Effects of Thresholding on Voxel-Wise Correspondence of Breath-Hold and Resting-State Maps of Cerebrovascular Reactivity.
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ABSTRACT:
SUBMITTER: Fesharaki NJ
PROVIDER: S-EPMC8421787 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A pseudocontinuous arterial spin labeling (PCASL) sequence combined with background suppression and single-shot accelerated 3D RARE stack-of-spirals was used to evaluate cerebrovascular reactivity (CVR) induced by breath-holding (BH) in ten healthy volunteers. Four different models designed using the measured change in PETCO2 induced by BH were compared, for CVR quantification. The objective of this comparison was to understand which regressor offered a better physiological model to characterize the cerebral blood flow response under BH. The BH task started with free breathing of 42 s, followed by interleaved end-expiration BHs of 21 s, for ten cycles. The total scan time was 12 min and 20 s. The accelerated readout allowed the acquisition of PCASL data with better temporal resolution than previously used, without compromising the post-labeling delay. Elevated CBF was observed in most cerebral regions under hypercapnia, which was delayed with respect to the BH challenge. Significant statistical differences in CVR were obtained between the different models in GM (p < 0.0001), with ramp models yielding higher values than boxcar models and between the two tissues, GM and WM, with higher values in GM, in all the models (p < 0.0001). The adjustment of the ramp amplitude during each BH cycle did not improve the results compared with a ramp model with a constant amplitude equal to the mean PETCO2 change during the experiment.
| S-EPMC7925895 | biostudies-literature
Project description:BACKGROUND:Hypercapnia during breath holding is believed to be the dominant driver behind the modulation of cerebral blood flow (CBF). However, increasing evidence show that mild hypoxia and mild hypercapnia in breath hold (BH) could work synergistically to enhance CBF response. We hypothesized that breath-by-breath O2-CO2 exchange ratio (bER), defined as the ratio of the change in partial pressure of oxygen (ΔPO2) to that of carbon dioxide (ΔPCO2) between end inspiration and end expiration, would be able to better correlate with the global and regional cerebral hemodynamic responses (CHR) to BH challenge. We aimed to investigate whether bER is a more useful index than end-tidal PCO2 to characterize cerebrovascular reactivity (CVR) under BH challenge. METHODS:We used transcranial Doppler ultrasound (TCD) to evaluate CHR under BH challenge by measuring cerebral blood flow velocity (CBFv) in the middle cerebral arteries. Regional changes in CHR to BH and exogenous CO2 challenges were mapped with blood oxygenation level dependent (BOLD) signal changes using functional magnetic resonance imaging (fMRI). We correlated respiratory gas exchange (RGE) metrics (bER, ΔPO2, ΔPCO2, end-tidal PCO2 and PO2, and time of breaths) with CHR (CBFv and BOLD) to BH challenge. Temporal features and frequency characteristics of RGE metrics and their coherence with CHR were examined. RESULTS:CHR to brief BH epochs and free breathing were coupled with both ΔPO2 and ΔPCO2. We found that bER was superior to either ΔPO2 or ΔPCO2 alone in coupling with the changes of CBFv and BOLD signals under breath hold challenge. The regional CVR results derived by regressing BOLD signal changes on bER under BH challenge resembled those derived by regressing BOLD signal changes on end-tidal PCO2 under exogenous CO2 challenge. CONCLUSION:Our findings provide a novel insight on the potential of using bER to better quantify CVR changes under BH challenge.
| S-EPMC7092994 | biostudies-literature
Project description:Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.
| S-EPMC8351526 | biostudies-literature
Project description:Task-evoked Blood-oxygenation-level-dependent (BOLD-fMRI) signal activation is widely used to interrogate eloquence of brain areas. However, data interpretation can be improved, especially in regions with absent BOLD-fMRI signal activation. Absent BOLD-fMRI signal activation may actually represent false-negative activation due to impaired cerebrovascular reactivity (BOLD-CVR) of the vascular bed. The relationship between impaired BOLD-CVR and BOLD-fMRI signal activation may be better studied in healthy subjects where neurovascular coupling is known to be intact. Using a model-based prospective end-tidal carbon dioxide (CO2) targeting algorithm, we performed two controlled 3 tesla BOLD-CVR studies on 17 healthy subjects: 1: at the subjects' individual resting end-tidal CO2 baseline. 2: Around +6.0 mmHg CO2 above the subjects' individual resting baseline. Two BOLD-fMRI finger-tapping experiments were performed at similar normo- and hypercapnic levels. Relative BOLD fMRI signal activation and t-values were calculated for BOLD-CVR and BOLD-fMRI data. For each component of the cerebral motor-network (precentral gyrus, postcentral gyrus, supplementary motor area, cerebellum und fronto-operculum), the correlation between BOLD-CVR and BOLD-fMRI signal changes and t-values was investigated. Finally, a voxel-wise quantitative analysis of the impact of BOLD-CVR on BOLD-fMRI was performed. For the motor-network, the linear correlation coefficient between BOLD-CVR and BOLD-fMRI t-values were significant (p<0.01) and in the range 0.33-0.55, similar to the correlations between the CVR and fMRI Δ%signal (p<0.05; range 0.34-0.60). The linear relationship between CVR and fMRI is challenged by our voxel-wise analysis of Δ%signal and t-value change between normo- and hypercapnia. Our main finding is that BOLD fMRI signal activation maps are markedly dampened in the presence of impaired BOLD-CVR and highlights the importance of a complementary BOLD-CVR assessment in addition to a task-evoked BOLD fMRI to identify brain areas at risk for false-negative BOLD-fMRI signal activation.
