Data-driven methods for characterizing individual differences in brain MRI
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[1] Carl-Fredrik Westin,et al. Fiber clustering versus the parcellation-based connectome , 2013, NeuroImage.
[2] K. Ashkan,et al. Subcortical Structure Volumes and Correlation to Clinical Variables in Parkinson's Disease , 2015, Journal of neuroimaging : official journal of the American Society of Neuroimaging.
[3] Emily S. Finn,et al. Individual variation in functional brain connectivity: implications for personalized approaches to psychiatric disease , 2016, Dialogues in clinical neuroscience.
[4] Feng-Tao Liu,et al. Toward precision medicine in Parkinson's disease. , 2016, Annals of translational medicine.
[5] K. Amunts,et al. Brodmann's Areas 17 and 18 Brought into Stereotaxic Space—Where and How Variable? , 2000, NeuroImage.
[6] Polina Golland,et al. BrainPrint: A discriminative characterization of brain morphology , 2015, NeuroImage.
[7] Kuldeep Kumar,et al. White Matter Fiber Segmentation Using Functional Varifolds , 2017, GRAIL/MFCA/MICGen@MICCAI.
[8] Thomas E. Nichols,et al. Functional connectomics from resting-state fMRI , 2013, Trends in Cognitive Sciences.
[9] Jean-Philippe Thiran,et al. Sparse regularization for fiber ODF reconstruction: from the suboptimality of $\ell_2$ and $\ell_1$ priors to $\ell_0$ , 2012, 1208.2247.
[10] Jean-Francois Mangin,et al. MR Diffusion-Based Inference of a Fiber Bundle Model from a Population of Subjects , 2005, MICCAI.
[11] Paul M. Thompson,et al. Segmentation of High Angular Resolution Diffusion MRI Using Sparse Riemannian Manifold Clustering , 2014, IEEE Transactions on Medical Imaging.
[12] William M. Wells,et al. A Feature-Based Developmental Model of the Infant Brain in Structural MRI , 2012, MICCAI.
[13] Kjersti Engan,et al. Method of optimal directions for frame design , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[14] Dustin Scheinost,et al. Can brain state be manipulated to emphasize individual differences in functional connectivity? , 2017, NeuroImage.
[15] J. Giedd,et al. Review of Twin and Family Studies on Neuroanatomic Phenotypes and Typical Neurodevelopment , 2007, Twin Research and Human Genetics.
[16] Mechthild Krause,et al. Radiation oncology in the era of precision medicine , 2016, Nature Reviews Cancer.
[17] Vince D. Calhoun,et al. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls , 2017, NeuroImage.
[18] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Abraham Z. Snyder,et al. Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.
[20] Carl-Fredrik Westin,et al. White Matter Tract Clustering and Correspondence in Populations , 2005, MICCAI.
[21] Feiping Nie,et al. Nonnegative Matrix Tri-factorization Based High-Order Co-clustering and Its Fast Implementation , 2011, 2011 IEEE 11th International Conference on Data Mining.
[22] Thomas E. Nichols,et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.
[23] G. Bruce Pike,et al. Quantitative functional MRI: Concepts, issues and future challenges , 2012, NeuroImage.
[24] 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.
[25] P. Thompson,et al. Diffusion imaging, white matter, and psychopathology. , 2011, Annual review of clinical psychology.
[26] Anna Vilanova,et al. Evaluation of fiber clustering methods for diffusion tensor imaging , 2005, VIS 05. IEEE Visualization, 2005..
[27] P. Basser,et al. Toward a quantitative assessment of diffusion anisotropy , 1996, Magnetic resonance in medicine.
[28] Tamer S. Ibrahim,et al. MR Imaging: Brief Overview and Emerging Applications1 , 2008 .
[29] Kuldeep Kumar,et al. White matter fiber analysis using kernel dictionary learning and sparsity priors , 2018, Pattern Recognit..
[30] Peter Jackson,et al. The Physics of Magnetic Resonance Imaging , 1988, Bristol medico-chirurgical journal.
[31] D. Halpern. Sex Differences in Cognitive Abilities , 1986 .
[32] Cordelia Schmid,et al. A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Gagan S. Wig,et al. Resting-State Network Topology Differentiates Task Signals across the Adult Life Span , 2017, The Journal of Neuroscience.
[34] Andrzej Cichocki,et al. Tensor Decompositions: A New Concept in Brain Data Analysis? , 2013, ArXiv.
[35] Maxime Descoteaux,et al. Robust clustering of massive tractography datasets , 2011, NeuroImage.
[36] M. Descoteaux. High angular resolution diffusion MRI : from local estimation to segmentation and tractography , 2008 .
[37] Alain Trouvé,et al. Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents , 2011, NeuroImage.
[38] Pietro Gori,et al. A Bayesian framework for joint morphometry of surface and curve meshes in multi‐object complexes , 2017, Medical Image Anal..
[39] Mark W. Woolrich,et al. Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.
[40] W. Eric L. Grimson,et al. Statistical modeling and EM clustering of white matter fiber tracts , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..
[41] Maya R. Gupta,et al. Learning kernels from indefinite similarities , 2009, ICML '09.
[42] Rama Chellappa,et al. Kernel dictionary learning , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[43] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[44] Pietro Gori,et al. Comparison of distances for supervised segmentation of white matter tractography , 2017, 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI).
