The Age-Related Perfusion Pattern Measured With Arterial Spin Labeling MRI in Healthy Subjects

Zhang, Nan; Gordon, M.S.; Ma, Yilong; Chi, Bradley; Gomar, Jesús J.; Peng, Shichun; Kingsley, Peter B.; Eidelberg, David; Goldberg, Terry E.

doi:10.3389/fnagi.2018.00214

Cited by 56 publications

(62 citation statements)

References 64 publications

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“…The observed average, gender and age effects were consistent with previous reports. The age effects on CBF values were in agreement with previous reports (Chen et al, 2011;Zhang et al, 2018), with decreases mainly found in regions that are associated with high perfusion and metabolic demand, including precuneus, cuneus, prefrontal cortices and cerebellum. We also observed age-related increase in CBF in temporal regions, which may reflect macro-vascular artifacts that are common to arterial spin labelling findings (Detre et al, 2012;Mutsaerts et al, 2017) due to prolonged arterial transit time with ageing (Dai et al, 2017).…”

Section: Neurovascular Signals and Age-differences In Rsfasupporting

confidence: 92%

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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Section: Neurovascular Signals and Age-differences In Rsfasupporting

confidence: 92%

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

Tsvetanov

Henson

Jones

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…The magnitudes of the observed CBF decreases in our study were larger than age-related CBF changes reported in the literature. For example, the percentage change in mean global GM CBF in this study is about −4.8% per year, more than 10 times larger than reported age-related changes, which range from −0.38 to −0.45% per year ( Biagi et al, 2007 , Parkes et al, 2004 , Wagner et al, 2012 , Zhang et al, 2018 ).…”

Section: Discussioncontrasting

confidence: 70%

Cerebral blood flow characteristics following hemodialysis initiation in older adults: A prospective longitudinal pilot study using arterial spin labeling imaging

Slinin

Zhang

et al. 2020

NeuroImage: Clinical

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“…Since we found significant correlations between PDRP z-scores and age in healthy controls, it could be suggested that ageing and PD share certain metabolic features. Metabolic decreases have been reported in the parietal cortex in normal aging [47,48]. This may cause some overlap with the PDRP.…”

Section: Discussionmentioning

confidence: 93%

Abnormal pattern of brain glucose metabolism in Parkinson’s disease: replication in three European cohorts

Meles

Renken

Pagani

et al. 2019

Eur J Nucl Med Mol Imaging

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Rationale In Parkinson's disease (PD), spatial covariance analysis of 18 F-FDG PET data has consistently revealed a characteristic PD-related brain pattern (PDRP). By quantifying PDRP expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. We provide a further validation of the PDRP by applying spatial covariance analysis to PD cohorts from the Netherlands (NL), Italy (IT), and Spain (SP). Methods The PDRP NL was previously identified (17 controls, 19 PD) and its expression was determined in 19 healthy controls and 20 PD patients from the Netherlands. The PDRP IT was identified in 20 controls and 20 "de-novo" PD patients from an Italian cohort. A further 24 controls and 18 "de-novo" Italian patients were used for validation. The PDRP SP was identified in 19 controls and 19 PD patients from a Spanish cohort with late-stage PD. Thirty Spanish PD patients were used for validation. Patterns of the three centers were visually compared and then cross-validated. Furthermore, PDRP expression was determined in 8 patients with multiple system atrophy. Results A PDRP could be identified in each cohort. Each PDRP was characterized by relative hypermetabolism in the thalamus, putamen/pallidum, pons, cerebellum, and motor cortex. These changes co-varied with variable degrees of hypometabolism in posterior parietal, occipital, and frontal cortices. Frontal hypometabolism was less pronounced in "de-novo" PD subjects (Italian cohort). Occipital hypometabolism was more pronounced in late-stage PD subjects (Spanish cohort). PDRP IT , PDRP NL , and PDRP SP were significantly expressed in PD patients compared with controls in validation cohorts from the same center (P < 0.0001), and maintained significance on cross-validation (P < 0.005). PDRP expression was absent in MSA.

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The Age-Related Perfusion Pattern Measured With Arterial Spin Labeling MRI in Healthy Subjects

Cited by 56 publications

References 64 publications

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

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

Cerebral blood flow characteristics following hemodialysis initiation in older adults: A prospective longitudinal pilot study using arterial spin labeling imaging

Abnormal pattern of brain glucose metabolism in Parkinson’s disease: replication in three European cohorts

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