Access the full text.
Sign up today, get DeepDyve free for 14 days.
M. Stone (1976)
Cross‐Validatory Choice and Assessment of Statistical PredictionsJournal of the royal statistical society series b-methodological, 36
C. Beckmann, Stephen Smith (2004)
Probabilistic independent component analysis for functional magnetic resonance imagingIEEE Transactions on Medical Imaging, 23
R. Woods, J. Mazziotta, S. Cherry (1993)
Automated image registrationAnnals of Nuclear Medicine, 7
R. Cox (1996)
AFNI: software for analysis and visualization of functional magnetic resonance neuroimages.Computers and biomedical research, an international journal, 29 3
C. Lehman, S. Peacock, Wendy DeMartini, Xiaoming Chen (2006)
A new automated software system to evaluate breast MR examinations: improved specificity without decreased sensitivity.AJR. American journal of roentgenology, 187 1
S. Strother (2006)
Evaluating fMRI preprocessing pipelinesIEEE Engineering in Medicine and Biology Magazine, 25
Carola Tegeler, S. Strother, Jon Anderson, Seong-Gi Kim (1999)
Reproducibility of BOLD‐based functional MRI obtained at 4 THuman Brain Mapping, 7
J. Haynes, G. Rees (2006)
Neuroimaging: Decoding mental states from brain activity in humansNature Reviews Neuroscience, 7
E. Bullmore, M. Brammer, G. Rouleau, B. Everitt, A. Simmons, T. Sharma, S. Frangou, R. Murray, G. Dunn (1995)
Computerized Brain Tissue Classification of Magnetic Resonance Images: A New Approach to the Problem of Partial Volume ArtifactNeuroImage, 2
K. Fissell, Eugene Tseytlin, Daniel Cunningha, K. Iyer, C. Carter, W. Schneider, J. Cohen (2003)
FiswidgetsNeuroinformatics, 1
A. Lukic, M. Wernick, S. Strother (2002)
An evaluation of methods for detecting brain activations from functional neuroimagesArtificial intelligence in medicine, 25 1
Steven Strother, Nicholas Lange, J. Anderson, K. Schaper, K. Rehm, Lars Hansen, D. Rottenberg (1997)
Activation pattern reproducibility: Measuring the effects of group size and data analysis modelsHuman Brain Mapping, 5
J. Grant, L. Somers, Yue Zhang, F. Manion, Ghislain Bidaut, M. Ochs (2004)
FGDP: functional genomics data pipeline for automated, multiple microarray data analysesBioinformatics, 20 2
R. Kustra, S. C. Strother (2001)
Penalized discriminant analysis of [15O] water PET brain images with prediction error selection of smoothing and regularization hyperparametersIEEE Transactions on Medical Imaging, 20
L. Hansen, J. Larsen, F. Nielsen, S. Strother, E. Rostrup, R. Savoy, N. Lange, J. Sidtis, C. Svarer, O. Paulson (1999)
Generalizable Patterns in Neuroimaging: How Many Principal Components?NeuroImage, 9
R. Sherlock, P. Mooney, A. Winstanley, Jan Husdal (2002)
Shortest Path Computation: A Comparative Analysis
J. Tanabe, David Miller, J. Tregellas, R. Freedman, François Meyer
See Blockindiscussions, Blockinstats, Blockinand Blockinauthor Blockinprofiles Blockinfor Blockinthis Blockinpublication Comparison Blockinof Blockindetrending Blockinmethods Blockinfor Optimal Blockinfmri Blockinpreprocessing
M. Chance, A. Bresnick, S. Burley, Jianrong Jiang, C. Lima, A. Sali, S. Almo, J. Bonanno, J. Buglino, S. Boulton, Hua Chen, N. Eswar, G. He, Raymond Huang, V. Ilyin, L. McMahan, U. Pieper, S. Ray, M. Vidal, L. Wang (2002)
Structural genomics: A pipeline for providing structures for the biologistProtein Science, 11
T. Hastie, A. Buja, R. Tibshirani (1995)
Penalized Discriminant AnalysisAnnals of Statistics, 23
R. Madsen (2003)
Multi-Subject fMRI Generalization with| Independent Component Representation
D. Cohn, L. Atlas, R. Ladner (1994)
Improving generalization with active learningMachine Learning, 15