[45] C. Sherwood,et al. Increased morphological asymmetry, evolvability and plasticity in human brain evolution , 2013, Proceedings of the Royal Society B: Biological Sciences.
[46] Carl-Fredrik Westin,et al. High-Dimensional White Matter Atlas Generation and Group Analysis , 2006, MICCAI.
[47] Hiroyuki Kudo,et al. Image reconstruction for sparse-view CT and interior CT-introduction to compressed sensing and differentiated backprojection. , 2013, Quantitative imaging in medicine and surgery.
[48] A. Alexander,et al. Diffusion tensor imaging of the brain , 2007, Neurotherapeutics.
[49] Mark Jenkinson,et al. The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.
[50] Douglas B. Kell,et al. Computational cluster validation in post-genomic data analysis , 2005, Bioinform..
[51] W. Eric L. Grimson,et al. Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI , 2005, MICCAI.
[52] Gaël Varoquaux,et al. A Comparison of Metrics and Algorithms for Fiber Clustering , 2013, 2013 International Workshop on Pattern Recognition in Neuroimaging.
[53] Kuldeep Kumar,et al. Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI , 2017, ArXiv.
[54] C. Jack,et al. Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging☆ , 2013, NeuroImage: Clinical.
[55] M D'Esposito,et al. The roles of prefrontal brain regions in components of working memory: effects of memory load and individual differences. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[56] Roland G. Henry,et al. Probabilistic streamline q-ball tractography using the residual bootstrap , 2008, NeuroImage.
[57] Yong He,et al. Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns , 2018, Human brain mapping.
[58] Daniel Rueckert,et al. Manifold Learning for Medical Image Registration, Segmentation, and Classification , 2012 .
[59] Rachid Deriche,et al. Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation , 2008, Int. J. Biomed. Imaging.
[60] Evan M. Gordon,et al. Functional System and Areal Organization of a Highly Sampled Individual Human Brain , 2015, Neuron.
[61] Christos Davatzikos,et al. HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework , 2017, NeuroImage.
[62] Kâmil Uğurbil,et al. The road to functional imaging and ultrahigh fields , 2012, NeuroImage.
[63] C. Westin,et al. A method for clustering white matter fiber tracts. , 2006, AJNR. American journal of neuroradiology.
[64] Maxime Descoteaux,et al. Robust and efficient linear registration of white-matter fascicles in the space of streamlines , 2015, NeuroImage.
[65] Alessandro Daducci,et al. Microstructure Informed Tractography: Pitfalls and Open Challenges , 2016, Front. Neurosci..
[66] E. Purcell,et al. Resonance Absorption by Nuclear Magnetic Moments in a Solid , 1946 .
[67] Paul M. Thompson,et al. Along-tract statistics allow for enhanced tractography analysis , 2012, NeuroImage.
[68] Jesper Andersson,et al. A multi-modal parcellation of human cerebral cortex , 2016, Nature.
[69] Graeme D. Jackson,et al. Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding , 2015, NeuroImage: Clinical.
[70] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[71] R. E. Gur,et al. Mental disorders of known aetiology and precision medicine in psychiatry: a promising but neglected alliance , 2016, Psychological Medicine.
[72] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[73] Carl-Fredrik Westin,et al. Unbiased Groupwise Registration of White Matter Tractography , 2012, MICCAI.
[74] R. Deriche,et al. Regularized, fast, and robust analytical Q‐ball imaging , 2007, Magnetic resonance in medicine.
[75] Paul M. Thompson,et al. Population learning of structural connectivity by white matter encoding and decoding , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).
[76] Suvrit Sra,et al. Approximation Algorithms for Tensor Clustering , 2009, ALT.
[77] John H. Gilmore,et al. Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis , 2005, MICCAI.
[78] R. Tibshirani,et al. A note on the group lasso and a sparse group lasso , 2010, 1001.0736.
[79] Maxime Descoteaux,et al. Tractometer: Towards validation of tractography pipelines , 2013, Medical Image Anal..
[80] Mariano Rivera,et al. Sparse and Adaptive Diffusion Dictionary (SADD) for recovering intra-voxel white matter structure , 2015, Medical Image Anal..
[81] Carl-Fredrik Westin,et al. Clustering Fiber Traces Using Normalized Cuts , 2004, MICCAI.
[82] Kaleem Siddiqi,et al. Recent advances in diffusion MRI modeling: Angular and radial reconstruction , 2011, Medical Image Anal..
[83] Jean-Francois Mangin,et al. Fiber Tracking in q-Ball Fields Using Regularized Particle Trajectories , 2005, IPMI.
[84] P. V. van Zijl,et al. Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.
[85] Michael Rabadi,et al. Kernel Methods for Machine Learning , 2015 .
[86] In-So Kweon,et al. Object recognition using a generalized robust invariant feature and Gestalt's law of proximity and similarity , 2008, Pattern Recognit..
[87] A. Anderson,et al. Classification and quantification of neuronal fiber pathways using diffusion tensor MRI , 2003, Magnetic resonance in medicine.
[88] Ryota Kanai,et al. What contributes to individual differences in brain structure? , 2014, Front. Hum. Neurosci..
[89] R. Cameron Craddock,et al. Individual differences in functional connectivity during naturalistic viewing conditions , 2016, NeuroImage.
[90] G. Rees,et al. The structural basis of inter-individual differences in human behaviour and cognition , 2011, Nature Reviews Neuroscience.
[91] Zhanpeng Jin,et al. Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics , 2015, Neurocomputing.
