A generalized signal model for dual-module velocity-selective arterial spin labeling.

PURPOSE: To develop a generalized signal model for dual-module velocity-selective arterial spin labeling (dm-VSASL) that can integrate arbitrary saturation and inversion profiles. THEORY AND METHODS: A recently developed mathematical framework for single-module VSASL is extended to address the increased complexity of dm-VSASL and to model the use of realistic velocity-selective profiles in the label-control and vascular crushing modules. Expressions for magnetization difference, arterial delivery functions, labeling efficiency, and cerebral blood flow (CBF) estimation error are presented. Sources of error are examined and timing requirements to minimize quantification errors are derived. RESULTS: For ideal velocity-selective profiles, the predicted signals match those of prior work. With realistic profiles, a CBF-dependent estimation error can occur when velocity-selective inversion (VSI) is used for the labeling modules and velocity-selective saturation (VSS) is used for the vascular crushing module. The error reflects a mismatch between the leading and trailing edges of the delivery function for the second bolus and can be minimized by choosing a nominal labeling cutoff velocity that is lower than the nominal saturation cutoff velocity. In the presence of B 0 $$ {\mathrm{B}}_0 $$ and B 1 $$ {\mathrm{B}}_1 $$ inhomogeneities, the labeling efficiency of dual-module VSI is more attenuated than that of dual-module VSS. CONCLUSION: The proposed signal model will enable researchers to more accurately assess and compare the performance of realistic dm-VSASL implementations and improve the quantification of dm-VSASL CBF measures.

A mathematical model for velocity-selective arterial spin labeling.

PURPOSE: To present a theoretical framework that rigorously defines and analyzes key concepts and quantities for velocity selective arterial spin labeling (VSASL). THEORY AND METHODS: An expression for the VSASL arterial delivery function is derived based on (1) labeling and saturation profiles as a function of velocity and (2) physiologically plausible approximations of changes in acceleration and velocity across the vascular system. The dependence of labeling efficiency on the amplitude and effective bolus width of the arterial delivery function is defined. Factors that affect the effective bolus width are examined, and timing requirements to minimize quantitation errors are derived. RESULTS: The model predicts that a flow-dependent negative bias in the effective bolus width can occur when velocity selective inversion (VSI) is used for the labeling module and velocity selective saturation (VSS) is used for the vascular crushing module. The bias can be minimized by choosing a nominal labeling cutoff velocity that is lower than the nominal cutoff velocity of the vascular crushing module. CONCLUSION: The elements of the model are specified in a general fashion such that future advances can be readily integrated. The model can facilitate further efforts to understand and characterize the performance of VSASL and provide critical theoretical insights that can be used to design future experiments and develop novel VSASL approaches.

Reduced B0/B1 + sensitivity in velocity-selective inversion arterial spin labeling using adiabatic refocusing pulses.

PURPOSE: To mitigate the B0/B1 + sensitivity of velocity-selective inversion (VSI) pulse trains for velocity-selective arterial spin labeling (VSASL) by implementing adiabatic refocusing. This approach aims to achieve artifact-free VSI-based perfusion imaging through single-pair label-control subtractions, reducing the need for the currently required four-pair dynamic phase-cycling (DPC) technique when using a velocity-insensitive control. METHODS: We introduce a Fourier-transform VSI (FT-VSI) train that incorporates sinc-modulated hard excitation pulses with MLEV-8-modulated adiabatic hyperbolic secant refocusing pairs. We compare performance between this train and the standard composite refocusing train, including with and without DPC, for dual-module VSI VSASL. We evaluate (1) simulated velocity-selective profiles and subtraction fidelity across a broad B0/B1 + range, (2) subtraction fidelity in phantoms, and (3) image quality, artifact presence, and gray-matter perfusion heterogeneity (as measured by the spatial coefficient of variation) in healthy human subjects. RESULTS: Adiabatic refocusing significantly improves FT-VSI robustness to B0/B1 + inhomogeneity for a single label-control subtraction. Subtraction fidelity is dramatically improved in both simulation and phantoms compared with composite refocusing without DPC, and is similar compared with DPC methods. In humans, marked artifacts seen with the non-DPC composite refocusing approach are eliminated, corroborated by significantly reduced gray-matter heterogeneity (via lower spatial coefficient of variation values). CONCLUSION: A novel VSASL labeling train using adiabatic refocusing pulses for VSI was found to reduce artifacts related to B0/B1 + inhomogeneity, thereby providing an alternative to DPC and its associated limitations, which include increased vulnerability to physiological noise and motion, reduced functional MRI applicability, and suboptimal data censoring.

