Multivariate Spatial Gaussian Mixture Modeling for statistical clustering of hemodynamic parameters in functional MRI, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.445-448, 2009. ,
DOI : 10.1109/ICASSP.2009.4959616
Machine learning for neuroimaging with scikit-learn. arXiv preprint, 2014. ,
DOI : 10.3389/fninf.2014.00014
URL : https://hal.archives-ouvertes.fr/hal-01093971
Perfusion fMRI for Functional Neuroimaging, Int Rev Neurobiol, vol.66, pp.213-236, 2005. ,
DOI : 10.1016/S0074-7742(05)66007-2
Model selection for hemodynamic brain parcellation in fMRI, 2014 22nd European Signal Processing Conference (EUSIPCO), pp.31-35, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01107475
Multi-subject joint parcellation detection estimation in functional MRI, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016. ,
DOI : 10.1109/ISBI.2016.7493214
URL : https://hal.archives-ouvertes.fr/hal-01261982
Hemodynamic brain parcellation using a nonparametric Bayesian approach, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01275622
Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magnetic resonance in medicine, pp.102-116, 2015. ,
Glial and neuronal control of brain blood ow, Nature, issue.7321, pp.468232-243, 2010. ,
Normalization of cerebral vasoreactivity using BOLD MRI after intravascular stenting, Human Brain Mapping, vol.29, issue.4, pp.1320-1324, 2014. ,
DOI : 10.1002/ana.410290302
Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets, Human Brain Mapping, vol.22, issue.8, pp.678-693, 2006. ,
DOI : 10.1002/hbm.20210
Hemodynamic Estimation Based on Consensus Clustering, 2013 International Workshop on Pattern Recognition in Neuroimaging, 2013. ,
DOI : 10.1109/PRNI.2013.61
URL : https://hal.archives-ouvertes.fr/hal-00854621
Multi-session extension of the joint-detection framework in fMRI, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.1512-1515, 2013. ,
DOI : 10.1109/ISBI.2013.6556822
URL : https://hal.archives-ouvertes.fr/hal-00854624
Clinical perfusion MRI: techniques and applications, 2013. ,
DOI : 10.1017/CBO9781139004053
Variational algorithms for approximate Bayesian inference, 2003. ,
An arteriolar compliance model of the cerebral blood ow response to neural stimulus ,
Multi-level bootstrap analysis of stable clusters in resting-state fMRI, NeuroImage, vol.51, issue.3, pp.1126-1139, 2010. ,
DOI : 10.1016/j.neuroimage.2010.02.082
Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the royal statistical society. Series B (Methodological), pp.289-300, 1995. ,
Pattern recognition, Machine Learning, 2006. ,
Relationship Between Flow and Metabolism in BOLD Signals: Insights from Biophysical Models, Brain Topography, vol.24, issue.7, pp.40-53, 2011. ,
DOI : 10.1016/j.envsoft.2008.12.002
URL : https://hal.archives-ouvertes.fr/inserm-00613116
Brodmann's: Localisation in the cerebral cortex, 2007. ,
Diagnostic classiication of arterial spin labeling and structural MRI in presenile early stage dementia, Human brain mapping, vol.35, issue.9, pp.4916-4931, 2014. ,
The physics of functional magnetic resonance imaging (fMRI), Reports on Progress in Physics, vol.76, issue.9, p.96601, 2013. ,
DOI : 10.1088/0034-4885/76/9/096601
A general kinetic model for quantitative perfusion imaging with arterial spin labeling, Magnetic Resonance in Medicine, vol.37, issue.3, pp.383-396, 1998. ,
DOI : 10.1038/jcbfm.1985.9
Dynamics of blood flow and oxygenation changes during brain activation: The balloon model, Magnetic Resonance in Medicine, vol.77, issue.6, pp.855-864, 1998. ,
DOI : 10.1161/01.RES.77.6.1201
