Surface-based analysis increases the specificity of cortical activation patterns and connectivity results
暂无分享,去创建一个
Stefan Brodoehl | Otto W. Witte | Christian Gaser | Robert Dahnke | Carsten M. Klingner | O. Witte | Christian Gaser | R. Dahnke | C. Klingner | S. Brodoehl
[1] Stephen M. Smith,et al. Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.
[2] Marisa O. Hollinshead,et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.
[3] Paul L Gribble,et al. Functional Connectivity Between Somatosensory and Motor Brain Areas Predicts Individual Differences in Motor Learning by Observing , 2017, bioRxiv.
[4] Karl J. Friston,et al. Statistical parametric mapping , 2013 .
[5] Takeo Ishigaki,et al. Contralateral and ipsilateral responses in primary somatosensory cortex following electrical median nerve stimulation—an fMRI study , 2005, Clinical Neurophysiology.
[6] Jae-Hun Kim,et al. Surface-based functional magnetic resonance imaging analysis of partial brain echo planar imaging data at 1.5 T. , 2009, Magnetic resonance imaging.
[7] Hans Knutsson,et al. Correction for Eklund et al., Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.
[8] Robert Chen,et al. Digit somatotopy within cortical areas of the postcentral gyrus in humans. , 2008, Cerebral cortex.
[9] Joshua Carp,et al. The secret lives of experiments: Methods reporting in the fMRI literature , 2012, NeuroImage.
[10] David C Van Essen,et al. The impact of traditional neuroimaging methods on the spatial localization of cortical areas , 2018, Proceedings of the National Academy of Sciences.
[11] Andrea Bergmann,et al. Statistical Parametric Mapping The Analysis Of Functional Brain Images , 2016 .
[12] Brian Knutson,et al. Spatial smoothing systematically biases the localization of reward-related brain activity , 2013, NeuroImage.
[13] Carsten Klingner,et al. Dependence of the negative BOLD response on somatosensory stimulus intensity , 2010, NeuroImage.
[14] Timothy O. Laumann,et al. Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.
[15] Jae-Hun Kim,et al. Spatial accuracy of fMRI activation influenced by volume- and surface-based spatial smoothing techniques , 2007, NeuroImage.
[16] Jan Ruben,et al. Sub-area-specific Suppressive Interaction in the BOLD responses to simultaneous finger stimulation in human primary somatosensory cortex: evidence for increasing rostral-to-caudal convergence. , 2006, Cerebral cortex.
[17] L. Wu,et al. Intrinsic Functional Plasticity of the Sensory-Motor Network in Patients with Cervical Spondylotic Myelopathy , 2015, Scientific Reports.
[18] John G. Csernansky,et al. Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia , 2008, NeuroImage.
[19] Matthew F. Glasser,et al. Lost in Space: The Impact of Traditional Neuroimaging Methods on the Spatial Localization of Cortical Areas , 2018, bioRxiv.
[20] Russell A. Poldrack,et al. Handbook of Functional MRI Data Analysis: Visualizing, localizing, and reporting fMRI data , 2011 .
[21] César Caballero-Gaudes,et al. Methods for cleaning the BOLD fMRI signal , 2016, NeuroImage.
[22] Karl J. Friston,et al. To Smooth or Not to Smooth? Bias and Efficiency in fMRI Time-Series Analysis , 2000, NeuroImage.
[23] Jesper Andersson,et al. A multi-modal parcellation of human cerebral cortex , 2016, Nature.
[24] Colin Humphries,et al. Tonotopic organization of human auditory cortex , 2010, NeuroImage.
[25] Christian Gaser,et al. Topological correction of brain surface meshes using spherical harmonics , 2010, MICCAI.
[26] W. Backes,et al. Somatosensory cortex responses to median nerve stimulation: fMRI effects of current amplitude and selective attention , 2000, Clinical Neurophysiology.
[27] Karl J. Friston,et al. MRI and PET Coregistration—A Cross Validation of Statistical Parametric Mapping and Automated Image Registration , 1997, NeuroImage.
[28] Zikuan Chen,et al. Effect of Spatial Smoothing on Task fMRI ICA and Functional Connectivity , 2018, Front. Neurosci..
[29] Benjamin D. Singer,et al. Retinotopic Organization of Human Ventral Visual Cortex , 2009, The Journal of Neuroscience.
[30] Stephen M. Smith,et al. Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data , 2017, NeuroImage.
[31] John C Gore,et al. Distinct fine‐scale fMRI activation patterns of contra‐ and ipsilateral somatosensory areas 3b and 1 in humans , 2014, Human brain mapping.
[32] Mary Beth Nebel,et al. The impact of T1 versus EPI spatial normalization templates for fMRI data analyses , 2017, Human brain mapping.
[33] Junjie Liu,et al. Laminar profiles of functional activity in the human brain , 2007, NeuroImage.
[34] Ayse Pinar Saygin,et al. Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data , 2006, NeuroImage.
[35] Anders M. Dale,et al. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature , 2010, NeuroImage.
[36] Steen Moeller,et al. The Human Connectome Project's neuroimaging approach , 2016, Nature Neuroscience.
[37] Daniel Rueckert,et al. Multimodal surface matching with higher-order smoothness constraints , 2017, NeuroImage.
[38] Olaf Blanke,et al. Human finger somatotopy in areas 3b, 1, and 2: A 7T fMRI study using a natural stimulus , 2014, Human brain mapping.
[39] Satrajit S. Ghosh,et al. Evaluation of volume-based and surface-based brain image registration methods , 2010, NeuroImage.