Functional connectome fingerprinting using shallow feedforward neural networks.

Although individual subjects can be identified with high accuracy using correlation matrices computed from resting-state functional MRI (rsfMRI) data, the performance significantly degrades as the scan duration is decreased. Recurrent neural networks can achieve high accuracy with short-duration (72 s) data segments but are designed to use temporal features not present in the correlation matrices. Here we show that shallow feedforward neural networks that rely solely on the information in rsfMRI correlation matrices can achieve state-of-the-art identification accuracies ([Formula: see text]) with data segments as short as 20 s and across a range of input data size combinations when the total number of data points (number of regions × number of time points) is on the order of [Formula: see text].

Greater accelerometer-measured physical activity is associated with better cognition and cerebrovascular health in older adults.

OBJECTIVES: Physical activity (PA) may help maintain brain structure and function in aging. Since the intensity of PA needed to effect cognition and cerebrovascular health remains unknown, we examined associations between PA and cognition, regional white matter hyperintensities (WMH), and regional cerebral blood flow (CBF) in older adults. METHOD: Forty-three older adults without cognitive impairment underwent magnetic resonance imaging (MRI) and comprehensive neuropsychological assessment. Waist-worn accelerometers objectively measured PA for approximately one week. RESULTS: Higher time spent in moderate to vigorous PA (MVPA) was uniquely associated with better memory and executive functioning after adjusting for all light PA. Higher MVPA was also uniquely associated with lower frontal WMH volume although the finding was no longer significant after additionally adjusting for age and accelerometer wear time. MVPA was not associated with CBF. Higher time spent in all light PA was uniquely associated with higher CBF but not with cognitive performance or WMH volume. CONCLUSIONS: Engaging in PA may be beneficial for cerebrovascular health, and MVPA in particular may help preserve memory and executive function in otherwise cognitively healthy older adults. There may be differential effects of engaging in lighter PA and MVPA on MRI markers of cerebrovascular health although this needs to be confirmed in future studies with larger samples. Future randomized controlled trials that increase PA are needed to elucidate cause-effect associations between PA and cerebrovascular health.

Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events.

