adhd200_pubs.bib

@article{KadkhodaeianBakhtiari2012,
author = {Bakhtiari, Shahab Kadkhodaeian and Hossein-Zadeh, Gholam-Ali},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Algorithms,Brain,Brain: physiology,Child,Female,Humans,Magnetic Resonance Imaging,Male,Nerve Net,Nerve Net: physiology,Rest,Rest: physiology,adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = apr,
number = {2},
pages = {1236--49},
title = {{Subspace-based Identification Algorithm for characterizing causal networks in resting brain.}},
url = {http://www.sciencedirect.com/science/article/pii/S105381191200016X},
volume = {60},
year = {2012}
}
@article{Bellec2012,
author = {Bellec, Pierre and Lavoie-Courchesne, S\'{e}bastien and Dickinson, Phil and Lerch, Jason P and Zijdenbos, Alex P and Evans, Alan C},
doi = {10.3389/fninf.2012.00007},
issn = {1662-5196},
journal = {Frontiers in neuroinformatics},
keywords = {high-performance computing,matlab,octave,open-source,parallel computing,pipeline,pipeline, workflow, Octave, Matlab, open-source, p,workflow},
month = jan,
number = {April},
pages = {7},
pmid = {22493575},
title = {{The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3318188\&tool=pmcentrez\&rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Bohland2012,
author = {Bohland, Jason W and Saperstein, Sara and Pereira, Francisco and Rapin, J\'{e}r\'{e}my and Grady, Leo},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = jan,
pages = {78},
title = {{Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3527894\&tool=pmcentrez\&rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Chang2012,
author = {Chang, Che-Wei and Ho, Chien-Chang and Chen, Jyh-Horng},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {adhd200 preprc},
language = {English},
mendeley-tags = {adhd200 preprc},
month = jan,
pages = {66},
publisher = {Frontiers},
title = {{ADHD classification by a texture analysis of anatomical brain MRI data.}},
url = {http://www.frontiersin.org/Journal/10.3389/fnsys.2012.00066/abstract},
volume = {6},
year = {2012}
}
@article{Cheng2012,
author = {Cheng, Wei and Ji, Xiaoxi and Zhang, Jie and Feng, Jianfeng},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = jan,
pages = {58},
title = {{Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3412279\&tool=pmcentrez\&rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Colby2012,
author = {Colby, John B and Rudie, Jeffrey D and Brown, Jesse A and Douglas, Pamela K and Cohen, Mark S and Shehzad, Zarrar},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {adhd200 preprc},
language = {English},
mendeley-tags = {adhd200 preprc},
month = jan,
pages = {59},
publisher = {Frontiers},
title = {{Insights into multimodal imaging classification of ADHD.}},
url = {http://www.frontiersin.org/Journal/10.3389/fnsys.2012.00059/abstract},
volume = {6},
year = {2012}
}
@article{Colby2012a,
author = {Colby, John Benjamin},
keywords = {Biomedical engineering,MRI,Neurosciences,Statistics,brain development,diffusion tensor imaging,neuroimaging,tractography,white matter},
month = jan,
title = {{Development of human brain connectivity in health and disease}},
url = {http://escholarship.org/uc/item/2p3471tj\#page-2},
year = {2012}
}
@article{Dai2012,
author = {Dai, Dai and Wang, Jieqiong and Hua, Jing and He, Huiguang},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = jan,
pages = {63},
title = {{Classification of ADHD children through multimodal magnetic resonance imaging.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3432508\&tool=pmcentrez\&rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Dey2012,
author = {Dey, Soumyabrata and Rao, A Ravishankar and Shah, Mubarak},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {adhd200 preprc},
language = {English},
mendeley-tags = {adhd200 preprc},
month = jan,
pages = {75},
publisher = {Frontiers},
title = {{Exploiting the brain's network structure in identifying ADHD subjects.}},
url = {http://www.frontiersin.org/Journal/10.3389/fnsys.2012.00075/abstract},
volume = {6},
year = {2012}
}
@article{Dey2014,
author={Dey},
issn = {1662-5110},
journal = {Frontiers in Neural Circuits},
keywords = {Attention Deficit Hyperactive Disorder,Attributed Graph,Support vector machine,functional magnetic resonance imaging,multidimensional scaling},
language = {English},
month = jun,
publisher = {Frontiers},