MR Imaging, Journal of Computer Assisted Tomography, vol.9, issue.4, pp.659-675, 1985. ,
DOI : 10.1097/00004728-198507010-00002
Impaired cerebral vasoreactivity to CO 2 in Alzheimer's disease using BOLD fMRI, Neuroimage, issue.2, pp.58579-587, 2011. ,
EM procedures using mean field-like approximations for Markov model-based image segmentation, Pattern Recognition, vol.36, issue.1, pp.131-144, 2003. ,
DOI : 10.1016/S0031-3203(02)00027-4
URL : https://hal.archives-ouvertes.fr/inria-00072526
Fast Joint Detection-Estimation of Evoked Brain Activity in Event-Related fMRI Using a Variational Approach, IEEE Transactions on Medical Imaging, vol.32, issue.5, pp.821-837, 2013. ,
DOI : 10.1109/TMI.2012.2225636
URL : https://hal.archives-ouvertes.fr/inserm-00753873
Subject-level joint parcellationdetection-estimation in fMRI, 2016. ,
Quantification of Cerebral Blood Flow as Biomarker of Drug Effect: Arterial Spin Labeling phMRI After a Single Dose of Oral Citalopram, Clinical Pharmacology & Therapeutics, vol.313, issue.2, pp.251-258, 2011. ,
DOI : 10.1002/hbm.20035
A whole brain fMRI atlas generated via spatially constrained spectral clustering, Human Brain Mapping, vol.22, issue.Pt 1, pp.1914-1928, 2012. ,
DOI : 10.1097/WCO.0b013e32832d95db
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3838923/pdf
Technical aspects and utility of fMRI using BOLD and ASL, Clinical Neurophysiology, vol.113, issue.5, pp.621-634, 2002. ,
DOI : 10.1016/S1388-2457(02)00038-X
Perfusion imaging, Magnetic Resonance in Medicine, vol.3, issue.1, pp.37-45, 1992. ,
DOI : 10.1038/jcbfm.1985.9
Applications of arterial spin labeled MRI in the brain, Journal of Magnetic Resonance Imaging, vol.6, issue.6 Pt 1 ,
DOI : 10.1371/journal.pone.0017096
Fluctuating and sensory-induced vasodynamics in rodent cortex extend arteriole capacity, Proceedings of the National Academy of Sciences, pp.8473-8478, 2011. ,
DOI : 10.1093/cercor/bhn085
URL : http://www.pnas.org/content/108/20/8473.full.pdf
Cerebral hypoperfusion in multiple sclerosis is reversible and mediated by endothelin-1, Proceedings of the National Academy of Sciences, issue.14, pp.1105654-5658, 2013. ,
Qualitative mapping of cerebral blood flow and functional localization with echo-planar MR imaging and signal targeting with alternating radio frequency., Radiology, vol.192, issue.2, pp.513-520, 1994. ,
DOI : 10.1148/radiology.192.2.8029425
Quantitative perfusion measurements using pulsed arterial spin labeling: Effects of large region-of-interest analysis, Journal of Magnetic Resonance Imaging, vol.46, issue.6, pp.676-682, 2005. ,
DOI : 10.1002/jmri.20329
Bayesian fMRI data analysis with sparse spatial basis function priors, NeuroImage, vol.34, issue.3, pp.1108-1125, 2007. ,
DOI : 10.1016/j.neuroimage.2006.10.005
Rapid calculation of T1 using variable flip angle gradient refocused imaging, Magnetic Resonance Imaging, vol.5, issue.3, pp.201-208, 1987. ,
DOI : 10.1016/0730-725X(87)90021-X
Hemodynamically informed parcellation of cerebral FMRI data, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2079-2083, 2014. ,
DOI : 10.1109/ICASSP.2014.6853965
URL : https://hal.archives-ouvertes.fr/hal-01100186
Physiologically Informed Bayesian Analysis of ASL fMRI Data, Bayesian and grAphical Models for Biomedical Imaging, pp.37-48, 2014. ,
DOI : 10.1007/978-3-319-12289-2_4
URL : https://hal.archives-ouvertes.fr/hal-01107613
Physiological models comparison for the analysis of ASL FMRI data, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp.1348-1351, 2015. ,
DOI : 10.1109/ISBI.2015.7164125
