diff --git a/adhd200_pubs.bib b/adhd200_pubs.bib index dd2c00e..123548c 100644 --- a/adhd200_pubs.bib +++ b/adhd200_pubs.bib @@ -83,7 +83,7 @@ @article{Colby2012 volume = {6}, year = {2012} } -@article{Colby2012a, +@phdthesis{Colby2012a, author = {Colby, John Benjamin}, keywords = {Biomedical engineering,MRI,Neurosciences,Statistics,brain development,diffusion tensor imaging,neuroimaging,tractography,white matter}, month = jan, @@ -133,7 +133,7 @@ @article{Dey2014 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}, +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}, @@ -158,25 +158,30 @@ @article{Eloyan2012 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}, +author = {Fujita, Andr\'{e} and Takahashi, Daniel Y and Patriota, Alexandre G and Sato, Jo{\~a}o R}, journal = {Statistics in medicine}, month = sep, title = {{A non-parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data.}}, +volume = {33}, +number = {28}, +issn = {1097-0258}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25185759}, +doi = {10.1002/sim.6292}, +page = {4949--4962}, 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}, +author = {Fujita, Andr\'{e} and Takahashi, Daniel Y. and Patriota, Alexandre G. and Sato, Jo{\~a}o Ricardo}, +journal = {Statistics in medicine}, +number = {28}, keywords = {adhd200 preprc}, mendeley-tags = {adhd200 preprc}, -month = nov, +month = dec, +pages = {4949--4962}, 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} +url = {https://www-ncbi-nlm-nih-gov.proxy.wexler.hunter.cuny.edu/pubmed/25185759}, +volume = {33}, +year = {2014} } @inproceedings{He2013, address = {Philadelphia, PA}, @@ -189,7 +194,7 @@ @inproceedings{He2013 url = {http://epubs.siam.org/doi/abs/10.1137/1.9781611973440.15}, year = {2013} } -@article{Ji2011, +@misc{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}, @@ -212,6 +217,7 @@ @inproceedings{Kong2013 keywords = {adhd200 preprc}, mendeley-tags = {adhd200 preprc}, month = jan, +pages = {82--93}, title = {{Discriminative Feature Selection for Uncertain Graph Classification}}, url = {http://arxiv.org/abs/1301.6626}, year = {2013} @@ -228,7 +234,7 @@ @article{Lavoie-Courchesne2012b volume = {341}, year = {2012} } -@article{Li2013, +@misc{Li2013, archivePrefix = {arXiv}, arxivId = {1304.5637}, author = {Li, Xiaoshan and Zhou, Hua and Li, Lexin}, @@ -253,18 +259,17 @@ @inproceedings{Liang2012 url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6409719}, year = {2012} } -@article{Liu2012, -archivePrefix = {arXiv}, -arxivId = {1203.3896}, +@article{Liu2015, author = {Liu, Weidong and Luo, Xi}, -eprint = {1203.3896}, -keywords = {adhd200 preprc}, +journal = {Journal of multivariate analysis}, +keywords = {adhd200 preprc, adaptivity, coordinate descent, cross validation, Gaussian graphical models, Lasso, covergence rates}, 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} +pages = {153--162}, +title = {{Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions}}, +url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4347526/}, +volume = {135}, +year = {2015} } @incollection{Mahanand2013, address = {Cham}, @@ -295,7 +300,7 @@ @article{Olivetti2012 year = {2012} } @article{Sato2012, -author = {Sato, Jo\~{a}o Ricardo and Hoexter, Marcelo Queiroz and Castellanos, Xavier Francisco and Rohde, Luis A}, +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 = {::}, @@ -314,7 +319,7 @@ @article{Sato2012 year = {2012} } @article{Sato2012a, -author = {Sato, Jo\~{a}o Ricardo and Hoexter, Marcelo Queiroz and Fujita, Andr\'{e} and Rohde, Luis Augusto}, +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}, @@ -330,7 +335,7 @@ @article{Sato2012a 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}}, +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}, @@ -342,11 +347,13 @@ @article{Sato2013 volume = {77}, year = {2013} } -@article{She2014, +@misc{She2014, +archivePrefix = {arXiv}, +arxivId = {1410.1174}, 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}, +url = {https://arxiv.org/abs/1410.1174}, year = {2014} } @inproceedings{Solmaz2012, @@ -369,11 +376,12 @@ @inproceedings{Tabas2014 pages = {1--4}, publisher = {IEEE}, title = {{Spatial discriminant ICA for RS-fMRI characterisation}}, +journal = {International Workshop on Pattern Recognition in Neuroimaging}, 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}}, +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}, @@ -381,7 +389,7 @@ @article{Takahashi2012 month = jan, number = {12}, pages = {e49949}, -title = {{Discriminating different classes of biological networks by analyzing the graphs spectra distribution.