title = {{Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects}},
url = {http://journal.frontiersin.org/Journal/10.3389/fncir.2014.00064/abstract},
volume = {8},
year = {2014}
}
@article{DosSantosSiqueira2014,
author = {{dos Santos Siqueira}, Anderson and {Biazoli Junior}, Claudinei Eduardo and Comfort, William Edgar and Rohde, Luis Augusto and Sato, Jo\~{a}o Ricardo},
doi = {10.1155/2014/380531},
file = {:Users/cameron/Documents/papers/dos Santos Siqueira et al. - 2014 - Abnormal Functional Resting-State Networks in ADHD Graph Theory and Pattern Recognition Analysis of.pdf:pdf},
issn = {2314-6133},
journal = {BioMed Research International},
pages = {1--10},
title = {{Abnormal Functional Resting-State Networks in ADHD: Graph Theory and Pattern Recognition Analysis of fMRI Data}},
url = {http://www.hindawi.com/journals/bmri/2014/380531/},
volume = {2014},
year = {2014}
}
@article{Eloyan2012,
author = {Eloyan, Ani and Muschelli, John and Nebel, Mary Beth and Liu, Han and Han, Fang and Zhao, Tuo and Barber, Anita D and Joel, Suresh and Pekar, James J and Mostofsky, Stewart H and Caffo, Brian},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = jan,
pages = {61},
title = {{Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3431009\&tool=pmcentrez\&rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Fujita2014,
author = {Fujita, Andr\'{e} and Takahashi, Daniel Y and Patriota, Alexandre G and Sato, Jo\~{a}o R},
issn = {1097-0258},
journal = {Statistics in medicine},
month = sep,
title = {{A non-parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25185759},
year = {2014}
}
@article{Fujita2013,
archivePrefix = {arXiv},
arxivId = {1311.6732},
author = {Fujita, Andr\'{e} and Takahashi, Daniel Y. and Patriota, Alexandre G. and Sato, Jo\~{a}o Ricardo},
eprint = {1311.6732},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = nov,
title = {{A statistical test to identify differences in clustering structures}},
url = {http://webcache.googleusercontent.com/search?q=cache:hW0LJGfI6mUJ:my.arxiv.org/arxiv/FilterServlet/abs/1311.6732+\&cd=1\&hl=en\&ct=clnk\&gl=us http://arxiv.org/abs/1311.6732},
year = {2013}
}
@inproceedings{He2013,
address = {Philadelphia, PA},
author = {He, Lifang and Kong, Xiangnan and Yu, Philip S. and Ragin, Ann B. and Hao, Zhifeng and Yang, Xiaowei},
booktitle = {Proc of the Thirteenth SIAM International Conference on Data Mining (SDM 2013)},
doi = {http://epubs.siam.org/doi/abs/10.1137/1.9781611973440.15},
language = {en},
pages = {127--135},
title = {{DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages}},
url = {http://epubs.siam.org/doi/abs/10.1137/1.9781611973440.15},
year = {2013}
}
@article{Ji2011,
archivePrefix = {arXiv},
arxivId = {1112.3496},
author = {Ji, Xiaoxi and Cheng, Wei and Zhang, Jie and Ge, Tian and Sun, Li and Wang, Yufeng and Feng, Jianfeng},
eprint = {1112.3496},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = dec,
title = {{Increased Coupling in the Saliency Network is the main cause/effect of Attention Deficit Hyperactivity Disorder}},
url = {http://arxiv.org/abs/1112.3496},
year = {2011}
}
@inproceedings{Kong2013,
address = {Philadelphia, PA},
archivePrefix = {arXiv},
arxivId = {1301.6626},
author = {Kong, Xiangnan and Yu, Philip S. and Wang, Xue and Ragin, Ann B.},
booktitle = {Proc of the Thirteenth SIAM International Conference on Data Mining (SDM 2013)},
eprint = {1301.6626},
file = {:Users/cameron/Documents/papers/Kong et al. - 2013 - Discriminative Feature Selection for Uncertain Graph Classification.pdf:pdf},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = jan,
title = {{Discriminative Feature Selection for Uncertain Graph Classification}},
url = {http://arxiv.org/abs/1301.6626},
year = {2013}
}
@article{Lavoie-Courchesne2012b,
author = {Lavoie-Courchesne, S. and Rioux, P. and Chouinard-Decorte, F. and Sherif, T. and Rousseau, M. -E and Das, S. and Adalat, R. and Doyon, J. and Craddock, C. and Margulies, D. and Chu, Carlton and Lyttelton, O. and Evans, A. C. and Bellec, P.},
journal = {Journal of Physics: Conference Series},
keywords = {adhd200 preprc,cbrain,pipeline,psom},
mendeley-tags = {adhd200 preprc},
number = {1},
pages = {012032+},
title = {{Integration of a neuroimaging processing pipeline into a pan-canadian computing grid}},
url = {http://dx.doi.org/10.1088/1742-6596/341/1/012032},
