Low frequency BOLD fluctuations during resting wakefulness and light sleep: A simultaneous EEG-fMRI study. Human Brain Mapping, 29(6), 671-682

Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, Maryland, USA.
Human Brain Mapping (Impact Factor: 6.92). 06/2008; 29(6):671-82. DOI: 10.1002/hbm.20428
Source: PubMed

ABSTRACT Recent blood oxygenation level dependent functional MRI (BOLD fMRI) studies of the human brain have shown that in the absence of external stimuli, activity persists in the form of distinct patterns of temporally correlated signal fluctuations. In this work, we investigated the spontaneous BOLD signal fluctuations during states of reduced consciousness such as drowsiness and sleep. For this purpose, we performed BOLD fMRI on normal subjects during varying levels of consciousness, from resting wakefulness to light (non-slow wave) sleep. Depth of sleep was determined based on concurrently acquired EEG data. During light sleep, significant increases in the fluctuation level of the BOLD signal were observed in several cortical areas, among which visual cortex was the most significant. Correlations among brain regions involved with the default-mode network persisted during light sleep. These results suggest that activity in areas such as the default-mode network and primary sensory cortex, as measured from BOLD fMRI fluctuations, does not require a level of consciousness typical of wakefulness.

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Available from: Silvina Horovitz, Apr 07, 2015
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    • "In the last decade, more and more fMRI studies are investigating the temporal dynamics of functional connectivity in the human brain (Hutchison et al., 2013a). Functional brain connectivity has been reported to exhibit changes due to task demands (Esposito et al., 2006; Fornito et al., 2012a; Fransson, 2006), learning (Bassett et al., 2011), maturation (Uddin et al., 2011), and large state transition such as sleep (Horovitz et al., 2008, 2009). Brain connectivity under dynamic changes within time scales of seconds to minutes has also been reported in fMRI data (Chang and Glover, 2010; Hutchison et al., 2013b; Kang et al., 2011; Kiviniemi et al., 2011; Li et al., 2013, 2014; Sakoglu et al., 2010). "
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    ABSTRACT: Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia which may underscore the abnormal brain performance in this mental illness. Copyright © 2014 Elsevier Inc. All rights reserved.
    NeuroImage 12/2014; 107. DOI:10.1016/j.neuroimage.2014.12.020 · 6.36 Impact Factor
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    • "Intrinsic networks are consistent across individuals (Damoiseaux et al., 2006), development (Fransson et al., 2007), different behavioral states (Horovitz et al., 2008), and even species (Vincent et al., 2007), and possibly represent a basic organization principle of the mammalian brain. They are functional networks i.e. their areas commonly coactivate during both non-task and task states, suggesting intrinsic networks to implement specific aspects of cognition and behavior (Laird et al., 2011; Smith et al., 2009). "
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    ABSTRACT: Although pronounced and lasting deficits in selective attention have been observed for preterm born individuals it is unknown which specific attentional sub-mechanisms are affected and how they relate to brain networks. We used the computationally specified 'Theory of Visual Attention' together with whole- and partial-report paradigms to compare attentional sub-mechanisms of pre- (n=33) and full-term (n=32) born adults. Resting-state fMRI was used to evaluate both between-group differences and inter-individual variance in changed functional connectivity of intrinsic brain networks relevant for visual attention. In preterm born adults, we found specific impairments of visual short-term memory (vSTM) storage capacity while other sub-mechanisms such as processing speed or attentional weighting were unchanged. Furthermore, changed functional connectivity was found in unimodal visual and supramodal attention-related intrinsic networks. Among preterm born adults, the individual pattern of changed connectivity in occipital and parietal cortices was systematically associated with vSTM in such a way that the more distinct the connectivity differences, the better the preterm adults' storage capacity. These findings provide first evidence for selectively changed attentional sub-mechanisms in preterm born adults and their relation to altered intrinsic brain networks. In particular, data suggest that cortical changes in intrinsic functional connectivity may compensate adverse developmental consequences of prematurity on visual short-term storage capacity. Copyright © 2014 Elsevier Inc. All rights reserved.
    NeuroImage 12/2014; 107C:95-106. DOI:10.1016/j.neuroimage.2014.11.062 · 6.36 Impact Factor
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    • "However, it is conceivable that the use of anesthesia may have modified the connectivity patterns. Nevertheless , spontaneous BOLD fluctuations, which are thought to have its neuronal origin (Liu et al., 2011), demonstrate generally consistent patterns in the anesthetized monkey (Vincent et al., 2007) and rat (Lu et al., 2007); and the connectivity patterns in sleeping humans (Horovitz et al., 2008) are similar to that seen in the awake human. The rsFC maps derived from our housing control group are in good agreement with known MCL anatomical connections (Alexander et al., 1986; Heidbreder and Groenewegen, 2003; Hoover and Vertes 2007; Jones et al., 2005; Voorn et al., 2004), are consistent with previous rsFC studies (Lu et al., 2007, 2011; Pawela et al., 2010), support our analysis pipeline examining group differences, and speak to the neurobiological relevance of the findings. "
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    ABSTRACT: Previous preclinical studies have emphasized that drugs of abuse, via actions within and between mesocorticolimbic (MCL) regions, usurp learning and memory processes normally involved in the pursuit of naturally rewarding stimuli. To distinguish MCL circuit pathobiological neuroadaptations that accompany addiction from general learning processes associated with natural reward goal-directed behaviour, we trained two groups of rats to self-administer either cocaine (IV) or sucrose (orally) followed by an identically enforced 30 day abstinence period previously shown to induce behavioral self administration (SA) plasticity. A third group of sedentary animals served as a negative control group for general handling effects. We examined low frequency spontaneous fluctuations in the fMRI signal, known as resting-state functional connectivity (rsFC), as a measure of intrinsic neurobiological interactions between brain regions. Decreased rsFC was seen in the cocaine-SA compared with both sucrose-SA and housing control groups between prelimbic cortex (PrL) and entopeduncular nucleus and between nucleus accumbens core (AcbC) and dorsomedial prefrontal cortex (dmPFC). Moreover, individual differences in cocaine SA escalation predicted connectivity strength only in the Acb-dmPFC circuit. These data provide evidence of fronto-striatal plasticity across the addiction trajectory, are consistent with Acb-PFC hypoactivity seen in abstinent human drug addicts and suggest potential circuit level biomarkers that may inform therapeutic interventions. They further suggest that available data from cross sectional human studies may reflect the consequence of rather than a predispositional predecessor to their dependence.
    07/2014; 4(7). DOI:10.1089/brain.2014.0264
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