}}, +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} @@ -393,7 +401,7 @@ @phdthesis{Wang2013 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}, +url = {https://etd.auburn.edu/handle/10415/3623}, year = {2013} } @article{Wang2013a, @@ -409,7 +417,7 @@ @article{Wang2013a volume = {82}, year = {2013} } -@article{Yang2012, +@misc{Yang2012, archivePrefix = {arXiv}, arxivId = {1209.2139}, author = {Yang, Sen and Lu, Zhaosong and Shen, Xiaotong and Wonka, Peter and Ye, Jieping}, @@ -455,13 +463,17 @@ @phdthesis{Dey2014 } @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}}, +volume = {9}, +number = {2}, url = {http://works.bepress.com/phil\_reiss/29}, +doi = {10.1214/15-AOAS829}, +pages = {1076--1101}, +keywords = {ADHD-200, elastic net, functional confounding, functional magnetic resonance imaging, functional regression, sparse principal component regression, sparse partial least squares}, year = {2014} } -@article{Rangarajan2014, +@misc{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)}, @@ -470,26 +482,29 @@ @article{Rangarajan2014 year = {2014} } @phdthesis{Vidal2014, -address = {S\~{a}o Paulo}, +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}, +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}}, +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, +@article{Olivetti2015, 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}, +doi = { 10.1007/s40708-014-0007-6}, issn = {2198-4018}, journal = {Brain Informatics}, +volume = {2}, +number = {1}, title = {{Statistical independence for the evaluation of classifier-based diagnosis}}, -url = {http://link.springer.com/10.1007/s40708-014-0007-6}, -year = {2014} +url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883157/}, +pages = {13--19}, +month = mar; +year = {2015} } @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}, +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}, @@ -501,29 +516,31 @@ @article{DosSantosSiqueira2014 year = {2014} } -@article{Chen2015, +@article{Chen2016, 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}, +volume = {111}, +number = {515}, keywords = {Covariance model,Dynamic covariance,Functional connectivity,High Dimensionality,Marginal independence,Rate of convergence,Sparsity,Uniform consistency}, language = {en}, month = aug, -pages = {1--55}, +pages = {1196--1207}, 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} +url = {http://dx.doi.org/10.1080/01621459.2015.1077712}, +year = {2016} } @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}, +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}, +pages = {2544--2557}, 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}, @@ -542,9 +559,9 @@ @article{Kyeong2015 journal = {PloS one}, month = jan, number = {9}, -pages = {e0137296}, +pages = {1--15}, 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.}}, +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} @@ -552,10 +569,10 @@ @article{Kyeong2015 @article{Rangarajan2015, author = {Rangarajan, B. and Subramaian, K. and Suresh, S.}, doi = {10.1109/CCIP.2015.7100722}, +month = {mar}, 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}, @@ -583,20 +600,23 @@ @inproceedings{Hou2015 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}}, +title = {{Hierarchical tucker tensor regression: Application to brain imaging data analysis}}, +pages = {1344--1348}, +doi= {10.1109/ICIP.2015.7351019}, +month = {sept}, 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}, +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}, +number = {7}, +pages = {2544--2557}, title = {{Sensation-to-cognition cortical streams in attention-deficit/hyperactivity disorder}}, url = {http://doi.wiley.com/10.1002/hbm.22790}, -volume = {00}, +volume = {36}, year = {2015} } @article{Ahn2015, @@ -605,8 +625,10 @@ @article{Ahn2015 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}, +volume = {25}, +number = {1}, title = {{A Sparse Reduced Rank Framework for Group Analysis of Functional Neuroimaging Data}}, +pages = {295--312}, url = {http://www3.stat.sinica.edu.tw/statistica/J25N1/J25N117/J25N117.html}, year = {2015} } @@ -624,7 +646,7 @@ @article{Deshpande2015 volume = {45}, year = {2015} } -@article{Li2015, +@misc{Li2015, archivePrefix = {arXiv}, arxivId = {1501.07815}, author = {Li, Lexin and Zhang, Xin}, @@ -632,7 +654,6 @@ @article{Li2015 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} } @@ -650,7 +671,7 @@ @incollection{Han2015 } @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}, +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}, @@ -662,3 +683,48 @@ @incollection{Nunez-Garcia2015 url = {http://link.springer.com/10.1007/978-3-319-24888-2\_8}, year = {2015} } +@article{nachamai2016sub, + title={Sub-Type Discernment of Attention Deficit Hyperactive Disorder in Children using a Cluster Partitioning Algorithm}, + author={Nachamai, M}, + journal={Indian Journal of Science and Technology}, + volume={9}, + number={8}, + pages = {}, + year={2016} +} + +@article{Brown20161238, +title = "Connected brains and minds—The \{UMCD\} repository for brain connectivity matrices ", +journal = "NeuroImage ", +volume = "124, Part B", +number = "", +pages = "1238--1241", +year = "2016", +note = "Sharing the wealth: Brain Imaging Repositories in 2015 ", +issn = "1053-8119", +doi = "http://dx.doi.org/10.1016/j.neuroimage.2015.08.043", +url = "http://www.sciencedirect.com/science/article/pii/S1053811915007624", +author = "Jesse A. Brown and John D. Van Horn", +abstract = "Abstract We describe the \{USC\} Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an interactive web-based platform for brain connectivity matrix sharing and analysis. The site enables users to download connectivity matrices shared by other users, upload matrices from their own published studies, or select a specific matrix and perform a real-time graph theory-based analysis and visualization of network properties. The data shared on the site span a broad spectrum of functional and structural brain connectivity information from humans across the entire age range (fetal to age 89), representing an array of different neuropsychiatric and neurodegenerative disease populations (autism spectrum disorder, ADHD, and APOE-4 carriers). An analysis combining 7 different datasets shared on the site illustrates the diversity of the data and the potential for yielding deeper insight by assessing new connectivity matrices with respect to population-wide network properties represented in the UMCD. " +} + +@article{Yu2016, +title = "Partial functional linear quantile regression for neuroimaging data analysis ", +journal = "Neurocomputing ", +volume = "195", +number = "", +pages = "74--87", +year = "2016", +note = "", +issn = "0925-2312", +doi = "http://dx.doi.org/10.1016/j.neucom.2015.08.116", +url = "http://www.sciencedirect.com/science/article/pii/S0925231216001181", +author = "Dengdeng Yu and Linglong Kong and Ivan Mizera", +keywords = "Functional linear quantile regression", +keywords = "Partial quantile covariance", +keywords = "PQR basis", +keywords = "SIMPQR", +keywords = "ADHD", +keywords = "ADNI ", +abstract = "Abstract We propose a prediction procedure for the functional linear quantile regression model by using partial quantile covariance techniques and develop a simple partial quantile regression (SIMPQR) algorithm to efficiently extract partial quantile regression (PQR) basis for estimating functional coefficients. We further extend our partial quantile covariance techniques to functional composite quantile regression (CQR) defining partial composite quantile covariance. There are three major contributions. (1) We define partial quantile covariance between two scalar variables through linear quantile regression. We compute \{PQR\} basis by sequentially maximizing the partial quantile covariance between the response and projections of functional covariates. (2) In order to efficiently extract \{PQR\} basis, we develop a \{SIMPQR\} algorithm analog to simple partial least squares (SIMPLS). (3) Under the homoscedasticity assumption, we extend our techniques to partial composite quantile covariance and use it to find the partial composite quantile regression (PCQR) basis. The \{SIMPQR\} algorithm is then modified to obtain the \{SIMPCQR\} algorithm. Two simulation studies show the superiority of our proposed methods. Two real data from ADHD-200 sample and \{ADNI\} are analyzed using our proposed methods. " +}