Controlling target brain regions by optimal selection of input nodes.

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.

Fetal brain MRI atlases and datasets: A review.

Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.

The Role of Cognitive Reserve in Protecting Cerebellar Volumes of Older Adults with mild Cognitive Impairment.

The present study aims to investigate the relationship between cerebellar volumes and cognitive reserve in individuals with Mild Cognitive Impairment (MCI). A description of proxies of cerebellar cognitive reserve in terms of different volumes across lobules is also provided. 36 individuals with MCI underwent neuropsychological (MoCA, MMSE, Clock test, CRIq) assessment and neuroimaging acquisition with magnetic resonance imaging at 3 T. Simple linear correlations were applied between cerebellar volumes and cognitive measures. Multiple linear regression models were then used to estimate standardized regression coefficients and 95% confidence intervals. Simple linear correlations between cerebellar lobules volumes and cognitive features highlighted a significant association between CRIq_Working activity and specific motor cerebellar volumes: Left_V (ρ = 0.40, p = 0.02), Right_V (r = 0.42, p = 0.002), Vermis_VIIIb (ρ = 0.47, p = 0.003), Left_X (ρ = -0.46, p = 0.002) and Vermis_X (r = 0.35, p = 0.03). Furthermore, CRIq_Working activity scores correlated with certain cerebellar lobules implicated in cognition: Left_Crus_II, Vermis VIIb, Left_IX. MMSE was associated only with the Right_VIIB volume (r = 0.35, p = 0.02), while Clock Drawing Test scores correlated with both Left_Crus_I and Right_Crus_I (r = -0.42 and r = 0.42, p = 0.02, respectively). This study suggests that a higher cognitive reserve is associated with specific cerebellar lobule volumes and that Working activity may play a predominant role in this association. These findings contribute to the understanding of the relationship between cerebellar volumes and cognitive reserve, highlighting the potential modulatory role of Working activity on cerebellum response to cognitive decline.

Variability of regional glucose metabolism and the topology of functional networks in the human brain.

The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e., the balance of cell membranes' resting potentials and subthreshold spontaneous activity. Spontaneous activity fluctuates across brain regions in a correlated fashion that defines multi-scale hierarchical networks called resting-state networks (RSNs). Different regions of the brain display different metabolic consumption, but the relationship between regional brain metabolism and RSNs is still under investigation. Here, we examine the variability of glucose metabolism across brain regions, measured with the relative standard uptake value (SUVR) using 18F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC). We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs.

Unveiling whole-brain dynamics in normal aging through Hidden Markov Models.

During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs-fMRI) data showing a link between aging and the increase of between-networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data-driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs-fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph-based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process.

Sparse DCM for whole-brain effective connectivity from resting-state fMRI data.

Contemporary neuroscience has embraced network science and dynamical systems to study the complex and self-organized structure of the human brain. Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed interactions in whole brain networks, referred to as effective connectivity, as well as their role in the emergent brain dynamics is still lacking. The main reason is that estimating brain connectivity requires solving a formidable large-scale inverse problem from indirect and noisy measurements. Building on the dynamic causal modelling framework, the present study offers a novel method for estimating whole-brain effective connectivity from resting-state functional magnetic resonance data. To this purpose sparse estimation methods are adapted to infer the parameters of our novel model, which is based on a linearized, region-specific haemodynamic response function. The resulting algorithm, referred to as sparse DCM, is shown to compare favorably with state-of-the art methods when tested on both synthetic and real data. We also provide a graph-theoretical analysis on the whole-brain effective connectivity estimated using data from a cohort of healthy individuals, which reveals properties such as asymmetry in the connectivity structure as well as the different roles of brain areas in favoring segregation or integration.

Archetypes of human cognition defined by time preference for reward and their brain correlates: An evolutionary trade-off approach.