volume = {341},
year = {2012}
}
@article{Li2013,
archivePrefix = {arXiv},
arxivId = {1304.5637},
author = {Li, Xiaoshan and Zhou, Hua and Li, Lexin},
eprint = {1304.5637},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = apr,
title = {{Tucker Tensor Regression and Neuroimaging Analysis}},
url = {http://arxiv.org/abs/1304.5637},
year = {2013}
}
@inproceedings{Liang2012,
author = {Liang, Sheng-Fu and Hsieh, Tsung-Hao and Chen, Pin-Tzu and Wu, Ming-Long and Kung, Chun-Chia and Lin, Chun-Yu and Shaw, Fu-Zen},
booktitle = {2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012)},
keywords = {adhd200 preprc},
language = {English},
mendeley-tags = {adhd200 preprc},
month = nov,
pages = {294--298},
publisher = {IEEE},
title = {{Differentiation between resting-state fMRI data from ADHD and normal subjects: Based on functional connectivity and machine learning}},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6409719},
year = {2012}
}
@article{Liu2012,
archivePrefix = {arXiv},
arxivId = {1203.3896},
author = {Liu, Weidong and Luo, Xi},
eprint = {1203.3896},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = mar,
pages = {30},
title = {{High-dimensional Sparse Precision Matrix Estimation via Sparse Column Inverse Operator}},
url = {http://arxiv.org/abs/1203.3896},
year = {2012}
}
@incollection{Mahanand2013,
address = {Cham},
author = {Mahanand, B. S. and Savitha, R. and Suresh, S.},
booktitle = {AI 2013: Advances in Artificial Intelligence},
doi = {10.1007/978-3-319-03680-9},
editor = {Cranefield, Stephen and Nayak, Abhaya},
isbn = {978-3-319-03679-3},
pages = {386--395},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
title = {{Computer Aided Diagnosis of ADHD Using Brain Magnetic Resonance Images}},
url = {http://link.springer.com/10.1007/978-3-319-03680-9},
volume = {8272},
year = {2013}
}
@article{Olivetti2012,
author = {Olivetti, Emanuele and Greiner, Susanne and Avesani, Paolo},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = jan,
pages = {70},
title = {{ADHD diagnosis from multiple data sources with batch effects.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3465911\&tool=pmcentrez\&rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Sato2012,
author = {Sato, Jo\~{a}o Ricardo and Hoexter, Marcelo Queiroz and Castellanos, Xavier Francisco and Rohde, Luis A},
doi = {10.1371/journal.pone.0045671},
editor = {Fan, Yong},
file = {::},
issn = {1932-6203},
journal = {PloS one},
keywords = {Adolescent,Adult,Age Factors,Artificial Intelligence,Attention Deficit Disorder with Hyperactivity,Attention Deficit Disorder with Hyperactivity: phy,Automated,Brain,Brain Mapping,Brain Mapping: methods,Brain: pathology,Child,Computer-Assisted,Female,Gyrus Cinguli,Gyrus Cinguli: physiology,Humans,Image Processing,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Middle Aged,Models,Neural Pathways,Neural Pathways: physiology,Pattern Recognition,Reproducibility of Results,Statistical,Support Vector Machines,Young Adult,adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = jan,
number = {9},
pages = {e45671},
pmid = {23049834},
publisher = {Public Library of Science},
title = {{Abnormal brain connectivity patterns in adults with ADHD: a coherence study.}},
url = {http://dx.plos.org/10.1371/journal.pone.0045671},
volume = {7},
year = {2012}
}
@article{Sato2012a,
author = {Sato, Jo\~{a}o Ricardo and Hoexter, Marcelo Queiroz and Fujita, Andr\'{e} and Rohde, Luis Augusto},
doi = {10.3389/fnsys.2012.00068},
file = {::},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = jan,
pages = {68},
pmid = {23015782},
title = {{Evaluation of pattern recognition and feature extraction methods in ADHD prediction.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3449288\&tool=pmcentrez\&rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Sato2013,
author = {Sato, Jo\~{a}o Ricardo and Takahashi, Daniel Yasumasa and Hoexter, Marcelo Queiroz and Massirer, Katlin Brauer and Fujita, Andr\'{e}},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Attention Deficit Disorder with Hyperactivity,Attention Deficit Disorder with Hyperactivity: phy,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiopathology,Child,Computer-Assisted,Computer-Assisted: methods,Entropy,Female,Humans,Image Interpretation,Magnetic Resonance Imaging,Male,Nerve Net,Nerve Net: physiopathology,adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = aug,
