Access the full text.
Sign up today, get DeepDyve free for 14 days.
K. Franke, E. Luders, A. May, M. Wilke, Christian Gaser (2012)
Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRINeuroImage, 63
K. Franke, Christian Gaser (2012)
Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease, 25
Franziskus Liem, G. Varoquaux, J. Kynast, F. Beyer, S. Masouleh, Julia Huntenburg, L. Lampe, M. Rahim, Alexandre Abraham, Cameron Craddock, S. Riedel-Heller, T. Luck, M. Löffler, M. Schroeter, A. Witte, A. Villringer, D. Margulies (2016)
Predicting brain-age from multimodal imaging data captures cognitive impairmentNeuroImage, 148
Christian Gaser, K. Franke, S. Klöppel, N. Koutsouleris, H. Sauer (2013)
BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s DiseasePLoS ONE, 8
G. Ziegler, R. Dahnke, L. Jäncke, R. Yotter, A. May, Christian Gaser (2012)
Brain structural trajectories over the adult lifespanHuman Brain Mapping, 33
Pierre Bellec, P. Rosa-Neto, O. Lyttelton, H. Benali, Alan Evans (2009)
Multi-level bootstrap analysis of stable clusters in resting-state fMRINeuroImage, 51
B. Thirion, G. Varoquaux, Elvis Dohmatob, J. Poline (2014)
Which fMRI clustering gives good brain parcellations?Frontiers in Neuroscience, 8
A. Schaefer, Ru Kong, Evan Gordon, Timothy Laumann, X. Zuo, A. Holmes, S. Eickhoff, B. Yeo (2017)
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRIbioRxiv
Franke (2014)
Dementia classification based on brain age estimationProc. MICCAI Work Chall. Comput. Diagn. Dement. Based Struct. MRI Data
Ylva Østby, C. Tamnes, A. Fjell, L. Westlye, P. Due-Tønnessen, K. Walhovd (2009)
Heterogeneity in Subcortical Brain Development: A Structural Magnetic Resonance Imaging Study of Brain Maturation from 8 to 30 YearsThe Journal of Neuroscience, 29
S. Genon, A. Reid, Hai Li, L. Fan, V. Müller, E. Cieslik, F. Hoffstaedter, R. Langner, C. Grefkes, A. Laird, P. Fox, T. Jiang, K. Amunts, S. Eickhoff (2017)
The heterogeneity of the left dorsal premotor cortex evidenced by multimodal connectivity-based parcellation and functional characterizationNeuroImage, 170
(1996)
Regression selection and shrinkage via the lasso
Pingyue Wang, Kewei Chen, L. Yao, Bin Hu, Xia Wu, Jia-cai Zhang, Q. Ye, Xiaojuan Guo (2016)
Multimodal Classification of Mild Cognitive Impairment Based on Partial Least Squares.Journal of Alzheimer's disease : JAD, 54 1
M. Glasser, Timothy Coalson, E. Robinson, C. Hacker, John Harwell, E. Yacoub, K. Uğurbil, J. Andersson, C. Beckmann, M. Jenkinson, Stephen Smith, D. Essen (2016)
A multi-modal parcellation of human cerebral cortexNature, 536
G. Ziegler, G. Ridgway, R. Dahnke, Christian Gaser (2014)
Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjectsNeuroimage, 97
(2014)
normal older adults: The SPARE-AD index. Brain 132:2026–2035
E. Dougherty, Jianping Hua, Chao Sima (2009)
Performance of Feature Selection MethodsCurrent Genomics, 10
C. Davatzikos (2016)
Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learningMedical image analysis, 33
P. Zhao, Bin Yu (2006)
On Model Selection Consistency of LassoJ. Mach. Learn. Res., 7
D. García-Pérez, Juan Castillo, Yahya Al-Hazmi, Josep Martrat, K. Kavoussanakis, Alastair Hume, Celia López, G. Landi, T. Wauters, M. Gienger, D. Margery (2014)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
R. Petersen (2010)
Alzheimer's disease: progress in predictionThe Lancet Neurology, 9
Christos Boutsidis, Efstratios Gallopoulos (2008)
SVD based initialization: A head start for nonnegative matrix factorizationPattern Recognit., 41
(1985)
VBM tutorial
Xiaobai Liu, Shuicheng Yan, Hai Jin (2010)
Projective Nonnegative Graph EmbeddingIEEE Transactions on Image Processing, 19
