A group model for stable multi-subject ICA on fMRI datasets.
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How machine learning is shaping cognitive neuroimaging.Which fMRI clustering gives good brain parcellations?An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD.Robust data driven model order estimation for independent component analysis of FMRI data with low contrast to noise.Detecting Spatio-Temporal Modes in Multivariate Data by Entropy Field DecompositionInstability of default mode network connectivity in major depression: a two-sample confirmation study.Comparison of multi-subject ICA methods for analysis of fMRI data.Imaging human connectomes at the macroscale.The relation of ongoing brain activity, evoked neural responses, and cognition.Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study.A novel group ICA approach based on multi-scale individual component clustering. Application to a large sample of fMRI data.A functional network estimation method of resting-state fMRI using a hierarchical Markov random field.Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraintsA hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG.Machine learning for neuroimaging with scikit-learnLarge-scale probabilistic functional modes from resting state fMRISubject-specific functional parcellation via prior based eigenanatomy.Intact bilateral resting-state networks in the absence of the corpus callosum.Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task.Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging.Machine-learning to characterise neonatal functional connectivity in the preterm brain.Time course based artifact identification for independent components of resting-state FMRI.Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI.Select and Cluster: A Method for Finding Functional Networks of Clustered Voxels in fMRI.Dynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition.Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks.Neuroimaging paradigms for tonotopic mapping (I): the influence of sound stimulus type.Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.Detection of epileptic activity in fMRI without recording the EEG.Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis.When three is greater than five: EEG and fMRI signatures of errors in numerical and physical comparisons.Extracting intrinsic functional networks with feature-based group independent component analysis.Functional connectivity changes differ in early and late-onset Alzheimer's disease.The effects of serotonin modulation on medial prefrontal connectivity strength and stability: A pharmacological fMRI study with citalopram.Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI.Statistical Learning for Resting-State fMRI: Successes and Challenges
P2860
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P2860
A group model for stable multi-subject ICA on fMRI datasets.
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2010 nî lūn-bûn
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2010年学术文章
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name
A group model for stable multi-subject ICA on fMRI datasets.
@en
A group model for stable multi-subject ICA on fMRI datasets.
@nl
type
label
A group model for stable multi-subject ICA on fMRI datasets.
@en
A group model for stable multi-subject ICA on fMRI datasets.
@nl
prefLabel
A group model for stable multi-subject ICA on fMRI datasets.
@en
A group model for stable multi-subject ICA on fMRI datasets.
@nl
P2093
P1433
P1476
A group model for stable multi-subject ICA on fMRI datasets.
@en
P2093
A Kleinschmidt
J B Poline
P304
P356
10.1016/J.NEUROIMAGE.2010.02.010
P407
P577
2010-02-12T00:00:00Z