Purpose

The goal of this study was to introduce a new noncontrast enhanced 4D dynamic MR angiography (dMRA) technique termed multibolus TrueFISP-based spin tagging with alternating radiofrequency (TrueSTAR).

Methods

Multibolus TrueFISP-based spin tagging with alternating radiofrequency was developed by taking advantage of the phenomenon that the steady-state signal of TrueFISP is minimally disturbed by periodically inserted magnetization preparations (e.g., spin tagging) that are sandwiched by two α/2 RF pulses. Both theoretical analysis and experimental studies were carried out to optimize the proposed method which was compared with both pulsed and pseudo-continuous arterial spin labeling-based dMRA in healthy volunteers. Optimized multibolus dMRA was also applied in a patient with arteriovenous malformation to demonstrate its potential clinical utility.

Results

Multibolus dMRA offered a prolonged tagging bolus compared to the standard single-bolus dMRA, and allowed improved visualization of the draining veins in the arteriovenous malformation patient. Compared to pseudo-continuous arterial spin labeling-based dMRA, multibolus dMRA provided visualization of the full passage of the labeled blood with the flexibility for both static and dynamic magnetic resonance angiography.

Conclusion

By combining the benefits of pulsed and pseudo-continuous arterial spin labeling-based dMRA, multibolus TrueFISP-based spin tagging with alternating radiofrequency can prolong and enhance the tagging bolus without sacrificing imaging speed or temporal resolution.

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.

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.

Transcranial direct current stimulation (tDCS) is a low intensity neuromodulation technique shown to elicit therapeutic effects in a number of neuropsychological conditions. Independent randomized sham-controlled trials and meta- and mega-analyses demonstrate that tDCS targeted to the left dorsolateral prefrontal cortex can produce a clinically meaningful response in patients with major depressive disorder (MDD), but effects are small to moderate in size. However, the heterogeneous presentation, and the neurobiology underlying particular features of depression suggest clinical outcomes might benefit from empirically informed patient selection. In this review, we summarize the status of tDCS research in MDD with focus on the clinical, biological, and intrinsic and extrinsic factors shown to enhance or predict antidepressant response. We also discuss research strategies for optimizing tDCS to improve patient-specific clinical outcomes. TDCS appears suited for both bipolar and unipolar depression, but is less effective in treatment resistant depression. TDCS may also better target core aspects of depressed mood over vegetative symptoms, while pretreatment patient characteristics might inform subsequent response. Peripheral blood markers of gene and immune system function have not yet proven useful as predictors or correlates of tDCS response. Though further research is needed, several lines of evidence suggest that tDCS administered in combination with pharmacological and cognitive behavioral interventions can improve outcomes. Tailoring stimulation to the functional and structural anatomy and/or connectivity of individual patients can maximize physiological response in targeted networks, which in turn could translate to therapeutic benefits.

One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large-scale networks. In resting state fMRI (rs-fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs-fMRI signal. Here, we develop a wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs-fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of "lagged" subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an "objective" estimate of noise level at each scale. We found that the brain's default mode network (DMN) areas manifest a higher level of irregularity in rs-fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet-based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs-fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs-fMRI as well as other biological signals.

The effect of high dose isoflurane on cerebral blood flow (CBF) was investigated in adult macaque monkeys receiving 1% to 2% isoflurane with the pseudo continuous arterial-spin-labeling (pCASL) MRI technique. High concentration (2%) of isoflurane resulted in significant increase in the mean CBF of the global, cortical, subcortical regions and the regional CBF in all subcortical structures and most cortical structures (such as motor cortex, anterior cingulate cortex, but not media prefrontal cortex). In addition, the changes of regional CBF in the affected regions correlated linearly with increasing isoflurane concentrations. The study demonstrates region-specific CBF abnormal increase in adult macaque monkeys under high dose (2%) isoflurane and suggests that the brain functionality in the corresponding structures may be affected and need to be taken consideration in either human or non-human primate neuroimaging studies.