pages = {44--51},
title = {{Measuring network's entropy in ADHD: a new approach to investigate neuropsychiatric disorders.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23571416},
volume = {77},
year = {2013}
}
@article{She2014,
author = {She, Yiyuan and He, Yuejia and Wu, Dapeng},
file = {:Users/cameron/Documents/papers/She, He, Wu - Unknown - Learning Topology and Dynamics of Large Recurrent Neural Networks.pdf:pdf},
title = {{Learning Topology and Dynamics of Large Recurrent Neural Networks}},
url = {http://www.wu.ece.ufl.edu/mypapers/sigmoid\_IEEE\_doublecolumn.pdf},
year = {2014}
}
@inproceedings{Solmaz2012,
author = {Solmaz, Berkan and Dey, Soumyabrata and Rao, A. Ravishankar and Shah, Mubarak},
booktitle = {Medical Imaging 2012: Image Processing. Edited by Haynor},
editor = {Haynor, David R. and Ourselin, S\'{e}bastien},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = feb,
pages = {83144T},
title = {{ADHD classification using bag of words approach on network features}},
url = {http://adsabs.harvard.edu/abs/2012SPIE.8314E.164S},
volume = {8314},
year = {2012}
}
@inproceedings{Tabas2014,
author={Tabas},
language = {English},
month = jun,
pages = {1--4},
publisher = {IEEE},
title = {{Spatial discriminant ICA for RS-fMRI characterisation}},
url = {http://ieeexplore.ieee.org.proxy.wexler.hunter.cuny.edu/articleDetails.jsp?arnumber=6858546},
year = {2014}
}
@article{Takahashi2012,
author = {Takahashi, Daniel Yasumasa and Sato, Jo\~{a}o Ricardo and Ferreira, Carlos Eduardo and Fujita, Andr\'{e}},
issn = {1932-6203},
journal = {PloS one},
keywords = {Attention Deficit Disorder with Hyperactivity,Attention Deficit Disorder with Hyperactivity: dia,Attention Deficit Disorder with Hyperactivity: met,Child,Cluster Analysis,Computational Biology,Computational Biology: methods,Computer Graphics,Humans,Magnetic Resonance Imaging,Protein Interaction Maps,ROC Curve,adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = jan,
number = {12},
pages = {e49949},
title = {{Discriminating different classes of biological networks by analyzing the graphs spectra distribution.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3526608\&tool=pmcentrez\&rendertype=abstract},
volume = {7},
year = {2012}
}
@phdthesis{Wang2013,
author = {Wang, Peng},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
school = {Auburn University},
title = {{Machine Learning Approaches for Disease State Classification from Neuroimaging Data}},
type = {Masters Thesis},
url = {http://etd.auburn.edu/etd/handle/10415/3623},
year = {2013}
}
@article{Wang2013a,
author = {Wang, Xunheng and Jiao, Yun and Tang, Tianyu and Wang, Hui and Lu, Zuhong},
issn = {1872-7727},
journal = {European journal of radiology},
keywords = {Adult,Attention Deficit Disorder with Hyperactivity,Attention Deficit Disorder with Hyperactivity: dia,Attention Deficit Disorder with Hyperactivity: phy,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiopathology,Female,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Nerve Net,Nerve Net: physiopathology,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity,Support Vector Machines},
month = sep,
number = {9},
pages = {1552--7},
title = {{Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder.}},
url = {http://www.sciencedirect.com/science/article/pii/S0720048X13002040},
volume = {82},
year = {2013}
}
@article{Yang2012,
archivePrefix = {arXiv},
arxivId = {1209.2139},
author = {Yang, Sen and Lu, Zhaosong and Shen, Xiaotong and Wonka, Peter and Ye, Jieping},
eprint = {1209.2139},
keywords = {adhd200 preprc},
mendeley-tags = {adhd200 preprc},
month = sep,
title = {{Fused Multiple Graphical Lasso}},
url = {http://arxiv.org/abs/1209.2139},
year = {2012}
}
@article{Yao2013,
author = {Yao, Y and Lu, W L and Xu, B and Li, C B and Lin, C P and Waxman, D and Feng, J F},
issn = {2045-2322},
journal = {Scientific reports},
language = {en},
month = jan,
pages = {2853},
publisher = {Nature Publishing Group},
title = {{The increase of the functional entropy of the human brain with age.}},
url = {http://www.nature.com/srep/2013/131009/srep02853/full/srep02853.html},
volume = {3},
year = {2013}
}
@misc{patent,
author = {Yao, Y},
keywords = {patent},
mendeley-tags = {patent},
month = aug,
