demonstrates early promise, and the technique may have a future role in providing diagnostic and prognostic information for neurologic and psychiatric diseases.},
author = {Lee, M H and Smyser, C D and Shimony, J S},
doi = {10.3174/ajnr.A3263},
file = {:home/rtungaraza/PapersBooks/1866.full.pdf:pdf},
issn = {1936-959X},
journal = {AJNR. American journal of neuroradiology},
keywords = {Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Preoperative Care,Preoperative Care: methods,Rest,Rest: physiology},
month = oct,
number = {10},
pages = {1866--72},
pmid = {22936095},
title = {{Resting-state fMRI: a review of methods and clinical applications.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22936095},
volume = {34},
year = {2013}
}
@article{Mennes2013,
abstract = {Over a decade ago, the fMRI Data Center (fMRIDC) pioneered open-access data sharing in the task-based functional neuroimaging community. Well ahead of its time, the fMRIDC effort encountered logistical, sociocultural and funding barriers that impeded the field-wise instantiation of open-access data sharing. In 2009, ambitions for open-access data sharing were revived in the resting state functional MRI community in the form of two grassroots initiatives: the 1000 Functional Connectomes Project (FCP) and its successor, the International Neuroimaging Datasharing Initiative (INDI). Beyond providing open access to thousands of clinical and non-clinical imaging datasets, the FCP and INDI have demonstrated the feasibility of large-scale data aggregation for hypothesis generation and testing. Yet, the success of the FCP and INDI should not be confused with widespread embracement of open-access data sharing. Reminiscent of the challenges faced by fMRIDC, key controversies persist and include participant privacy, the role of informatics, and the logistical and cultural challenges of establishing an open science ethos. We discuss the FCP and INDI in the context of these challenges, highlighting the promise of current initiatives and suggesting solutions for possible pitfalls.},
author = {Mennes, Maarten and Biswal, Bharat B and Castellanos, F Xavier and Milham, Michael P},
doi = {10.1016/j.neuroimage.2012.10.064},
file = {:home/rtungaraza/PapersBooks/mennesINDI2013.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Databases, Factual,Humans,Information Dissemination,Information Dissemination: ethics,Information Dissemination: methods,Magnetic Resonance Imaging,Medical Informatics,Medical Informatics: ethics,Medical Informatics: methods,Medical Informatics: organization \& administration},
month = nov,
pages = {683--91},
pmid = {23123682},
publisher = {Elsevier Inc.},
title = {{Making data sharing work: the FCP/INDI experience.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23123682},
volume = {82},
year = {2013}
}
@article{Meskaldji2013,
abstract = {Brain connectivity can be represented by a network that enables the comparison of the different patterns of structural and functional connectivity among individuals. In the literature, two levels of statistical analysis have been considered in comparing brain connectivity across groups and subjects: 1) the global comparison where a single measure that summarizes the information of each brain is used in a statistical test; 2) the local analysis where a single test is performed either for each node/connection which implies a multiplicity correction, or for each group of nodes/connections where each subset is summarized by one single test in order to reduce the number of tests to avoid a penalizing multiplicity correction. We comment on the different levels of analysis and present some methods that have been proposed at each scale. We highlight as well the possible factors that could influence the statistical results and the questions that have to be addressed in such an analysis.},
author = {Meskaldji, Djalel Eddine and Fischi-Gomez, Elda and Griffa, Alessandra and Hagmann, Patric and Morgenthaler, Stephan and Thiran, Jean-Philippe},
doi = {10.1016/j.neuroimage.2013.04.084},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Meskaldji et al. - 2013 - Comparing connectomes across subjects and populations at different scales.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Animals,Brain,Brain: physiology,Connectome,Connectome: methods,Data Interpretation, Statistical,Humans,Models, Anatomic,Models, Neurological,Models, Statistical,Nerve Net,Nerve Net: physiology},
month = oct,
pages = {416--25},
pmid = {23631992},
publisher = {Elsevier Inc.},
title = {{Comparing connectomes across subjects and populations at different scales.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23631992},
volume = {80},
year = {2013}
}
@article{Milham2012,
abstract = {The neuroimaging community is at a crossroads. Long characterized by individualism, the data and computational and analytic needs of the connectome-wide association era necessitate cultural reform. Emerging initiatives have demonstrated the feasibility and utility of adopting an open neuroscience model to accelerate the pace and success of scientific discovery.},
author = {Milham, Michael Peter},
doi = {10.1016/j.neuron.2011.11.004},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Milham - 2012 - Open neuroscience solutions for the connectome-wide association era.pdf:pdf},
issn = {1097-4199},
journal = {Neuron},
keywords = {Interdisciplinary Communication,Neurosciences,Software},
month = jan,
number = {2},
pages = {214--8},
pmid = {22284177},
title = {{Open neuroscience solutions for the connectome-wide association era.}},
url = {http://www.sciencedirect.com/science/article/pii/S0896627311010038},
volume = {73},
year = {2012}
}
@article{Richiardi2013,
author = {Richiardi, Jonas and Achard, Sophie and Bunke, Horst and Ville, Dimitri Van De},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Richiardi et al. - 2013 - Machine Learning with Brain Graphs.pdf:pdf},
number = {April},
pages = {58--70},
title = {{Machine Learning with Brain Graphs}},
year = {2013}
}
@article{Richiardi2011,
abstract = {Functional connectivity analysis of fMRI data can reveal synchronised activity between anatomically distinct brain regions. Here, we extract the characteristic connectivity signatures of different brain states to perform classification, allowing us to decode the different states based on the functional connectivity patterns. Our approach is based on polythetic decision trees, which combine powerful discriminative ability with interpretability of results. We also propose to use ensemble of classifiers within specific frequency subbands, and show that they bring systematic improvement in classification accuracy. Exploiting multi-band classification of connectivity graphs is also proposed, and we explain theoretical reasons why the technique could bring further improvement in classification performance. The choice of decision trees as classifier is shown to provide a practical way to identify a subset of connections that distinguishes best between the conditions, permitting the extraction of very compact representations for differences between brain states, which we call discriminative graphs. Our experimental results based on strict train/test separation at all stages of processing show that the method is applicable to inter-subject brain decoding with relatively low error rates for the task considered.},
author = {Richiardi, Jonas and Eryilmaz, Hamdi and Schwartz, Sophie and Vuilleumier, Patrik and {Van De Ville}, Dimitri},
doi = {10.1016/j.neuroimage.2010.05.081},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Richiardi et al. - 2011 - Decoding brain states from fMRI connectivity graphs.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Artificial Intelligence,Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy \& histology,Brain: physiology,Decision Trees,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Magnetic Resonance Imaging,Nerve Net,Nerve Net: anatomy \& histology,Nerve Net: physiology},
month = may,
number = {2},
pages = {616--26},
pmid = {20541019},
publisher = {Elsevier Inc.},
title = {{Decoding brain states from fMRI connectivity graphs.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20541019},
volume = {56},
year = {2011}
}
@book{Riesen2010,
address = {Singapore},
author = {Riesen, Kaspar and Bunke, Horst},
publisher = {World Scientific},
title = {{Graph Classification and Clustering based on Vector space embedding}},
year = {2010}
}
@article{Rubinov2010,
abstract = {Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets.},
author = {Rubinov, Mikail and Sporns, Olaf},
doi = {10.1016/j.neuroimage.2009.10.003},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Rubinov, Sporns - 2010 - Complex network measures of brain connectivity uses and interpretations.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain: physiology,Models, Neurological,Nerve Net,Nerve Net: physiology,Neural Networks (Computer)},
month = sep,
number = {3},
pages = {1059--69},
pmid = {19819337},
publisher = {Elsevier Inc.},
title = {{Complex network measures of brain connectivity: uses and interpretations.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19819337},
volume = {52},
year = {2010}
}
@article{Smith1999,
abstract = {Low frequency drift (0.0-0.015 Hz) has often been reported in time series fMRI data. This drift has often been attributed to physiological noise or subject motion, but no studies have been done to test this assumption. Time series T*2-weighted volumes were acquired on two clinical 1.5 T MRI systems using spiral and EPI readout gradients from cadavers, a normal volunteer, and nonhomogeneous and homogeneous phantoms. The data were tested for significant differences (P = 0.001) from Gaussian noise in the frequency range 0.0-0.015 Hz. The percentage of voxels that were significant in data from the cadaver, normal volunteer, nonhomogeneous and homogeneous phantoms were 13.7-49.0\%, 22.1-61.9\%, 46.4-68.0\%, and 1.10\%, respectively. Low frequency drift was more pronounced in regions with high spatial intensity gradients. Significant drifting was present in data acquired from cadavers and nonhomogeneous phantoms and all pulse sequences tested, implying that scanner instabilities and not motion or physiological noise may be the major cause of the drift.},
author = {Smith, a M and Lewis, B K and Ruttimann, U E and Ye, F Q and Sinnwell, T M and Yang, Y and Duyn, J H and Frank, J a},
doi = {10.1006/nimg.1999.0435},
file = {:home/rtungaraza/PapersBooks/driftfmrisignalSmith1999.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Adult,Aged,Artifacts,Cadaver,Case-Control Studies,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Middle Aged,Motion,Normal Distribution,Phantoms, Imaging},