[92] Peter Savadjiev,et al. Whole brain white matter connectivity analysis using machine learning: An application to autism , 2017, NeuroImage.
[93] Guillermo Sapiro,et al. Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.
[94] J. Laidlaw,et al. ANATOMY OF THE HUMAN BODY , 1967, The Ulster Medical Journal.
[95] Paul M. Thompson,et al. Discovery of genes that affect human brain connectivity: A genome-wide analysis of the connectome , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).
[96] Peter F. Neher,et al. The challenge of mapping the human connectome based on diffusion tractography , 2017, Nature Communications.
[97] Christian F. Beckmann,et al. Modelling with independent components , 2012, NeuroImage.
[98] Steven A. Chance,et al. Distinctively human: cerebral lateralisation and language in Homo sapiens , 2007 .
[99] B. Wandell,et al. Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification , 2012, PloS one.
[100] Jonathan D. Power,et al. Prediction of Individual Brain Maturity Using fMRI , 2010, Science.
[101] Michael Elad,et al. On the Role of Sparse and Redundant Representations in Image Processing , 2010, Proceedings of the IEEE.
[102] Guillermo Sapiro,et al. Online dictionary learning for sparse coding , 2009, ICML '09.
[103] Kuldeep Kumar,et al. Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[104] Steen Moeller,et al. Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data , 2015, NeuroImage.
[105] Jean Gotman,et al. SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity , 2016, NeuroImage.
[106] Alan C. F. Colchester,et al. Resolving complex fibre configurations using two-tensor random-walk stochastic algorithms , 2011, Medical Imaging.
[107] C. Jack,et al. Alzheimer's Disease Neuroimaging Initiative , 2008 .
[108] William M. Wells,et al. Efficient and robust model-to-image alignment using 3D scale-invariant features , 2013, Medical Image Anal..
[109] Martha Elizabeth Shenton,et al. Neural Tractography Using an Unscented Kalman Filter , 2009, IPMI.
[110] Thomas T. Liu,et al. The development of event-related fMRI designs , 2012, NeuroImage.
[111] Paul M. Thompson,et al. Fractional anisotropy of cerebral white matter and thickness of cortical gray matter across the lifespan , 2011, NeuroImage.
[112] Jiayu Zhou,et al. Multi-Modality Disease Modeling via Collective Deep Matrix Factorization , 2017, KDD.
[113] Allen Y. Yang,et al. Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[114] Paul M. Thompson,et al. Genetic analysis of structural brain connectivity using DICCCOL models of diffusion MRI in 522 twins , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).
[115] John D. E. Gabrieli,et al. Knowledge-Based Classification of Neuronal Fibers in Entire Brain , 2005, MICCAI.
[116] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[117] M. Descoteaux. High Angular Resolution Diffusion Imaging (HARDI) , 2015 .
[118] Thomas E. Nichols,et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data , 2014, Brain Imaging and Behavior.
[119] Stephen M Smith,et al. The relationship between spatial configuration and functional connectivity of brain regions , 2017, bioRxiv.
[120] Fang-Cheng Yeh,et al. Connectometry: A statistical approach harnessing the analytical potential of the local connectome , 2016, NeuroImage.
[121] Mark W. Woolrich,et al. FSL , 2012, NeuroImage.
[122] Klaus-Robert Müller,et al. Feature Discovery in Non-Metric Pairwise Data , 2004, J. Mach. Learn. Res..
[123] Timothy D. Verstynen,et al. Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy , 2013, PloS one.
[124] John C Gore,et al. Assessing functional connectivity in the human brain by fMRI. , 2007, Magnetic resonance imaging.
[125] G. Ascoli,et al. Computational Neuroanatomy , 2002, Humana Press.
[126] Andrew R. McKinstry-Wu,et al. Connectome: How the Brain’s Wiring Makes Us Who We Are , 2013 .
[127] M. Chun,et al. Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.
[128] Jean-Philippe Thiran,et al. Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI , 2015, NeuroImage.
[129] Evan M. Gordon,et al. Long-term neural and physiological phenotyping of a single human , 2015, Nature Communications.
[130] Christophe Lenglet,et al. Automatic clustering and population analysis of white matter tracts using maximum density paths , 2014, NeuroImage.
[131] K. Whittingstall,et al. Tractography in the Study of the Human Brain: A Neurosurgical Perspective , 2012, Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques.
[132] A. Bruckstein,et al. K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .
[133] P. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .
[134] W. Eric L. Grimson,et al. Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts , 2007, IPMI.
[135] E. Duchesnay,et al. A framework to study the cortical folding patterns , 2004, NeuroImage.
[136] Daniel Rueckert,et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.
[137] Evan M. Gordon,et al. Individual-specific features of brain systems identified with resting state functional correlations , 2017, NeuroImage.
[138] Stephen T. C. Wong,et al. A hybrid approach to automatic clustering of white matter fibers , 2010, NeuroImage.
[139] Paul M. Thompson,et al. Genetics of the connectome , 2013, NeuroImage.
[140] Ellen M. Voorhees,et al. Evaluating evaluation measure stability , 2000, SIGIR '00.
[141] Richard H. Bartels,et al. Algorithm 432 [C2]: Solution of the matrix equation AX + XB = C [F4] , 1972, Commun. ACM.
[142] Paul M. Thompson,et al. Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29 , 2011, NeuroImage.
[143] P. Basser,et al. MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.
