MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain
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Hai Li | Chuyang Ye | Zhengyi Yang | Lingzhong Fan | Bin Gao | Tianzi Jiang | Bin He | Bo You | T. Jiang | Zhengyi Yang | Chuyang Ye | L. Fan | Hai Li | Bin He | Bin Gao | Bo You
[1] Daniel S. Margulies,et al. An Open Resource for Non-human Primate Imaging , 2018, Neuron.
[2] Alfred Anwander,et al. Segregating the core computational faculty of human language from working memory , 2009, Proceedings of the National Academy of Sciences.
[3] Bilwaj Gaonkar,et al. Multi-atlas skull-stripping. , 2013, Academic radiology.
[4] Angela R. Laird,et al. Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation , 2011, NeuroImage.
[5] G. Varoquaux,et al. Connectivity‐based parcellation: Critique and implications , 2015, Human brain mapping.
[6] Angela R. Laird,et al. Cytoarchitecture, probability maps and functions of the human frontal pole , 2014, NeuroImage.
[7] L. Ferré. Selection of components in principal component analysis: a comparison of methods , 1995 .
[8] Timothy Edward John Behrens,et al. Diffusion-Weighted Imaging Tractography-Based Parcellation of the Human Parietal Cortex and Comparison with Human and Macaque Resting-State Functional Connectivity , 2011, The Journal of Neuroscience.
[9] A. Toga,et al. Mapping brain maturation , 2006, Trends in Neurosciences.
[10] H. Cramér. Mathematical Methods of Statistics (PMS-9), Volume 9 , 1946 .
[11] Tianzi Jiang,et al. Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation , 2013, Neuroscience Bulletin.
[12] Timothy Edward John Behrens,et al. Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[13] Mert R. Sabuncu,et al. Multi-atlas segmentation of biomedical images: A survey , 2014, Medical Image Anal..
[14] Jiaojian Wang,et al. Parcellation of Macaque Cortex with Anatomical Connectivity Profiles , 2018, Brain Topography.
[15] Yu Zhang,et al. ATPP: A Pipeline for Automatic Tractography-Based Brain Parcellation , 2017, Front. Neuroinform..
[16] Guido Gerig,et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.
[17] David R. Haynor,et al. Anatomically Informed Metrics for Connectivity-Based Cortical Parcellation From Diffusion MRI , 2015, IEEE Journal of Biomedical and Health Informatics.
[18] Danny J. J. Wang,et al. Imbalance of Functional Connectivity and Temporal Entropy in Resting-State Networks in Autism Spectrum Disorder: A Machine Learning Approach , 2018, Front. Neurosci..
[19] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[20] R. Mars,et al. Dichotomous organization of amygdala/temporal-prefrontal bundles in both humans and monkeys , 2019, eLife.
[21] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[22] Alfie Gleeson,et al. Mapping a brain. , 2018, BioTechniques.
[23] D. Leopold,et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited , 2014, Proceedings of the National Academy of Sciences.
[24] William Rea,et al. How Many Components should be Retained from a Multivariate Time Series PCA , 2016 .
[25] Sébastien Ourselin,et al. Brain MAPS: An automated, accurate and robust brain extraction technique using a template library , 2011, NeuroImage.
[26] Marina Meila,et al. Comparing Clusterings by the Variation of Information , 2003, COLT.
[27] Timothy Edward John Behrens,et al. Integrity of white matter in the corpus callosum correlates with bimanual co-ordination skills , 2007, NeuroImage.
[28] K. Amunts,et al. Architectonic Mapping of the Human Brain beyond Brodmann , 2015, Neuron.
[29] Martin Styner,et al. Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline , 2014, Front. Neuroinform..
[30] Brian B. Avants,et al. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..
[31] K. Brodmann. Vergleichende Lokalisationslehre der Großhirnrinde : in ihren Prinzipien dargestellt auf Grund des Zellenbaues , 1985 .
[32] Joseph A. Maldjian,et al. Multi-Atlas Library for Eliminating Normalization Failures in Non-Human Primates , 2015, Neuroinformatics.
[33] Matthew J. McAuliffe,et al. Medical Image Processing, Analysis and Visualization in clinical research , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.
[34] Jesper Andersson,et al. A multi-modal parcellation of human cerebral cortex , 2016, Nature.
[35] Yu Zhang,et al. Tractography‐based parcellation of the human left inferior parietal lobule , 2012, NeuroImage.
[36] Stuart Crozier,et al. Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images , 2015, Physics in medicine and biology.
[37] Arno Klein,et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.
[38] Xiaoying Tang,et al. Simultaneous skull-stripping and lateral ventricle segmentation via fast multi-atlas likelihood fusion , 2015, Medical Imaging.
[39] Timothy Edward John Behrens,et al. Between session reproducibility and between subject variability of diffusion MR and tractography measures , 2006, NeuroImage.
[40] David C. Van Essen,et al. Application of Information Technology: An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex , 2001, J. Am. Medical Informatics Assoc..
[41] Klaas E. Stephan,et al. The anatomical basis of functional localization in the cortex , 2002, Nature Reviews Neuroscience.
[42] Juha Koikkalainen,et al. Fast and robust multi-atlas segmentation of brain magnetic resonance images , 2010, NeuroImage.
[43] G. Pasini. PRINCIPAL COMPONENT ANALYSIS FOR STOCK PORTFOLIO MANAGEMENT , 2017 .
[44] James Dean Brown,et al. Statistics Corner Questions and answers about language testing statistics: Choosing the Right Number of Components or Factors in PCA and EFA Choosing the Right Number of Components or Factors in PCA and EFA , 2009 .
[45] Carlos Ortiz-de-Solorzano,et al. Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data , 2009, IEEE Transactions on Medical Imaging.
