Blood Oxygen Level-Dependent Signal Variability Is More than Just Noise

Garrett, Douglas D.; Kovačević, Nataša Žunić; McIntosh, Anthony R.; Grady, Cheryl L.

doi:10.1523/jneurosci.5166-09.2010

Cited by 360 publications

(519 citation statements)

References 38 publications

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“…Notably, brain activity variability has been linked to task performance, development and disease and it has been suggested that signal variability is not "noise" but crucial for spontaneous or stimulated reorganizations of the brain (Garrett et al, 2010;McIntosh et al, 2008). Just as studies on brain signal variability provided novel insights into brain function, we believe that studies on brain connectivity variability will further our understanding of the brain's functional organization.…”

Section: Variability As a Signal In Its Own Rightmentioning

confidence: 92%

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

et al. 2013

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Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.© 2013 Elsevier Inc. All rights reserved. IntroductionSpontaneous fluctuations of the functional MRI (fMRI) bloodoxygen-level-dependent (BOLD) signal are not random but temporally coherent between distinct brain regions. While these fluctuations were long considered as "noise", Biswal et al. (1995) showed that fluctuations of motor areas were correlated even in the absence of a motor task. Several other networks of coherent BOLD activity between remote brain regions have since been identified, including visual, auditory, language and attention networks, and a network called the "default mode network" (DMN) which reduces its activity during attentiondemanding tasks. These networks of regions with coherent activity during rest are consistent across subjects and closely resemble the brain's functional organization of evoked responses (Damoiseaux et al., 2006;Fox and Raichle, 2007;Laird et al., 2011;Smith et al., 2009). Coherent BOLD activity persists during sleep and in anesthetized monkeys, suggesting that it reflects a fundamental property of the brain's functional organization (Larson-Prior et al., 2009;Vincent et al., 2007).Coherent BOLD activity, known as "functional connectivity" (FC), is modulated by learning (Bassett et al., 2011), cognitive and affective states (Cribben et al., 2012;Ekman et al., 2012;Eryilmaz et al., 2011;Richiardi et al., 2011;Shirer et al., 2012) and also spontaneously Chang and Glover, 2010; Kitzbichler et al., 2009). Chang andGlover (2010) showed that FC between the posterior cingulate cortex, a key region of the default mode network, and various other brain regions was highly dy...

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Section: Variability As a Signal In Its Own Rightmentioning

confidence: 92%

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

et al. 2013

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“…To note, BOLD-and CBF-ALFF were sensitive to physiological noises and were higher in the regions near large vessels and CSF. Although advanced preprocessing strategy including ICA noise removal Lowe, 2007, 2010) and additional regression of head motion, CSF and white matter time series (Garrett et al, 2010), could help to eliminate noise contaminations, the observations of residual noises suggests more work on denoising is needed such as physiological noise modeling using simultaneously recorded physiological pulsations and more robust indices for measurement of fluctuation amplitude.…”

Section: Group Average Maps Of Cbf-mean Bold-and Cbf-alff and Bold-mentioning

confidence: 99%

“…Although advanced preprocessing strategy including ICA noise components removal Lowe, 2007, 2010) and additional regression of head motion, CSF and white matter time series (Garrett et al, 2010), could help to eliminate noise contaminations, the observations of residual noises contamination suggested future work on denoising such as physiological noise modeling using simultaneously recorded physiological pulsations and more robust indices for measurement of fluctuation amplitude. Fractional ALFF (fALFF) was proposed to suppress these physiological noises by normalizing ALFF using a ratio between amplitude of low-frequency fluctuations and the amplitude of the overall frequency range.…”

Section: Test-retest Reliability Of Bold and Cbfmentioning

confidence: 99%

“…To minimize the potential contaminations of physiological pulsations and head motion, we employed extended data preprocessing strategies including ICA denoising Lowe, 2007, 2010), regression of covariates of no interests including head motion parameters, mean signals within the white matter and CSF masks (Garrett et al, 2010). After denoising, test-retest reliability of BOLD-ALFF decreased (Table 3 and Fig.…”