month = may,
number = {5},
pages = {526--33},
pmid = {10329292},
title = {{Investigation of low frequency drift in fMRI signal.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/10329292},
volume = {9},
year = {1999}
}
@article{Sotiropoulos2013,
abstract = {The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013.},
author = {Sotiropoulos, Stamatios N and Jbabdi, Saad and Xu, Junqian and Andersson, Jesper L and Moeller, Steen and Auerbach, Edward J and Glasser, Matthew F and Hernandez, Moises and Sapiro, Guillermo and Jenkinson, Mark and Feinberg, David a and Yacoub, Essa and Lenglet, Christophe and {Van Essen}, David C and Ugurbil, Kamil and Behrens, Timothy E J},
doi = {10.1016/j.neuroimage.2013.05.057},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sotiropoulos et al. - 2013 - Advances in diffusion MRI acquisition and processing in the Human Connectome Project.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain: anatomy \& histology,Brain: physiology,Connectome,Connectome: trends,Diffusion Tensor Imaging,Diffusion Tensor Imaging: trends,Humans,Models, Anatomic,Models, Neurological,Nerve Net,Nerve Net: anatomy \& histology,Nerve Net: physiology},
month = oct,
pages = {125--43},
pmid = {23702418},
publisher = {Elsevier Inc.},
title = {{Advances in diffusion MRI acquisition and processing in the Human Connectome Project.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23702418},
volume = {80},
year = {2013}
}
@article{Sporns2013,
abstract = {The human connectome refers to a map of the brain's structural connections, rendered as a connection matrix or network. This article attempts to trace some of the historical origins of the connectome, in the process clarifying its definition and scope, as well as its putative role in illuminating brain function. Current efforts to map the connectome face a number of significant challenges, including the issue of capturing network connectivity across multiple spatial scales, accounting for individual variability and structural plasticity, as well as clarifying the role of the connectome in shaping brain dynamics. Throughout, the article argues that these challenges require the development of new approaches for the statistical analysis and computational modeling of brain network data, and greater collaboration across disciplinary boundaries, especially with researchers in complex systems and network science.},
author = {Sporns, Olaf},
doi = {10.1016/j.neuroimage.2013.03.023},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sporns - 2013 - The human connectome origins and challenges.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain: anatomy \& histology,Brain: physiology,Connectome,Connectome: methods,Humans,Models, Anatomic,Models, Neurological,Nerve Net,Nerve Net: anatomy \& histology,Nerve Net: physiology},
month = oct,
pages = {53--61},
pmid = {23528922},
publisher = {Elsevier Inc.},
title = {{The human connectome: origins and challenges.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23528922},
volume = {80},
year = {2013}
}
@article{Sporns2005,
abstract = {The connection matrix of the human brain (the human "connectome") represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.},
author = {Sporns, Olaf and Tononi, Giulio and K\"{o}tter, Rolf},
doi = {10.1371/journal.pcbi.0010042},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sporns, Tononi, K\"{o}tter - 2005 - The human connectome A structural description of the human brain.pdf:pdf},
issn = {1553-7358},
journal = {PLoS computational biology},
keywords = {Animals,Brain,Brain: anatomy \& histology,Brain: cytology,Brain: metabolism,Humans,Nerve Net,Neurons,Neurons: metabolism,Synapses,Synapses: metabolism},
month = sep,
number = {4},
pages = {e42},
pmid = {16201007},
title = {{The human connectome: A structural description of the human brain.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1239902\&tool=pmcentrez\&rendertype=abstract},
volume = {1},
year = {2005}
}
@article{VanEssen2012,
abstract = {The opportunity to explore the human connectome using cutting-edge neuroimaging methods has elicited widespread interest. How far will the field be able to progress in deciphering long-distance connectivity patterns and in relating differences in connectivity to phenotypic characteristics in health and disease? We discuss the daunting nature of this challenge in relation to specific complexities of brain circuitry and known limitations of in vivo imaging methods. We also discuss the excellent prospects for continuing improvements in data acquisition and analysis. Accordingly, we are optimistic that major insights will emerge from human connectomics in the coming decade.},
author = {{Van Essen}, D C and Ugurbil, K},
doi = {10.1016/j.neuroimage.2012.01.032},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Van Essen, Ugurbil - 2012 - The future of the human connectome.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Animals,Brain,Brain Mapping,Brain Mapping: methods,Brain Mapping: trends,Brain: physiology,History, 20th Century,History, 21st Century,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: history,Image Processing, Computer-Assisted: methods,Image Processing, Computer-Assisted: trends,Nerve Net,Nerve Net: physiology,Neuroimaging,Neuroimaging: history,Neuroimaging: methods,Neuroimaging: trends},
month = aug,
number = {2},
pages = {1299--310},
pmid = {22245355},
title = {{The future of the human connectome.}},
url = {http://www.sciencedirect.com/science/article/pii/S1053811912000493},
volume = {62},
year = {2012}
}
@article{VanHorn2013,
abstract = {Neuroimaging and the discipline of cognitive neuroscience have grown together in lock-step with each pushing the other toward an improved ability to explore and examine brain function and form. However successful neuroimaging and the examination of cognitive processes may seem today, the culture of data sharing in these fields remains underdeveloped. In this article, we discuss our own experience in the development of the fMRI Data Center (fMRIDC) - a large-scale effort to gather, curate, and openly share the complete data sets from published research articles of brain activation studies using fMRI. We outline the fMRIDC effort's beginnings, how it operated, note some of the sociological reactions we received, and provide several examples of prominent new studies performed using data drawn from the archive. Finally, we provide comment on what considerations are needed for successful neuroimaging databasing and data sharing as existing and emerging efforts take the next steps in archiving and disseminating the field's valuable and irreplaceable data.},
author = {{Van Horn}, John Darrell and Gazzaniga, Michael S},
doi = {10.1016/j.neuroimage.2012.11.010},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Van Horn, Gazzaniga - 2013 - Why share data Lessons learned from the fMRIDC.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Databases as Topic,Databases as Topic: organization \& administration,Humans,Information Dissemination,Magnetic Resonance Imaging},
month = nov,
pages = {677--82},
pmid = {23160115},
title = {{Why share data? Lessons learned from the fMRIDC.}},
url = {http://www.sciencedirect.com/science/article/pii/S1053811912011068},
volume = {82},
year = {2013}
}
@article{Varoquaux2013,
author = {Varoquaux, Ga\"{e}l and Craddock, R. Cameron},
doi = {10.1016/j.neuroimage.2013.04.007},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Varoquaux, Craddock - 2013 - Learning and comparing functional connectomes across subjects.pdf:pdf},
issn = {10538119},
journal = {NeuroImage},
keywords = {Connectome,Effective connectivity,Group study,Resting-state,fMRI,functional connectivity},
month = oct,
pages = {405--415},
publisher = {Elsevier Inc.},
title = {{Learning and comparing functional connectomes across subjects}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811913003340},
volume = {80},
year = {2013}
}
@article{Vogelstein2013,
author = {J. T. Vogelstein and W. G. Roncal and R. J. Vogelstein and C. E. Priebe},
title = {Graph Classification Using Signal-Subgraphs: Applications in Statistical Connectomics},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {35},
number = {7},
issn = {0162-8828},
year = {2013},
pages = {1539-1551},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.235},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
@article{Zalesky2012,
abstract = {The scenario considered here is one where brain connectivity is represented as a network and an experimenter wishes to assess the evidence for an experimental effect at each of the typically thousands of connections comprising the network. To do this, a univariate model is independently fitted to each connection. It would be unwise to declare significance based on an uncorrected threshold of $\alpha$=0.05, since the expected number of false positives for a network comprising N=90 nodes and N(N-1)/2=4005 connections would be 200. Control of Type I errors over all connections is therefore necessary. The network-based statistic (NBS) and spatial pairwise clustering (SPC) are two distinct methods that have been used to control family-wise errors when assessing the evidence for an experimental effect with mass univariate testing. The basic principle of the NBS and SPC is the same as supra-threshold voxel clustering. Unlike voxel clustering, where the definition of a voxel cluster is unambiguous, 'clusters' formed among supra-threshold connections can be defined in different ways. The NBS defines clusters using the graph theoretical concept of connected components. SPC on the other hand uses a more stringent pairwise clustering concept. The purpose of this article is to compare the pros and cons of the NBS and SPC, provide some guidelines on their practical use and demonstrate their utility using a case study involving neuroimaging data.},
author = {Zalesky, Andrew and Cocchi, Luca and Fornito, Alex and Murray, Micah M and Bullmore, Ed},
doi = {10.1016/j.neuroimage.2012.01.068},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Zalesky et al. - 2012 - Connectivity differences in brain networks.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain connectivity,Brain network,Connectome,Family-wise errors,Network-based statistic,Spatial pairwise clustering},
month = jan,
number = {2},
pages = {1055--1062},
pmid = {22273567},
publisher = {Elsevier Inc.},
title = {{Connectivity differences in brain networks.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22273567},
volume = {60},
year = {2012}
}