Importance: Whether protein risk scores derived from a single plasma sample could be useful for risk assessment for atherosclerotic cardiovascular disease (ASCVD), in conjunction with clinical risk factors and polygenic risk scores, is uncertain. Objective: To develop protein risk scores for ASCVD risk prediction and compare them to clinical risk factors and polygenic risk scores in primary and secondary event populations. Design, Setting, and Participants: The primary analysis was a retrospective study of primary events among 13 540 individuals in Iceland (aged 40-75 years) with proteomics data and no history of major ASCVD events at recruitment (study duration, August 23, 2000 until October 26, 2006; follow-up through 2018). We also analyzed a secondary event population from a randomized, double-blind lipid-lowering clinical trial (2013-2016), consisting of individuals with stable ASCVD receiving statin therapy and for whom proteomic data were available for 6791 individuals. Exposures: Protein risk scores (based on 4963 plasma protein levels and developed in a training set in the primary event population); polygenic risk scores for coronary artery disease and stroke; and clinical risk factors that included age, sex, statin use, hypertension treatment, type 2 diabetes, body mass index, and smoking status at the time of plasma sampling. Main Outcomes and Measures: Outcomes were composites of myocardial infarction, stroke, and coronary heart disease death or cardiovascular death. Performance was evaluated using Cox survival models and measures of discrimination and reclassification that accounted for the competing risk of non-ASCVD death. Results: In the primary event population test set (4018 individuals [59.0% women]; 465 events; median follow-up, 15.8 years), the protein risk score had a hazard ratio (HR) of 1.93 per SD (95% CI, 1.75 to 2.13). Addition of protein risk score and polygenic risk scores significantly increased the C index when added to a clinical risk factor model (C index change, 0.022 [95% CI, 0.007 to 0.038]). Addition of the protein risk score alone to a clinical risk factor model also led to a significantly increased C index (difference, 0.014 [95% CI, 0.002 to 0.028]). Among White individuals in the secondary event population (6307 participants; 432 events; median follow-up, 2.2 years), the protein risk score had an HR of 1.62 per SD (95% CI, 1.48 to 1.79) and significantly increased C index when added to a clinical risk factor model (C index change, 0.026 [95% CI, 0.011 to 0.042]). The protein risk score was significantly associated with major adverse cardiovascular events among individuals of African and Asian ancestries in the secondary event population. Conclusions and Relevance: A protein risk score was significantly associated with ASCVD events in primary and secondary event populations. When added to clinical risk factors, the protein risk score and polygenic risk score both provided statistically significant but modest improvement in discrimination.

A geometric view of signal sensitivity metrics in multi-echo fMRI.

In multi-echo fMRI (ME-fMRI), two metrics have been widely used to measure the performance of various acquisition and analysis approaches. These are temporal SNR (tSNR) and differential contrast-to-noise ratio (dCNR). A key step in ME-fMRI is the weighted combination of the data from multiple echoes, and prior work has examined the dependence of tSNR and dCNR on the choice of weights. However, most studies have focused on only one of these two metrics, and the relationship between the two metrics has not been examined. In this work, we present a geometric view that offers greater insight into the relation between the two metrics and their weight dependence. We identify three major regimes: (1) a tSNR robust regime in which tSNR is robust to the weight selection with most weight variants achieving close to optimal performance, whereas dCNR shows a pronounced dependence on the weights with most variants achieving suboptimal performance; (2) a dCNR robust regime in which dCNR is robust to the weight selection with most weight variants achieving close to optimal performance, while tSNR exhibits a strong dependence on the weights with most variants achieving significantly lower than optimal performance; and (3) a within-type robust regime in which both tSNR and dCNR achieve nearly optimal performance when the form of the weights are variants of their respective optimal weights and exhibit a moderate decrease in performance for other weight variants. Insight into the behavior observed in the different regimes is gained by considering spherical representations of the weight dependence of the components used to form each metric. For multi-echo acquisitions, dCNR is shown to be more directly related than tSNR to measures of CNR and signal-to-noise ratio (SNR) for task-based and resting-state fMRI scans, respectively.

Vibronic fingerprints in the luminescence of graphene quantum dots at cryogenic temperature.

Atomically precise graphene quantum dots synthesized by bottom-up chemistry are promising versatile single emitters with potential applications for quantum photonic technologies. Toward a better understanding and control of graphene quantum dot (GQD) optical properties, we report on single-molecule spectroscopy at cryogenic temperature. We investigate the effect of temperature on the GQDs' spectral linewidth and vibronic replica, which we interpret building on density functional theory calculations. Finally, we highlight that the vibronic signatures are specific to the GQD geometry and can be used as a fingerprint for identification purposes.

Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes.