A. Tucholka, B. Thirion, M. Perrot, P. Pinel, J. Mangin, J. Poline (2008)
Probabilistic Anatomo-Functional Parcellation of the Cortex: How Many Regions?Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 11 Pt 2
(2015)
Biobank prospective epidemiological study
Daniel Lee, H. Seung (1999)
Learning the parts of objects by non-negative matrix factorizationNature, 401
(2017)
35 correlated designs with randomization and clustering
(2016)
and adolescent brain and effects of genetic variation. Neuropsychol
S. Eickhoff, R. Constable, B. Yeo (2017)
Topographic organization of the cerebral cortex and brain cartographyNeuroImage, 170
E. Finn, X. Shen, D. Scheinost, M. Rosenberg, Jessica Huang, M. Chun, X. Papademetris, R. Constable (2015)
Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivityNature neuroscience, 18
Hasse Karlsson (2009)
The prevalence of what?Nordic Journal of Psychiatry, 63
Daniel Weinberger (2014)
Reporting Checklist for Nature Neuroscience
J. Giedd, M. Stockman, Catherine Weddle, M. Liverpool, A. Alexander-Bloch, G. Wallace, N. Lee, F. Lalonde, R. Lenroot (2010)
Anatomic Magnetic Resonance Imaging of the Developing Child and Adolescent Brain and Effects of Genetic VariationNeuropsychology Review, 20
(2014)
adolescents using structural MRI. Neuroimage 63:1305–1312
S. Burgmans, M. Boxtel, E. Vuurman, F. Smeets, E. Gronenschild, H. Uylings, J. Jolles (2009)
The prevalence of cortical gray matter atrophy may be overestimated in the healthy aging brain.Neuropsychology, 23 5
K. Franke, Christian Gaser, B. Manor, V. Novak (2013)
Advanced BrainAGE in older adults with type 2 diabetes mellitusFrontiers in Aging Neuroscience, 5
(2014)
Dementia classification based on brain age estimation. Proc MICCAI Work Chall Comput Diagnosis Dement Based Struct MRI Data
K. Franke, M. Ristow, Christian Gaser (2014)
Gender-specific impact of personal health parameters on individual brain aging in cognitively unimpaired elderly subjectsFrontiers in Aging Neuroscience, 6
A. Mechelli, Karl Friston, Richard Frackowiak, C. Price (2005)
Structural Covariance in the Human CortexThe Journal of Neuroscience, 25
L. Hubert, P. Arabie (1985)
Comparing partitionsJournal of Classification, 2
J. Suzuki (2006)
On Strong Consistency of Model Selection in ClassificationIEEE Transactions on Information Theory, 52
C. Kelly, R. Toro, A. Martino, Christine Cox, Pierre Bellec, F. Castellanos, M. Milham (2012)
A convergent functional architecture of the insula emerges across imaging modalitiesNeuroImage, 61
C. Davatzikos, Yong Fan, Xiaoying Wu, D. Shen, S. Resnick (2008)
Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imagingNeurobiology of Aging, 29
J. Ashburner (2007)
A fast diffeomorphic image registration algorithmNeuroImage, 38
C. Davatzikos (2004)
Why voxel-based morphometric analysis should be used with great caution when characterizing group differencesNeuroImage, 23
N. Koutsouleris, C. Davatzikos, S. Borgwardt, Christian Gaser, R. Bottlender, T. Frodl, P. Falkai, A. Riecher-Rössler, H. Möller, M. Reiser, C. Pantelis, E. Meisenzahl (2014)
Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders.Schizophrenia bulletin, 40 5
(2012)
Author manuscript, published in "International Conference on Machine Learning (2012)" Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering
R. Craddock, G. James, P. Holtzheimer, Xiaoping Hu, H. Mayberg (2012)
A whole brain fMRI atlas generated via spatially constrained spectral clusteringHuman Brain Mapping, 33
Michael Tipping, Anita Faul (2003)
Fast Marginal Likelihood Maximisation for Sparse Bayesian Models
Isabelle Guyon, A. Elisseeff (2003)
An Introduction to Variable and Feature SelectionJ. Mach. Learn. Res., 3
