Large-amplitude, spatially correlated fluctuations in BOLD fMRI signals during extended rest and early sleep stages

Fukunaga, Masaki; Horovitz, Silvina G.; Gelderen, Peter van; Zwart, Jacco A. de; Jansma, J.; Ikonomidou, Vasiliki N.; Chu, Renxin; Deckers, Roel; Leopold, David A.; Duyn, Jeff H.

doi:10.1016/j.mri.2006.04.018

Cited by 349 publications

(323 citation statements)

References 68 publications

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“…However, the volume of cortical structures involved especially by the negative correlation and the t-values were small and the group analysis method may not reveal such regions as significant due to smoothing (Worsley et al, 2002). Individual results were coherent with the general finding of a decrease of brain activity during sleep stages compared to wakefulness or REM sleep (Kaufmann et al, 2006;Fukunaga et al, 2006). BOLD changes related to theta have only been evaluated in two studies.…”

Section: Cerebral Correlates Of Thetasupporting

confidence: 73%

“…However, in our condition of recording, without specific cognitive task, the theta rhythm specifically related to light sleep was more likely to be predominant. Whatever the type of theta rhythm analyzed (our study, Scheeringa et al, 2007;Kaufmann et al, 2006;Fukunaga et al, 2006), structures negatively correlated with BOLD were similar. Theta seems related to the default mode network (Raichle et al, 2001).…”

Section: Cerebral Correlates Of Thetasupporting

confidence: 55%

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Effects of fluctuating physiological rhythms during prolonged EEG-fMRI studies

Tyvaert¹,

LeVan²,

Grova³

et al. 2008

Clinical Neurophysiology

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Objective-We evaluated BOLD correlates of alertness fluctuations commonly seen during prolonged EEG-fMRI studies to better define the brain areas active at different phases of vigilance and to assess the contribution of these fluctuations to the BOLD signal.Methods-We evaluated BOLD changes specifically related to the main physiological EEG rhythms (alpha, beta, theta, delta, spindles) in 15 epilepsy patients with rare discharges (all the regressors were included in the same general linear model to improve specificity).Results-We found a consistent effect of spindles, alpha and theta. For alpha, BOLD was positively correlated in thalami and putamen, and negatively correlated in the occipital, parietal and frontal lobes. For theta, a negative correlation was found over the parietal, temporal and frontal lobes. Spindles were correlated with a positive BOLD in thalami and putamen. Rhythm regressors added as confounds in the fMRI analysis explained at least 5% of BOLD signal variance in 6.8 ± 8.9% of gray matter voxels, a contribution which is of the order of typical changes in fMRI studies.Conclusion-First, we found specific cerebral structures involved in each main EEG rhythm generation. Second, fluctuations of these rhythms following vigilance changes are responsible for noteworthy BOLD changes. Significance: Physiological EEG rhythms may be integrated to the analysis of EEG-fMRI in studies with fluctuation of alertness, to eliminate possible confounding factors.

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Section: Cerebral Correlates Of Thetasupporting

confidence: 73%

Section: Cerebral Correlates Of Thetasupporting

confidence: 55%

Effects of fluctuating physiological rhythms during prolonged EEG-fMRI studies

Tyvaert¹,

LeVan²,

Grova³

et al. 2008

Clinical Neurophysiology

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Section: [Figure 1]mentioning

confidence: 99%

“…Moreover, up to ten cortico-cortical and subcortical resting-state networks have been reported by Beckmann et al (2005) and Damoiseaux et al (2006). Surprisingly, the persistence of these resting-state networks is overt during both sleep (Fukunaga et al, 2006;Honey et al, 2009) and anesthesia (Fox et al, 2005;Vincent et al, 2007).One such network called the default-mode network (DMN) or task-negative network (Fox et al, 2005;Fox and Greicius, 2010) has been extensively studied and has also been used as a potential diagnostic tool in neuropathological disorders (He et al, 2007;Greicius, 2008). DMN is a set of brain regions initially detected by Shulman et al (1994), including posterior cingulate, the medial prefrontal and the medial and lateral parietal cortex.…”

mentioning

confidence: 98%

Resting-state Abnormalities in Heroin-dependent Individuals

Pandria

Kovatsi

Vivas³

et al. 2018

Neuroscience

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Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. AbstractDrug addiction is a major health problem worldwide. Recent neuroimaging studies have shed light into the underlying mechanisms of drug addiction as well as its consequences to the human brain. The most vulnerable, to heroin addiction, brain regions have been reported to be specific regions in the prefrontal, parietal, occipital, and temporal lobes, as well as, some subcortical regions.The brain regions involvedare linked with reward, motivation/drive, memory/learning, inhibition as well as emotional control andseem to form circuitsthat interact with each other. So, along with neuroimaging studies, recent advances in resting-state dynamics might allow further assessments upon the multilayer complexity of addiction. In the current manuscript, we comprehensively reviewand discuss all the existing resting-state neuroimaging findings classified into three overlapping and interconnected groups: functional connectivity alterations, structural deficits and abnormal topological properties. Moreover, behavioral traits of heroin-addicted individuals as well as the limitations of the currently available studies are also reviewed. Finally, in need of a contemporary therapya multimodal therapeutic approach is suggested using classical treatment practices along with current neurotechonologies, such as neurofeedback and goal-oriented video-games Keywords:Heroin addiction, resting-state, functional and structural connectivity, small-world properties, behavioral traits of heroin-dependent individuals. Radial Diffusivity (RD) 5 Drug addiction or substance dependence (Koob and Le Moal, 2005)is defined as a multifaceted neuropsychiatric disorder resulting from complex interactions between neurobiological, environmental, genetic and pharmacological variables (Sutherland et al., 2012).It affects both cognitive as well as emotional processes . Furthermore, the compulsive seeking and taking of the drug despite the negative consequences, (including downward social mobility (Denier et al., 2013b)), constitutes its behavioral phenotype (Volkow ...

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“…The approach has been used to investigate functional brain differences in groups and topics that would otherwise not be capable of task participation, such as other species Vincent et al, 2007;Vincent et al, 2010;Biswal and Kannurpatti, 2009), and different levels of consciousness in humans (Boly et al, 2008;Horovitz et al, 2008;Fukunaga et al, 2006). Since all that is required during data collection is that the participant remains motionless in the scanner environment, resting state fMRI data has also opened the possibility of data-sharing across institutions on an unprecedented scale (Biswal et al, 2010).…”

Section: History Of Resting State Research: Lineage II -Spontaneous Bmentioning

confidence: 99%