URL : https://hal.archives-ouvertes.fr/hal-01249014
Variational Physiologically Informed Solution to Hemodynamic and Perfusion Response Estimation from ASL fMRI Data, 2015 International Workshop on Pattern Recognition in NeuroImaging, p.2015 ,
DOI : 10.1109/PRNI.2015.12
URL : https://hal.archives-ouvertes.fr/hal-01249015
Comparison of Stochastic and Variational Solutions to ASL fMRI Data Analysis, Medical Image Computing and Computer-Assisted Intervention?MICCAI 2015, pp.85-92, 2015. ,
DOI : 10.1109/ICASSP.2013.6637800
URL : https://hal.archives-ouvertes.fr/hal-01249018
Posterior probability maps and SPMs, NeuroImage, vol.19, issue.3, pp.1240-1249, 2003. ,
DOI : 10.1016/S1053-8119(03)00144-7
Event-Related fMRI: Characterizing Differential Responses, NeuroImage, vol.7, issue.1, pp.30-40, 1998. ,
DOI : 10.1006/nimg.1997.0306
Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics, NeuroImage, vol.12, issue.4, pp.466-477, 2000. ,
DOI : 10.1006/nimg.2000.0630
Classical and Bayesian Inference in Neuroimaging: Applications, NeuroImage, vol.16, issue.2, pp.484-512, 2002. ,
DOI : 10.1006/nimg.2002.1091
URL : http://www.fil.ion.ucl.ac.uk/spm/doc/papers/karl_peb_app.pdf
Classical and Bayesian Inference in Neuroimaging: Theory, NeuroImage, vol.16, issue.2, pp.465-483, 2002. ,
DOI : 10.1006/nimg.2002.1090
URL : http://orbi.ulg.ac.be/bitstream/2268/84738/1/Friston_K_2002_Neuroimage_16_2_465.pdf
A new representation of fMRI data using anatomo-functional constraints, Proc. 8th HBM, 2002. ,
URL : https://hal.archives-ouvertes.fr/inria-00615928
Pseudo-continuous ow driven adiabatic inversion for arterial spin labeling, In Proc Int Soc Magn Reson Med, vol.13, p.37, 2005. ,
Age dependence of hemodynamic response characteristics in human functional magnetic resonance imaging, Neurobiology of Aging, vol.34, issue.5, pp.1469-1485, 2013. ,
DOI : 10.1016/j.neurobiolaging.2012.11.002
Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate, NeuroImage, vol.15, issue.4, pp.870-878, 2002. ,
DOI : 10.1006/nimg.2001.1037
Introducing Markov chain Monte Carlo. Markov chain Monte Carlo in practice, p.19, 1996. ,
Deconvolution of Impulse Response in Event-Related BOLD fMRI1, NeuroImage, vol.9, issue.4, pp.416-429, 1999. ,
DOI : 10.1006/nimg.1998.0419
Arterial spin labelling: final steps to make it a clinical reality, Magnetic Resonance Materials in Physics, Biology and Medicine, vol.17, issue.10, pp.79-82, 2012. ,
DOI : 10.1111/j.1468-1331.2010.03040.x
Perfusion Imaging Using Arterial Spin Labeling, Topics in Magnetic Resonance Imaging, vol.15, issue.1, pp.10-27, 2004. ,
DOI : 10.1097/00002142-200402000-00003
Bayesian Modeling of the Hemodynamic Response Function in BOLD fMRI, NeuroImage, vol.14, issue.1 ,
DOI : 10.1006/nimg.2001.0795
A neuroradiologist???s guide to arterial spin labeling MRI in clinical practice, Neuroradiology, vol.26, issue.8, pp.1181-1202, 2015. ,
DOI : 10.1002/nbm.2836
The influence of extra- and intracranial artery disease on the BOLD signal in FMRI, NeuroImage, vol.20, issue.2, pp.1393-1399, 2003. ,
DOI : 10.1016/S1053-8119(03)00384-7
Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses, NeuroImage, vol.21, issue.4, pp.1639-1651, 2004. ,
DOI : 10.1016/j.neuroimage.2003.11.029
Multivariate autoregressive modeling of fMRI time series, NeuroImage, vol.19, issue.4, pp.1477-1491, 2003. ,
DOI : 10.1016/S1053-8119(03)00160-5
Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering, NeuroImage, vol.56, issue.4, pp.2109-2128, 2011. ,
DOI : 10.1016/j.neuroimage.2011.03.005
The choice of basis functions in event-related fMRI, NeuroImage, vol.13, issue.6, pp.149-149, 2001. ,
