Probabilistic independent component analysis for functional magnetic resonance imaging

By Christian F. Beckmann, Stephen M. Smith. IEEE Transactions on Medical Imaging, 2004

2,813 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Radiology, Nuclear Medicine and Imaging, Signal Processing

View Paper: https://www.semanticscholar.org/paper/97dd13e512a6305c7eb9273714cd20c55c4a6474

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Citations in the corpus

Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis (2012, methodology): “Melodic was used for the PICA calculation of RSN (Beckmann and Smith, 2004) in Figure 2 as well as for denoising motion artifacts.”
How default is the default mode of brain function? Further evidence from intrinsic BOLD signal fluctuations. (2006, methodology): “The pre-processing steps nvolved transforming the smoothed, normalized and realigned EPI volumes into D datasets which were subsequently analyzed with the FSL MELODIC softare (Beckmann & Smith, 2004).”
Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control (2007, methodology): “ICA is a statistical technique that separates a set of signals into independent (uncorrelated and nonGaussian) spatiotemporal components (Beckmann and Smith, 2004).”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, positive): “Given ICA results, as in (11) or PICA [57], STR first estimates subject-specific TCs, , then SMs, , via multiple regression.”
Discovering dynamic brain networks from big data in rest and task (2017, methodology): “Indeed, the ICA decompositions emphasise different areas in the two data sets.”
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI (2007, methodology): “Additionally, component-based techniques, utilizing independent component analysis (ICA) or principal component analysis (PCA), have shown potential in identifying spatial and temporal patterns of structured noise (Thomas et al., 2002; McKeown et al., 2003; Beckmann and Smith, 2004).”
Estimating the number of independent components for functional magnetic resonance imaging data (2007, methodology): “(5), an eigenspectrum adjustment (Beckmann and Smith, 2004) is used to mitigate the finite sample effect.”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “Since the late 1990s, spatial ICA (sICA) has been applied to fMRI as a data-driven approach that estimates networks from the entire spatiotemporal dataset at once (Beckmann and Smith, 2004; Calhoun and Adali, 2012; Calhoun et al., 2001; McKeown et al., 1998).”
ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI (2015, methodology): “One of the techniques used for this purpose is Independent Component Analysis (ICA)—a data-driven technique that facilitates comparison of functional networks in the brain without requiring a priori selected seed regions (Beckmann and Smith, 2004).”
Advances in functional and structural MR image analysis and implementation as FSL. (2004, methodology): “In [4] we proposed a probabilistic ICA (PICA) model for FMRI which models the observations as mixtures of spatially non-Gaussian signals and artefacts in the presence of Gaussian noise.”
Linked independent component analysis for multimodal data fusion (2011, methodology): “There is also the possibility of applying these techniques to non-MRI neuroimaging data, for example by combining FMRI volumes and EEG epochs on a trial-by-trial (rather than subject-by-subject) basis.”
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data (2006, neutral): “…analysis, one could perfectly well feed the entire (sparse) 4D dataset into a multivariate approach such as ICA (independent component analysis (Beckmann and Smith, 2004)), and not only generate added benefit from modelling the spatial aspects of the signal, but potentially find cross-subject…”
Financial time series forecasting using independent component analysis and support vector regression (2009, methodology): “Even though the basic ICA model has been widely applied in signal processing, face recognition, feature extraction and quality control [3, 17, 28, 29, 31, 42, 43, 58], there are still few applications using ICA in financial time series forecasting.”
The WU-Minn Human Connectome Project: An Overview (2013, methodology): “FSL's MELODIC tool (Beckmann and Smith, 2004) is used to decompose the data into multiple (typically ~230) components, each comprising a single spatial map and an associated timecourse.”
The Human Connectome Project’s Neuroimaging Approach (2016, methodology): “(i) In HCP data, the global signal (after ICA+FIX cleanup) is primarily a gray matter signal, with a near-zero amplitude in white matter voxels and low-level anti-correlation with cerebrospinal fluid ( Supplementary Fig.”
MSM: a new flexible framework for Multimodal Surface Matching☆ (2014, positive): “…this, a set of matched components (spatial maps and associated timecourses) were generated across subjects through the process of Dual Regression (Beckmann and Smith, 2004); first regressing group maps onto subjects’ timeseries data to produce a set of component timecourses, 24 AC C EP TE D M AN…”