[144] Ning Yang,et al. Fusing DTI and fMRI data: A survey of methods and applications , 2014, NeuroImage.
[145] V. Calhoun,et al. In Search of Multimodal Neuroimaging Biomarkers of Cognitive Deficits in Schizophrenia , 2015, Biological Psychiatry.
[146] P. Visscher,et al. Nature Genetics Advance Online Publication , 2022 .
[147] Russell A. Poldrack,et al. The future of fMRI in cognitive neuroscience , 2012, NeuroImage.
[148] Matthew Toews,et al. Multi-modal brain fingerprinting: A manifold approximation based framework , 2017, NeuroImage.
[149] M. Herbert,et al. Motor stereotypies and volumetric brain alterations in children with Autistic Disorder. , 2013, Research in autism spectrum disorders.
[150] Niklas Peinecke,et al. Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids , 2006, Comput. Aided Des..
[151] Woei-Chyn Chu,et al. Sex-linked white matter microstructure of the social and analytic brain , 2011, NeuroImage.
[152] Dustin Scheinost,et al. Using connectome-based predictive modeling to predict individual behavior from brain connectivity , 2017, Nature Protocols.
[153] D. Tuch. Q‐ball imaging , 2004, Magnetic resonance in medicine.
[154] Christa Neuper,et al. Individual differences in mathematical competence predict parietal brain activation during mental calculation , 2007, NeuroImage.
[155] Steen Moeller,et al. Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.
[156] Falk Scholer,et al. User performance versus precision measures for simple search tasks , 2006, SIGIR.
[157] Shimon Ullman,et al. Object recognition with informative features and linear classification , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[158] Jitendra Malik,et al. Spectral grouping using the Nystrom method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[159] Dimitri Van De Ville,et al. Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time , 2014, Human brain mapping.
[160] T. Yarkoni. Neurobiological substrates of personality: A critical overview. , 2015 .
[161] Michael C. Hout,et al. Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.
[162] Carl-Fredrik Westin,et al. Sparse deconvolution of higher order tensor for fiber orientation distribution estimation , 2015, Artif. Intell. Medicine.
[163] Daniel Rueckert,et al. Automated morphological analysis of magnetic resonance brain imaging using spectral analysis , 2008, NeuroImage.
[164] Michael I. Miller,et al. Multi-Modal MRI Analysis with Disease-Specific Spatial Filtering: Initial Testing to Predict Mild Cognitive Impairment Patients Who Convert to Alzheimer’s Disease , 2011, Front. Neur..
[165] M. Lustig,et al. Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.
[166] Jean-Philippe Thiran,et al. Structural connectomics in brain diseases , 2013, NeuroImage.
[167] J. Thiran,et al. Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.
[168] Martijn P. van den Heuvel,et al. The parcellation-based connectome: Limitations and extensions , 2013, NeuroImage.
[169] Michael Elad,et al. Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation , 2010, IEEE Transactions on Signal Processing.
[170] R. Kahn,et al. Genetic influences on human brain structure: A review of brain imaging studies in twins , 2007, Human brain mapping.
[171] Ghassan Hamarneh,et al. Multi-region competitive tractography via graph-based random walks , 2012, 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis.
[172] Christian Desrosiers,et al. Group sparse kernelized dictionary learning for the clustering of white matter fibers , 2014 .
[173] Rachid Deriche,et al. Towards quantitative connectivity analysis: reducing tractography biases , 2014, NeuroImage.
[174] W. Eric L. Grimson,et al. A unified framework for clustering and quantitative analysis of white matter fiber tracts , 2008, Medical Image Anal..
[175] T. Brown,et al. Individual differences in human brain development , 2016, Wiley interdisciplinary reviews. Cognitive science.
[176] D. Tank,et al. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.
[177] Alfred O. Hero,et al. Fiber Tract Clustering on Manifolds With Dual Rooted-Graphs , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[178] Alois Knoll,et al. The Human Brain Project: Creating a European Research Infrastructure to Decode the Human Brain , 2016, Neuron.
[179] M. Fox,et al. Individual Variability in Functional Connectivity Architecture of the Human Brain , 2013, Neuron.
[180] Hayit Greenspan,et al. Co-registration of White Matter Tractographies by Adaptive-Mean-Shift and Gaussian Mixture Modeling , 2010, IEEE Transactions on Medical Imaging.
[181] Jun Zhang,et al. Shape modeling and clustering of white matter fiber tracts using fourier descriptors , 2009, 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.
[182] Sven Haller,et al. Medical Image Retrieval Using Multi-graph Learning for MCI Diagnostic Assistance , 2015, MICCAI.
[183] Chong-Wah Ngo,et al. Towards optimal bag-of-features for object categorization and semantic video retrieval , 2007, CIVR '07.
[184] Chun-Hui Che,et al. Toward precision medicine in amyotrophic lateral sclerosis. , 2016, Annals of translational medicine.
[185] Marko Pfeifer. Human Brain Anatomy In Computerized Images , 2016 .
[186] David G. Lowe,et al. Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.
[187] Ian Davidson,et al. Network discovery via constrained tensor analysis of fMRI data , 2013, KDD.
[188] Essa Yacoub,et al. The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.
[189] Bharat B. Biswal,et al. Resting state fMRI: A personal history , 2012, NeuroImage.
[190] V. Wedeen,et al. Generalized -Sampling Imaging , 2010 .
[191] P. Elliott,et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.