Biological systems carry out multiple tasks in their lifetime, which, in the course of evolution, may lead to trade-offs. In fact phenotypes (different species, individuals within a species, circuits, bacteria, proteins, etc.) cannot be optimal at all tasks, and, according to Pareto optimality theory, lay into a well-defined geometrical distribution (polygons and/or polyhedrons) in the space of traits. The vertices of this distribution contain archetypes, namely phenotypes that are specialists at one of the tasks, whereas phenotypes toward the center of the geometrical distribution show average performance across tasks. We applied this theory to the variability of cognitive and behavioral scores measured in 1206 individuals from the Human Connectome Project. Among all possible combinations of pairs of traits, we found the best fit to Pareto optimality when individuals were plotted in the trait-space of time preferences for reward, evaluated with the Delay Discounting Task (DDT). The DDT measures subjects' preference in choosing either immediate smaller rewards or delayed larger rewards. Time preference for reward was described by a triangular distribution in which each of the three vertices included individuals who used a particular strategy to discount reward. These archetypes accounted for variability on many cognitive, personality, and socioeconomic status variables, as well as differences in brain structure and functional connectivity, with only a weak influence of genetics. In summary, time preference for reward reflects a core variable that biases human phenotypes via natural and cultural selection.

Inflammatory intrathecal profiles and cortical damage in multiple sclerosis.

OBJECTIVE: Gray matter (GM) damage and meningeal inflammation have been associated with early disease onset and a more aggressive disease course in multiple sclerosis (MS), but can these changes be identified in the patient early in the disease course? METHODS: To identify possible biomarkers linking meningeal inflammation, GM damage, and disease severity, gene and protein expression were analyzed in meninges and cerebrospinal fluid (CSF) from 27 postmortem secondary progressive MS and 14 control cases. Combined cytokine/chemokine CSF profiling and 3T magnetic resonance imaging (MRI) were performed at diagnosis in 2 independent cohorts of MS patients (35 and 38 subjects) and in 26 non-MS patients. RESULTS: Increased expression of proinflammatory cytokines (IFNγ, TNF, IL2, and IL22) and molecules related to sustained B-cell activity and lymphoid-neogenesis (CXCL13, CXCL10, LTα, IL6, and IL10) was detected in the meninges and CSF of postmortem MS cases with high levels of meningeal inflammation and GM demyelination. Similar proinflammatory patterns, including increased levels of CXCL13, TNF, IFNγ, CXCL12, IL6, IL8, and IL10, together with high levels of BAFF, APRIL, LIGHT, TWEAK, sTNFR1, sCD163, MMP2, and pentraxin III, were detected in the CSF of MS patients with higher levels of GM damage at diagnosis. INTERPRETATION: A common pattern of intrathecal (meninges and CSF) inflammatory profile strongly correlates with increased cortical pathology, both at the time of diagnosis and at death. These results suggest a role for detailed CSF analysis combined with MRI as a prognostic marker for more aggressive MS. Ann Neurol 2018 Ann Neurol 2018;83:739-755.

A robust deconvolution method to disentangle multiple water pools in diffusion MRI.

The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.

Multiparametric quantitative MRI assessment of thigh muscles in limb-girdle muscular dystrophy 2A and 2B.

INTRODUCTION: The aim of this study was to apply quantitative MRI (qMRI) to assess structural modifications in thigh muscles of subjects with limb girdle muscular dystrophy (LGMD) 2A and 2B with long disease duration. METHODS: Eleven LGMD2A, 9 LGMD2B patients and 11 healthy controls underwent a multi-parametric 3T MRI examination of the thigh. The protocol included structural T1-weighted images, DIXON sequences for fat fraction calculation, T2 values quantification and diffusion MRI. Region of interest analysis was performed on 4 different compartments (anterior compartment, posterior compartment, gracilis, sartorius). RESULTS: Patients showed high levels of fat infiltration as measured by DIXON sequences. Sartorius and anterior compartment were more infiltrated in LGMD2B than LGMD2A patients. T2 values were mildly reduced in both disorders. Correlations between clinical scores and qMRI were found. CONCLUSIONS: qMRI measures may help to quantify muscular degeneration, but careful interpretation is needed when fat infiltration is massive. Muscle Nerve 58: 550-558, 2018.