title = {{Method and system for modeling and processing fmri image data using a bag-of-words approach}},
url = {http://www.google.com/patents/US20130211229},
year = {2013}
}


@phdthesis{Dey2014,
author = {Dey, Soumyabrata},
file = {:Users/cameron.craddock/Documents/papers/Dey - 2014 - Automatic Detection of Brain Functional Disorder Using Imaging Data.pdf:pdf},
school = {University of Central Florida},
title = {{Automatic Detection of Brain Functional Disorder Using Imaging Data}},
type = {PhD Dissertation},
year = {2014}
}
@article{Reiss2014,
author = {Reiss, Philip T and Huo, Lan and Zhao, Yihong and Kelly, Clare and Ogden, R. Todd},
file = {:Users/cameron.craddock/Documents/papers/Reiss et al. - 2014 - Wavelet-domain Regression and Predictive Inference in Psychiatric Neuroimaging.pdf:pdf},
journal = {The SelectedWorks of Philip T. Reiss},
title = {{Wavelet-domain Regression and Predictive Inference in Psychiatric Neuroimaging}},
url = {http://works.bepress.com/phil\_reiss/29},
year = {2014}
}
@article{Rangarajan2014,
author = {Rangarajan, B and Suresh, S. and Mahanand, B. S.},
file = {:Users/cameron.craddock/Documents/papers/Rangarajan, Suresh, Mahanand - 2014 - Identification of Potential Biomarkers in the Hippocampus Region for the Diagnosis of ADHD using P.pdf:pdf},
journal = {13th International Conference on Control, Automation, Robotics and Vision, (ICARCV 2014)},
title = {{Identification of Potential Biomarkers in the Hippocampus Region for the Diagnosis of ADHD using PBL-McRBFN Approach}},
volume = {2},
year = {2014}
}
@phdthesis{Vidal2014,
address = {S\~{a}o Paulo},
author = {Vidal, Maciel Calebe},
file = {:Users/cameron.craddock/Documents/papers/Vidal - 2014 - An\'{a}lise da estrutura de clusteriza\c{c}\~{a}o das redes de conectividade funcional do c\'{e}rebro para investigar as bases das de.pdf:pdf},
pages = {64},
school = {Universidade de S\~{a}o Paulo},
title = {{An\'{a}lise da estrutura de clusteriza\c{c}\~{a}o das redes de conectividade funcional do c\'{e}rebro para investigar as bases das desordens do espectro autista}},
year = {2014}
}
@article{Olivetti2014,
author = {Olivetti, Emanuele and Greiner, Susanne and Avesani, Paolo},
doi = {10.1007/s40708-014-0007-6},
file = {:Users/cameron.craddock/Documents/papers/Olivetti, Greiner, Avesani - 2014 - Statistical independence for the evaluation of classifier-based diagnosis.pdf:pdf},
issn = {2198-4018},
journal = {Brain Informatics},
title = {{Statistical independence for the evaluation of classifier-based diagnosis}},
url = {http://link.springer.com/10.1007/s40708-014-0007-6},
year = {2014}
}
@article{DosSantosSiqueira2014,
author = {{dos Santos Siqueira}, Anderson and {Biazoli Junior}, Claudinei Eduardo and Comfort, William Edgar and Rohde, Luis Augusto and Sato, Jo\~{a}o Ricardo},
doi = {10.1155/2014/380531},
file = {::},
issn = {2314-6133},
journal = {BioMed Research International},
pages = {1--10},
title = {{Abnormal Functional Resting-State Networks in ADHD: Graph Theory and Pattern Recognition Analysis of fMRI Data}},
url = {http://www.hindawi.com/journals/bmri/2014/380531/},
volume = {2014},
year = {2014}
}

@article{Chen2015,
abstract = {An important problem in contemporary statistics is to understand the relationship among a large number of variables based on a dataset, usually with p, the number of the variables, much larger than n, the sample size. Recent efforts have focused on modeling static covariance matrices where pairwise covariances are considered invariant. In many real systems, however, these pairwise relations often change. To characterize the changing correlations in a high dimensional system, we study a class of dynamic covariance models (DCMs) assumed to be sparse, and investigate for the first time a unified theory for understanding their non-asymptotic error rates and model selection properties. In particular, in the challenging high dimension regime, we highlight a new uniform consistency theory in which the sample size can be seen as n4/5 when the bandwidth parameter is chosen as h∝n−1/5 for accounting for the dynamics. We show that this result holds uniformly over a range of the variable used for modeling the dynamic...},
author = {Chen, Ziqi and Leng, Chenlei},
doi = {10.1080/01621459.2015.1077712},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
