Cerebral transit time of 99mtechnetium sodium pertechnetate before and after cerebral arteriography

Crandell, Donna C.; Moinuddin, Muhammad; Fields, Murray L.; Friedman, Barry; Robertson, James S.

doi:10.3171/jns.1973.38.5.0545

Cited by 11 publications

(13 citation statements)

References 5 publications

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“…Figure 5(d) shows their z-value vs. time shift. The time for the wave to pass the brain is about 6.48 s, which is consistent with the literature (Crandell et al, 1973). We acknowledge that this is an unsophisticated calculation, which does not take into account the fact that the fMRI voxels are likely to contain blood in multiple compartments.…”

Section: Resultssupporting

confidence: 91%

Time lag dependent multimodal processing of concurrent fMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory origin for low-frequency oscillation signals in human brain

2010

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Low frequency oscillations (LFOs), characterized by frequencies in the range 0.01~0.1 Hz are commonly observed in blood-related brain functional measurements such as near-infrared spectroscopy (NIRS) and functional magnetic resonance imaging (fMRI). While their physiological origin and implications are not fully understood, these signals are believed to reflect some types of neuronal signaling, systemic hemodynamics, and/or cerebral vascular autoregulation processes. Here, we examine a new method of integrated processing of concurrent NIRS and fMRI data collected on six human subjects during a whole brain resting state acquisition. The method combines the high spatial resolution offered by fMRI (~3 mm) and the high temporal resolution offered by NIRS (~ 80 ms) to allow for the quantitative assessment of temporal relationships between the LFOs observed at different spatial locations in fMRI data. This temporal relationship allowed us to infer that the origin of a large proportion of the LFOs is independent of the baseline neural activity. The spatio-temporal pattern of LFOs detected by NIRS and fMRI evolves temporally through the brain in a way that resembles cerebral blood flow dynamics. Our results suggest that a major component of the LFOs arise from fluctuations in the blood flow and hemoglobin oxygenation at a global circulatory system level.

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Section: Resultssupporting

confidence: 91%

Time lag dependent multimodal processing of concurrent fMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory origin for low-frequency oscillation signals in human brain

2010

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“…0.4s×13=5.2s), which is in the range of the cerebral circulation time of healthy participants (Crandell, et al, 1973; Schreiber, et al, 2002). …”

Section: Resultsmentioning

confidence: 99%

“…Finally, we can see that the time span of the correlation graph in Figure 3 is about 5.2 s (i.e., 0.4 s 3 13 5 5.2 s), which is in the range of the cerebral circulation time of [Crandell et al, 1973;Schreiber et al, 2002].…”

Section: Optimized Proceduresmentioning

confidence: 89%

“…First, the TR determines the time resolution of the dynamic map (TR 5 evolving step in time). The cerebral circulation time of cerebral blood flow is about 4-7 s [Crandell et al, 1973;Ibaraki et al, 2007;Schreiber et al, 2002], as shown in Figure 3. With a TR value of 2 or 3 s, only two to four time points can be generated, which r Tracking Cerebral Blood Flow in BOLD fMRI r r 5481 r dramatically reduces the resolution in dynamic maps.…”

Section: Robustness Of the Methodsmentioning

confidence: 99%

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Tracking cerebral blood flow in BOLD fMRI using recursively generated regressors

Tong

Frederick

2014

Human Brain Mapping

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BOLD fMRI data is dominated by low frequency signals, many of them of unclear origin. We have recently shown that some portion of the low frequency oscillations found in BOLD fMRI are systemic signals closely related to the blood circulation (Tong, et al., 2013). They are commonly treated as physiological noise in fMRI studies. In the present study, we propose and test a novel data-driven analytical method that uses these systemic low frequency oscillations in the BOLD signal as a tracer to follow cerebral blood flow dynamically. Our findings demonstrate that: (1) systemic oscillations pervade the BOLD signal; (2) the temporal traces evolve as the blood propagates though the brain; and, (3) they can be effectively extracted via a recursive procedure and used to derive the cerebral circulation map. Moreover, this method is independent from functional analyses, and thus allows simultaneous and independent assessment of information about cerebral blood flow to be conducted in parallel with the functional studies. In this study, the method was applied to data from the resting state scans, acquired using a multiband EPI sequence (fMRI scan with much shorter TRs), of 7 healthy participants. Dynamic maps with consistent features resembling cerebral blood circulation were derived, confirming the robustness and repeatability of the method.

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“…This number comes from our previous observations over a number studies, and has been reported in radiotracer studies. For example, Crandell et al (1973), show that in controls, blood from the carotids takes 2, 5.4, and 8.5 s to reach the anterior and middle cerebral arteries, gray matter in the cerebral hemispheres, and the superior sagittal sinus, respectively. In contrast cardiac pulsations appear to move through the vasculature much more quickly, as they are transient pressure waves that move faster than the blood carrying them, rapidly affecting the total hemoglobin in a voxel.…”

Section: Methodsmentioning

confidence: 99%

Physiological denoising of BOLD fMRI data using Regressor Interpolation at Progressive Time Delays (RIPTiDe) processing of concurrent fMRI and near-infrared spectroscopy (NIRS)

2012

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Confounding noise in BOLD fMRI data arises primarily from fluctuations in blood flow and oxygenation due to cardiac and respiratory effects, spontaneous low frequency oscillations (LFO) in arterial pressure, and non-task related neural activity. Cardiac noise is particularly problematic, as the low sampling frequency of BOLD fMRI ensures that these effects are aliased in recorded data. Various methods have been proposed to estimate the noise signal through measurement and transformation of the cardiac and respiratory waveforms (e.g. RETROICOR and respiration volume per time (RVT)) and model-free estimation of noise variance through examination of spatial and temporal patterns. We have previously demonstrated that by applying a voxel-specific time delay to concurrently acquired near infrared spectroscopy (NIRS) data, we can generate regressors that reflect systemic blood flow and oxygenation fluctuations effects. Here, we apply this method to the task of removing physiological noise from BOLD data. We compare the efficacy of noise removal using various sets of noise regressors generated from NIRS data, and also compare the noise removal to RETROICOR+RVT. We compare the results of resting state analyses using the original and noise filtered data, and we evaluate the bias for the different noise filtration methods by computing null distributions from the resting data and comparing them with the expected theoretical distributions. Using the best set of processing choices, six NIRS-generated regressors with voxel-specific time delays explain a median of 10.5% of the variance throughout the brain, with the highest reductions being seen in gray matter. By comparison, the nine RETROICOR+RVT regressors together explain a median of 6.8% of the variance in the BOLD data. Detection of resting state networks was enhanced with NIRS denoising, and there were no appreciable differences in the bias of the different techniques. Physiological noise regressors generated using Regressor Interpolation at Progressive Time Delays (RIPTiDe) offer an effective method for efficiently removing hemodynamic noise from BOLD data.

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Cerebral transit time of 99mtechnetium sodium pertechnetate before and after cerebral arteriography

Cited by 11 publications

References 5 publications

Time lag dependent multimodal processing of concurrent fMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory origin for low-frequency oscillation signals in human brain

Time lag dependent multimodal processing of concurrent fMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory origin for low-frequency oscillation signals in human brain

Tracking cerebral blood flow in BOLD fMRI using recursively generated regressors

Physiological denoising of BOLD fMRI data using Regressor Interpolation at Progressive Time Delays (RIPTiDe) processing of concurrent fMRI and near-infrared spectroscopy (NIRS)

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