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.

BackgroundNeuroimaging studies can shed light on the neurobiological underpinnings of autism spectrum disorders (ASD). Studies of the resting brain have shown both altered baseline metabolism from PET/SPECT and altered functional connectivity (FC) of intrinsic brain networks based on resting-state fMRI. To date, however, no study has investigated these two physiological parameters of resting brain function jointly, or explored the relationship between these measures and ASD symptom severity.MethodsHere, we used pseudo-continuous arterial spin labeling with 3D background-suppressed GRASE to assess resting cerebral blood flow (CBF) and FC in 17 youth with ASD and 22 matched typically developing (TD) children.ResultsA pattern of altered resting perfusion was found in ASD versus TD children including frontotemporal hyperperfusion and hypoperfusion in the dorsal anterior cingulate cortex. We found increased local FC in the anterior module of the default mode network (DMN) accompanied by decreased CBF in the same area. In our cohort, both alterations were associated with greater social impairments as assessed with the Social Responsiveness Scale (SRS-total T scores). While FC was correlated with CBF in TD children, this association between FC and baseline perfusion was disrupted in children with ASD. Furthermore, there was reduced long-range FC between anterior and posterior modules of the DMN in children with ASD.ConclusionTaken together, the findings of this study – the first to jointly assess resting CBF and FC in ASD – highlight new avenues for identifying novel imaging markers of ASD symptomatology.

Purpose To explore the use of approximate entropy (ApEn) as an index of the complexity and the synchronicity of resting state BOLD fMRI in normal aging and cognitive decline associated with familial Alzheimer’s disease (fAD). Materials and Methods Resting state BOLD fMRI data were acquired at 3T from 2 independent cohorts of subjects consisting of healthy young (age 23±2 years, n=8) and aged volunteers (age 66±3 years, n=8), as well as 22 fAD associated subjects (14 mutation carriers, age 41.2±15.8 years; and 8 non-mutation carrying family members, age 28.8±5.9 years). Mean ApEn values were compared between the two age groups, and correlated with cognitive performance in the fAD group. Cross-ApEn (C-ApEn) was further calculated to assess the asynchrony between precuneus and the rest of the brain. Results Complexity of brain activity measured by mean ApEn in gray and white matter decreased with normal aging. In the fAD group, cognitive impairment was associated with decreased mean ApEn in gray matter as well as decreased regional ApEn in right precuneus, right lateral parietal regions, left precentral gyrus, and right paracentral gyrus. A pattern of asynchrony between BOLD fMRI series emerged from C-ApEn analysis, with significant regional anti-correlation with cross-correlation coefficient of functional connectivity analysis. Conclusion ApEn and C-ApEn may be useful for assessing the complexity and synchronicity of brain activity in normal aging and cognitive decline associated with neurodegenerative diseases

The present study explored multi-scale entropy (MSE) analysis to investigate the entropy of resting state fMRI signals across multiple time scales. MSE analysis was developed to distinguish random noise from complex signals since the entropy of the former decreases with longer time scales while the latter signal maintains its entropy due to a self-resemblance” across time scales. A long resting state BOLD fMRI (rs-fMRI) scan with 1000 data points was performed on five healthy young volunteers to investigate the spatial and temporal characteristics of entropy across multiple time scales. A shorter rs-fMRI scan with 240 data points was performed on a cohort of subjects consisting of healthy young (age 23±2 years, n=8) and aged volunteers (age 66±3 years, n=8) to investigate the effect of healthy aging on the entropy of rs-fMRI. The results showed that MSE of gray matter, rather than white matter, resembles closely that of f−1 noise over multiple time scales. By filtering out high frequency random fluctuations, MSE analysis is able to reveal enhanced contrast in entropy between gray and white matter, as well as between age groups at longer time scales. Our data support the use of MSE analysis as a validation metric for quantifying the complexity of rs-fMRI signals.