@article{DagliMSIngeholmJE1999,
author = {{Dagli MS, Ingeholm JE}, and Haxby JV},
journal = {NeuroImage},
pages = {407--415},
title = {{Localization of cardiac-in- duced signal change in fMRI}},
volume = {9},
year = {1999}
}

@article{Eloyan2014,
abstract = {Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.},
author = {Eloyan, Ani and Li, Shanshan and Muschelli, John and Pekar, Jim J and Mostofsky, Stewart H and Caffo, Brian S},
doi = {10.1371/journal.pone.0089470},
file = {:home/rtungaraza/PapersBooks/caffoFMRIpaper.pdf:pdf},
issn = {1932-6203},
journal = {PloS one},
month = jan,
number = {2},
pages = {e89470},
pmid = {24586801},
title = {{Analytic programming with FMRI data: a quick-start guide for statisticians using R.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3938835\&tool=pmcentrez\&rendertype=abstract},
volume = {9},
year = {2014}
}

@article{Fornito2014,
abstract = {In recent years, pathophysiological models of brain disorders have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disturbances of interconnected neural systems. This shift has paralleled rapid advances in connectomics, a field concerned with comprehensively mapping the neural elements and inter-connections that constitute the brain. Magnetic resonance imaging (MRI) has played a central role in these efforts, as it allows relatively cost-effective in vivo assessment of the macro-scale architecture of brain network connectivity. In this paper, we provide a brief introduction to some of the basic concepts in the field and review how recent developments in imaging connectomics are yielding new insights into brain disease, with a particular focus on Alzheimer's disease and schizophrenia. Specifically, we consider how research into circuit-level, connectome-wide and topological changes is stimulating the development of new aetiopathological theories and biomarkers with potential for clinical translation. The findings highlight the advantage of conceptualizing brain disease as a result of disturbances in an interconnected complex system, rather than discrete pathology in isolated sub-sets of brain regions.},
author = {Fornito, Alex and Bullmore, Edward T},
doi = {10.1016/j.euroneuro.2014.02.011},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Fornito, Bullmore - 2014 - Connectomics A new paradigm for understanding brain disease.pdf:pdf},
issn = {1873-7862},
journal = {European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology},
month = mar,
pmid = {24726580},
title = {{Connectomics: A new paradigm for understanding brain disease.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24726580},
year = {2014}
}

@article{Lee2013,
abstract = {SUMMARY: Resting-state fMRI measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. Application of this technique has allowed the identification of various RSNs, or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. Various methods exist for analyzing resting-state data, including seed-based approaches, independent component analysis, graph methods, clustering algorithms, neural networks, and pattern classifiers. Clinical applications of resting-state fMRI are at an early stage of development. However, its use in presurgical planning for patients with brain tumor and epilepsy demonstrates early promise, and the technique may have a future role in providing diagnostic and prognostic information for neurologic and psychiatric diseases.},
author = {Lee, M H and Smyser, C D and Shimony, J S},
doi = {10.3174/ajnr.A3263},
file = {:home/rtungaraza/PapersBooks/1866.full.pdf:pdf},
issn = {1936-959X},
journal = {AJNR. American journal of neuroradiology},
keywords = {Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Preoperative Care,Preoperative Care: methods,Rest,Rest: physiology},
month = oct,
number = {10},
pages = {1866--72},
pmid = {22936095},
title = {{Resting-state fMRI: a review of methods and clinical applications.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22936095},
volume = {34},
year = {2013}
}

@article{Sporns2013,
abstract = {The human connectome refers to a map of the brain's structural connections, rendered as a connection matrix or network. This article attempts to trace some of the historical origins of the connectome, in the process clarifying its definition and scope, as well as its putative role in illuminating brain function. Current efforts to map the connectome face a number of significant challenges, including the issue of capturing network connectivity across multiple spatial scales, accounting for individual variability and structural plasticity, as well as clarifying the role of the connectome in shaping brain dynamics. Throughout, the article argues that these challenges require the development of new approaches for the statistical analysis and computational modeling of brain network data, and greater collaboration across disciplinary boundaries, especially with researchers in complex systems and network science.},
author = {Sporns, Olaf},
doi = {10.1016/j.neuroimage.2013.03.023},
file = {:home/rtungaraza/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sporns - 2013 - The human connectome origins and challenges.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain: anatomy \& histology,Brain: physiology,Connectome,Connectome: methods,Humans,Models, Anatomic,Models, Neurological,Nerve Net,Nerve Net: anatomy \& histology,Nerve Net: physiology},
month = oct,
pages = {53--61},
pmid = {23528922},
publisher = {Elsevier Inc.},
title = {{The human connectome: origins and challenges.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23528922},
volume = {80},
year = {2013}
}

Buckner, Randy L.; Roffman, Joshua L. ; Smoller, Jordan W. , 2014, "Brain Genomics Superstruct Project (GSP)", <a href="http://dx.doi.org/10.7910/DVN/25833">doi:10.7910/DVN/25833</a> Harvard Dataverse Network [Distributor] V9 [Version]

@ELECTRONIC{BucknerGSP2014,
  author = {Randy L. Buckner and Joshua L. Roffman and Jordan W. Smoller},
  year = {2014},
  title = {{B}rain {G}enomics {S}uperstruct {P}roject {(GSP)}},
  language = {English},
  howpublished = {\url=http://dx.doi.org/10.7910/DVN/25833},
  doi = {10.7910/DVN/25833}
}

@misc{NDAR,
  author = {{NIMH}},
  title = {{N}ational {D}atabase for {A}utism {R}esearch {(NDAR)}},
  language = {English},
  url = {http://ndar.nih.gov},
  note = {Accessed 12 13 2014}
}


@misc{JerniganPING,
  author = {Terry L. Jernigan and  Connor McCabe and  Linda Chang and  Natacha Akshoomoff and  Erik Newman and  Anders M. Dale and  Thomas Ernst and  Peter Van Zijl and  Joshua Kuperman and  Sarah Murray and  Cinnamon Bloss and  Nicholas J. Schork and  Mark Appelbaum and  Anthony Gamst and  Wesley Thompson and  Hauke Bartsch and  Brian Keating and  David Amaral and  Elizabeth Sowell and  Walter Kaufmann and  Peter Van Zijl and  Stewart Mostofsky and  B.J. Casey and  Erika J. Ruberry and  Alisa Powers and  Bruce Rosen and  Tal Kenet and  Jean Frazier and  M.D. and  David Kennedy and  Jeffrey Gruen},
  title = {{P}ediatric {I}maging, {N}eurocognition, and {G}enetics {(PING)} Study},
  language = {English},
  url = {http://pingstudy.ucsd.edu},
  note = {Accessed 12 13 2014}
}

