Neural Underpinnings of Learning in Dementia Populations: A Review of Motor Learning Studies Combined with Neuroimaging.

The intent of this review article is to serve as an overview of current research regarding the neural characteristics of motor learning in Alzheimer disease (AD) as well as prodromal phases of AD: at-risk populations, and mild cognitive impairment. This review seeks to provide a cognitive framework to compare various motor tasks. We will highlight the neural characteristics related to cognitive domains that, through imaging, display functional or structural changes because of AD progression. In turn, this motivates the use of motor learning paradigms as possible screening techniques for AD and will build upon our current understanding of learning abilities in AD populations.

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications.

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.

Multimodal comparisons of QSM and PET in neurodegeneration and aging.

Quantitative susceptibility mapping (QSM) has been used to study susceptibility changes that may occur based on tissue composition and mineral deposition. Iron is a primary contributor to changes in magnetic susceptibility and of particular interest in applications of QSM to neurodegeneration and aging. Iron can contribute to neurodegeneration through inflammatory processes and via interaction with aggregation of disease-related proteins. To better understand the local susceptibility changes observed on QSM, its signal has been studied in association with other imaging metrics such as positron emission tomography (PET). The associations of QSM and PET may provide insight into the pathophysiology of disease processes, such as the role of iron in aging and neurodegeneration, and help to determine the diagnostic utility of QSM as an indirect indicator of disease processes typically evaluated with PET. In this review we discuss the proposed mechanisms and summarize prior studies of the associations of QSM and amyloid PET, tau PET, TSPO PET, FDG-PET, 15O-PET, and F-DOPA PET in evaluation of neurologic diseases with a focus on aging and neurodegeneration.

Characterizing systemic physiological effects on the blood oxygen level dependent signal of resting-state fMRI in time-frequency space using wavelets.

Systemic physiological dynamics, such as heart rate variability (HRV) and respiration volume per time (RVT), are known to account for significant variance in the blood oxygen level dependent (BOLD) signal of resting-state functional magnetic resonance imaging (rsfMRI). However, synchrony between these cardiorespiratory changes and the BOLD signal could be due to neuronal (i.e., autonomic activity inducing changes in heart rate and respiration) or vascular (i.e., cardiorespiratory activity facilitating hemodynamic changes and thus the BOLD signal) effects and the contributions of these effects may differ spatially, temporally, and spectrally. In this study, we characterize these brain-body dynamics using a wavelet analysis in rapidly sampled rsfMRI data with simultaneous pulse oximetry and respiratory monitoring of the Human Connectome Project. Our time-frequency analysis across resting-state networks (RSNs) revealed differences in the coherence of the BOLD signal and heartbeat interval (HBI)/RVT dynamics across frequencies, with unique profiles per network. Somatomotor (SMN), visual (VN), and salience (VAN) networks demonstrated the greatest synchrony with both systemic physiological signals when compared to other networks; however, significant coherence was observed in all RSNs regardless of direct autonomic involvement. Our phase analysis revealed distinct frequency profiles of percentage of time with significant coherence between BOLD and systemic physiological signals for different phase offsets across RSNs, suggesting that the phase offset and temporal order of signals varies by frequency. Lastly, our analysis of temporal variability of coherence provides insight on potential influence of autonomic state on brain-body communication. Overall, the novel wavelet analysis enables an efficient characterization of the dynamic relationship between cardiorespiratory activity and the BOLD signal in spatial, temporal, and spectral dimensions to inform our understanding of autonomic states and improve our interpretation of the BOLD signal.

Modeling venous bias in resting state functional MRI metrics.

Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations in the blood oxygen-level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD-derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high-resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI-derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel-wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.

18F-FSPG PET/CT Imaging of System xC- Transporter Activity in Patients with Primary and Metastatic Brain Tumors.