S. LaConte, Jon Anderson, S. Muley, J. Ashe, S. Frutiger, K. Rehm, L. Hansen, E. Yacoub, Xiaoping Hu, D. Rottenberg, S. Strother (2003)
The Evaluation of Preprocessing Choices in Single-Subject BOLD fMRI Using NPAIRS Performance MetricsNeuroImage, 18
V. Iyengar, C. Apté, Tong Zhang (2000)
Active learning using adaptive resampling
S. Strother, Jon Anderson, L. Hansen, U. Kjems, R. Kustra, J. Sidtis, S. Frutiger, S. Muley, S. LaConte, D. Rottenberg (2000)
The Quantitative Evaluation of Functional Neuroimaging Experiments: The NPAIRS Data Analysis FrameworkNeuroImage, 15
A. Gevins, N. Morgan, S. Bressler, B. Cutillo, R. White, J. Illes, D. Greer, J. Doyle, G. Zeitlin (1987)
Human neuroelectric patterns predict performance accuracy.Science, 235 4788
Marnie Shaw, S. Strother, M. Gavrilescu, Katherine Podzebenko, A. Waites, J. Watson, Jon Anderson, G. Jackson, G. Egan (2003)
Evaluating subject specific preprocessing choices in multisubject fMRI data sets using data-driven performance metricsNeuroImage, 19
U. Kjems, L. Hansen, Jon Anderson, S. Frutiger, S. Muley, J. Sidtis, D. Rottenberg, S. Strother (2002)
The Quantitative Evaluation of Functional Neuroimaging Experiments: Mutual Information Learning CurvesNeuroImage, 15
E. Herskovits, J. Gerring (2003)
Application of a data-mining method based on Bayesian networks to lesion-deficit analysisNeuroImage, 19
L. Liberman, E. Morris, Cathleen Kim, J. Kaplan, A. Abramson, J. Menell, K. Zee, D. Dershaw (2003)
MR imaging findings in the contralateral breast of women with recently diagnosed breast cancer.AJR. American journal of roentgenology, 180 2
D. Bluemke, C. Gatsonis, Mei-Hsiu Chen, G. Deangelis, N. Debruhl, S. Harms, S. Heywang-Köbrunner, N. Hylton, C. Kuhl, C. Lehman, E. Pisano, P. Causer, S. Schnitt, S. Smazal, C. Stelling, P. Weatherall, M. Schnall (2004)
Magnetic resonance imaging of the breast prior to biopsy.JAMA, 292 22
J. D. Haynes, G. Rees (2006)
Decoding mental states from brain activity in humansNature Reviews. Neuroscience, 7
T. Le, Xiaoping Hu (1997)
Methods for assessing accuracy and reliability in functional MRINMR in Biomedicine, 10
Chong Yu (2005)
Test–Retest Reliability
A. Lukic, M. Wernick, S. Strother (1999)
An evaluation of methods for detecting brain activations from PET or fMRI images1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019), 2
B. Efron, G. Gong (1983)
A leisurely look at the bootstrap, the jackknife, and cross-validationThe American Statistician, 37
C. R. Genovese, D. C. Noll, W. F. Eddy (1997)
Estimating test-retest reliability in fMRI I: Statistical methodologyMagnetic Resonance in Medicine, 38
L. Prechelt (2000)
An Empirical Comparison of Seven Programming LanguagesComputer, 33
P. Skudlarski, R. Constable, J. Gore (1999)
ROC Analysis of Statistical Methods Used in Functional MRI: Individual SubjectsNeuroImage, 9
R. Maitra, S. Roys, R. Gullapalli (2002)
Test‐retest reliability estimation of functional MRI dataMagnetic Resonance in Medicine, 48
B. Efron, Gail Gong (1983)
A Leisurely Look at the Bootstrap, the Jackknife, and
C. Genovese, D. Noll, W. Eddy (1997)
Estimating test‐retest reliability in functional MR imaging I: Statistical methodologyMagnetic Resonance in Medicine, 38
B. Blankertz, G. Curio, K. Müller (2001)
Classifying Single Trial EEG: Towards Brain Computer Interfacing
R. Woods, Scott Grafton, C. Holmes, S. Cherry, J. Mazziotta (1998)
Automated image registration: I. General methods and intrasubject, intramodality validation.Journal of computer assisted tomography, 22 1
A. Holmes (1994)
Statistical issues in functional brain mapping.