DOI : 10.1016/S1053-8119(01)91492-2
Quantitative analysis of arterial spin labeling FMRI data using a general linear model, Magnetic Resonance Imaging, vol.28, issue.7, pp.919-927, 2010. ,
DOI : 10.1016/j.mri.2010.03.035
Nonparametric Analysis of Statistic Images from Functional Mapping Experiments, Journal of Cerebral Blood Flow & Metabolism, vol.13, issue.1, pp.7-22, 1996. ,
DOI : 10.1038/jcbfm.1993.135
Investigating Human Neurovascular Coupling Using Functional Neuroimaging: A Critical Review of Dynamic Models, Frontiers in Neuroscience, vol.6, 2015. ,
DOI : 10.1038/nn980
URL : https://hal.archives-ouvertes.fr/hal-01266115
Glial regulation of the cerebral microvasculature, Nature Neuroscience, vol.281, issue.11, pp.1369-1376, 2007. ,
DOI : 10.1161/01.RES.64.1.136
FSL, NeuroImage, vol.62, issue.2, pp.782-790, 2012. ,
DOI : 10.1016/j.neuroimage.2011.09.015
URL : https://hal.archives-ouvertes.fr/inserm-01149484
An Introduction to Variational Methods for Graphical Models, Machine learning, vol.37, issue.2, pp.183-233, 1999. ,
DOI : 10.1007/978-94-011-5014-9_5
Interpreting and extending classical agglomerative clustering algorithms using a model-based approach, ICML, 2002. ,
Comparison of continuous overt speech fMRI using BOLD and arterial spin labeling, Human Brain Mapping, vol.42, issue.3, pp.173-183, 2005. ,
DOI : 10.1155/2000/421719
Activelets: Wavelets for sparse representation of hemodynamic responses, Signal Processing, vol.91, issue.12, pp.912810-2821, 2011. ,
DOI : 10.1016/j.sigpro.2011.03.008
Model of the Transient Neurovascular Response Based on Prompt Arterial Dilation, Journal of Cerebral Blood Flow & Metabolism, vol.94, issue.9, pp.1429-1439, 2013. ,
DOI : 10.1007/s00259-003-1430-8
Reduced Neuronal Activity in Language-Related Regions After Transcranial Magnetic Stimulation Therapy for Auditory Verbal Hallucinations, Biological Psychiatry, vol.73, issue.6, pp.73518-524, 2013. ,
DOI : 10.1016/j.biopsych.2012.06.019
Regional Impairment of Cerebrovascular Reactivity and BOLD Signal in Adults After Stroke, Stroke, vol.36, issue.6, pp.1146-1152, 2005. ,
DOI : 10.1161/01.STR.0000166178.40973.a7
URL : https://hal.archives-ouvertes.fr/inserm-00391163
Functional imaging of cerebral perfusion, Diagnostic and interventional imaging, pp.941259-1278, 2013. ,
DOI : 10.1016/j.diii.2013.08.004
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation, Proceedings of the National Academy of Sciences, pp.5675-5679, 1992. ,
Adaptive hemodynamic-informed parcellation of fMRI data in a variational joint detection estimation framework, 15th Proc. MICCAIPart III), pp.180-188, 2012. ,
Reproducibility of BOLD, perfusion, and CMRO2 measurements with calibrated-BOLD fMRI, NeuroImage, vol.35, issue.1, pp.175-184, 2007. ,
DOI : 10.1016/j.neuroimage.2006.10.044
Measurement of cerebral perfusion with arterial spin labeling: Part 1. Methods, Journal of the International Neuropsychological Society, vol.192, issue.03, pp.517-525, 2007. ,
DOI : 10.1073/pnas.0503082102
A signal processing model for arterial spin labeling functional MRI, NeuroImage, vol.24, issue.1, pp.207-215, 2005. ,
DOI : 10.1016/j.neuroimage.2004.09.047
Interpreting the BOLD Signal, Annual Review of Physiology, vol.66, issue.1, pp.735-769, 2004. ,
DOI : 10.1146/annurev.physiol.66.082602.092845
Neurophysiological investigation of the basis of the fMRI signal, Nature, vol.412, issue.6843, pp.412150-157, 2001. ,
DOI : 10.1038/35084005