We implemented pseudo-continuous ASL (pCASL) with 2D and 3D balanced steady state free precession (bSSFP) readout for mapping blood flow in the human brain, retina, and kidney, free of distortion and signal dropout, which are typically observed in the most commonly used echo-planar imaging acquisition. High resolution functional brain imaging in the human visual cortex was feasible with 3D bSSFP pCASL. Blood flow of the human retina could be imaged with pCASL and bSSFP in conjunction with a phase cycling approach to suppress the banding artifacts associated with bSSFP. Furthermore, bSSFP based pCASL enabled us to map renal blood flow within a single breath hold. Control and test-retest experiments suggested that the measured blood flow values in retina and kidney were reliable. Because there is no specific imaging tool for mapping human retina blood flow and the standard contrast agent technique for mapping renal blood flow can cause problems for patients with kidney dysfunction, bSSFP based pCASL may provide a useful tool for the diagnosis of retinal and renal diseases and can complement existing imaging techniques.

Background: Two approaches to understanding the etiology of neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) involve network level functional connectivity (FC) and the dynamics of neuronal signaling. The former approach has revealed both increased and decreased FC in individuals with ASD. The latter approach has found high frequency EEG oscillations and higher levels of epilepsy in children with ASD. Together, these findings have led to the hypothesis that atypical excitatory-inhibitory neural signaling may lead to imbalanced association pathways. However, simultaneously reconciling local temporal dynamics with network scale spatial connectivity remains a difficult task and thus empirical support for this hypothesis is lacking. Methods: We seek to fill this gap by combining two powerful resting-state functional MRI (rs-fMRI) methods-functional connectivity (FC) and wavelet-based regularity analysis. Wavelet-based regularity analysis is an entropy measure of the local rs-fMRI time series signal. We examined the relationship between the RSN entropy and integrity in individuals with ASD and controls from the Autism Brain Imaging Data Exchange (ABIDE) cohort using a putative set of 264 functional brain regions-of-interest (ROI). Results: We observed that an imbalance in intra- and inter-network FC across 11 RSNs in ASD individuals (p = 0.002) corresponds to a weakened relationship with RSN temporal entropy (p = 0.02). Further, we observed that an estimated RSN entropy model significantly distinguished ASD from controls (p = 0.01) and was associated with level of ASD symptom severity (p = 0.003). Conclusions: Imbalanced brain connectivity and dynamics at the network level coincides with their decoupling in ASD. The association with ASD symptom severity presents entropy as a potential biomarker.

Information on renal perfusion is essential for the diagnosis and prognosis of kidney function. Quantification using gadolinium chelates is limited as a result of filtration through renal glomeruli and safety concerns in patients with kidney dysfunction. Arterial spin labeling MRI is a noninvasive technique for perfusion quantification that has been applied to humans and animals. However, because of the low sensitivity and vulnerability to motion and susceptibility artifacts, its application to mice has been challenging. In this article, mouse renal perfusion was studied using flow-sensitive alternating inversion recovery at 7 T. Good perfusion image quality was obtained with spin-echo echo-planar imaging after controlling for respiratory, susceptibility and fat artifacts by triggering, high-order shimming and water excitation, respectively. High perfusion was obtained in the renal cortex relative to the medulla, and signal was absent in scans carried out post mortem. Cortical perfusion increased from 397 ± 36 (mean ± standard deviation) to 476 ± 73 mL/100 g/min after switching from 100% oxygen to carbogen with 95% oxygen and 5% carbon dioxide. The perfusion in the medulla was 2.5 times lower than that in the cortex and changed from 166 ± 41 mL/100 g/min under oxygen to 203 ± 40 mL/100 g/min under carbogen. T1 decreased in both the cortex (from 1570 ± 164 to 1377 ± 72 ms, p < 0.05) and medulla (from 1788 ± 107 to 1573 ± 144 ms, p < 0.05) under carbogen relative to 100% oxygen. The results showed the potential of the use of ASL for perfusion quantification in mice and in models of renal diseases.