Section: Effect Of Preprocessing On Test-retest Reliabilitymentioning

confidence: 99%

“…Moreover, it has been demonstrated that denoising strategies such as noise components removal using independent component analysis (ICA) and regression of physiological noise could boost the predictability of age using BOLD-ALFF (Garrett et al, 2010) and leading to more accurate estimates of group maps of BOLD-FC . However, physiological pulsations and motion (Van Dijk et al, 2012; showed moderate reliability, which might inflate the reliability of ALFF and FC of BOLD data.…”

Section: Introductionmentioning

confidence: 99%

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Reliability comparison of spontaneous brain activities between BOLD and CBF contrasts in eyes-open and eyes-closed resting states

et al. 2015

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Blood oxygenation level dependent (BOLD) and arterial spin labeling (ASL) are two predominant resting-state fMRI techniques in mapping spontaneous brain activities. At single voxel level, cerebral blood flow (CBF) measured by ASL and amplitude of low frequency fluctuations (ALFF) of BOLD have been recognized as useful indices to characterize brain function in health and disease. However, no study has directly compared the test-retest reliability between BOLD and CBF contrasts on the same group of subjects at single voxel level. Moreover, both eyes-open and eyes-closed conditions have been employed as resting states, but it is still not clear which state is more reliable. Here we collected BOLD and ASL data during eyes-open and eyes-closed states across three scanning visits on twenty-two healthy young subjects. CBF-mean, BOLD-and CBF-ALFF were computed to characterize corresponding brain activities at single voxel level. Seed-based functional connectivity (FC) with the posterior cingulate cortex (PCC) was further calculated for both BOLD and CBF data. Intra-class correlation was used as the index of long-term reliability between visits 1 and 2 (two months apart) and short-term reliability between visits 2 and 3 (on the same day). Both short-and long-term reliabilities for CBF-mean and BOLD-ALFF were high, but were lower for CBF-ALFF, BOLD-and CBF-FC in both eyes-open and eyes-closed states. Direct comparisons showed that brain regions with the highest reliability of CBF-mean were mainly in the gray matter. The reliability of CBF-ALFF and BOLD-FC was lower than that of BOLD-ALFF, and the reliability of CBF-FC was lower than those of both CBF-ALFF and BOLD-FC. Furthermore, we observed that reliabilities of the eyes-open state were higher than those of the eyes-closed state for both imaging contrasts, though the effect size was small. Voxel-wise comparisons demonstrated that the long-term reliability of BOLD-ALFF was significantly higher with eyes open in the visual system, and both the short-and long-term reliability of BOLD-FC was slightly higher with eyes open in the default mode network. Moreover, we showed that denoising decreased the reliability of both ALFF and FC of both BOLD and ASL contrasts. In conclusion, our results indicated that CBF-mean and BOLD-ALFF could both be used as reliable indices for characterizing resting-state brain activities at single voxel level and recommended the eyesopen state for resting-state studies, especially for those targeting the visual system and default mode network.

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Critical Brain Dynamics at Large Scale

Chialvo

2014

Criticality in Neural Systems

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Essentially, all modeling of brain function from studying models of neural networks has ignored the self-organized aspects of the process, but has concentrated on designing a working brain by engineering all the connections of inputs and outputs.Per Bak[1]Abstract -Highly correlated brain dynamics produces synchronized states with no behavioral value, while weakly correlated dynamics prevent information flow. In between these states, the unique dynamical features of the critical state endow the brain with properties which are fundamental for adaptive behavior. We discuss the idea put forward two decades ago by Per Bak that the working brain stays at an intermediate (critical) regime characterized by power-law correlations. This proposal is now supported by a wide body of empirical evidence at different scales demonstrating that the spatiotemporal brain dynamics exhibit key signatures of critical dynamics, previously recognized in other complex systems. The rationale behind this program is discussed in these notes, followed by an account of the most recent results.In "Criticality in Neural Systems", Niebur E, Plenz D, Schuster HG. (eds.) 2013 (in press).

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Blood Oxygen Level-Dependent Signal Variability Is More than Just Noise

Cited by 360 publications

References 38 publications

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

Reliability comparison of spontaneous brain activities between BOLD and CBF contrasts in eyes-open and eyes-closed resting states

Critical Brain Dynamics at Large Scale

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