Hemodynamic scaling of fMRI-BOLD signal: validation of low-frequency spectral amplitude as a scalability factor

Biswal, Bharat B.; Kannurpatti, Sridhar S.; Rypma, Bart

doi:10.1016/j.mri.2007.03.022

Cited by 71 publications

(99 citation statements)

References 44 publications

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“…The rs-fMRI family of techniques (Biswal et al, 1995;Fox and Greicius, 2010;Hedden et al, 2009;van Dijk et al, 2010) is commonly used to capture intrinsic brain activity. However, the rs-fMRI signal also encompasses substantial non-neural contributions (Biswal and Kannurpatti, 2009;Biswal et al, 2007;Tong and Frederick, 2010), notably through intrinsic physiological processes. These non-neural components constitute the majority of the rsfMRI signal variability.…”

Section: Introductionmentioning

confidence: 99%

“…The normalized resting-state fluctuation amplitude (RSFA) -the RSFA was originally computed as the temporal standard deviation of the preprocessed and band-pass filtered BOLD time course , and been found to be related to breath-hold CVR previously (Biswal et al, 2007). In this work, in order to better distinguish the RSFA from the ALFF, we adopted the normalized version of RSFA, defined as the standard deviation divided by the temporal mean of the rs-fMRI signal ).…”

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confidence: 99%

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Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

2016

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In conventional neuroimaging, cerebrovascular reactivity (CVR) is quantified primarily using the blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) signal, specifically, as the BOLD response to intravascular carbon dioxide (CO 2 ) modulations, in units of [%ΔBOLD/ mmHg]. While this method has achieved wide appeal and clinical translation, the tolerability of CO 2 -related tasks amongst patients and the elderly remains a challenge in more routine and largescale applications. In this work, we propose an improved method to quantify CVR by exploiting intrinsic fluctuations in CO 2 and corresponding changes in the resting-state BOLD signal (rsqCVR). Our rs-qCVR approach requires simultaneous monitoring of PETCO 2 , cardiac pulsation and respiratory volume. In 16 healthy adults, we compare our quantitative CVR estimation technique to the prospective CO 2 -targeting based CVR quantification approach (qCVR, the "standard"). We also compare our rs-CVR to non-quantitative alternatives including the restingstate fluctuation amplitude (RSFA), amplitude of low-frequency fluctuation (ALFF) and globalsignal regression. When all subjects were pooled, only RSFA and ALFF were significantly associated with qCVR. However, for characterizing regional CVR variations within each subject, only the PETCO 2 -based rs-qCVR measure is strongly associated with standard qCVR in 100% of the subjects (p<=0.1). In contrast, for the more qualitative CVR measures, significant withinsubject association with qCVR was only achieved in 50-70% of the subjects. Our work establishes the feasibility of extracting quantitative CVR maps using rs-fMRI, opening the possibility of mapping functional connectivity and qCVR simultaneously.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

2016

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“…Recently, our group has shown a positive correlation between the ALFF and functional connectivity (Di et al, 2013b). ALFF is highly correlated with the breath hold responses that reflect mostly the vascular activities of the local brain regions (Biswal et al, 2007;Di et al, 2013a). Furthermore, as shown in Supplementary Figure S2, hemispheric differences in ALFF were observed.…”

Section: Discussionmentioning

confidence: 78%

Lateralized Resting-State Functional Connectivity in the Task-Positive and Task-Negative Networks

Kim

Chen

et al. 2014

Brain Connectivity

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Studies on functional brain lateralization using functional magnetic resonance imaging (fMRI) have generally focused on lateralization of local brain regions. To explore the lateralization on the whole-brain level, lateralization of functional connectivity using resting-state fMRI (N = 87, right handed) was analyzed and left-and rightlateralized networks were mapped. Four hundred two equally spaced regions of interest (ROI) covering the entire gray matter were divided into 358 task-positive and 44 task-negative ROIs. Lateralization of functional connectivity was analyzed separately for the task-positive and task-negative regions to prevent spuriously high lateralization indices caused by negative correlations between task-positive and task-negative regions. Lateralized functional connections were obtained using k-means clustering analysis. Within the task-positive network, the right-lateralized functional connections were between the occipital and inferior/middle frontal regions among other connections, whereas the left-lateralized functional connections were among fusiform gyrus and inferior frontal and inferior/superior parietal regions. Within the task-negative network, the left-lateralized connections were mainly between the precuneus and medial prefrontal regions. Specific brain regions exhibited different leftor right-lateralized connections with other regions, which suggest the importance of reporting lateralized connections over lateralized seed regions. The mean lateralization indices of the left-and right-lateralized connections were correlated, suggesting that the lateralization of connectivity may result from complementary processes between the lateralized networks. The potential functions of the lateralized networks were discussed.

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“…However, little is known regarding the correction for CVR, in particular within the resting-state signal, and is, therefore, not directly addressed in this investigation. Physiological noises such as cardiac and respiratory signal have not been systematically measured, though such contributions have been suggested to influence fluctuations at higher frequencies outside of the slow-5 oscillation range: 0.04 Hz for hypercapnic conditions induced by breath hold (Biswal et al, 2007), and 0.03 Hz for respiratory variations (Birn et al, 2006).…”

Section: Limitationsmentioning

confidence: 99%

Implication of the Slow-5 Oscillations in the Disruption of the Default-Mode Network in Healthy Aging and Stroke

Nair

Mossahebi

et al. 2016

Brain Connectivity

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The processes of normal aging and aging-related pathologies subject the brain to an active re-organization of its brain networks. Among these, the default-mode network (DMN) is consistently implicated with a demonstrated reduction in functional connectivity within the network. However, no clear stipulation on the underlying mechanisms of the de-synchronization has yet been provided. In this study, we examined the spectral distribution of the intrinsic low-frequency oscillations (LFOs) of the DMN sub-networks in populations of young normals, older subjects, and acute and subacute ischemic stroke patients. The DMN sub-networks were derived using a midorder group independent component analysis with 117 eyes-closed resting-state functional magnetic resonance imaging (rs-fMRI) sessions from volunteers in those population groups, isolating three robust components of the DMN among other resting-state networks. The posterior component of the DMN presented noticeable differences. Measures of amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) of the network component demonstrated a decrease in resting-state cortical oscillation power in the elderly (normal and patient), specifically in the slow-5 (0.01-0.027 Hz) range of oscillations. Furthermore, the contribution of the slow-5 oscillations during the resting state was diminished for a greater influence of the slow-4 (0.027-0.073 Hz) oscillations in the subacute stroke group, not only suggesting a vulnerability of the slow-5 oscillations to disruption but also indicating a change in the distribution of the oscillations within the resting-state frequencies. The reduction of network slow-5 fALFF in the posterior DMN component was found to present a potential association with behavioral measures, suggesting a brain-behavior relationship to those oscillations, with this change in behavior potentially resulting from an altered network integrity induced by a weakening of the slow-5 oscillations during the resting state. The repeated identification of those frequencies in the disruption of DMN stresses a critical role of the slow-5 oscillations in network disruption, and it accentuates the importance of managing those oscillations in the health of the DMN.

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Hemodynamic scaling of fMRI-BOLD signal: validation of low-frequency spectral amplitude as a scalability factor

Cited by 71 publications

References 44 publications

Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

Lateralized Resting-State Functional Connectivity in the Task-Positive and Task-Negative Networks

Implication of the Slow-5 Oscillations in the Disruption of the Default-Mode Network in Healthy Aging and Stroke

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