Falling asleep is common in fMRI studies. By using long eyelid closures to detect microsleep onset, we showed that the onset and termination of short sleep episodes invokes a systematic sequence of BOLD signal changes that are large, widespread, and consistent across different microsleep durations. The signal changes are intimately intertwined with shifts in respiration and heart rate, indicating that autonomic contributions are integral to the brain physiology evaluated using fMRI and cannot be simply treated as nuisance signals. Additionally, resting state functional connectivity (RSFC) was altered in accord with the frequency of falling asleep and in a manner that global signal regression does not eliminate. Our findings point to the need to develop a consensus among neuroscientists using fMRI on how to deal with microsleep intrusions. SIGNIFICANCE STATEMENT: Sleep, breathing and cardiac action are influenced by common brainstem nuclei. We show that falling asleep and awakening are associated with a sequence of BOLD signal changes that are large, widespread and consistent across varied durations of sleep onset and awakening. These signal changes follow closely those associated with deceleration and acceleration of respiration and heart rate, calling into question the separation of the latter signals as 'noise' when the frequency of falling asleep, which is commonplace in RSFC studies, correlates with the extent of RSFC perturbation. Autonomic and central nervous system contributions to BOLD signal have to be jointly considered when interpreting fMRI and RSFC studies.

Intrathecal Baclofen Pump Infection With Meningitis: Effective Treatment by Radical Debridement and Intrareservoir Baclofen-Vancomycin Co-Infusion.

OBJECTIVES: Intrathecal baclofen pump associated central nervous system (CNS) infection and meningitis is a rare but serious complication and may have dire consequences. Due to bacterial biofilm formation, the optimal treatment strategy is usually for removal of the pump, followed by systemic antibiotics for treatment of local and CNS infection. We describe this case of a patient with recurrent Staphylococcus aureus pump site empyema and meningitis leading to status dystonicus, who was successfully managed with radical debridement and intrareservoir baclofen-vancomycin co-infusion. MATERIALS AND METHODS: We retrospectively report a case of infected intrathecal baclofen pump with meningitis and provide a full review of literature. CONCLUSIONS: To the best of our knowledge, this is the first reported case of intrathecal baclofen (ITB)-associated pump site empyema and meningitis successfully treated with this technique. In selected cases where surgical explantation is deemed not feasible, this method can provide clinicians with an additional option for pump salvage and retention, while eradicating CNS infection and maintaining optimal control of spasticity and dystonia.

Negatively Curved Nanographene with Heptagonal and [5]Helicene Units.

Negatively curved nanographene (NG) 4, having two heptagons and a [5]helicene, was unexpectedly obtained by aryl rearrangement and stepwise cyclodehydrogenations. X-ray crystallography confirmed the saddle-shaped structures of intermediate 3 and NG 4. The favorability of rearrangement over helicene formation following radical cation or arenium cation mechanisms is supported by theoretical calculations. NG 4 demonstrates a reversible mechanochromic color change and solid-state emission, presumably benefiting from its loose crystal packing. After resolution by chiral high-performance liquid chromatography, the circular dichroism spectra of enantiomers 4-(P) and 4-(M) were measured and showed moderate Cotton effects at 350 nm (|Δε| = 148 M-1 cm-1).

Evolocumab and Clinical Outcomes in Patients with Cardiovascular Disease.