@INPROCEEDINGS{RosenHCP2010,
  author = {Bruce Rosen and Van J. Wedeen and John D. Van Horn and Bruce Fischl and Randy L. Buckner and Lawrence Wald and Matti Hamalainen and Steven Stufflebeam and Joshua Roffman and David W. Shattuck and Thompson PM and Roger P. Woods and Nelson Freimer and Robert Bilder and and Arthur W. Toga},
  title = {The {H}uman {C}onnectome {P}roject},
  booktitle = {Proceedings Organization for Human Brain Mapping 16th Annual Meeting},
  year = {2010},
  address = {Barcelona},
  owner = {Richard Craddock},
}

@Article{Satterthwaite2014,
   Author="Satterthwaite, T. D.  and Elliott, M. A.  and Ruparel, K.  and Loughead, J.  and Prabhakaran, K.  and Calkins, M. E.  and Hopson, R.  and Jackson, C.  and Keefe, J.  and Riley, M.  and Mentch, F. D.  and Sleiman, P.  and Verma, R.  and Davatzikos, C.  and Hakonarson, H.  and Gur, R. C.  and Gur, R. E. ",
   Title="{{N}euroimaging of the {P}hiladelphia neurodevelopmental cohort}",
   Journal="Neuroimage",
   Year="2014",
   Volume="86",
   Pages="544--553",
   Month="Feb",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3947233}{PMC3947233}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2013.07.064}{10.1016/j.neuroimage.2013.07.064}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/23921101}{23921101}] }
}

@Article{Varela2001,
   Author="Varela, F.  and Lachaux, J. P.  and Rodriguez, E.  and Martinerie, J. ",
   Title="{{T}he brainweb: phase synchronization and large-scale integration}",
   Journal="Nat. Rev. Neurosci.",
   Year="2001",
   Volume="2",
   Number="4",
   Pages="229--239",
   Month="Apr",
   Note={[DOI:\href{http://dx.doi.org/10.1038/35067550}{10.1038/35067550}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/11283746}{11283746}] }
}

% 21908183 
@Article{Behrens2012,
   Author="Behrens, T. E.  and Sporns, O. ",
   Title="{{H}uman connectomics}",
   Journal="Curr. Opin. Neurobiol.",
   Year="2012",
   Volume="22",
   Number="1",
   Pages="144--153",
   Month="Feb",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3294015}{PMC3294015}] [DOI:\href{http://dx.doi.org/10.1016/j.conb.2011.08.005}{10.1016/j.conb.2011.08.005}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/21908183}{21908183}] }
}

@Article{Kapur2012,
   Author="Kapur, S.  and Phillips, A. G.  and Insel, T. R. ",
   Title="{{W}hy has it taken so long for biological psychiatry to develop clinical tests and what to do about it?}",
   Journal="Mol. Psychiatry",
   Year="2012",
   Volume="17",
   Number="12",
   Pages="1174--1179",
   Month="Dec",
   Note={[DOI:\href{http://dx.doi.org/10.1038/mp.2012.105}{10.1038/mp.2012.105}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/22869033}{22869033}] }
}

@Article{Biswal1995,
   Author="Biswal, B.  and Yetkin, F. Z.  and Haughton, V. M.  and Hyde, J. S. ",
   Title="{{F}unctional connectivity in the motor cortex of resting human brain using echo-planar {M}{R}{I}}",
   Journal="Magn Reson Med",
   Year="1995",
   Volume="34",
   Number="4",
   Pages="537--541",
   Month="Oct",
   Note={[PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/8524021}{8524021}] }
}

@Article{Kelly2012,
   Author="Kelly, C.  and Biswal, B. B.  and Craddock, R. C.  and Castellanos, F. X.  and Milham, M. P. ",
   Title="{{C}haracterizing variation in the functional connectome: promise and pitfalls}",
   Journal="Trends Cogn. Sci. (Regul. Ed.)",
   Year="2012",
   Volume="16",
   Number="3",
   Pages="181--188",
   Month="Mar",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3882689}{PMC3882689}] [DOI:\href{http://dx.doi.org/10.1016/j.tics.2012.02.001}{10.1016/j.tics.2012.02.001}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/22341211}{22341211}] }
}

% 23523803 
@Article{Blumensath2013,
   Author="Blumensath, T.  and Jbabdi, S.  and Glasser, M. F.  and Van Essen, D. C.  and Ugurbil, K.  and Behrens, T. E.  and Smith, S. M. ",
   Title="{{S}patially constrained hierarchical parcellation of the brain with resting-state f{M}{R}{I}}",
   Journal="Neuroimage",
   Year="2013",
   Volume="76",
   Pages="313--324",
   Month="Aug",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3758955}{PMC3758955}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2013.03.024}{10.1016/j.neuroimage.2013.03.024}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/23523803}{23523803}] }
}

@Article{Bellec2006,
   Author="Bellec, P.  and Perlbarg, V.  and Jbabdi, S.  and Pelegrini-Issac, M.  and Anton, J. L.  and Doyon, J.  and Benali, H. ",
   Title="{{I}dentification of large-scale networks in the brain using f{M}{R}{I}}",
   Journal="Neuroimage",
   Year="2006",
   Volume="29",
   Number="4",
   Pages="1231--1243",
   Month="Feb",
   Note={[DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2005.08.044}{10.1016/j.neuroimage.2005.08.044}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/16246590}{16246590}] }
}

@Article{Thirion2006,
   Author="Thirion, B.  and Flandin, G.  and Pinel, P.  and Roche, A.  and Ciuciu, P.  and Poline, J. B. ",
   Title="{{D}ealing with the shortcomings of spatial normalization: multi-subject parcellation of f{M}{R}{I} datasets}",
   Journal="Hum Brain Mapp",
   Year="2006",
   Volume="27",
   Number="8",
   Pages="678--693",
   Month="Aug",
   Note={[DOI:\href{http://dx.doi.org/10.1002/hbm.20210}{10.1002/hbm.20210}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/16281292}{16281292}] }
}

@Article{Zalesky2010,
   Author="Zalesky, A.  and Fornito, A.  and Harding, I. H.  and Cocchi, L.  and Yucel, M.  and Pantelis, C.  and Bullmore, E. T. ",
   Title="{{W}hole-brain anatomical networks: does the choice of nodes matter?}",
   Journal="Neuroimage",
   Year="2010",
   Volume="50",
   Number="3",
   Pages="970--983",
   Month="Apr",
   Note={[DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2009.12.027}{10.1016/j.neuroimage.2009.12.027}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/20035887}{20035887}] }
}

@INPROCEEDINGS{Flandin2002, 
author={Flandin, G. and Kherif, F. and Pennec, X. and Riviere, D. and Ayache, N. and Poline, J.-B.}, 
booktitle={Biomedical Imaging, 2002. Proceedings. 2002 IEEE International Symposium on}, 
title={Parcellation of brain images with anatomical and functional constraints for fMRI data analysis}, 
year={2002}, 
month={}, 
pages={907-910}, 
keywords={biomedical MRI;brain;differential geometry;image resolution;image segmentation;medical image processing;3D cortex;K-means clustering adaptation;adjustable resolution;anatomical constraints;automatic parcellation;brain image parcellation;connectivity studies;fMRI data analysis;functional constraints;geodesic distances;intermediate dimensionality;modality fusion;multivariate analyses;nonconvex domain;region of interest;voxel;weighted geodesic distances;Anatomy;Brain;Clustering algorithms;Data analysis;Image analysis;Image resolution;Magnetic analysis;Signal resolution;Spatial resolution;Testing}, 
doi={10.1109/ISBI.2002.1029408},}

@Article{Thirion2014,
   Author="Thirion, B.  and Varoquaux, G.  and Dohmatob, E.  and Poline, J. B. ",
   Title="{{W}hich f{M}{R}{I} clustering gives good brain parcellations?}",
   Journal="Front Neurosci",
   Year="2014",
   Volume="8",
   Pages="167",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4076743}{PMC4076743}] [DOI:\href{http://dx.doi.org/10.3389/fnins.2014.00167}{10.3389/fnins.2014.00167}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/25071425}{25071425}] }
}