Background The PET tracer (4S)-4-(3-[18F]fluoropropyl)-l-glutamate (18F-FSPG) targets the system xC- cotransporter, which is overexpressed in various tumors. Purpose To assess the role of 18F-FSPG PET/CT in intracranial malignancies. Materials and Methods Twenty-six patients (mean age, 54 years ± 12; 17 men; 48 total lesions) with primary brain tumors (n = 17) or brain metastases (n = 9) were enrolled in this prospective, single-center study (ClinicalTrials.gov identifier: NCT02370563) between November 2014 and March 2016. A 30-minute dynamic brain 18F-FSPG PET/CT scan and a static whole-body (WB) 18F-FSPG PET/CT scan at 60-75 minutes were acquired. Moreover, all participants underwent MRI, and four participants underwent fluorine 18 (18F) fluorodeoxyglucose (FDG) PET imaging. PET parameters and their relative changes were obtained for all lesions. Kinetic modeling was used to estimate the 18F-FSPG tumor rate constants using the dynamic and dynamic plus WB PET data. Imaging parameters were correlated to lesion outcomes, as determined with follow-up MRI and/or pathologic examination. The Mann-Whitney U test or Student t test was used for group mean comparisons. Receiver operating characteristic curve analysis was used for performance comparison of different decision measures. Results 18F-FSPG PET/CT helped identify all 48 brain lesions. The mean tumor-to-background ratio (TBR) on the whole-brain PET images at the WB time point was 26.6 ± 24.9 (range: 2.6-150.3). When 18F-FDG PET was performed, 18F-FSPG permitted visualization of non-18F-FDG-avid lesions or allowed better lesion differentiation from surrounding tissues. In participants with primary brain tumors, the predictive accuracy of the relative changes in influx rate constant Ki and maximum standardized uptake value to discriminate between poor and good lesion outcomes were 89% and 81%, respectively. There were significant differences in the 18F-FSPG uptake curves of lesions with good versus poor outcomes in the primary brain tumor group (P < .05) but not in the brain metastases group. Conclusion PET/CT imaging with (4S)-4-(3-[18F]fluoropropyl)-l-glutamate (18F-FSPG) helped detect primary brain tumors and brain metastases with a high tumor-to-background ratio. Relative changes in 18F-FSPG uptake with multi-time-point PET appear to be helpful in predicting lesion outcomes. Clinical trial registration no. NCT02370563 © RSNA, 2022 Online supplemental material is available for this article.

Cerebrovascular reactivity measurements using simultaneous 15O-water PET and ASL MRI: Impacts of arterial transit time, labeling efficiency, and hematocrit.

Cerebrovascular reactivity (CVR) reflects the capacity of the brain to meet changing physiological demands and can predict the risk of cerebrovascular diseases. CVR can be obtained by measuring the change in cerebral blood flow (CBF) during a brain stress test where CBF is altered by a vasodilator such as acetazolamide. Although the gold standard to quantify CBF is PET imaging, the procedure is invasive and inaccessible to most patients. Arterial spin labeling (ASL) is a non-invasive and quantitative MRI method to measure CBF, and a consensus guideline has been published for the clinical application of ASL. Despite single post labeling delay (PLD) pseudo-continuous ASL (PCASL) being the recommended ASL technique for CBF quantification, it is sensitive to variations to the arterial transit time (ATT) and labeling efficiency induced by the vasodilator in CVR studies. Multi-PLD ASL controls for the changes in ATT, and velocity selective ASL is in theory insensitive to both ATT and labeling efficiency. Here we investigate CVR using simultaneous 15O-water PET and ASL MRI data from 19 healthy subjects. CVR and CBF measured by the ASL techniques were compared using PET as the reference technique. The impacts of blood T1 and labeling efficiency on ASL were assessed using individual measurements of hematocrit and flow velocity data of the carotid and vertebral arteries measured using phase-contrast MRI. We found that multi-PLD PCASL is the ASL technique most consistent with PET for CVR quantification (group mean CVR of the whole brain = 42±19% and 40±18% respectively). Single-PLD ASL underestimated the CVR of the whole brain significantly by 15±10% compared with PET (p<0.01, paired t-test). Changes in ATT pre- and post-acetazolamide was the principal factor affecting ASL-based CVR quantification. Variations in labeling efficiency and blood T1 had negligible effects.

Quantification of brain oxygen extraction and metabolism with [15O]-gas PET: A technical review in the era of PET/MRI.

Oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen (CMRO2) are key cerebral physiological parameters to identify at-risk cerebrovascular patients and understand brain health and function. PET imaging with [15O]-oxygen tracers, either through continuous or bolus inhalation, provides non-invasive assessment of OEF and CMRO2. Numerous tracer delivery, PET acquisition, and kinetic modeling approaches have been adopted to map brain oxygenation. The purpose of this technical review is to critically evaluate different methods for [15O]-gas PET and its impact on the accuracy and reproducibility of OEF and CMRO2 measurements. We perform a meta-analysis of brain oxygenation PET studies in healthy volunteers and compare between continuous and bolus inhalation techniques. We also describe OEF metrics that have been used to detect hemodynamic impairment in cerebrovascular disease. For these patients, advanced techniques to accelerate the PET scans and potential synthesis with MRI to avoid arterial blood sampling would facilitate broader use of [15O]-oxygen PET for brain physiological assessment.

