Long-access heroin self-administration induces region specific reduction of grey matter volume and microglia reactivity in the rat.

In opioid use disorder (OUD) patients, a decrease in brain grey matter volume (GMV) has been reported. It is unclear whether this is the consequence of prolonged exposure to opioids or is a predisposing causal factor in OUD development. To investigate this, we conducted a structural MRI longitudinal study in NIH Heterogeneous Stock rats exposed to heroin self-administration and age-matched naïve controls housed in the same controlled environment. Structural MRI scans were acquired before (MRI1) and after (MRI2) a prolonged period of long access heroin self-administration resulting in escalation of drug intake. Heroin intake resulted in reduced GMV in various cortical and sub-cortical brain regions. In drug-naïve controls no difference was found between MRI1 and MRI2. Notably, the degree of GMV reduction in the medial prefrontal cortex (mPFC) and the insula positively correlated with the amount of heroin consumed and the escalation of heroin use. In a preliminary gene expression analysis, we identified a number of transcripts linked to immune response and neuroinflammation. This prompted us to hypothesize a link between changes in microglia homeostasis and loss of GMV. For this reason, we analyzed the number and morphology of microglial cells in the mPFC and insula. The number of neurons and their morphology was also evaluated. The primary motor cortex, where no GMV change was observed, was used as negative control. We found no differences in the number of neurons and microglia cells following heroin. However, in the same regions where reduced GMV was detected, we observed a shift towards a rounder shape and size reduction in microglia, suggestive of their homeostatic change towards a reactive state. Altogether these findings suggest that escalation of heroin intake correlates with loss of GMV in specific brain regions and that this phenomenon is linked to changes in microglial morphology.

Increased network centrality of the anterior insula in early abstinence from alcohol.

Abnormal resting-state functional connectivity, as measured by functional magnetic resonance imaging (MRI), has been reported in alcohol use disorders (AUD), but findings are so far inconsistent. Here, we exploited recent developments in graph-theoretical analyses, enabling improved resolution and fine-grained representation of brain networks, to investigate functional connectivity in 35 recently detoxified alcohol dependent patients versus 34 healthy controls. Specifically, we focused on the modular organization, that is, the presence of tightly connected substructures within a network, and on the identification of brain regions responsible for network integration using an unbiased approach based on a large-scale network composed of more than 600 a priori defined nodes. We found significant reductions in global connectivity and region-specific disruption in the network topology in patients compared with controls. Specifically, the basal brain and the insular-supramarginal cortices, which form tightly coupled modules in healthy subjects, were fragmented in patients. Further, patients showed a strong increase in the centrality of the anterior insula, which exhibited stronger connectivity to distal cortical regions and weaker connectivity to the posterior insula. Anterior insula centrality, a measure of the integrative role of a region, was significantly associated with increased risk of relapse. Exploratory analysis suggests partial recovery of modular structure and insular connectivity in patients after 2 weeks. These findings support the hypothesis that, at least during the early stages of abstinence, the anterior insula may drive exaggerated integration of interoceptive states in AUD patients with possible consequences for decision making and emotional states and that functional connectivity is dynamically changing during treatment.

Scale-resolved analysis of brain functional connectivity networks with spectral entropy.

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between a functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure. We apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks. We show that network sparsification, in combination with motion correction algorithms, dramatically improves detection of large scale network structure.

Aberrant insular cortex connectivity in abstinent alcohol-dependent rats is reversed by dopamine D3 receptor blockade.

A few studies have reported aberrant functional connectivity in alcoholic patients, but the specific neural circuits involved remain unknown. Moreover, it is unclear whether these alterations can be reversed upon treatment. Here, we used functional MRI to study resting state connectivity in rats following chronic intermittent exposure to ethanol. Further, we evaluated the effects of SB-277011-a, a selective dopamine D3 receptor antagonist, known to decrease ethanol consumption. Alcohol-dependent and control rats (N = 13/14 per group), 3 weeks into abstinence, were administered SB-277011-a or vehicle before fMRI sessions. Resting state connectivity networks were extracted by independent component analysis. A dual-regression analysis was performed using independent component maps as spatial regressors, and the effects of alcohol history and treatment on connectivity were assessed. A history of alcohol dependence caused widespread reduction of the internal coherence of components. Weaker correlation was also found between the insula cortex (IC) and cingulate cortices, key constituents of the salience network. Similarly, reduced connectivity was observed between a component comprising the anterior insular cortex, together with the caudate putamen (CPu-AntIns), and the posterior part of the IC. On the other hand, postdependent rats showed strengthened connectivity between salience and reward networks. In particular, higher connectivity was observed between insula and nucleus accumbens, between the ventral tegmental area and the cingulate cortex and between the VTA and CPu-AntIns. Interestingly, aberrant connectivity in postdependent rats was partially restored by acute administration of SB-277011-a, which, conversely, had no significant effects in naïve rats.

Community detection in weighted brain connectivity networks beyond the resolution limit.