Simulation study of brain blood flow regulation by intra-cortical arterioles in an anatomically accurate large human vascular network. Part II: Flow variations induced by global or localized modifications of arteriolar diameters, NeuroImage, vol.54, issue.4, pp.2840-2853, 2011. ,
DOI : 10.1016/j.neuroimage.2010.10.040
QUIPSS II with thin-slice TI 1 periodic saturation: a method for improving accuracy of quantitative perfusion imaging using pulsed arterial spin labeling, Magnetic resonance in medicine, issue.6, pp.411246-1254, 1999. ,
Comparison of simultaneously measured perfusion and BOLD signal increases during brain activation withT1-based tissue identification, Magnetic Resonance in Medicine, vol.40, issue.1, pp.137-143, 2000. ,
DOI : 10.1148/radiology.192.3.8058920
Joint detection-estimation of brain activity in functional MRI: a Multichannel Deconvolution solution, IEEE Transactions on Signal Processing, vol.53, issue.9, pp.3488-3502, 2005. ,
DOI : 10.1109/TSP.2005.853303
A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI, NeuroImage, vol.41, issue.3, pp.41941-969, 2008. ,
DOI : 10.1016/j.neuroimage.2008.02.017
URL : https://hal.archives-ouvertes.fr/cea-00333624
Estimating Biophysical Parameters from BOLD Signals through Evolutionary-Based Optimization, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.528-535, 2015. ,
DOI : 10.1016/j.neuroimage.2007.06.033
URL : https://hal.archives-ouvertes.fr/hal-01221126
A Differential Evolution-Based Approach for Fitting a Nonlinear Biophysical Model to fMRI BOLD Data, IEEE Journal of Selected Topics in Signal Processing, vol.10, issue.2, pp.416-427, 2016. ,
DOI : 10.1109/JSTSP.2015.2502553
URL : https://hal.archives-ouvertes.fr/hal-01221115
Estimation efficiency and statistical power in arterial spin labeling fMRI, NeuroImage, vol.33, issue.1, pp.103-114, 2006. ,
DOI : 10.1016/j.neuroimage.2006.05.040
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2772871/pdf
A view of the EM algorithm that justiies incremental, sparse, and other variants, Learning in graphical models, pp.355-368, 1998. ,
Controlling the familywise error rate in functional neuroimaging: a comparative review. Statistical methods in medical research, pp.419-446, 2003. ,
Nonparametric permutation tests for functional neuroimaging: A primer with examples, Human Brain Mapping, vol.4, issue.1, pp.1-25, 2002. ,
DOI : 10.1002/(SICI)1097-0193(1996)4:1<58::AID-HBM4>3.0.CO;2-O
Cortical responses to amphetamine exposure studied by pCASL MRI and pharmacokinetic/pharmacodynamic dose modeling, NeuroImage, vol.68, pp.75-82, 2013. ,
DOI : 10.1016/j.neuroimage.2012.11.035
Cerebral vascular response to hypercapnia: Determination with perfusion MRI at 1.5 and 3.0 Tesla using a pulsed arterial spin labeling technique, Journal of Magnetic Resonance Imaging, vol.52, issue.6 ,
DOI : 10.1113/jphysiol.1993.sp019913
Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the balloon model to the interpretation of BOLD transients, NeuroImage, vol.21, issue.1, pp.144-153, 2004. ,
DOI : 10.1016/j.neuroimage.2003.08.040
Brain magnetic resonance imaging with contrast dependent on blood oxygenation., Proceedings of the National Academy of Sciences, pp.9868-9872, 1990. ,
DOI : 10.1073/pnas.87.24.9868
URL : http://www.pnas.org/content/87/24/9868.full.pdf
Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model, Biophysical Journal, vol.64, issue.3, p.803, 1993. ,
DOI : 10.1016/S0006-3495(93)81441-3
Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment, IEEE Transactions on Medical Imaging, vol.22, issue.10, pp.1235-1251, 2003. ,
DOI : 10.1109/TMI.2003.817759
URL : https://hal.archives-ouvertes.fr/cea-00333694
Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00650905
Variational Bayesian inference for fMRI time series, NeuroImage, vol.19, issue.3, pp.727-741, 2003. ,
DOI : 10.1016/S1053-8119(03)00071-5
URL : http://www.fil.ion.ucl.ac.uk/~wpenny/publications/glmar.pdf
Classical and Bayesian Inference in fMRI, 2004. ,
DOI : 10.1201/9781420028669.ch17
Bayesian fMRI time series analysis with spatial priors, NeuroImage, vol.24, issue.2 ,
DOI : 10.1016/j.neuroimage.2004.08.034
Statistical parametric mapping: the analysis of functional brain images Academic press, 2011. ,
Localization of the hand motor area by arterial spin labeling and blood oxygen level-dependent functional magnetic resonance imaging, Human Brain Mapping, vol.120, issue.1, pp.96-108, 2013. ,
DOI : 10.1093/brain/120.1.141
Fast reproducible identiication and large-scale databasing of individual functional cognitive networks, BMC neuroscience, vol.8, issue.1, p.1, 2007. ,
Hypercapnia-Induced Cerebral Hyperperfusion: An Underrecognized Clinical Entity, American Journal of Neuroradiology, vol.43, issue.2, pp.378-385, 2009. ,
DOI : 10.1016/j.annemergmed.2003.08.003
URL : http://www.ajnr.org/content/ajnr/30/2/378.full.pdf
Functional network organization of the human brain, Neuron, vol.72, issue.4, pp.665-678, 2011. ,
Arterial spin labeling for motor activation mapping at 3T with a 32-channel coil: Reproducibility and spatial accuracy in comparison with BOLD fMRI, NeuroImage, vol.58, issue.1, pp.157-167, 2011. ,
DOI : 10.1016/j.neuroimage.2011.06.011
URL : https://hal.archives-ouvertes.fr/inserm-00604941
Correlated gene expression supports synchronous activity in brain networks, Science, vol.27, issue.2_Part_1, pp.3481241-1244, 2015. ,
DOI : 10.1016/j.neuroimage.2013.12.039
Monte Carlo statistical methods, 2013. ,
Markov chain concepts related to sampling algorithms. Markov chain Monte Carlo in practice, 1996. ,
A Four-Dimensional Registration Algorithm With Application to Joint Correction of Motion and Slice Timing in fMRI, IEEE Transactions on Medical Imaging, vol.30, issue.8, pp.1546-1554, 2011. ,
DOI : 10.1109/TMI.2011.2131152
Mixed-effect statistics for group analysis in fMRI: A nonparametric maximum likelihood approach, NeuroImage, vol.38, issue.3, pp.501-510, 2007. ,
DOI : 10.1016/j.neuroimage.2007.06.043
URL : https://hal.archives-ouvertes.fr/cea-00333625
Optimized simultaneous ASL and BOLD functional imaging of the whole brain, Journal of Magnetic Resonance Imaging, vol.108, issue.Pt 1, pp.1104-1117, 2014. ,
DOI : 10.1073/pnas.1103228108
Slice-timing effects and their correction in functional MRI, NeuroImage, vol.58, issue.2, pp.588-594, 2011. ,
DOI : 10.1016/j.neuroimage.2011.06.078
URL : https://doi.org/10.1016/j.neuroimage.2011.06.078
Comparing hemodynamic models with DCM, NeuroImage, vol.38, issue.3, pp.387-401, 2007. ,
DOI : 10.1016/j.neuroimage.2007.07.040
URL : https://doi.org/10.1016/j.neuroimage.2007.07.040
Sensitivity analysis of parcellation in the joint detection-estimation of brain activity in fMRI, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.568-571, 2008. ,
DOI : 10.1109/ISBI.2008.4541059
Which fMRI clustering gives good brain parcellations? Frontiers in neuroscience, p.167, 2014. ,
DOI : 10.3389/fnins.2014.00167
URL : http://journal.frontiersin.org/article/10.3389/fnins.2014.00167/pdf
Quantitative assessment of the reproducibility of functional activation measured with BOLD and MR perfusion imaging: Implications for clinical trial design, NeuroImage, vol.27, issue.2, pp.393-401, 2005. ,
DOI : 10.1016/j.neuroimage.2005.04.021