[192] Matthew P. G. Allin,et al. Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography , 2011, NeuroImage.
[193] Stanley Durrleman,et al. Statistical models of currents for measuring the variability of anatomical curves, surfaces and their evolution. (Modèles statistiques de courants pour mesurer la variabilité anatomique de courbes, de surfaces et de leur évolution) , 2010 .
[194] Timothy E. J. Behrens,et al. Measuring macroscopic brain connections in vivo , 2015, Nature Neuroscience.
[195] Hans-Peter Seidel,et al. Estimating Crossing Fibers: A Tensor Decomposition Approach , 2008, IEEE Transactions on Visualization and Computer Graphics.
[196] P. Lauterbur,et al. Image Formation by Induced Local Interactions: Examples Employing Nuclear Magnetic Resonance , 1973, Nature.
[197] Robert P. W. Duin,et al. A Generalized Kernel Approach to Dissimilarity-based Classification , 2002, J. Mach. Learn. Res..
[198] Christos Davatzikos,et al. Regional Manifold Learning for Deformable Registration of Brain MR Images , 2012, MICCAI.
[199] Chong-Wah Ngo,et al. Evaluating bag-of-visual-words representations in scene classification , 2007, MIR '07.
[200] Peter A. Bandettini,et al. Twenty years of functional MRI: The science and the stories , 2012, NeuroImage.
[201] Steen Moeller,et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.
[202] Sidong Liu,et al. Multimodal neuroimaging computing: the workflows, methods, and platforms , 2015, Brain Informatics.
[203] J. Thiran,et al. Fiber tracts of high angular resolution diffusion MRI are easily segmented with spectral clustering. , 2005 .
[204] Carl-Fredrik Westin,et al. The white matter query language: a novel approach for describing human white matter anatomy , 2015, Brain Structure and Function.
[205] Kuldeep Kumar,et al. Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis , 2017, NeuroImage.
[206] Inderjit S. Dhillon,et al. Kernel k-means: spectral clustering and normalized cuts , 2004, KDD.
[207] N. Logothetis. What we can do and what we cannot do with fMRI , 2008, Nature.
[208] Tian Ge,et al. Multidimensional heritability analysis of neuroanatomical shape , 2016, Nature Communications.
[209] Barnabás Póczos,et al. Local Connectome Fingerprinting Reveals the Uniqueness of Individual White Matter Architecture , 2016 .
[210] J. Rademacher,et al. Variability and asymmetry in the human precentral motor system. A cytoarchitectonic and myeloarchitectonic brain mapping study. , 2001, Brain : a journal of neurology.
[211] Fang-Cheng Yeh,et al. NTU-90: A high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction , 2011, NeuroImage.
[212] Fang-Cheng Yeh,et al. Local connectome phenotypes predict social, health, and cognitive factors , 2017, bioRxiv.
[213] Rachid Deriche,et al. Unsupervised white matter fiber clustering and tract probability map generation: Applications of a Gaussian process framework for white matter fibers , 2010, NeuroImage.
[214] D. Glahn,et al. Fractional anisotropy of water diffusion in cerebral white matter across the lifespan , 2012, Neurobiology of Aging.
[215] Sidong Liu,et al. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders , 2015, Brain Informatics.
[216] Agatha D. Lee,et al. Genetics of Brain Fiber Architecture and Intellectual Performance , 2009, The Journal of Neuroscience.
[217] Carl-Fredrik Westin,et al. Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas , 2007, IEEE Transactions on Medical Imaging.
[218] Paul M. Thompson,et al. Fast Approximate Stochastic Tractography , 2011, Neuroinformatics.
[219] R. Buckner,et al. Parcellating Cortical Functional Networks in Individuals , 2015, Nature Neuroscience.
[220] Takashi Yamada,et al. Linked alterations in gray and white matter morphology in adults with high-functioning autism spectrum disorder: A multimodal brain imaging study , 2014, NeuroImage: Clinical.
[221] N. Geschwind,et al. Cerebral lateralization. Biological mechanisms, associations, and pathology: I. A hypothesis and a program for research. , 1985, Archives of neurology.
[222] Rachid Deriche,et al. A computational diffusion MRI and parametric dictionary learning framework for modeling the diffusion signal and its features , 2013, Medical Image Anal..
[223] Fillia Makedon,et al. Fast Nonnegative Matrix Tri-Factorization for Large-Scale Data Co-Clustering , 2011, IJCAI.
[224] Polina Golland,et al. Keypoint Transfer Segmentation , 2015, IPMI.
[225] P. Basser,et al. In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.
[226] Felix C. Morency,et al. A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles , 2017, NeuroImage: Clinical.
[227] K. Amunts,et al. Individual variability is not noise , 2013, Trends in Cognitive Sciences.
[228] Xin-Wei Li,et al. Local manifold learning for multiatlas segmentation: application to hippocampal segmentation in healthy population and Alzheimer's disease , 2015, CNS neuroscience & therapeutics.
[229] Daniel S. Margulies,et al. Predicting brain-age from multimodal imaging data captures cognitive impairment , 2016, NeuroImage.
[230] J. Reichenbach,et al. Atlas-Guided Cluster Analysis of Large Tractography Datasets , 2013, PloS one.
[231] Peter F. Neher,et al. Strengths and weaknesses of state of the art fiber tractography pipelines - A comprehensive in-vivo and phantom evaluation study using Tractometer , 2015, Medical Image Anal..