G. Winterer, M. Ziller, B. Klöppel, A. Heinz, L. Schmidt, W. Herrmann (1997)
Analysis of Quantitative EEG with Artificial Neural Networks and Discriminant Analysis – A Methodological ComparisonNeuropsychobiology, 37
S. Strother, J. Liow, J. Moeller, J. Sidtis, V. Dhawan, D. Rottenberg (1991)
Absolute Quantitation in Neurological PET: Do We Need it?Journal of Cerebral Blood Flow & Metabolism, 11
J. Tanabe, D. Miller, J. Tregellas, R. Freedman, F. G. Meyer (2002)
Comparison of detrending methods for optimal fMRI preprocessingNeuroImage, 15
R. Kustra, S. Strother (2001)
Penalized Discriminant Analysis of [15O]-water PET Brain Images with Prediction Error Selection of Smoothness and RegularizationIEEE Trans. Medical Imaging, 20
N. Nicolaou, S. Nasuto (2004)
Temporal Independent Component Analysis for automatic artefact removal from EEG
N. Mørch, L. Hansen, S. Strother, C. Svarer, D. Rottenberg, B. Lautrup, R. Savoy, O. Paulson (1997)
Nonlinear versus Linear Models in Functional Neuroimaging: Learning Curves and Generalization Crossover
Stephen Smith, M. Jenkinson, M. Woolrich, C. Beckmann, Timothy Behrens, H. Johansen-Berg, P. Bannister, M. Luca, I. Drobnjak, D. Flitney, R. Niazy, James Saunders, J. Vickers, Yongyue Zhang, N. Stefano, J. Brady, P. Matthews (2004)
Advances in functional and structural MR image analysis and implementation as FSLNeuroImage, 23
A. Zijdenbos, R. Forghani, Alan Evans (2002)
Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosisIEEE Transactions on Medical Imaging, 21
Xuerui Wang, R. Hutchinson, Tom Mitchell (2003)
Training fMRI Classifiers to Discriminate Cognitive States across Multiple Subjects
A. S. Lukic, M. N. Wernick, S. C. Strother (2002)
An evaluation of methods for detecting brain activations from PET or fMRI imagesArtificial Intelligence in Medicine, 25
K. Fissell, E. Tseytlin, D. Cunningham, K. Iyer, C. S. Carter, W. Schneider (2003)
Fiswidgets: A graphical computing environment for neuroimaging analysisNeuroinformatics, 1
Kendrick Kay, S. David, R. Prenger, Kathleen Hansen, J. Gallant (2008)
Modeling low‐frequency fluctuation and hemodynamic response timecourse in event‐related fMRIHuman Brain Mapping, 29
R. Woods, Scott Grafton, J. Watson, N. Sicotte, J. Mazziotta (1998)
Automated image registration: II. Intersubject validation of linear and nonlinear models.Journal of computer assisted tomography, 22 1
Stephen Smith, C. Beckmann, N. Ramnani, M. Woolrich, P. Bannister, M. Jenkinson, P. Matthews, David McGonigle (2005)
Variability in fMRI: A re‐examination of inter‐session differencesHuman Brain Mapping, 24
J. Ford, F. Makedon, V. Megalooikonomou, Li Shen, T. Steinberg, A. Saykin (2001)
Spatial comparison of fMRI activation maps for data miningNeuroImage, 13
S. Strother, Stephen Conte, L. Hansen, Jon Anderson, Jin Zhang, Sujit Pulapura, D. Rottenberg (2004)
Optimizing the fMRI data-processing pipeline using prediction and reproducibility performance metrics: I. A preliminary group analysisNeuroImage, 23
(2003)
Posterior probability maps and SPMsNeuroImage, 19
D. Rex, Jeffrey Ma, A. Toga (2003)
The LONI Pipeline Processing EnvironmentNeuroImage, 19
R. Nandy, D. Cordes (2003)
Novel ROC‐type method for testing the efficiency of multivariate statistical methods in fMRIMagnetic Resonance in Medicine, 49
B. Lautrup, L. K. Hansen, I. Law, N. Morch, C. Svarer, S. C. Strother (1994)
Proceedings of the workshop on supercomputing in brain research: From tomography to neural networks
C. Mungall, S. Misra, BP Berman, J. Carlson, E. Frise, N. Harris, B. Marshall, S. Shu, JS Kaminker, SE Prochnik, CD Smith, E. Smith, JL Tupy, C. Wiel, G. Rubin, S. Lewis (2002)
An integrated computational pipeline and database to support whole-genome sequence annotationGenome Biology, 3
J. Kippenhan, W. Barker, S. Pascal, J. Nagel, R. Duara (1992)
Evaluation of a neural-network classifier for PET scans of normal and Alzheimer's disease subjects.Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 33 8
Lifeng Liu, D. Meier, Mariann Polgar-Turcsanyi, Pawel Karkocha, R. Bakshi, C. Guttmann (2005)
Multiple Sclerosis Medical Image Analysis and Information ManagementJournal of Neuroimaging, 15
N. Lange, S. Strother, Jon Anderson, F. Nielsen, A. Holmes, T. Kolenda, R. Savoy, L. Hansen (1999)
Plurality and Resemblance in fMRI Data AnalysisNeuroImage, 10
S. Gold, B. Christian, S. Arndt, G. Zeien, T. Cizadlo, D. Johnson, M. Flaum, N. Andreasen (1998)
Functional MRI statistical software packages: A comparative analysisHuman Brain Mapping, 6
S. LaConte, S. Strother, V. Cherkassky, Jon Anderson, Xiaoping Hu (2005)
Support vector machines for temporal classification of block design fMRI dataNeuroImage, 26
As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.
Neuroinformatics – Springer Journals
Published: May 28, 2008
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.