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
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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
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    • "). 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 et al., 1995; Zuo et al., 2010) and classifiers obtained by support vector machine (SVM) are sensitive to noise (Guyon et al., 1996). "
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    ABSTRACT: Resting-state functional magnetic resonance imaging studies examining low frequency fluctuations (0.01-0.08Hz) have revealed atypical whole brain functional connectivity patterns in adolescents with autism spectrum disorder (ASD), and these atypical patterns can be used to discriminate individuals with ASD from controls. However, at present it is unknown whether functional connectivity at specific frequency bands can be used to discriminate individuals with ASD from controls, and whether relationships with symptom severity are stronger in specific frequency bands. We selected 240 adolescent subjects (12-18 years old, 112 with autism spectrum disorder (101/11, males/females) and 128 healthy controls (104/24, males/females)) from 6 separate international sites in the Autism Brain Imaging Data Exchange database. Whole brain functional connectivity networks were constructed in the Slow-5 (0.01-0.027Hz) and Slow-4 (0.027-0.073Hz) frequency bands, which were then used as classification features. An accuracy of 79.17% (p<0.001) was obtained using support vector machine. Most of the discriminative features were concentrated on the Slow-4 band. In the Slow-4 band, atypical connections between the default mode network, fronto-parietal network and cingulo-opercular network were detected. A significant correlation was found between social and communication deficits as measured by the ADOS in individuals with ASD and the classification scores based on connectivity between the default mode network and the cingulo-opercular network. Connections of the thalamus were of the highest classification weight in the Slow-4 band. Our findings provide preliminary evidence for frequency-specific whole brain functional connectivity indices that may eventually be used to aid detection of ASD. Copyright © 2015. Published by Elsevier Inc.
    Progress in Neuro-Psychopharmacology and Biological Psychiatry 07/2015; 64. DOI:10.1016/j.pnpbp.2015.06.014 · 3.69 Impact Factor
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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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    ABSTRACT: Performance improvements in cognitive tasks requiring executive functions are evident with nicotinic acetylcholine receptor (nAChR) agonists and activation of the underlying neural circuitry supporting these cognitive effects is thought to involve dopamine neurotransmission. As individual difference in response to nicotine may be related to a functional polymorphism in the gene encoding catechol-O-methyltransferase (COMT), an enzyme that strongly influences cortical dopamine metabolism, this study examined the modulatory effects of the COMT Val158Met polymorphism on the neural response to acute nicotine as measured with resting state electroencephalographic (EEG) oscillations. In a sample of 62 healthy nonsmoking adult males, a single dose (6 mg) of nicotine gum administered in a randomized, double-blind, placebo controlled design was shown to affect α oscillatory activity, increasing power of upper α oscillations in fronto-central regions of Met/Met homozygotes and in parietal/occipital regions of Val/Met heterozygotes. Peak α frequency was also found to be faster with nicotine (vs. placebo) treatment in Val/Met heterozygotes, who exhibited a slower α frequency compared to Val/Val homozygotes. The data tentatively suggest that interindividual differences in brain α oscillations and their response to nicotinic agonist treatment are influenced by genetic mechanisms involving COMT. This article is protected by copyright. All rights reserved.
    Genes Brain and Behavior 06/2015; 14(6). DOI:10.1111/gbb.12226 · 3.66 Impact Factor
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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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