Independent component analysis of nondeterministic fMRI signal sources

By Vesa Kiviniemi, Juha-Heikki Kantola, Jukka Jauhiainen, Aapo Hyvärinen, Osmo Tervonen. NeuroImage, 2003

395 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e6fa8b95d7f677b7662193abba696b183224d738

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Signal Processing

Citations in the corpus

Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging (2007, methodology): “Finally, noise sources can be isolated from the BOLD data itself through techniques such as independent components analysis (ICA, see below”
Functional and Effective Connectivity: A Review (2011, neutral): “ICA has very similar objectives to principal component analysis (PCA), but assumes the modes are driven by non-Gaussian random ﬂuctu-ations (Calhoun and Adali, 2006; Kiviniemi et al., 2003; McKeown et al., 1998).”
Decoding subject-driven cognitive states with whole-brain connectivity patterns. (2012, background): “…memory, visuospatial attention, salience processing, and episodic memory (Biswal et al. 1995; Hampson et al. 2002; Greicius et al. 2003; Kiviniemi et al. 2003; Beckmann et al. 2005; Bellec et al. 2006; Damoiseaux et al. 2006; Seeley et al. 2007; Kiviniemi et al. 2009; Smith et al.…”
Resting state fMRI: A review of methods and clinical applications (2012, neutral): “patients with altered mental status, sedated patients, and those who are paretic or aphasic,(56-58) especially because BOLD activity seen in RS-fMRI has also been seen during sleep(59) and anesthesia.(60,61) Another advantage of RS-fMRI over task-based fMRI is”
REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing (2011, methodology): “This procedure is usually referred to as functional connectivity analysis, for which linear correlation [1] and independent component analysis (ICA) [9] are two frequently used methods.”
The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? (2008, neutral): “With ICA, statistically independent maps and their associated time courses are extracted from the data.”
Investigations into resting-state connectivity using independent component analysis (2005, positive): “Alternatively, it has been argued that these effects simply reflect vascular processes unrelated to neuronal function, which would make RSNs of less interest to neuroscience (although still of potential clinical interest ).”
Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study (2011, background): “…brain tissues [e.g., gray matter (GM) and white matter (WM)] (Biswal et al., 1995), between different brain regions (e.g., visual and auditory regions) (Kiviniemi et al., 2003), and between different physiological states (e.g., eyes closed vs. eyes open) (Yan et al., 2009; Yang et al., 2007).”
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, methodology): “This is not surprising given that ICA is eﬀective for denoising FMRI data by identifying and separating out eﬀects due to both physiological and subject motion (McKeown et al., 1998; Kiviniemi et al., 2003; ∗ SM accurately localize the group differences only to posterior cingulate (yellow).”
Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS) (2012, background): “…and can be observed under sleep (Fukunaga et al., 2006; Horovitz et al., 2009; Larson-Prior et al., 2009) and sedation (Greicius et al., 2008; Kiviniemi et al., 2003; Peltier et al., 2005; Vincent et al., 2007) allowing for comparisons across development (Dosenbach et al., 2010; Fair et al.,…”
Multimodal population brain imaging in the UK Biobank prospective epidemiological study (2016, neutral): “While this might not provide statistical certainty for a diagnosis or interventional recommendation, it should allow for single-patient imaging to be used in a similar way to current state-of-the-art patient-tuned genetic testing.”
Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers (2014, result): “Later, (e.g., Kiviniemi et al., 2003) it was shown that amongst the structured processes identi ﬁ able through ICA, resting-state networks could be found as components distinct from each other and from artefactual effects in the data.”
Resting-state FMRI confounds and cleanup (2013, positive): “Independent component analyses (ICA) is a successful technique for detecting consistent resting-state networks (Bartels and Zeki, 2004; Beckmann et al., 2005; Damoiseaux et al., 2006; Kiviniemi et al., 2003).”
Impairment and compensation coexist in amnestic MCI default mode network (2010, methodology): “For this reason, several recent studies have used model-free approaches involving independent component analysis (ICA) to depict RSNs (Damoiseaux et al., 2006; Greicius et al., 2004; Kiviniemi et al., 2003), and this approach does not require a”
Inter-Individual Differences in Resting State Functional Connectivity Predict Task-Induced BOLD Activity (2010, methodology): “…and their independence from the specific analytical approach (i.e., seed-based) used for calculating RSFC, we performed a follow-up analysis in which we applied ICA, a datadriven approach, to identify the default mode network (Damoiseaux et al., 2006; Kiviniemi et al., 2003; Smith et al., 2009).”
Assessing functional connectivity in the human brain by fMRI. (2007, methodology): “Independent components analysis as a functional connectivity technique is somewhat similar to PCA in terms of results and interpretation, but assumes that the sources of resulting features are statistically independent, not merely uncorrelated [43–45].”