| S-EPMC6502350 | biostudies-literature
Project description:Cerebrovascular reactivity (CVR), an index of brain vessel's dilatory capacity, is typically measured using hypercapnic gas inhalation or breath-holding as a vasoactive challenge. However, these methods require considerable subject cooperation and could be challenging in clinical studies. More recently, there have been attempts to use resting-state BOLD data to map CVR by utilizing spontaneous changes in breathing pattern. However, in subjects who have small fluctuations in their spontaneous breathing pattern, the CVR results could be noisy and unreliable. In this study, we aim to develop a new method for CVR mapping that does not require gas-inhalation yet provides substantially higher sensitivity than resting-state CVR mapping. This new method is largely based on resting-state scan, but introduces intermittent modulation of breathing pattern in the subject to enhance fluctuations in their end-tidal CO2 (EtCO2) level. Here we examined the comfort level, sensitivity, and accuracy of this method in two studies. First, in 8 healthy young subjects, we developed the intermittent breath-modulation method using two different modulation frequencies, 6 s per breath and 12 s per breath, respectively, and compared the results to three existing CVR methods, specifically hypercapnic gas inhalation, breath-holding, and resting-state. Our results showed that the comfort level of the 6-s breath-modulation method was significantly higher than breath-holding (p = 0.007) and CO2-inhalation (p = 0.015) methods, while not different from the resting-state, i.e. free breathing method (p = 0.52). When comparing the sensitivity of CVR methods, the breath-modulation methods revealed higher Z-statistics compared to the resting-state scan (p < 0.008) and was comparable to breath-holding results. Next, we tested the feasibility of breath-modulation CVR mapping (6 s per breath) in 21 cognitively normal elderly participants and compared quantitative CVR values to that obtained with the CO2-inhalation method. Whole-brain CVR was found to be 0.150 ± 0.055 and 0.154 ± 0.032 %ΔBOLD/mmHg for the breath-modulation and CO2-inhalation method, respectively, with a significant correlation between them (y = 0.97x, p = 0.007). CVR mapping with intermittent breath modulation may be a useful method that combines the advantages of resting-state and CO2-inhalation based approaches.
| S-EPMC7292765 | biostudies-literature
Project description:FMRI BOLD responses to changes in neural activity are influenced by the reactivity of the vasculature. By complementing a task-related BOLD acquisition with a vascular reactivity measure obtained through breath-holding or hypercapnia, this unwanted variance can be statistically reduced in the BOLD responses of interest. Recently, it has been suggested that vascular reactivity can also be estimated using a resting state scan. This study aimed to compare three breath-hold based analysis approaches (block design, sine-cosine regressor and CO2 regressor) and a resting state approach (CO2 regressor) to measure vascular reactivity. We tested BOLD variance explained by the model and repeatability of the measures. Fifteen healthy participants underwent a breath-hold task and a resting state scan with end-tidal CO2 being recorded during both. Vascular reactivity was defined as CO2-related BOLD percent signal change/mmHg change in CO2. Maps and regional vascular reactivity estimates showed high repeatability when the breath-hold task was used. Repeatability and variance explained by the CO2 trace regressor were lower for the resting state data based approach, which resulted in highly variable measures of vascular reactivity. We conclude that breath-hold based vascular reactivity estimations are more repeatable than resting-based estimates, and that there are limitations with replacing breath-hold scans by resting state scans for vascular reactivity assessment.
| S-EPMC4441043 | biostudies-literature
Project description:A network approach to brain and dynamics opens new perspectives towards understanding of its function. The functional connectivity from functional MRI recordings in humans is widely explored at large scale, and recently also at the voxel level. The networks of dynamical directed connections are far less investigated, in particular at the voxel level. To reconstruct full brain effective connectivity network and study its topological organization, we present a novel approach to multivariate Granger causality which integrates information theory and the architecture of the dynamical network to efficiently select a limited number of variables. The proposed method aggregates conditional information sets according to community organization, allowing to perform Granger causality analysis avoiding redundancy and overfitting even for high-dimensional and short datasets, such as time series from individual voxels in fMRI. We for the first time depicted the voxel-wise hubs of incoming and outgoing information, called Granger causality density (GCD), as a complement to previous repertoire of functional and anatomical connectomes. Analogies with these networks have been presented in most part of default mode network; while differences suggested differences in the specific measure of centrality. Our findings could open the way to a new description of global organization and information influence of brain function. With this approach is thus feasible to study the architecture of directed networks at the voxel level and individuating hubs by investigation of degree, betweenness and clustering coefficient.