[232] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[233] F. Collins,et al. A new initiative on precision medicine. , 2015, The New England journal of medicine.
[234] F Alfaro Almagro. The genetic basis of human brain structure and function: 1,262 genome-wide associations found from 3,144 GWAS of multimodal brain imaging phenotypes from 9,707 UK Biobank participants , 2017 .
[235] Maxime Descoteaux,et al. Dipy, a library for the analysis of diffusion MRI data , 2014, Front. Neuroinform..
[236] W. W. Hansen,et al. Nuclear Induction , 2011 .
[237] Christopher Hunt,et al. Notes on the OpenSURF Library , 2009 .
[238] J. K. Smith,et al. Vessel tortuosity and brain tumor malignancy: a blinded study. , 2005, Academic radiology.
[239] R. Kahn,et al. Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.
[240] W. Eric L. Grimson,et al. Tractography Segmentation Using a Hierarchical Dirichlet Processes Mixture Model , 2009, IPMI.
[241] Zhanpeng Jin,et al. CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification , 2016, IEEE Transactions on Information Forensics and Security.
[242] Nikos D. Sidiropoulos,et al. From K-Means to Higher-Way Co-Clustering: Multilinear Decomposition With Sparse Latent Factors , 2013, IEEE Transactions on Signal Processing.
[243] Klaus Reinhardt,et al. Human Brain Anatomy In Computerized Images , 2016 .
[244] Ming-Chang Chiang,et al. Predicting White Matter Integrity from Multiple Common Genetic Variants , 2012, Neuropsychopharmacology.
[245] Dustin Scheinost,et al. Considering factors affecting the connectome-based identification process: Comment on Waller et al. , 2018, NeuroImage.
[246] Tyrone D. Cannon,et al. Genetic influences on brain structure , 2001, Nature Neuroscience.
[247] Kuldeep Kumar,et al. A weighted total variation approach for the atlas-based reconstruction of brain MR data , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[248] Kuldeep Kumar,et al. A sparse coding approach for the efficient representation and segmentation of white matter fibers , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).
[249] J. R. Hughes. Cerebral lateralization: biological mechanisms, associations and pathology , 1987 .
[250] Guido Gerig,et al. Towards a shape model of white matter fiber bundles using diffusion tensor MRI , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).
[251] Geraint Rees,et al. Relating Introspective Accuracy to Individual Differences in Brain Structure , 2010, Science.
[252] William M. Wells,et al. A Feature-Based Approach to Big Data Analysis of Medical Images , 2015, IPMI.
[253] Guillermo Sapiro,et al. Dictionary learning and sparse coding for unsupervised clustering , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[254] Paul M. Thompson,et al. Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics , 2014, NeuroImage.
[255] S. Blakemore,et al. Studying individual differences in human adolescent brain development , 2018, Nature Neuroscience.
[256] Thomas R. Knösche,et al. White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.
[257] Jan K. Buitelaar,et al. Partition-based mass clustering of tractography streamlines , 2011, NeuroImage.
[258] Carl-Fredrik Westin,et al. fMRI-DTI modeling via landmark distance atlases for prediction and detection of fiber tracts , 2012, NeuroImage.
[259] Vince D. Calhoun,et al. Prediction of Individual Differences from Neuroimaging Data , 2017, NeuroImage.
[260] Jian Sun,et al. Deep ADMM-Net for Compressive Sensing MRI , 2016, NIPS.
[261] Roger C. Tam,et al. Manifold Learning of Brain MRIs by Deep Learning , 2013, MICCAI.
[262] Hayit Greenspan,et al. White Matter Fiber Representation Using Continuous Dictionary Learning , 2017, MICCAI.
[263] Luc Van Gool,et al. Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..
[264] M. Levandowsky,et al. Distance between Sets , 1971, Nature.
[265] B. Dubois,et al. A Precision Medicine Initiative for Alzheimer’s disease: the road ahead to biomarker-guided integrative disease modeling , 2017, Climacteric : the journal of the International Menopause Society.
[266] Dustin Scheinost,et al. Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility , 2017, Cerebral cortex.
[267] D. Louis Collins,et al. Feature-based morphometry: Discovering group-related anatomical patterns , 2010, NeuroImage.
[268] Kaleem Siddiqi,et al. 3D Stochastic Completion Fields for Mapping Connectivity in Diffusion MRI , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[269] X. Huo,et al. A Survey of Manifold-Based Learning Methods , 2007 .
[270] V. Calhoun,et al. Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness. , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.
[271] Chris H. Q. Ding,et al. Orthogonal nonnegative matrix t-factorizations for clustering , 2006, KDD '06.
[272] Ghassan Hamarneh,et al. Exact integration of diffusion orientation distribution functions for graph-based diffusion MRI analysis , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[273] Tongxing Lu,et al. Solution of the matrix equation AX−XB=C , 2005, Computing.
[274] Denis Le Bihan,et al. Imagerie de diffusion in-vivo par résonance magnétique nucléaire , 1985 .
[275] Lawrence L. Wald,et al. Fast Dictionary-Based Reconstruction for Diffusion Spectrum Imaging , 2013, IEEE Transactions on Medical Imaging.
[276] Hayit Greenspan,et al. Sparse Representation for White Matter Fiber Compression and Calculation of Inter-Fiber Similarity , 2016, MICCAI 2016.