B. Mwangi, T. Tian, J. Soares (2013)
A Review of Feature Reduction Techniques in NeuroimagingNeuroinformatics, 12
S. Burgmans, M. Boxtel, E. Vuurman, F. Smeets, E. Gronenschild, H. Uylings, J. Jolles (2010)
The Prevalence of Cortical Gray Matter Atrophy May Be Overestimated in the Healthy Aging Brain (Reply to Fjell et al. (2010) and Raz and Lindenberger (2010)Neuropsychology (journal), 24
I. Jolliffe (2002)
Principal Component Analysis
Bastian Goldluecke, Variational Method, Velvety Reflectance, Video Mosaicing, Zhigang Zhu, Cees Snoek, A. Smeulders, Vasu Parameswaran, Ashok Veeraraghavan (2019)
Variable SelectionModel-Based Clustering and Classification for Data Science
(2009)
Longitudinal progression of Alzheimers-like
K. Franke, G. Ziegler, S. Klöppel, Christian Gaser (2010)
Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parametersNeuroImage, 50
E. Moradi, A. Pepe, Christian Gaser, H. Huttunen, Jussi Tohka (2015)
Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjectsNeuroImage, 104
B. Mwangi, K. Hasan, J. Soares (2013)
Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: A machine learning approachNeuroImage, 75
J. Mortimer, R. Petersen (2008)
Detection of prodromal Alzheimer's diseaseAnnals of Neurology, 64
I. Beheshti, H. Demirel, H. Matsuda (2017)
Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithmComputers in biology and medicine, 83
C. Harada, Marissa Love, K. Triebel (2013)
Normal cognitive aging.Clinics in geriatric medicine, 29 4
C. Davatzikos, Feng Xu, Y. An, Yong Fan, S. Resnick (2009)
Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index.Brain : a journal of neurology, 132 Pt 8
Zhirong Yang, Zhijian Yuan, Jorma Laaksonen (2007)
Projective Non-Negative Matrix Factorization with Applications to Facial Image ProcessingInt. J. Pattern Recognit. Artif. Intell., 21
Zhijian Yuan, Zhirong Yang, E. Oja (2009)
Projective Nonnegative Matrix Factorization : Sparseness , Orthogonality , and Clustering
Kittipat Kampa, S. Mehta, C. Chou, W. Chaovalitwongse, T. Grabowski (2014)
Sparse optimization in feature selection: application in neuroimagingJournal of Global Optimization, 59
J. Cole, Rudra Poudel, Dimosthenis Tsagkrasoulis, M. Caan, C. Steves, T. Spector, G. Montana (2016)
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarkerNeuroImage, 163
Evan Gordon, Timothy Laumann, B. Adeyemo, Jeremy Huckins, W. Kelley, S. Petersen (2016)
Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations.Cerebral cortex, 26 1
(2010)
Alzheimer’s Dis 54:359–371
S. Genon, Hai Li, L. Fan, V. Müller, E. Cieslik, F. Hoffstaedter, A. Reid, R. Langner, C. Grefkes, P. Fox, S. Moebus, S. Caspers, K. Amunts, T. Jiang, S. Eickhoff (2016)
The Right Dorsal Premotor Mosaic: Organization, Functions, and ConnectivityCerebral Cortex, 27
C. Good, I. Johnsrude, J. Ashburner, R. Henson, Karl Friston, Richard Frackowiak (2001)
A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human BrainsNeuroImage, 14
Aristeidis Sotiras, J. Toledo, R. Gur, R. Gur, T. Satterthwaite, C. Davatzikos (2017)
Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansionProceedings of the National Academy of Sciences, 114
S. Schippling, A. Ostwaldt, P. Suppa, L. Spies, Praveena Manogaran, C. Gocke, H. Huppertz, R. Opfer (2017)
Global and regional annual brain volume loss rates in physiological agingJournal of Neurology, 264
M. Cugmas, A. Ferligoj (2015)
On comparing partitionsInternational Federation of Classification Societies
A. May (2011)
Experience-dependent structural plasticity in the adult human brainTrends in Cognitive Sciences, 15
(2014)
overestimated in the healthy aging brain. Neuropsychology 23:541–550