keywords = {Covariance model,Dynamic covariance,Functional connectivity,High Dimensionality,Marginal independence,Rate of convergence,Sparsity,Uniform consistency},
language = {en},
month = aug,
pages = {1--55},
publisher = {Taylor \& Francis},
title = {{Dynamic Covariance Models}},
url = {http://www.tandfonline.com/doi/abs/10.1080/01621459.2015.1077712 http://www.tandfonline.com/doi/full/10.1080/01621459.2015.1077712},
year = {2015}
}
@article{Carmona2015,
abstract = {We sought to determine whether functional connectivity streams that link sensory, attentional, and higher-order cognitive circuits are atypical in attention-deficit/hyperactivity disorder (ADHD). We applied a graph-theory method to the resting-state functional magnetic resonance imaging data of 120 children with ADHD and 120 age-matched typically developing children (TDC). Starting in unimodal primary cortex-visual, auditory, and somatosensory-we used stepwise functional connectivity to calculate functional connectivity paths at discrete numbers of relay stations (or link-step distances). First, we characterized the functional connectivity streams that link sensory, attentional, and higher-order cognitive circuits in TDC and found that systems do not reach the level of integration achieved by adults. Second, we searched for stepwise functional connectivity differences between children with ADHD and TDC. We found that, at the initial steps of sensory functional connectivity streams, patients display significant enhancements of connectivity degree within neighboring areas of primary cortex, while connectivity to attention-regulatory areas is reduced. Third, at subsequent link-step distances from primary sensory cortex, children with ADHD show decreased connectivity to executive processing areas and increased degree of connections to default mode regions. Fourth, in examining medication histories in children with ADHD, we found that children medicated with psychostimulants present functional connectivity streams with higher degree of connectivity to regions subserving attentional and executive processes compared to medication-na\"{\i}ve children. We conclude that predominance of local sensory processing and lesser influx of information to attentional and executive regions may reduce the ability to organize and control the balance between external and internal sources of information in ADHD.},
author = {Carmona, Susana and Hoekzema, Elseline and Castellanos, Francisco X and Garc\'{\i}a-Garc\'{\i}a, David and Lage-Castellanos, Agust\'{\i}n and {Van Dijk}, Koene R A and Navas-S\'{a}nchez, Francisco J and Mart\'{\i}nez, Kenia and Desco, Manuel and Sepulcre, Jorge},
issn = {1097-0193},
journal = {Human brain mapping},
month = jul,
number = {7},
pages = {2544--57},
title = {{Sensation-to-cognition cortical streams in attention-deficit/hyperactivity disorder.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4484811\&tool=pmcentrez\&rendertype=abstract},
volume = {36},
year = {2015}
}
@article{Kyeong2015,
abstract = {BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed by a diagnostic interview, mainly based on subjective reports from parents or teachers. It is necessary to develop methods that rely on objectively measureable neurobiological data to assess brain-behavior relationship in patients with ADHD. We investigated the application of a topological data analysis tool, Mapper, to analyze the brain functional connectivity data from ADHD patients.

METHODS: To quantify the disease severity using the neuroimaging data, the decomposition of individual functional networks into normal and disease components by the healthy state model (HSM) was performed, and the magnitude of the disease component (MDC) was computed. Topological data analysis using Mapper was performed to distinguish children with ADHD (n = 196) from typically developing controls (TDC) (n = 214).

RESULTS: In the topological data analysis, the partial clustering results of patients with ADHD and normal subjects were shown in a chain-like graph. In the correlation analysis, the MDC showed a significant increase with lower intelligence scores in TDC. We also found that the rates of comorbidity in ADHD significantly increased when the deviation of the functional connectivity from HSM was large. In addition, a significant correlation between ADHD symptom severity and MDC was found in part of the dataset.