On the Role of the Inferior Intraparietal Sulcus in Visual Working Memory for Lateralized Single-feature Objects.

A consolidated practice in cognitive neuroscience is to explore the properties of human visual working memory through the analysis of electromagnetic signals using cued change detection tasks. Under these conditions, EEG/MEG activity increments in the posterior parietal cortex scaling with the number of memoranda are often reported in the hemisphere contralateral to the objects' position in the memory array. This highly replicable finding clashes with several reported failures to observe compatible hemodynamic activity modulations using fMRI or fNIRS in comparable tasks. Here, we reconcile this apparent discrepancy by acquiring fMRI data on healthy participants and employing a cluster analysis to group voxels in the posterior parietal cortex based on their functional response. The analysis identified two distinct subpopulations of voxels in the intraparietal sulcus (IPS) showing a consistent functional response among participants. One subpopulation, located in the superior IPS, showed a bilateral response to the number of objects coded in visual working memory. A different subpopulation, located in the inferior IPS, showed an increased unilateral response when the objects were displayed contralaterally. The results suggest that a cluster of neurons in the inferior IPS is a candidate source of electromagnetic contralateral responses to working memory load in cued change detection tasks.

Protein synthesis is associated with high-speed dynamics and broad-band stability of functional hubs in the brain.

L-[1-11C]leucine PET can be used to measure in vivo protein synthesis in the brain. However, the relationship between regional protein synthesis and on-going neural dynamics is unclear. We use a graph theoretical approach to examine the relationship between cerebral protein synthesis (rCPS) and both static and dynamical measures of functional connectivity (measured using resting state functional MRI, R-fMRI). Our graph theoretical analysis demonstrates a significant positive relationship between protein turnover and static measures of functional connectivity. We compared these results to simple measures of metabolism in the cortex using [18F]FDG PET). Whilst some relationships between [18F]FDG binding and graph theoretical measures was present, there remained a significant relationship between protein turnover and graph theoretical measures, which were more robustly explained by L-[1-11C]Leucine than [18F]FDG PET. This relationship was stronger in dynamics at a faster temporal resolution relative to dynamics measured over a longer epoch. Using a Dynamic connectivity approach, we also demonstrate that broad-band dynamic measures of Functional Connectivity (FC), are inversely correlated with protein turnover, suggesting greater stability of FC in highly interconnected hub regions is supported by protein synthesis. Overall, we demonstrate that cerebral protein synthesis has a strong relationship independent of tissue metabolism to neural dynamics at the macroscopic scale.

Test-retest reproducibility of quantitative binding measures of [11C]Ro15-4513, a PET ligand for GABAA receptors containing alpha5 subunits.