The variational approximation for Bayesian inference, IEEE Signal Processing Magazine, vol.25, issue.6, pp.131-146, 2008. ,
DOI : 10.1109/MSP.2008.929620
Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity, Biennial International Conference on Information Processing in Medical Imaging, pp.562-573, 2011. ,
DOI : 10.1007/978-3-642-22092-0_46
URL : https://hal.archives-ouvertes.fr/inria-00588898
Spatially Adaptive Mixture Modeling for Analysis of fMRI Time Series, IEEE Transactions on Medical Imaging, vol.29, issue.4, pp.1059-1074, 2010. ,
DOI : 10.1109/TMI.2010.2042064
URL : https://hal.archives-ouvertes.fr/cea-00470594
Bayesian bold and perfusion source separation and deconvolution from functional ASL imaging, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1003-1007, 2013. ,
DOI : 10.1109/ICASSP.2013.6637800
URL : https://hal.archives-ouvertes.fr/hal-00859373
Bayesian Joint Detection-Estimation of Cerebral Vasoreactivity from ASL fMRI Data, 16th Proc. MICCAI, pp.616-623, 2013. ,
DOI : 10.1007/978-3-642-40763-5_76
URL : https://hal.archives-ouvertes.fr/hal-00854437
Potentials and Challenges for Arterial Spin Labeling in Pharmacological Magnetic Resonance Imaging, Journal of Pharmacology and Experimental Therapeutics, vol.337, issue.2, pp.359-366, 2011. ,
DOI : 10.1124/jpet.110.172577
Arterial spin labeling perfusion fMRI with very low task frequency, Magnetic Resonance in Medicine, vol.45, issue.5, pp.796-802, 2003. ,
DOI : 10.1002/mrm.10437
URL : http://onlinelibrary.wiley.com/doi/10.1002/mrm.10437/pdf
Regional reproducibility of pulsed arterial spin labeling perfusion imaging at 3T, NeuroImage, vol.54, issue.2, pp.1188-1195, 2011. ,
DOI : 10.1016/j.neuroimage.2010.08.043
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2997151/pdf
Empirical optimization of ASL data analysis using an ASL data processing toolbox: ASLtbx, Magnetic Resonance Imaging, vol.26, issue.2, pp.261-269, 2008. ,
DOI : 10.1016/j.mri.2007.07.003
Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, vol.58, issue.301, pp.236-244, 1963. ,
DOI : 10.1007/BF02289263
Magnetic resonance imaging of perfusion using spin inversion of arterial water., Proceedings of the National Academy of Sciences, pp.212-216, 1992. ,
DOI : 10.1073/pnas.89.1.212
Arterial spin labeling MRI, Current Opinion in Neurology, vol.25, issue.4, p.421, 2012. ,
DOI : 10.1097/WCO.0b013e328354ff0a
Velocity-selective arterial spin labeling, Magnetic Resonance in Medicine, vol.55, issue.6 ,
DOI : 10.1038/jcbfm.1984.15
Variational bayes inference of spatial mixture models for segmentation, IEEE Transactions on Medical Imaging, vol.25, issue.10 ,
DOI : 10.1109/TMI.2006.880682
Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data, NeuroImage, vol.14, issue.6, pp.1370-1386, 2001. ,
DOI : 10.1006/nimg.2001.0931
Fully Bayesian Spatio-Temporal Modeling of FMRI Data, IEEE Transactions on Medical Imaging, vol.23, issue.2, pp.213-231, 2004. ,
DOI : 10.1109/TMI.2003.823065
A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human Brain, Journal of Cerebral Blood Flow & Metabolism, vol.251, issue.6, pp.900-918, 1992. ,
DOI : 10.1126/science.2003220
Perfusion MR imaging with pulsed arterial spin-labeling: Basic principles and applications in functional brain imaging, Concepts in Magnetic Resonance, pp.347-357, 2002. ,
DOI : 10.1161/01.STR.5.5.630
Actual flip-angle imaging in the pulsed steady state: A method for rapid three-dimensional mapping of the transmitted radiofrequency field, Magnetic Resonance in Medicine, vol.55, issue.1, pp.192-200, 2007. ,
DOI : 10.1259/bjr.71.841.9534700