CONCLUSIONS: The application of HSM and topological data analysis methods in assessing the brain functional connectivity seem to be promising tools to quantify ADHD symptom severity and to reveal the hidden relationship between clinical phenotypic variables and brain connectivity.},
author = {Kyeong, Sunghyon and Park, Seonjeong and Cheon, Keun-Ah and Kim, Jae-Jin and Song, Dong-Ho and Kim, Eunjoo},
issn = {1932-6203},
journal = {PloS one},
month = jan,
number = {9},
pages = {e0137296},
publisher = {Public Library of Science},
title = {{A New Approach to Investigate the Association between Brain Functional Connectivity and Disease Characteristics of Attention-Deficit/Hyperactivity Disorder: Topological Neuroimaging Data Analysis.}},
url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137296},
volume = {10},
year = {2015}
}
@article{Rangarajan2015,
author = {Rangarajan, B. and Subramaian, K. and Suresh, S.},
doi = {10.1109/CCIP.2015.7100722},
file = {:Users/cameron.craddock/Documents/papers/Rangarajan, Subramaian, Suresh - 2015 - Importance of phenotypic information in ADHD diagnosis.pdf:pdf},
isbn = {978-1-4799-7171-8},
journal = {2015 International Conference on Cognitive Computing and Information Processing(CCIP)},
number = {MARCH},
pages = {1--6},
title = {{Importance of phenotypic information in ADHD diagnosis}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7100722},
year = {2015}
}
@article{Yang2015,
abstract = {In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating example is the analysis of brain networks of Alzheimer's disease using neuroimaging data. Specifically, we may wish to estimate a brain network for the normal controls (NC), a brain network for the patients with mild cognitive impairment (MCI), and a brain network for Alzheimer's patients (AD). We expect the two brain networks for NC and MCI to share common structures but not to be identical to each other; similarly for the two brain networks for MCI and AD. The proposed formulation can be solved using a second-order method. Our key technical contribution is to establish the necessary and sufficient condition for the graphs to be decomposable. Based on this key property, a simple screening rule is presented, which decomposes the large graphs into small subgraphs and allows an efficient estimation of multiple ...},
author = {Yang, Sen and Lu, Zhaosong and Shen, Xiaotong and Wonka, Peter and Ye, Jieping},
doi = {10.1137/130936397},
issn = {1052-6234},
journal = {SIAM Journal on Optimization},
keywords = {62J10,65K05,90C22,90C25,90C47,fused multiple graphical lasso,screening,second-order method},
language = {en},
month = may,
number = {2},
pages = {916--943},
publisher = {Society for Industrial and Applied Mathematics},
title = {{Fused Multiple Graphical Lasso}},
url = {http://epubs.siam.org/doi/abs/10.1137/130936397},
volume = {25},
year = {2015}
}
@inproceedings{Hou2015,
address = {Quebec, Canada},
author = {Hou, Ming and Chaib-draa, Brahim},
booktitle = {IEEE International Conference on Image Processing (ICIP '15)},
file = {:Users/cameron.craddock/Documents/papers/Hou, Chaib-draa - 2015 - HIERARCHICAL TUCKER TENSOR REGRESSION APPLICATION TO BRAIN IMAGING DATA ANALYSIS.pdf:pdf},
title = {{HIERARCHICAL TUCKER TENSOR REGRESSION : APPLICATION TO BRAIN IMAGING DATA ANALYSIS}},
year = {2015}
}
@article{Carmona2015a,
author = {Carmona, Susana and Hoekzema, Elseline and Castellanos, Francisco X. and Garc\'{\i}a-Garc\'{\i}a, David and Lage-Castellanos, Agust\'{\i}n and {Van Dijk}, Koene R.a. and Navas-S\'{a}nchez, Francisco J. and Mart\'{\i}nez, Kenia and Desco, Manuel and Sepulcre, Jorge},
doi = {10.1002/hbm.22790},
file = {:Users/cameron.craddock/Documents/papers//Carmona et al. - 2015 - Sensation-to-cognition cortical streams in attention-deficithyperactivity disorder.pdf:pdf},
issn = {10659471},
journal = {Human Brain Mapping},
number = {March},
pages = {n/a--n/a},
title = {{Sensation-to-cognition cortical streams in attention-deficit/hyperactivity disorder}},
url = {http://doi.wiley.com/10.1002/hbm.22790},
volume = {00},
year = {2015}
}
@article{Ahn2015,
author = {Ahn, Mihye and Shen, Haipeng and Lin, Weili and Zhu, Hongtu},