[46] R. Cattell. The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.
[47] Maximilian Reiser,et al. Multivariate network analysis of fiber tract integrity in Alzheimer’s disease , 2007, NeuroImage.
[48] K. Amunts,et al. The human inferior parietal lobule in stereotaxic space , 2008, Brain Structure and Function.
[49] Mark W. Woolrich,et al. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.
[50] G. Perretta. Non-Human Primate Models in Neuroscience Research , 2009 .
[51] Daniel Rueckert,et al. Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy , 2009, NeuroImage.
[52] H QUASTLER,et al. GENETIC EFFECTS. , 1964, New York state journal of medicine.
[53] F. Gage,et al. Age-associated neuronal atrophy occurs in the primate brain and is reversible by growth factor gene therapy. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[54] S. Wold. Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .
[55] Arno Klein,et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.
[56] D. Louis Collins,et al. BEaST: Brain extraction based on nonlocal segmentation technique , 2012, NeuroImage.
[57] Simon B. Eickhoff,et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.
[58] Simon B. Eickhoff,et al. Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps , 2006, NeuroImage.
[59] Thomas E. Nichols,et al. Best practices in data analysis and sharing in neuroimaging using MRI , 2017, Nature Neuroscience.
[60] Luke J. Chang,et al. Connectivity-Based Parcellation of the Human Orbitofrontal Cortex , 2012, The Journal of Neuroscience.
[61] Alexander Leemans,et al. Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics , 2018, Brain Structure and Function.
[62] Helen D'Arceuil,et al. Connectivity-based parcellation of the macaque frontal cortex, and its relation with the cytoarchitectonic distribution described in current atlases , 2017, Brain Structure and Function.
[63] W. Freiwald,et al. Functionally defined white matter of the macaque monkey brain reveals a dorso-ventral attention network , 2019, eLife.
[64] Paul A. Yushkevich,et al. Multi-Atlas Segmentation with Joint Label Fusion , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[65] Romain Valabregue,et al. Subdivision of the occipital lobes: An anatomical and functional MRI connectivity study , 2014, Cortex.
[66] Dinggang Shen,et al. Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates , 2014, PloS one.
[67] Dimitri Van De Ville,et al. Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest , 2013, NeuroImage.
[68] Timothy Edward John Behrens,et al. Diffusion-Weighted Imaging Tractography-Based Parcellation of the Human Lateral Premotor Cortex Identifies Dorsal and Ventral Subregions with Anatomical and Functional Specializations , 2007, The Journal of Neuroscience.
[69] D. Pandya,et al. Comparative cytoarchitectonic analysis of the human and the macaque ventrolateral prefrontal cortex and corticocortical connection patterns in the monkey , 2002, The European journal of neuroscience.
[70] Meritxell Bach Cuadra,et al. Automatic brain extraction in fetal MRI using multi-atlas-based segmentation , 2015, Medical Imaging.
[71] H. Kaiser. The Application of Electronic Computers to Factor Analysis , 1960 .
[72] P. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .
[73] Bin Gao,et al. Automated brain extraction and associated 3D inspection layers for the Rhesus macaque MRI datasets , 2016, VRCAI.
[74] Tianzi Jiang,et al. Genetic Effects on Fine-Grained Human Cortical Regionalization. , 2016, Cerebral cortex.
[75] Sang Won Seo,et al. Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: Functional connectivity-based parcellation method , 2010, NeuroImage.
[76] Anil K. Jain. Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..
[77] J. Munkres. ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .
[78] Tianzi Jiang,et al. Automated brain extraction and immersive exploration of its layers in virtual reality for the rhesus macaque MRI data sets , 2019, Comput. Animat. Virtual Worlds.
[79] D. V. van Essen,et al. Windows on the brain: the emerging role of atlases and databases in neuroscience , 2002, Current Opinion in Neurobiology.
[80] D. Amaral,et al. The amygdala and autism: implications from non‐human primate studies , 2003, Genes, brain, and behavior.
[81] D. Astle,et al. Mapping differential responses to cognitive training using machine learning , 2019, Developmental science.
[82] Timothy Edward John Behrens,et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging , 2003, Nature Neuroscience.
[83] Ahmed Serag,et al. Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods , 2016, Scientific Reports.
[84] G. Gerhardt,et al. Motor slowing and parkinsonian signs in aging rhesus monkeys mirror human aging. , 2000, The journals of gerontology. Series A, Biological sciences and medical sciences.
[85] R Cameron Craddock,et al. A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.
[86] Clinton Fookes,et al. A Comparative Analysis of Registration Tools: Traditional vs Deep Learning Approach on High Resolution Tissue Cleared Data , 2018, ArXiv.
[87] Karl Herholz,et al. Robustness of correlations between PCA of FDG-PET scans and biological variables in healthy and demented subjects , 2009, NeuroImage.
[88] Michael Wolf,et al. Spectrum Estimation: A Unified Framework for Covariance Matrix Estimation and PCA in Large Dimensions , 2013, J. Multivar. Anal..
[89] P. Kalavathi,et al. Methods on Skull Stripping of MRI Head Scan Images—a Review , 2016, Journal of Digital Imaging.
[90] Yu Zhang,et al. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture , 2016, Cerebral cortex.
[91] Stephen M Smith,et al. Fast robust automated brain extraction , 2002, Human brain mapping.
[92] Nicolas Costes,et al. A multi-atlas based method for automated anatomical Macaca fascicularis brain MRI segmentation and PET kinetic extraction , 2013, NeuroImage.
[93] A. Dale,et al. Hierarchical Genetic Organization of Human Cortical Surface Area , 2012, Science.
[94] David N. Kennedy,et al. The Resource Identification Initiative: A cultural shift in publishing , 2015, Neuroinformatics.