BACKGROUND: Evolocumab is a monoclonal antibody that inhibits proprotein convertase subtilisin-kexin type 9 (PCSK9) and lowers low-density lipoprotein (LDL) cholesterol levels by approximately 60%. Whether it prevents cardiovascular events is uncertain. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 27,564 patients with atherosclerotic cardiovascular disease and LDL cholesterol levels of 70 mg per deciliter (1.8 mmol per liter) or higher who were receiving statin therapy. Patients were randomly assigned to receive evolocumab (either 140 mg every 2 weeks or 420 mg monthly) or matching placebo as subcutaneous injections. The primary efficacy end point was the composite of cardiovascular death, myocardial infarction, stroke, hospitalization for unstable angina, or coronary revascularization. The key secondary efficacy end point was the composite of cardiovascular death, myocardial infarction, or stroke. The median duration of follow-up was 2.2 years. RESULTS: At 48 weeks, the least-squares mean percentage reduction in LDL cholesterol levels with evolocumab, as compared with placebo, was 59%, from a median baseline value of 92 mg per deciliter (2.4 mmol per liter) to 30 mg per deciliter (0.78 mmol per liter) (P<0.001). Relative to placebo, evolocumab treatment significantly reduced the risk of the primary end point (1344 patients [9.8%] vs. 1563 patients [11.3%]; hazard ratio, 0.85; 95% confidence interval [CI], 0.79 to 0.92; P<0.001) and the key secondary end point (816 [5.9%] vs. 1013 [7.4%]; hazard ratio, 0.80; 95% CI, 0.73 to 0.88; P<0.001). The results were consistent across key subgroups, including the subgroup of patients in the lowest quartile for baseline LDL cholesterol levels (median, 74 mg per deciliter [1.9 mmol per liter]). There was no significant difference between the study groups with regard to adverse events (including new-onset diabetes and neurocognitive events), with the exception of injection-site reactions, which were more common with evolocumab (2.1% vs. 1.6%). CONCLUSIONS: In our trial, inhibition of PCSK9 with evolocumab on a background of statin therapy lowered LDL cholesterol levels to a median of 30 mg per deciliter (0.78 mmol per liter) and reduced the risk of cardiovascular events. These findings show that patients with atherosclerotic cardiovascular disease benefit from lowering of LDL cholesterol levels below current targets. (Funded by Amgen; FOURIER ClinicalTrials.gov number, NCT01764633 .).

Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression.

In resting-state functional MRI, the correlation between blood-oxygenation-level-dependent (BOLD) signals across brain regions is used to estimate the functional connectivity (FC) of the brain. FC estimates are prone to the influence of nuisance factors including scanner-related artifacts and physiological modulations of the BOLD signal. Nuisance regression is widely performed to reduce the effect of nuisance factors on FC estimates on a per-scan basis. However, a dedicated analysis of nuisance effects on the variability of FC metrics across a collection of scans has been lacking. This work investigates the effects of nuisance factors on the variability of FC estimates across a collection of scans both before and after nuisance regression. Inter-scan variations in FC estimates are shown to be significantly correlated with the geometric norms of various nuisance terms, including head motion measurements, signals derived from white-matter and cerebrospinal regions, and the whole-brain global signal (GS) both before and after nuisance regression. In addition, it is shown that GS regression (GSR) can introduce GS norm-related fluctuations that are negatively correlated with inter-scan FC estimates. The empirical results are shown to be largely consistent with the predictions of a theoretical framework previously developed for the characterization of dynamic FC measures. This work shows that caution must be exercised when interpreting inter-scan FC measures across scans both before and after nuisance regression.

Nuisance effects and the limitations of nuisance regression in dynamic functional connectivity fMRI.

In resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain's intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from different brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the effects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between seed-specific sliding window correlation-based DFC estimates and nuisance factors. We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors. Strong correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not necessarily eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed between the DFC estimates and nuisance norms even after nuisance regression. We present theoretical bounds on the difference between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the efficacy of nuisance regression with regards to DFC estimates.

APOE modifies the interaction of entorhinal cerebral blood flow and cortical thickness on memory function in cognitively normal older adults.