[277] Maro G. Machizawa,et al. Neural measures reveal individual differences in controlling access to working memory , 2005, Nature.
[278] Paul M. Thompson,et al. Automatic Population HARDI White Matter Tract Clustering by Label Fusion of Multiple Tract Atlases , 2012, MBIA.
[279] Maxime Descoteaux,et al. On the Origin of Individual Functional Connectivity Variability: The Role of White Matter Architecture , 2017, Brain Connect..
[280] Bruce Fischl,et al. AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity , 2016, NeuroImage.
[281] Matthew Toews,et al. Diffusion Orientation Histograms (DOH) for Diffusion Weighted Image Analysis , 2018 .
[282] Chih-Fong Tsai,et al. Bag-of-Words Representation in Image Annotation: A Review , 2012 .
[283] Xi-Nian Zuo,et al. Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability , 2016, bioRxiv.
[284] J. Tukey,et al. Variations of Box Plots , 1978 .
[285] Perminder S. Sachdev,et al. Genetics of ageing-related changes in brain white matter integrity – A review , 2013, Ageing Research Reviews.
[286] Steen Moeller,et al. The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.
[287] Evan M. Gordon,et al. Precision Functional Mapping of Individual Human Brains , 2017, Neuron.
[288] Alain Trouvé,et al. The Fshape Framework for the Variability Analysis of Functional Shapes , 2014, Found. Comput. Math..
[289] Samuel D. Gale,et al. A Basal Ganglia Pathway Drives Selective Auditory Responses in Songbird Dopaminergic Neurons via Disinhibition , 2010, The Journal of Neuroscience.
[290] D. Salat,et al. Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala. , 2016, Brain : a journal of neurology.
[291] Y. Nesterov. Gradient methods for minimizing composite objective function , 2007 .
[292] Susumu Mori,et al. Fiber tracking: principles and strategies – a technical review , 2002, NMR in biomedicine.
[293] Hao He,et al. Combination of FMRI-SMRI-EEG data improves discrimination of schizophrenia patients by ensemble feature selection , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[294] Danai Dima,et al. Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder , 2017, NeuroImage.
[295] Oscar Miranda-Dominguez,et al. Heritability of the human connectome: A connectotyping study , 2017, Network Neuroscience.
[296] Sushil Sharma,et al. Translational Multimodality Neuroimaging. , 2017, Current drug targets.
[297] Henry Markram,et al. The future of human cerebral cartography: a novel approach , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.
[298] Theo G. M. van Erp,et al. Multisite reliability of MR-based functional connectivity , 2017, NeuroImage.
[299] Kuldeep Kumar,et al. Brain Fiber Clustering Using Non-negative Kernelized Matching Pursuit , 2015, MLMI.
[300] Barnabás Póczos,et al. Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints , 2016, bioRxiv.
[301] Dinggang Shen,et al. Application of neuroanatomical features to tractography clustering , 2013, Human brain mapping.
[302] A. Babakhani,et al. A Spectral-Scanning Nuclear Magnetic Resonance Imaging (MRI) Transceiver , 2009, IEEE Journal of Solid-State Circuits.
[303] Evan M. Gordon,et al. Individual Variability of the System‐Level Organization of the Human Brain , 2015, Cerebral cortex.
[304] R. Adolphs,et al. Building a Science of Individual Differences from fMRI , 2016, Trends in Cognitive Sciences.
[305] Maxime Descoteaux,et al. Real-time multi-peak tractography for instantaneous connectivity display , 2014, Front. Neuroinform..
[306] Hyunsoo Kim,et al. Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares , 2006 .
[307] M. Raichle,et al. Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[308] Alain Trouvé,et al. The Varifold Representation of Nonoriented Shapes for Diffeomorphic Registration , 2013, SIAM J. Imaging Sci..
[309] Vince D. Calhoun,et al. A review of multivariate methods for multimodal fusion of brain imaging data , 2012, Journal of Neuroscience Methods.
[310] Arthur W. Toga,et al. Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling , 2014, NeuroImage.
[311] Pietro Gori,et al. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles , 2016, IEEE Transactions on Medical Imaging.
[312] Stephen M. Smith,et al. Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.
[313] Andrew Zalesky,et al. Building connectomes using diffusion MRI: why, how and but , 2017, NMR in biomedicine.
[314] Yihong Gong,et al. Linear spatial pyramid matching using sparse coding for image classification , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[315] Gabriela Csurka,et al. Visual categorization with bags of keypoints , 2002, eccv 2004.
[316] H. Aronen,et al. [Functional magnetic resonance imaging of the brain]. , 1997, Duodecim; laaketieteellinen aikakauskirja.
[317] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[318] Daniel Rueckert,et al. Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling , 2013, NeuroImage.
[319] Ross T. Whitaker,et al. Manifold modeling for brain population analysis , 2010, Medical Image Anal..
[320] Kaiming Li,et al. Review of methods for functional brain connectivity detection using fMRI , 2009, Comput. Medical Imaging Graph..
[321] C. Westin,et al. Automated white matter fiber tract identification in patients with brain tumors , 2016, NeuroImage: Clinical.
[322] Mary A. Rutherford,et al. Combining Morphological Information in a Manifold Learning Framework: Application to Neonatal MRI , 2010, MICCAI.
[323] Damien A. Fair,et al. Connectotyping: Model Based Fingerprinting of the Functional Connectome , 2014, PloS one.
[324] Philip S. Yu,et al. Brain network analysis: a data mining perspective , 2014, SKDD.
