Quantification of Network Perfusion in ASL Cerebral Blood Flow Data with Seed Based and ICA Approaches

Jann, Kay; Orosz, Ariane; Dierks, Thomas; Wang, Danny J.J.; Wiest, Roland; Federspiel, Andrea

doi:10.1007/s10548-013-0280-3

Cited by 26 publications

(31 citation statements)

References 53 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Absolute CBF values obtained using ASL in the whole brain and specific brain regions have been shown to be reproducible across time scales of minutes, hours to days (Chen et al, 2011; Jain et al, 2012; Jann et al, 2013; Wu et al, 2011). There is a good correlation between ASL CBF and the gold standard of 15 O-PET in both resting state and activation studies (Feng et al, 2004; Kilroy et al, 2013; Ye et al, 2000).…”

Section: Discussionmentioning

confidence: 99%

“…There is a good correlation between ASL CBF and the gold standard of 15 O-PET in both resting state and activation studies (Feng et al, 2004; Kilroy et al, 2013; Ye et al, 2000). Besides providing a robust mean CBF averaged across a few minute scan, it has been shown to be feasible to perform dynamic FC analysis of the ASL perfusion image series (Chuang et al, 2008; Dai et al, 2013; Jann et al, 2013; Liang et al, 2012; Zou et al, 2009), similar to BOLD FC analysis. To date, however, very few studies have systematically addressed the similarity of RBNs detected using BOLD and ASL contrasts, as well as their reliability across sessions and scanners.…”

Section: Discussionmentioning

confidence: 99%

“…The majority of rs-fcMRI studies have used blood oxygen level dependent (BOLD) contrast due to its technical simplicity, high sensitivity and temporal resolution. Recently, a growing number of rs-fcMRI studies have employed arterial spin labeled (ASL) perfusion MRI (Chuang et al, 2008; Dai et al, 2013; Jann et al, 2013; Liang et al, 2011, 2012; Zou et al, 2009), which measures cerebral blood flow (CBF) using magnetically labeled arterial blood water as an endogenous tracer (Detre et al, 1992). Compared to BOLD, perfusion-based FC analysis provides more direct and quantitative measures of the physiology and metabolism of specific networks (Buxton et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Functional connectivity in BOLD and CBF data: Similarity and reliability of resting brain networks

Jann¹,

Gee²,

Kilroy³

et al. 2015

NeuroImage

Self Cite

113

122

View full text Add to dashboard Cite

Resting-state functional connectivity (FC) fMRI (rs-fcMRI) offers an appealing approach to mapping the brain’s intrinsic functional organization. Blood oxygen level dependent (BOLD) and arterial spin labeling (ASL) are the two main rs-fcMRI approaches to assess alterations in brain networks associated with individual differences, behavior and psychopathology. While the BOLD signal is stronger with a higher temporal resolution, ASL provides quantitative, direct measures of the physiology and metabolism of specific networks. This study systematically investigated the similarity and reliability of resting brain networks (RBNs) in BOLD and ASL. A 2 × 2 × 2 factorial design was employed where each subject underwent repeated BOLD and ASL rs-fcMRI scans on two occasions on two MRI scanners respectively. Both independent and joint FC analyses revealed common RBNs in ASL and BOLD rs-fcMRI with a moderate to high level of spatial overlap, verified by Dice Similarity Coefficients. Test–retest analyses indicated more reliable spatial network patterns in BOLD (average modal Intraclass Correlation Coefficients: 0.905 ± 0.033 between-sessions; 0.885 ± 0.052 between-scanners) than ASL (0.545 ± 0.048; 0.575 ± 0.059). Nevertheless, ASL provided highly reproducible (0.955 ± 0.021; 0.970 ± 0.011) network-specific CBF measurements. Moreover, we observed positive correlations between regional CBF and FC in core areas of all RBNs indicating a relationship between network connectivity and its baseline metabolism. Taken together, the combination of ASL and BOLD rs-fcMRI provides a powerful tool for characterizing the spatiotemporal and quantitative properties of RBNs. These findings pave the way for future BOLD and ASL rs-fcMRI studies in clinical populations that are carried out across time and scanners.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Functional connectivity in BOLD and CBF data: Similarity and reliability of resting brain networks

