FMRI resting state networks define distinct modes of long-distance interactions in the human brain

Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, UK.
NeuroImage (Impact Factor: 6.36). 03/2006; 29(4):1359-67. DOI: 10.1016/j.neuroimage.2005.08.035
Source: PubMed

ABSTRACT Functional magnetic resonance imaging (fMRI) studies of the human brain have suggested that low-frequency fluctuations in resting fMRI data collected using blood oxygen level dependent (BOLD) contrast correspond to functionally relevant resting state networks (RSNs). Whether the fluctuations of resting fMRI signal in RSNs are a direct consequence of neocortical neuronal activity or are low-frequency artifacts due to other physiological processes (e.g., autonomically driven fluctuations in cerebral blood flow) is uncertain. In order to investigate further these fluctuations, we have characterized their spatial and temporal properties using probabilistic independent component analysis (PICA), a robust approach to RSN identification. Here, we provide evidence that: i. RSNs are not caused by signal artifacts due to low sampling rate (aliasing); ii. they are localized primarily to the cerebral cortex; iii. similar RSNs also can be identified in perfusion fMRI data; and iv. at least 5 distinct RSN patterns are reproducible across different subjects. The RSNs appear to reflect "default" interactions related to functional networks related to those recruited by specific types of cognitive processes. RSNs are a major source of non-modeled signal in BOLD fMRI data, so a full understanding of their dynamics will improve the interpretation of functional brain imaging studies more generally. Because RSNs reflect interactions in cognitively relevant functional networks, they offer a new approach to the characterization of state changes with pathology and the effects of drugs.

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Available from: Paul M Matthews, Jan 11, 2015
    • "Slow-2 (above 0.198Hz) (Zuo, Di Martino, 2010). Slow-5 and Slow-4 bands were investigated in the current study, as previous work has demonstrated that resting-state FC is primarily located within Slows 4-5 (De Luca et al. , 2006, Salvador et al. , 2008). Slow-6, Slow-3 and Slow-2 frequency bands were discarded as they mainly reflect low-frequency drifting, white matter signals and high-frequency physiological noise (Biswal, Yetkin, 1995, Zuo, Di Martino, 2010) and classifiers obtained by support vector machine (SVM) are sensitive to noise (Guyon et al. , 1996). "
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    • "working memory) task performance. There is increasing evidence, however, that the resting-state activity and deactivation of these neural networks, such as the default mode network (DMN) (regions that are active during non-task conditions and are suppressed by goal-directed cognitive demands; Raichle et al. 2001), determine the ability of task-positive networks to perform tasks, as measured by task-related fMRI (De Luca et al. 2006) and vary across individuals to predict behavioral performance (Kelly et al. 2008). There are a number of studies of dopaminergic modulation of task-induced changes in the DMN with transient dopamine depletion (Carbonell et al. 2014; Nagano-Saito et al. 2008), administration of dopamine receptor agonists and antagonists (Cole et al. 2013), levodopa (Delaveau et al. 2010), apomorphine (Nagano-Saito et al. 2009) and with pharmacological blockade (Minzenberg et al. 2011) and natural variation in dopamine transporter binding (Tomasi et al. 2009). "
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    • "An early study demonstrated that spontaneous low frequency (b0.1 Hz) flow-weighted fluctuations are highly synchronized within the motor system (Biswal et al., 1997). De Luca et al. (2006) observed several brain networks using resting-state ASL data from a single subject. Recently, Chuang et al. (2008) developed a strategy to reduce BOLD contamination in the resting-state CBF fluctuations and reported connectivity within the sensorimotor network. "
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