@article{Desikan2006,
title = "An automated labeling system for subdividing the human cerebral cortex on \{MRI\} scans into gyral based regions of interest ",
journal = "NeuroImage ",
volume = "31",
number = "3",
pages = "968 - 980",
year = "2006",
note = "",
issn = "1053-8119",
doi = "http://dx.doi.org/10.1016/j.neuroimage.2006.01.021",
url = "http://www.sciencedirect.com/science/article/pii/S1053811906000437",
author = "Rahul S. Desikan and Florent Ségonne and Bruce Fischl and Brian T. Quinn and Bradford C. Dickerson and Deborah Blacker and Randy L. Buckner and Anders M. Dale and R. Paul Maguire and Bradley T. Hyman and Marilyn S. Albert and Ronald J. Killiany"
}

@ARTICLE{Klein2012,
  AUTHOR={Klein, Arno  and  Tourville, Jason},   
  TITLE={101 labeled brain images and a consistent human cortical labeling protocol},      
  JOURNAL={Frontiers in Neuroscience},      
  VOLUME={6},      
  YEAR={2012},      
  NUMBER={171},
  URL={http://www.frontiersin.org/brain_imaging_methods/10.3389/fnins.2012.00171/abstract},       
  DOI={10.3389/fnins.2012.00171},      
  ISSN={1662-453X}
}

@Article{AAL2002,
   Author="Tzourio-Mazoyer, N.  and Landeau, B.  and Papathanassiou, D.  and Crivello, F.  and Etard, O.  and Delcroix, N.  and Mazoyer, B.  and Joliot, M. ",
   Title="{{A}utomated anatomical labeling of activations in {S}{P}{M} using a macroscopic anatomical parcellation of the {M}{N}{I} {M}{R}{I} single-subject brain}",
   Journal="Neuroimage",
   Year="2002",
   Volume="15",
   Number="1",
   Pages="273--289",
   Month="Jan",
   Abstract={An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.}
}

@article{Eickhoff2008,
author = {Eickhoff, Simon B. and Rottschy, Claudia and Kujovic, Milenko and Palomero-Gallagher, Nicola and Zilles, Karl}, 
title = {Organizational Principles of Human Visual Cortex Revealed by Receptor Mapping},
volume = {18}, 
number = {11}, 
pages = {2637-2645}, 
year = {2008}, 
doi = {10.1093/cercor/bhn024}, 
abstract ={This receptorarchitectonic study of the human visual cortex investigated interareal differences in mean receptor concentrations and laminar distribution patterns of 16 neurotransmitter receptors in the dorsal and ventral parts of areas V1, V2, V3 as well as in adjoining areas V4 (ventrally) and V3A (dorsally). Both the functional hierarchy of these areas and a distinction between dorsal and ventral visual cortices were reflected by significant receptorarchitectonic differences. The observation that dorso-ventral differences existed in all extrastriate areas (including V2) is particularly important for the discussion about the relationship between dorsal and ventral V3 as it indicates that a receptorarchitectonic distinction between the ventral and dorsal visual cortices is present in but not specific to V3. This molecular specificity is mirrored by previously reported differences in retinal microstructure and functional differences as revealed in behavioral experiments demonstrating differential advantages for stimulus processing in the upper and lower visual fields. We argue that these anatomical and functional differences may be regarded as the result of an evolutionary optimization adapting to the processing of the most relevant stimuli occurring in the upper and lower visual fields.}, 
URL = {http://cercor.oxfordjournals.org/content/18/11/2637.abstract}, 
eprint = {http://cercor.oxfordjournals.org/content/18/11/2637.full.pdf+html}, 
journal = {Cerebral Cortex} 
}

@Article{Smith2011,
   Author="Smith, S. M.  and Miller, K. L.  and Salimi-Khorshidi, G.  and Webster, M.  and Beckmann, C. F.  and Nichols, T. E.  and Ramsey, J. D.  and Woolrich, M. W. ",
   Title="{{N}etwork modelling methods for {F}{M}{R}{I}}",
   Journal="Neuroimage",
   Year="2011",
   Volume="54",
   Number="2",
   Pages="875--891",
   Month="Jan",
   Note={[DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2010.08.063}{10.1016/j.neuroimage.2010.08.063}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/20817103}{20817103}] }
}

@Article{Friston2011,
   Author="Friston, K. J. ",
   Title="{{F}unctional and effective connectivity: a review}",
   Journal="Brain Connect",
   Year="2011",
   Volume="1",
   Number="1",
   Pages="13--36",
   Note={[DOI:\href{http://dx.doi.org/10.1089/brain.2011.0008}{10.1089/brain.2011.0008}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/22432952}{22432952}] }
}

@Article{Friston2003,
   Author="Friston, K. J.  and Harrison, L.  and Penny, W. ",
   Title="{{D}ynamic causal modelling}",
   Journal="Neuroimage",
   Year="2003",
   Volume="19",
   Number="4",
   Pages="1273--1302",
   Month="Aug",
   Note={[PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/12948688}{12948688}] }
}

@article {Friston1994,
author = {Friston, Karl J.},
title = {Functional and effective connectivity in neuroimaging: A synthesis},
journal = {Human Brain Mapping},
volume = {2},
number = {1-2},
publisher = {Wiley Subscription Services, Inc., A Wiley Company},
issn = {1097-0193},
url = {http://dx.doi.org/10.1002/hbm.460020107},
doi = {10.1002/hbm.460020107},
pages = {56--78},
keywords = {functional connectivity, effective connectivity, PET, fMRI, eigenimages, spatial modes, multidimensional scaling, word generation, visual, modulation},
year = {1994},
}

@Article{Buchel1997,
   Author="Buchel, C.  and Friston, K. J. ",
   Title="{{M}odulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and f{M}{R}{I}}",
   Journal="Cereb. Cortex",
   Year="1997",
   Volume="7",
   Number="8",
   Pages="768--778",
   Month="Dec",
   Note={[PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/9408041}{9408041}] }
}

@article{Lohmann2012,
title = "Critical comments on dynamic causal modelling ",
journal = "NeuroImage ",
volume = "59",
number = "3",
pages = "2322 - 2329",
year = "2012",
note = "",
issn = "1053-8119",
doi = "http://dx.doi.org/10.1016/j.neuroimage.2011.09.025",
url = "http://www.sciencedirect.com/science/article/pii/S1053811911010718",
author = "Gabriele Lohmann and Kerstin Erfurth and Karsten Müller and Robert Turner",
keywords = "Dynamic causal modelling",
keywords = "Model validation",
keywords = "Combinatorial explosion "
}

@Article{Penny2010,
   Author="Penny, W. D.  and Stephan, K. E.  and Daunizeau, J.  and Rosa, M. J.  and Friston, K. J.  and Schofield, T. M.  and Leff, A. P. ",
   Title="{{C}omparing families of dynamic causal models}",
   Journal="PLoS Comput. Biol.",
   Year="2010",
   Volume="6",
   Number="3",
   Pages="e1000709",
   Month="Mar",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2837394}{PMC2837394}] [DOI:\href{http://dx.doi.org/10.1371/journal.pcbi.1000709}{10.1371/journal.pcbi.1000709}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/20300649}{20300649}] }
}

@Article{James2009,
   Author="James, G. A.  and Kelley, M. E.  and Craddock, R. C.  and Holtzheimer, P. E.  and Dunlop, B. W.  and Nemeroff, C. B.  and Mayberg, H. S.  and Hu, X. P. ",
   Title="{{E}xploratory structural equation modeling of resting-state f{M}{R}{I}: applicability of group models to individual subjects}",
   Journal="Neuroimage",
   Year="2009",
   Volume="45",
   Number="3",
   Pages="778--787",
   Month="Apr",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2653594}{PMC2653594}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2008.12.049}{10.1016/j.neuroimage.2008.12.049}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/19162206}{19162206}] }
}

@Article{Zhuang2005,
   Author="Zhuang, J.  and LaConte, S.  and Peltier, S.  and Zhang, K.  and Hu, X. ",
   Title="{{C}onnectivity exploration with structural equation modeling: an f{M}{R}{I} study of bimanual motor coordination}",
   Journal="Neuroimage",
   Year="2005",
   Volume="25",
   Number="2",
   Pages="462--470",
   Month="Apr",
   Note={[DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2004.11.007}{10.1016/j.neuroimage.2004.11.007}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/15784425}{15784425}] }}
   