doi = {10.5705/ss.2013.232w},
file = {:Users/cameron.craddock/Documents/papers/Ahn et al. - 2015 - A Sparse Reduced Rank Framework for Group Analysis of Functional Neuroimaging Data.pdf:pdf},
issn = {10170405},
journal = {Statistica Sinica},
number = {JANUARY},
title = {{A Sparse Reduced Rank Framework for Group Analysis of Functional Neuroimaging Data}},
url = {http://www3.stat.sinica.edu.tw/statistica/J25N1/J25N117/J25N117.html},
year = {2015}
}
@article{Deshpande2015,
author = {Deshpande, Gopikrishna and Wang, Peng and Rangaprakash, D. and Wilamowski, Bogdan},
doi = {10.1109/TCYB.2014.2379621},
file = {:Users/cameron.craddock/Documents/papers/Deshpande et al. - 2015 - Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Cl.pdf:pdf},
issn = {2168-2267},
journal = {IEEE Transactions on Cybernetics},
month = dec,
number = {12},
pages = {2668--2679},
title = {{Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7001645},
volume = {45},
year = {2015}
}
@article{Li2015,
archivePrefix = {arXiv},
arxivId = {1501.07815},
author = {Li, Lexin and Zhang, Xin},
doi = {arXiv:1501.07815},
eprint = {1501.07815},
file = {:Users/cameron.craddock/Documents/papers/Li, Zhang - 2015 - Parsimonious Tensor Response Regression.pdf:pdf},
journal = {ArXiv e-prints},
pages = {1501.07815},
title = {{Parsimonious Tensor Response Regression}},
year = {2015}
}
@incollection{Han2015,
author = {Han, Xiaobing and Zhong, Yanfei and He, Lifang and Yu, Philip S. and Zhang, Liangpei},
booktitle = {Brain Informatics and Health},
doi = {10.1007/978-3-319-23344-4\_16},
editor = {Guo, Yike and Friston, Karl and Aldo, Faisal and Hill, Sean and Peng, Hanchuan},
keywords = {Deep learning,Hierarchical convolutional sparse auto-encoder (HC,Neuroimaging classification,Sparse auto-encoder (SAE)},
pages = {156--166},
publisher = {Springer International Publishing},
title = {{The Unsupervised Hierarchical Convolutional Sparse Auto-Encoder for Neuroimaging Data Classification}},
url = {http://link.springer.com/10.1007/978-3-319-23344-4\_16},
year = {2015}
}
@incollection{Nunez-Garcia2015,
abstract = {Resting state fMRI is a powerful method of functional brain imaging, which can reveal information of functional connectivity between regions during rest. In this paper, we present a novel method, called Functional-Anatomical Discriminative Regions (FADR), for selecting a discriminative subset of functional-anatomical regions of the brain in order to characterize functional connectivity abnormalities in mental disorders. FADR integrates Independent Component Analysis with a sparse feature selection strategy, namely Elastic Net, in a supervised framework to extract a new sparse representation. In particular, ICA is used for obtaining group Resting State Networks and functional information is extracted from the subject-specific spatial maps. Anatomical information is incorporated to localize the discriminative regions. Thus, functional-anatomical information is combined in the new descriptor, which characterizes areas of different networks and carries discriminative power. Experimental results on the public database ADHD-200 validate the method being able to automatically extract discriminative areas and extending results from previous studies. The classification ability is evaluated showing that our method performs better than the average of the teams in the ADHD-200 Global Competition while giving relevant information about the disease by selecting the most discriminative regions at the same time.},
author = {Nu\~{n}ez-Garcia, Marta and Simpraga, Sonja and Jurado, Maria Angeles and Garolera, Maite and Pueyo, Roser and Igual, Laura},
booktitle = {Machine Learning in Medical Imaging},
doi = {10.1007/978-3-319-24888-2\_8},
editor = {Zhou, Luping and Wang, Li and Wang, Qian and Shi, Yinghuan},
keywords = {Classification,Elastic Net,Feature selection,Independent Component Analysis,Resting-state fMRI,athena},
mendeley-tags = {athena},
pages = {61--68},
publisher = {Springer International Publishing},
title = {{FADR: Functional-Anatomical Discriminative Regions for Rest fMRI Characterization}},
url = {http://link.springer.com/10.1007/978-3-319-24888-2\_8},
year = {2015}
}