Jorge Santos, M. Embrechts (2009)
On the Use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification
Michael Tipping (2001)
Sparse Bayesian Learning and the Relevance Vector MachineJ. Mach. Learn. Res., 1
G. Varoquaux, Pradeep Raamana, D. Engemann, Andrés Idrobo, Y. Schwartz, B. Thirion (2016)
Assessing and tuning brain decoders: Cross-validation, caveats, and guidelinesNeuroImage, 145
G. Erus, Harsha Battapady, T. Satterthwaite, H. Hakonarson, R. Gur, C. Davatzikos, R. Gur (2015)
Imaging patterns of brain development and their relationship to cognition.Cerebral cortex, 25 6
G. Douaud, A. Groves, C. Tamnes, L. Westlye, E. Duff, A. Engvig, K. Walhovd, A. James, A. Gass, A. Monsch, P. Matthews, A. Fjell, Stephen Smith, H. Johansen-Berg (2014)
A common brain network links development, aging, and vulnerability to diseaseProceedings of the National Academy of Sciences, 111
(2012)
mellitus. Front Aging Neurosci 5:1–9
S. Caspers, S. Moebus, S. Lux, N. Pundt, H. Schütz, Thomas Mühleisen, V. Gras, S. Eickhoff, S. Romanzetti, T. Stöcker, R. Stirnberg, M. Kirlangic, M. Minnerop, P. Pieperhoff, U. Mödder, Samir Das, Alan Evans, K. Jöckel, R. Erbel, S. Cichon, M. Nöthen, D. Sturma, A. Bauer, N. Shah, K. Zilles, K. Amunts (2014)
Studying variability in human brain aging in a population-based German cohort—rationale and design of 1000BRAINSFrontiers in Aging Neuroscience, 6
Débora Terribilli, M. Schaufelberger, F. Duran, M. Zanetti, P. Curiati, P. Menezes, M. Scazufca, E. Amaro, C. Leite, G. Busatto (2011)
Age-related gray matter volume changes in the brain during non-elderly adulthoodNeurobiology of Aging, 32
N. Raz, Paolo Ghisletta, K. Rodrigue, K. Kennedy, U. Lindenberger (2010)
Trajectories of brain aging in middle-aged and older adults: Regional and individual differencesNeuroImage, 51
D. Tisserand, J. Pruessner, E. Sanz-Arigita, M. Boxtel, Alan Evans, J. Jolles, H. Uylings (2002)
Regional Frontal Cortical Volumes Decrease Differentially in Aging: An MRI Study to Compare Volumetric Approaches and Voxel-Based MorphometryNeuroImage, 17
Cun-Hui Zhang, Jian Huang (2008)
The sparsity and bias of the Lasso selection in high-dimensional linear regressionAnnals of Statistics, 36
Zhirong Yang, E. Oja (2010)
Linear and Nonlinear Projective Nonnegative Matrix FactorizationIEEE Transactions on Neural Networks, 21
K. Singh, N. Basant, Shikha Gupta (2011)
Support vector machines in water quality management.Analytica chimica acta, 703 2
E. Luders, N. Cherbuin, Christian Gaser (2016)
Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitionersNeuroImage, 134
S. Haufe, F. Meinecke, Kai Görgen, Sven Dähne, J. Haynes, B. Blankertz, F. Biessmann (2014)
On the interpretation of weight vectors of linear models in multivariate neuroimagingNeuroImage, 87
F. Bunea, Yiyuan She, H. Ombao, A. Gongvatana, Kate Devlin, R. Cohen (2011)
Penalized least squares regression methods and applications to neuroimagingNeuroImage, 55
Aristeidis Sotiras, S. Resnick, C. Davatzikos (2015)
Finding imaging patterns of structural covariance via Non-Negative Matrix FactorizationNeuroImage, 108
O. Sporns (2013)
Network attributes for segregation and integration in the human brainCurrent Opinion in Neurobiology, 23
Himabindu Lakkaraju, Stephen Bach, J. Leskovec (2016)
Interpretable Decision Sets: A Joint Framework for Description and PredictionProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Jianping Hua, W. Tembe, E. Dougherty (2009)
Performance of feature-selection methods in the classification of high-dimension dataPattern Recognit., 42
NeuroImage – Unpaywall
Published: Mar 6, 2018
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.