@Article{Deshpande2011,
      Author="Deshpande, G.  and Santhanam, P.  and Hu, X. ",
      Title="{{I}nstantaneous and causal connectivity in resting state brain networks derived from functional {M}{R}{I} data}",
      Journal="Neuroimage",
      Year="2011",
      Volume="54",
      Number="2",
      Pages="1043--1052",
      Month="Jan",
      Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2997120}{PMC2997120}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2010.09.024}{10.1016/j.neuroimage.2010.09.024}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/20850549}{20850549}] }
   }
@Article{Lv2013,
   Author="Lv, Y.  and Margulies, D. S.  and Cameron Craddock, R.  and Long, X.  and Winter, B.  and Gierhake, D.  and Endres, M.  and Villringer, K.  and Fiebach, J.  and Villringer, A. ",
   Title="{{I}dentifying the perfusion deficit in acute stroke with resting-state functional magnetic resonance imaging}",
   Journal="Ann. Neurol.",
   Year="2013",
   Volume="73",
   Number="1",
   Pages="136--140",
   Month="Jan",
   Note={[DOI:\href{http://dx.doi.org/10.1002/ana.23763}{10.1002/ana.23763}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/23378326}{23378326}] }
}

@Article{Craddock2013b,
   Author="Craddock, R. C.  and Milham, M. P.  and LaConte, S. M. ",
   Title="{{P}redicting intrinsic brain activity}",
   Journal="Neuroimage",
   Year="2013",
   Volume="82",
   Pages="127--136",
   Month="Nov",
   Note={[DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2013.05.072}{10.1016/j.neuroimage.2013.05.072}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/23707580}{23707580}] }
}

@Article{James2009b,
   Author="James, G. A.  and Lu, Z. L.  and VanMeter, J. W.  and Sathian, K.  and Hu, X. P.  and Butler, A. J. ",
   Title="{{C}hanges in resting state effective connectivity in the motor network following rehabilitation of upper extremity poststroke paresis}",
   Journal="Top Stroke Rehabil",
   Year="2009",
   Volume="16",
   Number="4",
   Pages="270--281",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3595191}{PMC3595191}] [DOI:\href{http://dx.doi.org/10.1310/tsr1604-270}{10.1310/tsr1604-270}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/19740732}{19740732}] }
}

@Article{Brodersen2011,
   Author="Brodersen, K. H.  and Schofield, T. M.  and Leff, A. P.  and Ong, C. S.  and Lomakina, E. I.  and Buhmann, J. M.  and Stephan, K. E. ",
   Title="{{G}enerative embedding for model-based classification of f{M}{R}{I} data}",
   Journal="PLoS Comput. Biol.",
   Year="2011",
   Volume="7",
   Number="6",
   Pages="e1002079",
   Month="Jun",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3121683}{PMC3121683}] [DOI:\href{http://dx.doi.org/10.1371/journal.pcbi.1002079}{10.1371/journal.pcbi.1002079}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/21731479}{21731479}] }
}

@Article{Chang2010,
   Author="Chang, C.  and Glover, G. H. ",
   Title="{{T}ime-frequency dynamics of resting-state brain connectivity measured with f{M}{R}{I}}",
   Journal="Neuroimage",
   Year="2010",
   Volume="50",
   Number="1",
   Pages="81--98",
   Month="Mar",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2827259}{PMC2827259}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2009.12.011}{10.1016/j.neuroimage.2009.12.011}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/20006716}{20006716}] }
}

@Article{Keilholz2013,
   Author="Keilholz, S. D.  and Magnuson, M. E.  and Pan, W. J.  and Willis, M.  and Thompson, G. J. ",
   Title="{{D}ynamic properties of functional connectivity in the rodent}",
   Journal="Brain Connect",
   Year="2013",
   Volume="3",
   Number="1",
   Pages="31--40",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3621313}{PMC3621313}] [DOI:\href{http://dx.doi.org/10.1089/brain.2012.0115}{10.1089/brain.2012.0115}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/23106103}{23106103}] }
}

@Article{Majeed2011,
   Author="Majeed, W.  and Magnuson, M.  and Hasenkamp, W.  and Schwarb, H.  and Schumacher, E. H.  and Barsalou, L.  and Keilholz, S. D. ",
   Title="{{S}patiotemporal dynamics of low frequency {B}{O}{L}{D} fluctuations in rats and humans}",
   Journal="Neuroimage",
   Year="2011",
   Volume="54",
   Number="2",
   Pages="1140--1150",
   Month="Jan",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2997178}{PMC2997178}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2010.08.030}{10.1016/j.neuroimage.2010.08.030}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/20728554}{20728554}] }
}

@article{Smith2012,
author = {Smith, Stephen M. and Miller, Karla L. and Moeller, Steen and Xu, Junqian and Auerbach, Edward J. and Woolrich, Mark W. and Beckmann, Christian F. and Jenkinson, Mark and Andersson, Jesper and Glasser, Matthew F. and Van Essen, David C. and Feinberg, David A. and Yacoub, Essa S. and Ugurbil, Kamil}, 
title = {Temporally-independent functional modes of spontaneous brain activity},
volume = {109}, 
number = {8}, 
pages = {3131-3136}, 
year = {2012}, 
doi = {10.1073/pnas.1121329109}, 
abstract ={Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even “at rest,” the brain's different functional networks spontaneously fluctuate in their activity level; each network's spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks; one ideally wants a network model that explicitly allows overlap, for example, allowing a region's activity pattern to reflect one network's activity some of the time, and another network's activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging sampling rate. We identify multiple “temporal functional modes,” including several that subdivide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.}, 
URL = {http://www.pnas.org/content/109/8/3131.abstract}, 
eprint = {http://www.pnas.org/content/109/8/3131.full.pdf+html}, 
journal = {Proceedings of the National Academy of Sciences} 
}

@Article{Yang2014,
   Author="Yang, Z.  and Craddock, R. C.  and Margulies, D. S.  and Yan, C. G.  and Milham, M. P. ",
   Title="{{C}ommon intrinsic connectivity states among posteromedial cortex subdivisions: {I}nsights from analysis of temporal dynamics}",
   Journal="Neuroimage",
   Year="2014",
   Volume="93 Pt 1",
   Pages="124--137",
   Month="Jun",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4010223}{PMC4010223}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2014.02.014}{10.1016/j.neuroimage.2014.02.014}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/24560717}{24560717}] }
}

@article{Allen2014,
author = {Allen, Elena A. and Damaraju, Eswar and Plis, Sergey M. and Erhardt, Erik B. and Eichele, Tom and Calhoun, Vince D.}, 
title = {Tracking Whole-Brain Connectivity Dynamics in the Resting State},
volume = {24}, 
number = {3}, 
pages = {663-676}, 
year = {2014}, 
doi = {10.1093/cercor/bhs352}, 
abstract ={Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.}, 
URL = {http://cercor.oxfordjournals.org/content/24/3/663.abstract}, 
eprint = {http://cercor.oxfordjournals.org/content/24/3/663.full.pdf+html}, 
journal = {Cerebral Cortex} 
}

@Article{Handwerker2012,
   Author="Handwerker, D. A.  and Roopchansingh, V.  and Gonzalez-Castillo, J.  and Bandettini, P. A. ",
   Title="{{P}eriodic changes in f{M}{R}{I} connectivity}",
   Journal="Neuroimage",
   Year="2012",
   Volume="63",
   Number="3",
   Pages="1712--1719",
   Month="Nov",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180175}{PMC4180175}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2012.06.078}{10.1016/j.neuroimage.2012.06.078}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/22796990}{22796990}] }
}

@Article{Castellanos2013,
   Author="Castellanos, F. X.  and Di Martino, A.  and Craddock, R. C.  and Mehta, A. D.  and Milham, M. P. ",
   Title="{{C}linical applications of the functional connectome}",
   Journal="Neuroimage",
   Year="2013",
   Volume="80",
   Pages="527--540",
   Month="Oct",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809093}{PMC3809093}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2013.04.083}{10.1016/j.neuroimage.2013.04.083}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/23631991}{23631991}] }
}

