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[HTML][HTML] Methods for cleaning the BOLD fMRI signal
Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has
rapidly become a popular technique for the investigation of brain function in healthy …
rapidly become a popular technique for the investigation of brain function in healthy …
Classification and prediction of brain disorders using functional connectivity: promising but challenging
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI)
data, have been employed to reflect functional integration of the brain. Alteration in brain …
data, have been employed to reflect functional integration of the brain. Alteration in brain …
[HTML][HTML] Hand classification of fMRI ICA noise components
We present a practical “how-to” guide to help determine whether single-subject fMRI
independent components (ICs) characterise structured noise or not. Manual identification of …
independent components (ICs) characterise structured noise or not. Manual identification of …
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI
We proposed ICA-AROMA as a strategy for the removal of motion-related artifacts from fMRI
data (Pruim et al., 2015). ICA-AROMA automatically identifies and subsequently removes …
data (Pruim et al., 2015). ICA-AROMA automatically identifies and subsequently removes …
Advancing functional connectivity research from association to causation
Cognition and behavior emerge from brain network interactions, such that investigating
causal interactions should be central to the study of brain function. Approaches that …
causal interactions should be central to the study of brain function. Approaches that …
Noise contributions to the fMRI signal: An overview
The ability to discriminate signal from noise plays a key role in the analysis and
interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity …
interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity …
Artifact removal in the context of group ICA: A comparison of single‐subject and group approaches
Independent component analysis (ICA) has been widely applied to identify intrinsic brain
networks from fMRI data. Group ICA computes group‐level components from all data and …
networks from fMRI data. Group ICA computes group‐level components from all data and …
[PDF][PDF] Ten key observations on the analysis of resting-state functional MR imaging data using independent component analysis
Independent component analysis (ICA) has grown to be a widely used and continually
develo** staple for analyzing fMRI functional connectivity data. In this paper we discuss …
develo** staple for analyzing fMRI functional connectivity data. In this paper we discuss …
Machine learning identifies “rsfMRI epilepsy networks” in temporal lobe epilepsy
Objectives Experimental models have provided compelling evidence for the existence of
neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible …
neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible …
Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples
The search for clinically relevant neuroimaging biomarkers for neurological and psychiatric
disorders is rapidly accelerating. Here, we highlight some of these aspects, provide recent …
disorders is rapidly accelerating. Here, we highlight some of these aspects, provide recent …