Vascular autorescaling of fMRI (VasA fMRI) improves sensitivity of population studies: A pilot study

Kazan, Samira M.; Mohammadi, Siawoosh; Callaghan, Martina F.; Flandin, Guillaume; Huber, Laurentius; Leech, Robert; Kennerley, Aneurin J.; Windischberger, Christian; Weiskopf, Nikolaus

doi:10.1016/j.neuroimage.2015.09.033

Cited by 35 publications

(36 citation statements)

References 81 publications

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“…Jahanian et al further showed that coefficient of variance (CV, standard deviation divided by mean) of the BOLD signal was significantly higher in hypertensive elderly subjects with chronic kidney disease than in young healthy volunteers (Jahanian et al, 2014). Other studies have explored a parameter that quantifying the degree of low-frequency variation of the BOLD signal, referred to as Amplitude of low-frequency fluctuations (ALFF), and have demonstrated a correlation between ALFF and hypercapnia-derived CVR (Di et al, 2013; Kazan et al, 2016). These methods have not attempted to differentiate various components of the BOLD signal, thus may contain a variety of physiological origins.…”

Section: Discussionmentioning

confidence: 99%

Cerebrovascular reactivity mapping without gas challenges

et al. 2017

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Cerebrovascular reactivity (CVR), the ability of cerebral vessels to dilate or constrict, has been shown to provide valuable information in the diagnosis and treatment evaluation of patients with various cerebrovascular conditions. CVR mapping is typically performed using hypercapnic gas inhalation as a vasoactive challenge while collecting BOLD images, but the inherent need of gas inhalation and the associated apparatus setup present a practical obstacle in applying it in routine clinical use. Therefore, we aimed to develop a new method to map CVR using resting-state BOLD data without the need of gas inhalation. This approach exploits the natural variation in respiration and measures its influence on BOLD MRI signal. In this work, we first identified a surrogate of the arterial CO2 fluctuation during spontaneous breathing from the global BOLD signal. Second, we tested the feasibility and reproducibility of the proposed approach to use the above-mentioned surrogate as a regressor to estimate voxel-wise CVR. Third, we validated the “resting-state CVR map” with a conventional CVR map obtained with hypercapnic gas inhalation in healthy volunteers. Finally, we tested the utility of this new approach in detecting abnormal CVR in a group of patients with Moyamoya disease, and again validated the results using the conventional gas inhalation method. Our results showed that global BOLD signal fluctuation in the frequency range of 0.02–0.04 Hz contains the most prominent contribution from natural variation in arterial CO2. The CVR map calculated using this signal as a regressor is reproducible across runs (ICC=0.91±0.06), and manifests a strong spatial correlation with results measured with a conventional hypercapnia-based method in healthy subjects (r=0.88, p<0.001). We also found that resting-state CVR was able to identify vasodilatory deficit in patients with steno-occlusive disease, the spatial pattern of which matches that obtained using the conventional gas method (r=0.71±0.18). These results suggest that CVR obtained with resting-state BOLD may be a useful alternative in detecting vascular deficits in clinical applications when gas challenge is not feasible.

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Section: Discussionmentioning

confidence: 99%

Cerebrovascular reactivity mapping without gas challenges

et al. 2017

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“…In fact, one does not even need to acquire a separate resting-state scan here. In the same way that functional connectivity can be derived from the residuals of a General Linear Model (GLM) for task-based fMRI data [72], the amplitude of low frequency fluctuations in the residuals of task-based fMRI data (GLMres-ALFF) can also be used to rescale the BOLD signal change; this “vascular autorescaling” (VasA) technique was even shown to outperform RS-ALFF based normalization [73] (Figure 3(b)). Using data from the same fMRI run, as VasA does, is also desirable because it avoids contamination of the data with noise from a separate run.…”

Section: Validity: Are Individual Differences Attributable To Brain Fmentioning

confidence: 99%

“…(b) The ALFF of the residuals of task fMRI data (GLMres-ALFF) also looks very similar to RS-ALFF (left) and can be used to rescale the BOLD signal change (vascular autorescaling, or VasA). Figure modified with permission from [73]. (c) Decrease in BOLD response to a sensorimotor task as a function of increasing age in the occipital lobe (blue) is abolished after scaling the sensorimotor-task with RS-ALFF (red), where each point represents an individual.…”

Section: Figurementioning

confidence: 99%

Building a Science of Individual Differences from fMRI

Dubois

Adolphs

2016

Trends in Cognitive Sciences

592

482

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To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity and reliability that arise when the focus shifts to individual subjects and that are widely applicable across imaging modalities. We also emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability, sources of within-subject variability, and statistical power.

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“…Other means of RSFA-like estimates have been proposed for scaling BOLD activation data using fMRI BOLD data at different non-resting cognitive states, e.g. during task periods (Kazan et al, 2016) or fixation-/resting-periods succeeding task periods (Garrett et al, 2017). Given that short periods of cognitive engagement can modulate the BOLD signal in a subsequent resting state scan (Sami and Miall, 2013;Sami et al, 2014), future studies are required to generalise our findings to RSFAlike estimates derived from other types of fMRI BOLD acquisition.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Tsvetanov

Henson

Jones

et al. 2019

Preprint

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Accurate identification of brain function is necessary to understand neurocognitive ageing, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is therefore essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health and neurovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and neurovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and neurovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and neurovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age-differences in RSFA, after controlling for cardiovascular health. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and neurovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterise neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.

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Vascular autorescaling of fMRI (VasA fMRI) improves sensitivity of population studies: A pilot study

Cited by 35 publications

References 81 publications

Cerebrovascular reactivity mapping without gas challenges

Cerebrovascular reactivity mapping without gas challenges

Building a Science of Individual Differences from fMRI

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

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