@Article{Shirer2012,
   Author="Shirer, W. R.  and Ryali, S.  and Rykhlevskaia, E.  and Menon, V.  and Greicius, M. D. ",
   Title="{{D}ecoding subject-driven cognitive states with whole-brain connectivity patterns}",
   Journal="Cereb. Cortex",
   Year="2012",
   Volume="22",
   Number="1",
   Pages="158--165",
   Month="Jan",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3236795}{PMC3236795}] [DOI:\href{http://dx.doi.org/10.1093/cercor/bhr099}{10.1093/cercor/bhr099}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/21616982}{21616982}] }
}

@book{james2014introduction,
  title={An Introduction to Statistical Learning: with Applications in R},
  author={James, G. and Witten, D. and Hastie, T. and Tibshirani, R.},
  isbn={9781461471370},
  lccn={2013936251},
  series={Springer Texts in Statistics},
  url={http://books.google.com/books?id=at1bmAEACAAJ},
  year={2014},
  publisher={Springer New York}
}

@Article{Ryali2010,
   Author="Ryali, S.  and Supekar, K.  and Abrams, D. A.  and Menon, V. ",
   Title="{{S}parse logistic regression for whole-brain classification of f{M}{R}{I} data}",
   Journal="Neuroimage",
   Year="2010",
   Volume="51",
   Number="2",
   Pages="752--764",
   Month="Jun",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2856747}{PMC2856747}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2010.02.040}{10.1016/j.neuroimage.2010.02.040}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/20188193}{20188193}] }
}

@Article{Ryali2012,
   Author="Ryali, S.  and Chen, T.  and Supekar, K.  and Menon, V. ",
   Title="{{E}stimation of functional connectivity in f{M}{R}{I} data using stability selection-based sparse partial correlation with elastic net penalty}",
   Journal="Neuroimage",
   Year="2012",
   Volume="59",
   Number="4",
   Pages="3852--3861",
   Month="Feb",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3288428}{PMC3288428}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2011.11.054}{10.1016/j.neuroimage.2011.11.054}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/22155039}{22155039}] }
}

@Article{Ginestat2011,
   Author="Ginestet, C. E.  and Simmons, A. ",
   Title="{{S}tatistical parametric network analysis of functional connectivity dynamics during a working memory task}",
   Journal="Neuroimage",
   Year="2011",
   Volume="55",
   Number="2",
   Pages="688--704",
   Month="Mar",
   Note={[DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2010.11.030}{10.1016/j.neuroimage.2010.11.030}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/21095229}{21095229}] }
}

@article{Benjamini2001,
ajournal = "Ann. Statist.",
author = "Benjamini, Yoav and Yekutieli, Daniel",
doi = "10.1214/aos/1013699998",
journal = "The Annals of Statistics",
month = "08",
number = "4",
pages = "1165--1188",
publisher = "The Institute of Mathematical Statistics",
title = "The control of the false discovery rate in multiple testing
			 under dependency",
url = "http://dx.doi.org/10.1214/aos/1013699998",
volume = "29",
year = "2001"
}

@Article{Shehzad2014,
   Author="Shehzad, Z.  and Kelly, C.  and Reiss, P. T.  and Cameron Craddock, R.  and Emerson, J. W.  and McMahon, K.  and Copland, D. A.  and Castellanos, F. X.  and Milham, M. P. ",
   Title="{{A} multivariate distance-based analytic framework for connectome-wide association studies}",
   Journal="Neuroimage",
   Year="2014",
   Volume="93 Pt 1",
   Pages="74--94",
   Month="Jun",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4138049}{PMC4138049}] [DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2014.02.024}{10.1016/j.neuroimage.2014.02.024}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/24583255}{24583255}] }
}

@Article{Hutchison2013,
   Author="Hutchison, R. M.  and Womelsdorf, T.  and Allen, E. A.  and Bandettini, P. A.  and Calhoun, V. D.  and Corbetta, M.  and Della Penna, S.  and Duyn, J. H.  and Glover, G. H.  and Gonzalez-Castillo, J.  and Handwerker, D. A.  and Keilholz, S.  and Kiviniemi, V.  and Leopold, D. A.  and de Pasquale, F.  and Sporns, O.  and Walter, M.  and Chang, C. ",
   Title="{{D}ynamic functional connectivity: promise, issues, and interpretations}",
   Journal="Neuroimage",
   Year="2013",
   Volume="80",
   Pages="360--378",
   Month="Oct",
   Abstract={The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.}
}

@article{Thoma2010,
 author = {Thoma, Marisa and Cheng, Hong and Gretton, Arthur and Han, Jiawei and Kriegel, Hans-Peter and Smola, Alex and Song, Le and Yu, Philip S. and Yan, Xifeng and Borgwardt, Karsten M.},
 title = {Discriminative Frequent Subgraph Mining with Optimality Guarantees},
 journal = {Stat. Anal. Data Min.},
 issue_date = {October 2010},
 volume = {3},
 number = {5},
 month = oct,
 year = {2010},
 issn = {1932-1864},
 pages = {302--318},
 numpages = {17},
 url = {http://dx.doi.org/10.1002/sam.v3:5},
 doi = {10.1002/sam.v3:5},
 acmid = {1868844},
 publisher = {John Wiley \& Sons, Inc.},
 address = {New York, NY, USA},
 keywords = {feature selection, frequent subgraphs, graph mining, nested selection, submodularity},
} 

@Article{Gates2014,
   Author="Gates, K. M.  and Molenaar, P. C.  and Iyer, S. P.  and Nigg, J. T.  and Fair, D. A. ",
   Title="{{O}rganizing heterogeneous samples using community detection of {G}{I}{M}{M}{E}-derived resting state functional networks}",
   Journal="PLoS ONE",
   Year="2014",
   Volume="9",
   Number="3",
   Pages="e91322",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3958357}{PMC3958357}] [DOI:\href{http://dx.doi.org/10.1371/journal.pone.0091322}{10.1371/journal.pone.0091322}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/24642753}{24642753}] }
}

@Article{Grimes2002,
   Author="Grimes, D. A.  and Schulz, K. F. ",
   Title="{{U}ses and abuses of screening tests}",
   Journal="Lancet",
   Year="2002",
   Volume="359",
   Number="9309",
   Pages="881--884",
   Month="Mar",
   Note={[DOI:\href{http://dx.doi.org/10.1016/S0140-6736(02)07948-5}{10.1016/S0140-6736(02)07948-5}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/11897304}{11897304}] }
}

@Article{Altman1994,
   Author="Altman, D. G.  and Bland, J. M. ",
   Title="{{D}iagnostic tests 2: {P}redictive values}",
   Journal="BMJ",
   Year="1994",
   Volume="309",
   Number="6947",
   Pages="102",
   Month="Jul",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2540558}{PMC2540558}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/8038641}{8038641}] }
}

@misc{CDCMMWR,
  author = {{Centers for Disease Control and Prevention}},
  title = {{M}orbidity and {M}ortality {W}eekly {R}eport (MMWR)},
  language = {English},
  url = {http://www.cdc.gov/mmwr/},
  note = {Accessed 12 13 2014}
}

@article{Lugosi1992,
title = "Learning with an unreliable teacher ",
journal = "Pattern Recognition ",
volume = "25",
number = "1",
pages = "79 - 87",
year = "1992",
note = "",
issn = "0031-3203",
doi = "http://dx.doi.org/10.1016/0031-3203(92)90008-7",
url = "http://www.sciencedirect.com/science/article/pii/0031320392900087",
author = "Gábor Lugosi",
keywords = "Nonparametric classification",
keywords = "Imperfect supervision",
keywords = "Nonparametric estimation",
keywords = "\{MAP\} decision",
keywords = "Nearest neighbor rule",
keywords = "Bayes methods "
}