Predicting PET Cerebrovascular Reserve with Deep Learning by Using Baseline MRI: A Pilot Investigation of a Drug-Free Brain Stress Test.

Background Cerebrovascular reserve (CVR) may be measured by using an acetazolamide test to clinically evaluate patients with cerebrovascular disease. However, acetazolamide use may be contraindicated and/or undesirable in certain clinical settings. Purpose To predict CVR images generated from acetazolamide vasodilation with a deep learning network by using only images before acetazolamide administration. Materials and Methods Simultaneous oxygen 15 (15O)-labeled water PET/MRI before and after acetazolamide injection were retrospectively analyzed for patients with Moyamoya disease and healthy control participants from April 2017 to May 2019. Inputs to deep learning models were perfusion-based images (arterial spin labeling [ASL]), structural scans (T2 fluid-attenuated inversion-recovery, T1), and brain location. Two models, that is, 15O-labeled water PET cerebral blood flow (CBF) and MRI (PET-plus-MRI model) before acetazolamide administration and only MRI (MRI-only model) before acetazolamide administration, were trained and tested with sixfold cross-validation. The models learned to predict a voxelwise relative CBF change (rΔCBF) map by using rΔCBF measured with PET due to acetazolamide as ground truth. Quantitative analysis included image quality metrics (peak signal-to-noise ratio, root mean square error, and structural similarity index), as well as comparison between the various methods by using correlation and Bland-Altman analyses. Identification of vascular territories with impaired rΔCBF was evaluated by using receiver operating characteristic metrics. Results Thirty-six participants were included: 24 patients with Moyamoya disease (mean age ± standard deviation, 41 years ± 12; 17 women) and 12 age-matched healthy control participants (mean age, 39 years ± 16; nine women). The rΔCBF maps predicted by both deep learning models demonstrated better image quality metrics than did ASL (all P < .001 in patients) and higher correlation coefficient with PET than with ASL (PET-plus-MRI model, 0.704; MRI-only model, 0.690 vs ASL, 0.432; both P < .001 in patients). Both models also achieved high diagnostic performance in identifying territories with impaired rΔCBF (area under receiver operating characteristic curve, 0.95 for PET-plus-MRI model [95% confidence interval: 0.90, 0.99] and 0.95 for MRI-only model [95% confidence interval: 0.91, 0.98]). Conclusion By using only images before acetazolamide administration, PET-plus-MRI and MRI-only deep learning models predicted cerebrovascular reserve images without the need for vasodilator injection. © RSNA, 2020 Online supplemental material is available for this article.

Identifying Hypoperfusion in Moyamoya Disease With Arterial Spin Labeling and an [15O]-Water Positron Emission Tomography/Magnetic Resonance Imaging Normative Database.

Background and Purpose- Noninvasive imaging of brain perfusion has the potential to elucidate pathophysiological mechanisms underlying Moyamoya disease and enable clinical imaging of cerebral blood flow (CBF) to select revascularization therapies for patients. We used hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) technology to characterize the distribution of hypoperfusion in Moyamoya disease and its relationship to vessel stenosis severity, through comparisons with a normative perfusion database of healthy controls. Methods- To image CBF, we acquired [15O]-water PET as a reference and simultaneously acquired arterial spin labeling (ASL) MRI scans in 20 Moyamoya patients and 15 age-matched, healthy controls on a PET/MRI scanner. The ASL MRI scans included a standard single-delay ASL scan with postlabel delay of 2.0 s and a multidelay scan with 5 postlabel delays (0.7-3.0s) to estimate and account for arterial transit time in CBF quantification. The percent volume of hypoperfusion in patients (determined as the fifth percentile of CBF values in the healthy control database) was the outcome measure in a logistic regression model that included stenosis grade and location. Results- Logistic regression showed that anterior ( P<0.0001) and middle cerebral artery territory regions ( P=0.003) in Moyamoya patients were susceptible to hypoperfusion, whereas posterior regions were not. Cortical regions supplied by arteries with stenosis on MR angiography showed more hypoperfusion than normal arteries ( P=0.001), but the extent of hypoperfusion was not different between mild-moderate versus severe stenosis. Multidelay ASL did not perform differently from [15O]-water PET in detecting perfusion abnormalities, but standard ASL overestimated the extent of hypoperfusion in patients ( P=0.003). Conclusions- This simultaneous PET/MRI study supports the use of multidelay ASL MRI in clinical evaluation of Moyamoya disease in settings where nuclear medicine imaging is not available and application of a normative perfusion database to automatically identify abnormal CBF in patients.