[40] Riitta Hari,et al. Transient Suppression of Ipsilateral Primary Somatosensory Cortex during Tactile Finger Stimulation , 2006, The Journal of Neuroscience.
[41] F. Pizzagalli,et al. Local landmark alignment for high-resolution fMRI group studies: Toward a fine cortical investigation of hand movements in human , 2013, Journal of Neuroscience Methods.
[42] Hans Knutsson,et al. Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.
[43] Simon B Eickhoff,et al. Imaging-based parcellations of the human brain , 2018, Nature Reviews Neuroscience.
[44] Chandrajit L. Bajaj,et al. Surface-based analysis methods for high-resolution functional magnetic resonance imaging , 2011, Graph. Model..
[45] Amir Amedi,et al. Positive and Negative Somatotopic BOLD Responses in Contralateral Versus Ipsilateral Penfield Homunculus , 2017, Cerebral cortex.
[46] Jens Frahm,et al. Finger representations in human primary somatosensory cortex as revealed by high-resolution functional MRI of tactile stimulation , 2008, NeuroImage.
[47] Yufeng Zang,et al. Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space , 2013, NeuroImage.
[48] Thomas E. Nichols,et al. Validating cluster size inference: random field and permutation methods , 2003, NeuroImage.
[49] J. Karhu,et al. Simultaneous early processing of sensory input in human primary (SI) and secondary (SII) somatosensory cortices. , 1999, Journal of neurophysiology.
[50] Mark Jenkinson,et al. The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.
[51] Bertrand Thirion,et al. An empirical comparison of surface-based and volume-based group studies in neuroimaging , 2012, NeuroImage.
[52] Tianzi Jiang,et al. Disrupted effective connectivity of the sensorimotor network in amyotrophic lateral sclerosis , 2016, Journal of Neurology.
[53] Jonathan R. Polimeni,et al. Intracortical depth analyses of frequency-sensitive regions of human auditory cortex using 7TfMRI , 2016, NeuroImage.
[54] Muthuraman Muthuraman,et al. Source analysis of median nerve stimulated somatosensory evoked potentials and fields using simultaneously measured EEG and MEG signals , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[55] Carsten Klingner,et al. Parallel processing of somatosensory information: Evidence from dynamic causal modeling of MEG data , 2015, NeuroImage.
[56] M. Farah,et al. Progress and challenges in probing the human brain , 2015, Nature.
[57] Ravi Bansal,et al. Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions. , 2018, Magnetic resonance imaging.
[58] Mukund Balasubramanian,et al. High-resolution fMRI investigations of the fingertip somatotopy and variability in BA3b and BA1 of the primary somatosensory cortex , 2016, Neuroscience.
[59] Benoit M. Dawant,et al. Comparison of fMRI statistical software packages and strategies for analysis of images containing random and stimulus-correlated motion , 2007, Comput. Medical Imaging Graph..
[60] Peter Fransson,et al. Fingersomatotopy in area 3b: an fMRI-study , 2004, BMC Neuroscience.
[61] O. Witte,et al. Functional deactivations: Multiple ipsilateral brain areas engaged in the processing of somatosensory information , 2011, Human brain mapping.
[62] D. V. van Essen,et al. Surface-based approaches to spatial localization and registration in primate cerebral cortex. , 2004, NeuroImage.
[63] D Le Bihan,et al. Detection of fMRI activation using Cortical Surface Mapping , 2001, Human brain mapping.
[64] Peng Liu,et al. Functional overestimation due to spatial smoothing of fMRI data , 2017, Journal of Neuroscience Methods.
[65] Aina Puce,et al. A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies , 2017, Brain sciences.
[66] Arno Klein,et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.
[67] H. Railo,et al. Retinotopic Maps, Spatial Tuning, and Locations of Human Visual Areas in Surface Coordinates Characterized with Multifocal and Blocked fMRI Designs , 2012, PloS one.
[68] Alan C. Evans,et al. Cortical thickness analysis examined through power analysis and a population simulation , 2005, NeuroImage.
[69] Robert Turner,et al. Comparing Like with Like: The Power of Knowing Where You Are , 2014, Brain Connect..
[70] E. Ganz,et al. A Hitchhiker's Guide to Functional Magnetic Resonance Imaging , 2016, Front. Neurosci..
[71] Christian Gaser,et al. Cortical thickness and central surface estimation , 2013, NeuroImage.
[72] S. Kiebel,et al. Detecting Structural Changes in Whole Brain Based on Nonlinear Deformations—Application to Schizophrenia Research , 1999, NeuroImage.
[73] J. Brewer,et al. Default network correlations analyzed on native surfaces , 2011, Journal of Neuroscience Methods.
[74] Vince D. Calhoun,et al. SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability , 2012, NeuroImage.
[75] P. Hluštík,et al. Effects of spatial smoothing on fMRI group inferences. , 2008, Magnetic resonance imaging.
[76] Seong-Gi Kim,et al. Neural Interpretation of Blood Oxygenation Level-Dependent fMRI Maps at Submillimeter Columnar Resolution , 2007, The Journal of Neuroscience.
[77] Karl J. Friston,et al. Anatomically Informed Basis Functions , 2000, NeuroImage.
[78] C. Keysers,et al. The Observation and Execution of Actions Share Motor and Somatosensory Voxels in all Tested Subjects: Single-Subject Analyses of Unsmoothed fMRI Data , 2008, Cerebral cortex.
[79] Claus Lamm,et al. Unsmoothed functional MRI of the human amygdala and bed nucleus of the stria terminalis during processing of emotional faces , 2017, NeuroImage.