OBJECTIVE: The ε4 allele of the apolipoprotein E (APOE) gene increases risk for cognitive decline in normal and pathologic aging. However, precisely how APOE ε4 exerts its negative impact on cognition is poorly understood. The present study aimed to determine whether APOE genotype (ε4+ vs. ε4-) modifies the interaction of medial temporal lobe (MTL) resting cerebral blood flow (CBF) and brain structure (cortical thickness [CT], volume [Vo]) on verbal memory performance. METHODS: Multiple linear regression models were employed to investigate relationships between APOE genotype, arterial spin labeling MRI-measured CBF and FreeSurfer-based CT and Vo in four MTL regions of interest (left and right entorhinal cortex and hippocampus), and verbal memory performance among a sample of 117 cognitively normal older adults (41 ε4+, 76 ε4-) between the ages of 64 and 89 (mean age â€‹= â€‹73). RESULTS: Results indicated that APOE genotype modified the interaction of CBF and CT on memory in the left entorhinal cortex, such that the relationship between entorhinal CBF and memory was negative (lower CBF was associated with better memory) in non-carriers with higher entorhinal CT, positive (higher CBF was associated with better memory) in non-carriers with lower entorhinal CT, and negative (higher CBF was associated with worse memory) in ε4 carriers with lower entorhinal CT. CONCLUSIONS: Findings suggest that older adult APOE ε4 carriers may experience vascular dysregulation and concomitant morphological alterations in the MTL that interact to negatively affect memory even in the absence overt clinical symptoms, providing potential insight into the mechanistic link between APOE ε4 and detriments in cognition. Moreover, findings suggest a distinct multimodal neural signature in ε4 carriers (higher CBF and lower CT in the entorhinal cortex) that could aid in the identification of candidates for future clinical trials aimed at preventing or slowing cognitive decline. Differential findings with respect to ε4 carriers and non-carriers are discussed in the context of neurovascular compensation.

Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study.

There is ample evidence of atypical functional connectivity (FC) in autism spectrum disorders (ASDs). However, transient relationships between neural networks cannot be captured by conventional static FC analyses. Dynamic FC (dFC) approaches have been used to identify repeating, transient connectivity patterns ("states"), revealing spatiotemporal network properties not observable in static FC. Recent studies have found atypical dFC in ASDs, but questions remain about the nature of group differences in transient connectivity, and the degree to which states persist or change over time. This study aimed to: (a) describe and relate static and dynamic FC in typical development and ASDs, (b) describe group differences in transient states and compare them with static FC patterns, and (c) examine temporal stability and flexibility between identified states. Resting-state functional magnetic resonance imaging (fMRI) data were collected from 62 ASD and 57 typically developing (TD) children and adolescents. Whole-brain, data-driven regions of interest were derived from group independent component analysis. Sliding window analysis and k-means clustering were used to explore dFC and identify transient states. Across all regions, static overconnnectivity and increased variability over time in ASDs predominated. Furthermore, significant patterns of group differences emerged in two transient states that were not observed in the static FC matrix, with group differences in one state primarily involving sensory and motor networks, and in the other involving higher-order cognition networks. Default mode network segregation was significantly reduced in ASDs in both states. Results highlight that dynamic approaches may reveal more nuanced transient patterns of atypical FC in ASDs.

Template-based prediction of vigilance fluctuations in resting-state fMRI.

Changes in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions, finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as "templates" for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject's vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.

Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem.

In this paper, we develop a Bayesian evidence maximization framework to solve the sparse non-negative least squares problem (S-NNLS). We introduce a family of probability densities referred to as the Rectified Gaussian Scale Mixture (R-GSM), to model the sparsity enforcing prior distribution for the signal of interest. The R-GSM prior encompasses a variety of heavy-tailed distributions such as the rectified Laplacian and rectified Student-t distributions with a proper choice of the mixing density. We utilize the hierarchical representation induced by the R-GSM prior and develop an evidence maximization framework based on the Expectation-Maximization (EM) algorithm. Using the EM-based method, we estimate the hyper-parameters and obtain a point estimate for the solution of interest. We refer to this proposed method as rectified Sparse Bayesian Learning (R-SBL). We provide four EM-based R-SBL variants that offer a range of options to trade-off computational complexity to the quality of the E-step computation. These methods include the Markov Chain Monte Carlo EM, linear minimum mean square estimation, approximate message passing and a diagonal approximation. Using numerical experiments, we show that the proposed R-SBL method outperforms existing S-NNLS solvers in terms of both signal and support recovery, and is very robust against the structure of the design matrix.

Reprint of 'Noise contributions to the fMRI signal: An Overview'.

The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.