INTRODUCTION: Alteration of γ-aminobutyric acid "A" (GABAA) receptor-mediated neurotransmission has been associated with various neurological and psychiatric disorders. [11C]Ro15-4513 is a PET ligand with high affinity for α5-subunit-containing GABAA receptors, which are highly expressed in limbic regions of the human brain (Sur et al., 1998). We quantified the test-retest reproducibility of measures of [11C]Ro15-4513 binding derived from six different quantification methods (12 variants). METHODS: Five healthy males (median age 40 years, range 38-49 years) had a 90-min PET scan on two occasions (median interval 12 days, range 11-30 days), after injection of a median dose of 441 MegaBequerels of [11C]Ro15-4513. Metabolite-corrected arterial plasma input functions (parent plasma input functions, ppIFs) were generated for all scans. We quantified regional binding using six methods (12 variants), some of which were region-based (applied to the average time-activity curve within a region) and others were voxel-based: 1) Models requiring arterial ppIFs - regional reversible compartmental models with one and two tissue compartments (2kbv and 4kbv); 2) Regional and voxelwise Logan's graphical analyses (Logan et al., 1990), which required arterial ppIFs; 3) Model-free regional and voxelwise (exponential) spectral analyses (SA; (Cunningham and Jones, 1993)), which also required arterial ppIFs; 4) methods not requiring arterial ppIFs - voxelwise standardised uptake values (Kenney et al., 1941), and regional and voxelwise simplified reference tissue models (SRTM/SRTM2) using brainstem or alternatively cerebellum as pseudo-reference regions (Lammertsma and Hume, 1996; Gunn et al., 1997). To compare the variants, we sampled the mean values of the outcome parameters within six bilateral, non-reference grey matter regions-of-interest. Reliability was quantified in terms of median absolute percentage test-retest differences (MA-TDs; preferentially low) and between-subject coefficient of variation (BS-CV, preferentially high), both compounded by the intraclass correlation coefficient (ICC). These measures were compared between variants, with particular interest in the hippocampus. RESULTS: Two of the six methods (5/12 variants) yielded reproducible data (i.e. MA-TD <10%): regional SRTMs and voxelwise SRTM2s, both using either the brainstem or the cerebellum; and voxelwise SA. However, the SRTMs using the brainstem yielded a lower median BS-CV (7% for regional, 7% voxelwise) than the other variants (8-11%), resulting in lower ICCs. The median ICCs across six regions were 0.89 (interquartile range 0.75-0.90) for voxelwise SA, 0.71 (0.64-0.84) for regional SRTM-cerebellum and 0.83 (0.70-0.86) for voxelwise SRTM-cerebellum. The ICCs for the hippocampus were 0.89 for voxelwise SA, 0.95 for regional SRTM-cerebellum and 0.93 for voxelwise SRTM-cerebellum. CONCLUSION: Quantification of [11C]Ro15-4513 binding shows very good to excellent reproducibility with SRTM and with voxelwise SA which, however, requires an arterial ppIF. Quantification in the α5 subunit-rich hippocampus is particularly reliable. The very low expression of the α5 in the cerebellum (Fritschy and Mohler, 1995; Veronese et al., 2016) and the substantial α1 subunit density in this region may hamper the application of reference tissue methods.

Effects of perfusion on DTI and DKI estimates in the skeletal muscle.

PURPOSE: In this study, we evaluated the effects of perfusion of the skeletal muscle on diffusion tensor imaging (DTI) and diffusional kurtosis imaging (DKI) parameters and their reproducibility. METHODS: Diffusion tensor imaging and DKI models, with and without intravoxel incoherent motion (IVIM) correction, were applied to simulated data at different physiological conditions and signal-to-noise ratio levels. Next, the same models were applied to data of the right calf of five healthy volunteers, acquired twice at 3 telsa. For six muscles, we evaluated the correlation of the perfusion signal fraction, with parameters derived from DTI and DKI, and performed repeatability analysis with and without IVIM correction. Additionally, the IVIM correction was compared to a multishell acquisition approach that minimizes perfusion effects on DTI estimates. RESULTS: Simulations and acquired data showed that DTI and DKI estimates were biased proportionally to the perfusion signal fraction, and that IVIM correction was needed for accurate estimation of the DTI and DKI parameters. However, taking perfusion into account did not improve repeatability. CONCLUSION: Blood perfusion has an effect on DTI and DKI estimations, but it can be minimized with IVIM correction or multishell acquisition strategies. Magn Reson Med 78:233-246, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Stable spline deconvolution for dynamic susceptibility contrast MRI.

