Functional segmentation of the brain cortex using high model order group PICA

By Vesa Kiviniemi, Tuomo Starck, Jukka J. Remes, Xiangyu Long, Juha Nikkinen, Marianne Haapea, Juha Veijola, I. Moilanen, M. Isohanni, Yufeng Zang and 1 more. Human Brain Mapping, 2009

388 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/a4abe58ba21a22dfdedf16413638bc4aa74731b3

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In Cognitive Neuroscience

Citations in the corpus

A Baseline for the Multivariate Comparison of Resting-State Networks (2011, methodology): “We chose relatively high model order ICA (number of components, C = 75) as previous studies have demonstrated that such models yield refined components that correspond to known anatomical and functional segmentations (Kiviniemi et al., 2009; Smith et al., 2009; Abou-Elseoud et al., 2010; Ystad et al”
Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, positive): “ICA has been used to de ﬁ ne functional parcellations or nodes of functional connectomes by many groups (Kiviniemi et al., 2009; Rashid et al., 2014; Smith et al., 2015; Miller et al., 2016).”
Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study (2012, methodology): “…~60,000 intracranial voxels), and accordingly used a smaller number of true sources (C=25) than the number consistently and stably estimated in real data, which appears to be in the range of 70 to 100 (Kiviniemi et al., 2009; Ystad et al., 2010; Abou-Elseoud et al., 2010; Allen et al., 2011).”
Network modelling methods for FMRI (2011, positive): “However, if a higher numberof components is estimated (Kiviniemi et al., 2009), these are more likely to be smaller, isolated regions (functional parcels), which canmore sensibly be then considered as nodes for use in network analysis.”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, neutral): “High-dimensional spatial independent component analysis has also been used in this context (Kiviniemi et al., 2009).”
CLASSIFICATION OF SCHIZOPHRENIA AND BIPOLAR PATIENTS USING STATIC AND DYNAMIC RESTING-STATE FMRI BRAIN CONNECTIVITY (2016, methodology): “FC can also be investigated at the network level using spatial independent component analysis (ICA), and connectivity among spatial components is referred to as functional network connectivity (FNC) (Jafri et al., 2008).”
Which fMRI clustering gives good brain parcellations? (2014, methodology): “Parcellations can also be obtained from dictionary learning techniques such as independent components analysis (ICA) and variants of principal components analysis (PCA) ( Kiviniemi et al. , 2009; Varoquaux et al. , 2010, 2011, 2013; Abraham et al. , 2013).”
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.”
Behavioral Interpretations of Intrinsic Connectivity Networks (2011, methodology): “in repeated decompositions of resting state data (AbouElseoud et al., 2010; Kiviniemi et al., 2009).”
Decoding subject-driven cognitive states with whole-brain connectivity patterns. (2012, methodology): “Of the 30 components generated, 14 were selected visually as being ICNs based on previous reports by our group and others (Greicius et al. 2003; Fox et al. 2005; Damoiseaux et al. 2006; Seeley et al. 2007; Kiviniemi et al. 2009; Smith et al. 2009).”
Graph-Based Network Analysis of Resting-State Functional MRI (2010, positive): “…of the default-mode network into multiple sub-networks is also observed in previous ICA studies (Zuo et al., 2010a) and could imply functional segregation or heterogeneity within the default-mode network (Buckner et al., 2008; Harrison et al., 2008; Kiviniemi et al., 2009; Uddin et al., 2009).”
Abnormal intrinsic brain functional network dynamics in Parkinson’s disease (2017, methodology): “We then selected a higher-order ICA approach (100 independent components) to improve functional parcellation (Kiviniemi et al., 2009).”
Inter-Individual Differences in Resting State Functional Connectivity Predict Task-Induced BOLD Activity (2010, background): “1 Hz) fluctuations in the BOLD signal within distinct functional networks (Biswal et al., 1995; Damoiseaux et al., 2006; Fox and Raichle, 2007; Fox et al., 2005; Greicius et al., 2003; Kiviniemi et al., 2009; Margulies et al., 2007; Vincent et al., 2007).”
Association between heart rate variability and fluctuations in resting-state functional connectivity (2012, methodology): “Our analysis framework could be extended to a more comprehensive whole-brain investigation using parcellations derived from methods such as independent component analysis or clustering (Craddock et al., 2012; Kiviniemi et al., 2009), and to examine seed regions from other major networks (e.”
Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks (2019, positive): “…components or fewer) when attempting to recover macro-scale functional networks (Ray et al. 2013), and higher model order ICA (e.g. 100 components or greater) when aiming to achieve brain parcellation (Kiviniemi et al. 2009) or delineate ROIs to be used in subsequent analyses (Allen et al. 2014).”
The future of FMRI connectivity (2012, background): “However, if a higher number of components is estimated (Kiviniemi et al., 2009), these are more likely to be smaller, isolated regions (parcels), which can more sensibly be then considered as nodes for use in network analysis.”