@ARTICLE{Scott2013,
   author = {{Scott}, C. and {Blanchard}, G. and {Handy}, G. and {Pozzi}, S. and 
	{Flaska}, M.},
    title = "{Classification with Asymmetric Label Noise: Consistency and Maximal Denoising}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1303.1208},
 primaryClass = "stat.ML",
 keywords = {Statistics - Machine Learning, Computer Science - Learning},
     year = 2013,
    month = mar,
   adsurl = {http://adsabs.harvard.edu/abs/2013arXiv1303.1208S},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@Article{Insel2010,
   Author="Insel, T.  and Cuthbert, B.  and Garvey, M.  and Heinssen, R.  and Pine, D. S.  and Quinn, K.  and Sanislow, C.  and Wang, P. ",
   Title="{{R}esearch domain criteria ({R}{D}o{C}): toward a new classification framework for research on mental disorders}",
   Journal="Am J Psychiatry",
   Year="2010",
   Volume="167",
   Number="7",
   Pages="748--751",
   Month="Jul",
   Note={[DOI:\href{http://dx.doi.org/10.1176/appi.ajp.2010.09091379}{10.1176/appi.ajp.2010.09091379}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/20595427}{20595427}] }
}

@article{Franco2008,
  title={Multimodal and multi-tissue measures of connectivity revealed by joint independent component analysis},
  author={Franco, Alexandre R and Ling, Josef and Caprihan, Arvind and Calhoun, Vincent D and Jung, Rex E and Heileman, Gregory L and Mayer, Andy R},
  journal={Selected Topics in Signal Processing, IEEE Journal of},
  volume={2},
  number={6},
  pages={986--997},
  year={2008},
  publisher={IEEE}
}

@Article{Groves2011,
   Author="Groves, A. R.  and Beckmann, C. F.  and Smith, S. M.  and Woolrich, M. W. ",
   Title="{{L}inked independent component analysis for multimodal data fusion}",
   Journal="Neuroimage",
   Year="2011",
   Volume="54",
   Number="3",
   Pages="2198--2217",
   Month="Feb",
   Note={[DOI:\href{http://dx.doi.org/10.1016/j.neuroimage.2010.09.073}{10.1016/j.neuroimage.2010.09.073}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/20932919}{20932919}] }
}

@article{Calhoun2009,
  title={A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data},
  author={Calhoun, Vince D and Liu, Jingyu and Adal{\i}, T{\"u}lay},
  journal={Neuroimage},
  volume={45},
  number={1},
  pages={S163--S172},
  year={2009},
  publisher={Academic Press}
}

@article {Morup2011,
  author = {Mørup, Morten},
  title = {Applications of tensor (multiway array) factorizations and decompositions in data mining},
  journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
  volume = {1},
  number = {1},
  publisher = {John Wiley & Sons, Inc.},
  issn = {1942-4795},
  url = {http://dx.doi.org/10.1002/widm.1},
  doi = {10.1002/widm.1},
  pages = {24--40},
  year = {2011},
}

@article{Murphy2009,
title = "The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? ",
journal = "NeuroImage ",
volume = "44",
number = "3",
pages = "893 - 905",
year = "2009",
note = "",
issn = "1053-8119",
doi = "http://dx.doi.org/10.1016/j.neuroimage.2008.09.036",
url = "http://www.sciencedirect.com/science/article/pii/S1053811908010264",
author = "Kevin Murphy and Rasmus M. Birn and Daniel A. Handwerker and Tyler B. Jones and Peter A. Bandettini"
}

@article{Birn2012,
title = "The role of physiological noise in resting-state functional connectivity ",
journal = "NeuroImage ",
volume = "62",
number = "2",
pages = "864 - 870",
year = "2012",
issn = "1053-8119",
doi = "http://dx.doi.org/10.1016/j.neuroimage.2012.01.016",
url = "http://www.sciencedirect.com/science/article/pii/S105381191200033X",
author = "Rasmus M. Birn",
keywords = "functional Magnetic Resonance Imaging (fMRI)",
keywords = "Functional connectivity",
keywords = "Resting-state",
keywords = "Physiological noise "
}

@article{Power2012,
title = "Spurious but systematic correlations in functional connectivity \{MRI\} networks arise from subject motion ",
journal = "NeuroImage ",
volume = "59",
number = "3",
pages = "2142 - 2154",
year = "2012",
note = "",
issn = "1053-8119",
doi = "http://dx.doi.org/10.1016/j.neuroimage.2011.10.018",
url = "http://www.sciencedirect.com/science/article/pii/S1053811911011815",
author = "Jonathan D. Power and Kelly A. Barnes and Abraham Z. Snyder and Bradley L. Schlaggar and Steven E. Petersen",
keywords = "fMRI",
keywords = "fcMRI",
keywords = "Resting state",
keywords = "Network",
keywords = "Motion",
keywords = "Movement",
keywords = "Artifact",
keywords = "Noise "
}

@article{satterthwaite2012impact,
  title={Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth},
  author={Satterthwaite, Theodore D and Wolf, Daniel H and Loughead, James and Ruparel, Kosha and Elliott, Mark A and Hakonarson, Hakon and Gur, Ruben C and Gur, Raquel E},
  journal={Neuroimage},
  year={2012},
  publisher={Elsevier}
}

@article{satterthwaite2012improved,
  title={An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data},
  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 others},
  journal={NeuroImage},
  year={2012},
  publisher={Elsevier}
}

@article{VanDijk2012,
title = "The influence of head motion on intrinsic functional connectivity \{MRI\} ",
journal = "NeuroImage ",
volume = "59",
number = "1",
pages = "431 - 438",
year = "2012",
note = "Neuroergonomics: The human brain in action and at work ",
issn = "1053-8119",
doi = "http://dx.doi.org/10.1016/j.neuroimage.2011.07.044",
url = "http://www.sciencedirect.com/science/article/pii/S1053811911008214",
author = "Koene R.A. Van Dijk and Mert R. Sabuncu and Randy L. Buckner",
keywords = "Functional \{MRI\}",
keywords = "\{BOLD\}",
keywords = "Connectome",
keywords = "Resting-state",
keywords = "Movement "
}

@article{yan2013comprehensive,
  title={A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics},
  author={Yan, Chao-Gan and Cheung, Brian and Kelly, Clare and Colcombe, Stan and Craddock, R Cameron and Di Martino, Adriana and Li, Qingyang and Zuo, Xi-Nian and Castellanos, F Xavier and Milham, Michael P},
  journal={Neuroimage},
  volume={76},
  pages={183--201},
  year={2013},
  publisher={Academic Press}
}

@Article{Saad2012,
   Author="Saad, Z. S.  and Gotts, S. J.  and Murphy, K.  and Chen, G.  and Jo, H. J.  and Martin, A.  and Cox, R. W. ",
   Title="{{T}rouble at rest: how correlation patterns and group differences become distorted after global signal regression}",
   Journal="Brain Connect",
   Year="2012",
   Volume="2",
   Number="1",
   Pages="25--32",
   Note={[PubMed Central:\href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3484684}{PMC3484684}] [DOI:\href{http://dx.doi.org/10.1089/brain.2012.0080}{10.1089/brain.2012.0080}] [PubMed:\href{http://www.ncbi.nlm.nih.gov/pubmed/22432927}{22432927}] }
}

@article{strother2006,
  title={Evaluating fMRI preprocessing pipelines},
  author={Strother, Stephen C},
  journal={Engineering in Medicine and Biology Magazine, IEEE},
  volume={25},
  number={2},
  pages={27--41},
  year={2006},
  publisher={IEEE}
}

@article{strother2002quantitative,
  title={The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework},
  author={Strother, Stephen C and Anderson, Jon and Hansen, Lars Kai and Kjems, Ulrik and Kustra, Rafal and Sidtis, John and Frutiger, Sally and Muley, Suraj and LaConte, Stephen and Rottenberg, David},
  journal={NeuroImage},
  volume={15},
  number={4},
  pages={747--771},
  year={2002},
  publisher={Academic Press}
}

@article{laconte2003evaluation,
  title={The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics},
  author={LaConte, Stephen and Anderson, Jon and Muley, Suraj and Ashe, James and Frutiger, Sally and Rehm, Kelly and Hansen, Lars Kai and Yacoub, Essa and Hu, Xiaoping and Rottenberg, David and others},
  journal={NeuroImage},
  volume={18},
  number={1},
  pages={10--27},
  year={2003},
  publisher={Academic Press}
}



@misc{CraddockPCP,
  author = {R. Cameron Craddock and Pierre Bellec},
  year = {2014},
  title = {{P}reprocessed {C}onnectomes {P}roject {(PCP)}},
  language = {English},
  url = {http://preprocessed-connectomes-project.github.io},
  note = {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}
}