| S-EPMC3771991 | biostudies-literature
Project description:BackgroundThe physiological and pathophysiological mechanisms that govern diving, both self-contained underwater breathing apparatus (SCUBA) and breath-hold diving (BH-diving), are in large part well known, even if there are still many unknown aspects, in particular about cell metabolism during BH-diving. The scope of this study was to investigate changes in glycemia, insulinemia, and the catecholamine response to BH-diving, to better understand if the insulin-stimulated glucose uptake mechanism is involved in cellular metabolism in this sport.MethodsTwenty male experienced healthy breath-hold divers were studied. Anthropometric information was obtained. Glycemia, insulinemia, and catecholamine response were investigated before and after the series of BH-diving.ResultsWe found a statistically significant decrease in the blood glucose levels between before and after dives (mean 94.3?±?11.6 vs. 83.5?±?12.5 mg/dl) P?=?0.001 and a statistically significant increase in blood insulin value (median 4.5 range 3.4/6.4 vs. 7.0 range 4.2/10.2 mcgU/ml) P?<?0.0001. Also, we found a statistically significant increase of catecholamine production (median 14.0 range 8/18 vs. 15.5 range 10.0/21.0 ?g) P?<?0.0001.ConclusionsThe increase in blood insulin during BH-diving associated with the decrease of blood glucose levels could indicate that the upregulating cellular uptake is not caused by activation of the specific glucose transporters. Particular diving-related conditions such as the diving reflex, the intermittent hypoxia/hyperoxia, and the particular environmental condition could play an important role in the mechanism involved in glycemia decrease in BH-diving. Our data confirm that the adaptations to BH-diving are caused by complex mechanisms and involve many peculiar responses still in large part unknown.
| S-EPMC5871611 | biostudies-literature
Project description:As the multi-center studies with resting-state functional magnetic resonance imaging (RS-fMRI) have been more and more applied to neuropsychiatric studies, both intra- and inter-scanner reliability of RS-fMRI are becoming increasingly important. The amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) are 3 main RS-fMRI metrics in a way of voxel-wise whole-brain (VWWB) analysis. Although the intra-scanner reliability (i.e., test-retest reliability) of these metrics has been widely investigated, few studies has investigated their inter-scanner reliability. In the current study, 21 healthy young subjects were enrolled and scanned with blood oxygenation level dependent (BOLD) RS-fMRI in 3 visits (V1 - V3), with V1 and V2 scanned on a GE MR750 scanner and V3 on a Siemens Prisma. RS-fMRI data were collected under two conditions, eyes open (EO) and eyes closed (EC), each lasting 8 minutes. We firstly evaluated the intra- and inter-scanner reliability of ALFF, ReHo, and DC. Secondly, we measured systematic difference between two scanning visits of the same scanner as well as between two scanners. Thirdly, to account for the potential difference of intra- and inter-scanner local magnetic field inhomogeneity, we measured the difference of relative BOLD signal intensity to the mean BOLD signal intensity of the whole brain between each pair of visits. Last, we used percent amplitude of fluctuation (PerAF) to correct the difference induced by relative BOLD signal intensity. The inter-scanner reliability was much worse than intra-scanner reliability; Among the VWWB metrics, DC showed the worst (both for intra-scanner and inter-scanner comparisons). PerAF showed similar intra-scanner reliability with ALFF and the best reliability among all the 4 metrics. PerAF reduced the influence of BOLD signal intensity and hence increase the inter-scanner reliability of ALFF. For multi-center studies, inter-scanner reliability should be taken into account.
| S-EPMC6110941 | biostudies-literature
Project description:Background and Objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis and diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify patients with depressed ET at the individual level and help to reveal the brain network pathogenesis of depression in patients with ET. Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance. Results: While the total accuracy (40.45%) of four-class GPC was poor, the four-class GPC could discriminate depressed ET from non-depressed ET, primary depression, and HCs with a sensitivity of 70.73% (P < 0.001). At the same time, the sensitivity of using binary SVM to discriminate depressed ET from non-depressed ET, primary depression, and HCs was 73.17, 80.49, and 75.61%, respectively (P < 0.001). The significant discriminative features were mainly located in cerebellar-motor-prefrontal cortex circuits (P < 0.001), and a further correlation analysis showed that the GBC values of significant discriminative features in the right middle prefrontal gyrus, bilateral cerebellum VI, and Crus 1 were correlated with clinical depression severity in patients with depressed ET. Conclusion: Our findings demonstrated that GBC mapping combined with machine learning MVPA could be used to identify patients with depressed ET, and the GBC changes in cerebellar-prefrontal cortex circuits not only posed as the significant discriminative features but also helped to understand the network pathogenesis underlying depression in patients with ET.
| S-EPMC8545862 | biostudies-literature