[325] Yong He,et al. Sex- and brain size-related small-world structural cortical networks in young adults: a DTI tractography study. , 2011, Cerebral cortex.
[326] Thomas E. Nichols,et al. The heritability of multi-modal connectivity in human brain activity , 2017, eLife.
[327] M Symms,et al. A review of structural magnetic resonance neuroimaging , 2004, Journal of Neurology, Neurosurgery & Psychiatry.
[328] O. Sporns,et al. From regions to connections and networks: new bridges between brain and behavior , 2016, Current Opinion in Neurobiology.
[329] Jean-Francois Mangin,et al. Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas , 2012, NeuroImage.
[330] Danilo Bzdok,et al. Machine learning for precision psychiatry , 2017 .
[331] Horst Bunke,et al. Non-Euclidean or Non-metric Measures Can Be Informative , 2006, SSPR/SPR.
[332] I. Corouge,et al. Analysis of brain white matter via fiber tract modeling , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[333] Maxime Descoteaux,et al. Denoising and fast diffusion imaging with physically constrained sparse dictionary learning , 2014, Medical Image Anal..
[334] J. Shimony,et al. Resting-State fMRI: A Review of Methods and Clinical Applications , 2013, American Journal of Neuroradiology.
[335] M. Chou,et al. Principles and Limitations of Computational Algorithms in Clinical Diffusion Tensor MR Tractography , 2010, American Journal of Neuroradiology.
[336] Mark W. Woolrich,et al. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.
[337] Guy B. Williams,et al. QuickBundles, a Method for Tractography Simplification , 2012, Front. Neurosci..
[338] A. Toga,et al. Mapping brain asymmetry , 2003, Nature Reviews Neuroscience.
[339] Paul M. Thompson,et al. Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group , 2013, NeuroImage.
[340] Johann Daniel Kruschwitz,et al. Evaluating the replicability, specificity, and generalizability of connectome fingerprints , 2017, NeuroImage.
[341] Shuiwang Ji,et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation , 2015, NeuroImage.
[342] Bernard Ng,et al. Shape Analysis for Brain Structures , 2014 .
[343] A. Singleton,et al. The Parkinson Progression Marker Initiative (PPMI) , 2011, Progress in Neurobiology.
[344] P. Grenier,et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. , 1986, Radiology.
[345] Paul M. Thompson,et al. Investigating brain connectivity heritability in a twin study using diffusion imaging data , 2014, NeuroImage.
[346] Katrin Amunts,et al. White matter fiber tracts of the human brain: Three-dimensional mapping at microscopic resolution, topography and intersubject variability , 2006, NeuroImage.
[347] A. Alexander,et al. White matter tractography using diffusion tensor deflection , 2003, Human brain mapping.
[348] Nicolas Le Roux,et al. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.
[349] Rachid Deriche,et al. Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use? , 2015, Medical Image Anal..
[350] Christiane Reitz,et al. Toward precision medicine in Alzheimer's disease. , 2016, Annals of translational medicine.
[351] Jun Zhang,et al. Fiber Tractography in Diffusion Tensor Magnetic Resonance Imaging: A Survey and Beyond , 2005 .
[352] S. Holm. A Simple Sequentially Rejective Multiple Test Procedure , 1979 .
[353] David C. Van Essen,et al. The future of the human connectome , 2012, NeuroImage.
[354] Kilian M. Pohl,et al. Regional Manifold Learning for Disease Classification , 2014, IEEE Transactions on Medical Imaging.
[355] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[356] P. Hagmann,et al. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging , 2005, Magnetic resonance in medicine.
[357] Scott A. Huettel,et al. Event-related fMRI in cognition , 2012, NeuroImage.
[358] Jean-Philippe Thiran,et al. COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography , 2015, IEEE Transactions on Medical Imaging.
[359] S. Herculano‐Houzel. The Human Brain in Numbers: A Linearly Scaled-up Primate Brain , 2009, Front. Hum. Neurosci..
[360] Li Bai,et al. Brain tractography using Q-ball imaging and graph theory: Improved connectivities through fibre crossings via a model-based approach , 2010, NeuroImage.
[361] Henning Müller,et al. Overview of the ImageCLEF 2012 Medical Image Retrieval and Classification Tasks , 2012, CLEF.
[362] B. T. Thomas Yeo,et al. Inference in the age of big data: Future perspectives on neuroscience , 2017, NeuroImage.
[363] William M. Wells,et al. How are siblings similar? How similar are siblings? Large-scale imaging genetics using local image features , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).
[364] Alain Trouvé,et al. A Statistical Model of White Matter Fiber Bundles Based on Currents , 2009, IPMI.
[365] Suchitra Khoje,et al. Performance Comparison of Fourier Transform and Its Derivatives as Shape Descriptors for Mango Grading , 2012 .
[366] Mark W. Woolrich,et al. Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure , 2012, NeuroImage.
[367] Mark Jenkinson,et al. MSM: A new flexible framework for Multimodal Surface Matching , 2014, NeuroImage.
[368] Xuwei Liang,et al. Using Fourier Descriptor Features in the Classification of White Matter Fiber Tracts in DTI , 2013, 2013 International Conference on Computational and Information Sciences.
[369] Ivo D. Dinov,et al. An automatic framework for quantitative validation of voxel based morphometry measures of anatomical brain asymmetry , 2014, NeuroImage.