Jann¹,

Gee²,

Kilroy³

et al. 2015

NeuroImage

Self Cite

113

122

View full text Add to dashboard Cite

show abstract

“…For instance, due to the relatively low temporal resolution of ASL scans, perfusion time series from multiple subjects are often concatenated, for instance, in a group ICA (Jann et al, 2015a;Jann et al, 2013). In addition, as CBF and BOLD provide complementary information, it is appealing to combine ASL and BOLD for characterizing spatiotemporal and quantitative properties of specific brain networks, as proposed by a few recent studies (Jann et al, 2013;Zhu et al, 2013). Furthermore, joint CBF-and BOLD-based FC analyses offer mutual validation of findings using each modality, thereby improving confidence for statistical inferences.…”

Section: Perfusion-based Functional Connectivity Mappingmentioning

confidence: 99%

Characterizing Resting-State Brain Function Using Arterial Spin Labeling

2015

Self Cite

View full text Add to dashboard Cite

Arterial spin labeling (ASL) is an increasingly established magnetic resonance imaging (MRI) technique that is finding broader applications in studying the healthy and diseased brain. This review addresses the use of ASL to assess brain function in the resting state. Following a brief technical description, we discuss the use of ASL in the following main categories: (1) resting-state functional connectivity (FC) measurement: the use of ASL-based cerebral blood flow (CBF) measurements as an alternative to the blood oxygen level-dependent (BOLD) technique to assess resting-state FC; (2) the link between network CBF and FC measurements: the use of network CBF as a surrogate of the metabolic activity within corresponding networks; and (3) the study of resting-state dynamic CBF-BOLD coupling and cerebral metabolism: the use of dynamic CBF information obtained using ASL to assess dynamic CBF-BOLD coupling and oxidative metabolism in the resting state. In addition, we summarize some future challenges and interesting research directions for ASL, including slice-accelerated (multiband) imaging as well as the effects of motion and other physiological confounds on perfusion-based FC measurement. In summary, this work reviews the state-of-the-art of ASL and establishes it as an increasingly viable MRI technique with high translational value in studying resting-state brain function.

show abstract

“…6 In addition, it offers a more direct approach to measure neural activity than BOLD due to the close coupling of neural activity and CBF. 6,10 ASL has demonstrated its ability to detect resting state functional networks, [11][12][13][14][15][16][17] first in a sensorimotor network using seed-based analysis and later mapping large-scale networks using independent component analysis (ICA). Our ASL preliminary resting state studies have also been reported in abstract forms.…”

Section: Introductionmentioning

confidence: 99%

Quantifying fluctuations of resting state networks using arterial spin labeling perfusion MRI

Dai

Varma

Scheidegger

et al. 2015

J Cereb Blood Flow Metab

View full text Add to dashboard Cite

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) has been widely used to investigate spontaneous low-frequency signal fluctuations across brain resting state networks. However, BOLD only provides relative measures of signal fluctuations. Arterial Spin Labeling (ASL) MRI holds great potential for quantitative measurements of resting state network fluctuations. This study systematically quantified signal fluctuations of the large-scale resting state networks using ASL data from 20 healthy volunteers by separating them from global signal fluctuations and fluctuations caused by residual noise. Global ASL signal fluctuation was 7.59% AE 1.47% relative to the ASL baseline perfusion. Fluctuations of seven detected resting state networks vary from 2.96% AE 0.93% to 6.71% AE 2.35%. Fluctuations of networks and residual noise were 6.05% AE 1.18% and 6.78% AE 1.16% using 4-mm resolution ASL data applied with Gaussian smoothing kernel of 6mm. However, network fluctuations were reduced by 7.77% AE 1.56% while residual noise fluctuation was markedly reduced by 39.75% AE 2.90% when smoothing kernel of 12 mm was applied to the ASL data. Therefore, global and network fluctuations are the dominant structured noise sources in ASL data. Quantitative measurements of resting state networks may enable improved noise reduction and provide insights into the function of healthy and diseased brain.

show abstract

Quantification of Network Perfusion in ASL Cerebral Blood Flow Data with Seed Based and ICA Approaches

Cited by 26 publications

References 53 publications

Functional connectivity in BOLD and CBF data: Similarity and reliability of resting brain networks

Functional connectivity in BOLD and CBF data: Similarity and reliability of resting brain networks

Characterizing Resting-State Brain Function Using Arterial Spin Labeling

Quantifying fluctuations of resting state networks using arterial spin labeling perfusion MRI

Contact Info

Product

Resources

About