PURPOSE: To present the stable spline (SS) deconvolution method for the quantification of the cerebral blood flow (CBF) from dynamic susceptibility contrast MRI. METHODS: The SS method was compared with both the block-circulant singular value decomposition (oSVD) and nonlinear stochastic regularization (NSR) methods. oSVD is one of the most popular deconvolution methods in dynamic susceptibility contrast MRI (DSC-MRI). NSR is an alternative approach that we proposed previously. The three methods were compared using simulated data and two clinical data sets. RESULTS: The SS method correctly reconstructed the dispersed residue function and its peak in presence of dispersion, regardless of the delay. In absence of dispersion, SS performs similarly to oSVD and does not correctly reconstruct the residue function and its peak. SS and NSR better differentiate healthy and pathologic CBF values compared with oSVD in all simulated conditions. Using acquired data, SS and NSR provide more clinically plausible and physiological estimates of the residue function and CBF maps compared with oSVD. CONCLUSION: The SS method overcomes some of the limitations of oSVD, such as unphysiological estimates of the residue function and NSR, the latter of which is too computationally expensive to be applied to large data sets. Thus, the SS method is a valuable alternative for CBF quantification using DSC-MRI data. Magn Reson Med 78:1801-1811, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Brain PET and functional MRI: why simultaneously using hybrid PET/MR systems?

INTRODUCTION: In the last 20 years growing attention has been devoted to multimodal imaging. The recent literature is rich of clinical and research studies that have been performed using different imaging modalities on both separate and integrated positron emission tomography (PET) and magnetic resonance (MR) scanners. However, today, hybrid PET/MR systems measure signals related to brain structure, metabolism, neurochemistry, perfusion, and neuronal activity simultaneously, i.e. in the same physiological conditions. A frequently raised question at meeting and symposia is: "Do we really need a hybrid PET/MR system? Are there any advantages over acquiring sequential and separate PET and MR scans?" The present paper is an attempt to answer these questions specifically in relation to PET combined with functional magnetic resonance imaging (fMRI) and arterial spin labeling. EVIDENCE ACQUISITION: We searched (last update: June 2017) the databases PubMed, PMC, Google Scholar and Medline. We also included additional studies if they were cited in the selected articles. No language restriction was applied to the search, but the reviewed articles were all in English. Among all the retrieved articles, we selected only those performed using a hybrid PET/MR system. We found a total of 17 papers that were selected and discussed in three main groups according to the main radiopharmaceutical used: 18F-fluorodeoxyglucose (18F-FDG) (N.=8), 15O-water (15O-H2O) (N.=3) and neuroreceptors (N.=6). EVIDENCE SYNTHESIS: Concerning studies using 18F-FDG, simultaneous PET/fMRI revealed that global aspects of functional organization (e.g. graph properties of functional connections) are partially associated with energy consumption. There are remarkable spatial and functional similarities across modalities, but also discrepant findings. More work is needed on this point. There are only a handful of papers comparing blood flow measurements with PET 15O-H2O and MR arterial spin label (ASL) measures, and they show significant regional CBF differences between these two modalities. However, at least in one study the correlation at the level of gray, white matter, and whole brain is rather good (r=0.94, 0.8, 0.81 respectively). Finally, receptor studies show that simultaneous PET/fMRI could be a useful tool to characterize functional connectivity along with dynamic neuroreceptor adaptation in several physiological (e.g. working memory) or pathological (e.g. pain) conditions, with or without drug administrations. CONCLUSIONS: The simultaneous acquisition of PET (using a number of radiotracers) and functional MRI (using a number of sequences) offers exciting opportunities that we are just beginning to explore. The results thus far are promising in the evaluation of cerebral metabolism/flow, neuroreceptor adaptation, and network's energetic demand.

Measuring specific receptor binding of a PET radioligand in human brain without pharmacological blockade: The genomic plot.

PET studies allow in vivo imaging of the density of brain receptor species. The PET signal, however, is the sum of the fraction of radioligand that is specifically bound to the target receptor and the non-displaceable fraction (i.e. the non-specifically bound radioligand plus the free ligand in tissue). Therefore, measuring the non-displaceable fraction, which is generally assumed to be constant across the brain, is a necessary step to obtain regional estimates of the specific fractions. The nondisplaceable binding can be directly measured if a reference region, i.e. a region devoid of any specific binding, is available. Many receptors are however widely expressed across the brain, and a true reference region is rarely available. In these cases, the nonspecific binding can be obtained after competitive pharmacological blockade, which is often contraindicated in humans. In this work we introduce the genomic plot for estimating the nondisplaceable fraction using baseline scans only. The genomic plot is a transformation of the Lassen graphical method in which the brain maps of mRNA transcripts of the target receptor obtained from the Allen brain atlas are used as a surrogate measure of the specific binding. Thus, the genomic plot allows the calculation of the specific and nondisplaceable components of radioligand uptake without the need of pharmacological blockade. We first assessed the statistical properties of the method with computer simulations. Then we sought ground-truth validation using human PET datasets of seven different neuroreceptor radioligands, where nonspecific fractions were either obtained separately using drug displacement or available from a true reference region. The population nondisplaceable fractions estimated by the genomic plot were very close to those measured by actual human blocking studies (mean relative difference between 2% and 7%). However, these estimates were valid only when mRNA expressions were predictive of protein levels (i.e. there were no significant post-transcriptional changes). This condition can be readily established a priori by assessing the correlation between PET and mRNA expression.

Estimation of arterial arrival time and cerebral blood flow from QUASAR arterial spin labeling using stable spline.

PURPOSE: QUASAR arterial spin labeling (ASL) permits the application of deconvolution approaches for the absolute quantification of cerebral perfusion. Currently, oscillation index regularized singular value decomposition (oSVD) combined with edge-detection (ED) is the most commonly used method. Its major drawbacks are nonphysiological oscillations in the impulse response function and underestimation of perfusion. The aim of this work is to introduce a novel method to overcome these limitations. METHODS: A system identification method, stable spline (SS), was extended to address ASL peculiarities such as the delay in arrival of the arterial blood in the tissue. The proposed framework was compared with oSVD + ED in both simulated and real data. SS was used to investigate the validity of using a voxel-wise tissue T1 value instead of using a single global value (of blood T1 ). RESULTS: SS outperformed oSVD + ED in 79.9% of simulations. When applied to real data, SS exhibited a physiologically realistic range for perfusion and a higher mean value with respect to oSVD + ED (55.5 ± 9.5 SS, 34.9 ± 5.2 oSVD + ED mL/100 g/min). CONCLUSION: SS can represent an alternative to oSVD + ED for the quantification of QUASAR ASL data. Analysis of the retrieved impulse response function revealed that using a voxel wise tissue T1 might be suboptimal.

The methodology of TSPO imaging with positron emission tomography.

The 18-kDA translocator protein (TSPO) is consistently elevated in activated microglia of the central nervous system (CNS) in response to a variety of insults as well as neurodegenerative and psychiatric conditions. It is therefore a target of interest for molecular strategies aimed at imaging neuroinflammation in vivo. For more than 20 years, positron emission tomography (PET) has allowed the imaging of TSPO density in brain using [(11)C]-(R)-PK11195, a radiolabelled-specific antagonist of the TSPO that has demonstrated microglial activation in a large number pathological cohorts. The significant clinical interest in brain immunity as a primary or comorbid factor in illness has sparked great interest in the TSPO as a biomarker and a surprising number of second generation TSPO radiotracers have been developed aimed at improving the quality of TSPO imaging through novel radioligands with higher affinity. However, such major investment has not yet resulted in the expected improvement in image quality. We here review the main methodological aspects of TSPO PET imaging with particular attention to TSPO genetics, cellular heterogeneity of TSPO in brain tissue and TSPO distribution in blood and plasma that need to be considered in the quantification of PET data to avoid spurious results as well as ineffective development and use of these radiotracers.