Graph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or "communities", that are smaller than a specific scale. Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules (Nicolini and Bifone, 2016), in contrast with many previous reports. However, the use of Surprise is limited to binary networks, while brain networks are intrinsically weighted, reflecting a continuous distribution of connectivity strengths between different brain regions. Here, we propose Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground-truth modular structure. We compare Asymptotical Surprise with leading community detection methods currently in use and show its superior sensitivity in the detection of small modules even in the presence of noise and intersubject variability such as those observed in fMRI data. We apply our novel approach to functional connectivity networks from resting state fMRI experiments, and demonstrate a heterogeneous modular organization, with a wide distribution of clusters spanning multiple scales. Finally, we discuss the implications of these findings for the identification of connector hubs, the brain regions responsible for the integration of the different network elements, showing that the improved resolution afforded by Asymptotical Surprise leads to a different classification compared to current methods.

Self-similarity and quasi-idempotence in neural networks and related dynamical systems.

Self-similarity across length scales is pervasively observed in natural systems. Here, we investigate topological self-similarity in complex networks representing diverse forms of connectivity in the brain and some related dynamical systems, by considering the correlation between edges directly connecting any two nodes in a network and indirect connection between the same via all triangles spanning the rest of the network. We note that this aspect of self-similarity, which is distinct from hierarchically nested connectivity (coarse-grain similarity), is closely related to idempotence of the matrix representing the graph. We introduce two measures, ι(1) and ι(∞), which represent the element-wise correlation coefficients between the initial matrix and the ones obtained after squaring it once or infinitely many times, and term the matrices which yield large values of these parameters "quasi-idempotent". These measures delineate qualitatively different forms of "shallow" and "deep" quasi-idempotence, which are influenced by nodal strength heterogeneity. A high degree of quasi-idempotence was observed for partially synchronized mean-field Kuramoto oscillators with noise, electronic chaotic oscillators, and cultures of dissociated neurons, wherein the expression of quasi-idempotence correlated strongly with network maturity. Quasi-idempotence was also detected for macro-scale brain networks representing axonal connectivity, synchronization of slow activity fluctuations during idleness, and co-activation across experimental tasks, and preliminary data indicated that quasi-idempotence of structural connectivity may decrease with ageing. This initial study highlights that the form of network self-similarity indexed by quasi-idempotence is detectable in diverse dynamical systems, and draws attention to it as a possible basis for measures representing network "collectivity" and pattern formation.

Structural covariance networks in the mouse brain.

The presence of networks of correlation between regional gray matter volume as measured across subjects in a group of individuals has been consistently described in several human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain mapping modalities like functional and diffusion-weighted imaging, the approach can provide precious insights into the mutual influence of trophic and plastic processes in health and pathological states. To investigate whether analogous scMRI networks are present in lower mammal species amenable to genetic and experimental manipulation such as the laboratory mouse, we employed high resolution morphoanatomical MRI in a large cohort of genetically-homogeneous wild-type mice (C57Bl6/J) and mapped scMRI networks using a seed-based approach. We show that the mouse brain exhibits robust homotopic scMRI networks in both primary and associative cortices, a finding corroborated by independent component analyses of cortical volumes. Subcortical structures also showed highly symmetric inter-hemispheric correlations, with evidence of distributed antero-posterior networks in diencephalic regions of the thalamus and hypothalamus. Hierarchical cluster analysis revealed six identifiable clusters of cortical and sub-cortical regions corresponding to previously described neuroanatomical systems. Our work documents the presence of homotopic cortical and subcortical scMRI networks in the mouse brain, thus supporting the use of this species to investigate the elusive biological and neuroanatomical underpinnings of scMRI network development and its derangement in neuropathological states. The identification of scMRI networks in genetically homogeneous inbred mice is consistent with the emerging view of a key role of environmental factors in shaping these correlational networks.

COMT Genetic Reduction Produces Sexually Divergent Effects on Cortical Anatomy and Working Memory in Mice and Humans.

Genetic variations in catechol-O-methyltransferase (COMT) that modulate cortical dopamine have been associated with pleiotropic behavioral effects in humans and mice. Recent data suggest that some of these effects may vary among sexes. However, the specific brain substrates underlying COMT sexual dimorphisms remain unknown. Here, we report that genetically driven reduction in COMT enzyme activity increased cortical thickness in the prefrontal cortex (PFC) and postero-parieto-temporal cortex of male, but not female adult mice and humans. Dichotomous changes in PFC cytoarchitecture were also observed: reduced COMT increased a measure of neuronal density in males, while reducing it in female mice. Consistent with the neuroanatomical findings, COMT-dependent sex-specific morphological brain changes were paralleled by divergent effects on PFC-dependent working memory in both mice and humans. These findings emphasize a specific sex-gene interaction that can modulate brain morphological substrates with influence on behavioral outcomes in healthy subjects and, potentially, in neuropsychiatric populations.

Distributed BOLD and CBV-weighted resting-state networks in the mouse brain.

Laboratory mouse models represent a powerful tool to elucidate the biological foundations of disease, but translation to and from human studies rely upon valid cross-species measures. Resting-state functional connectivity (rsFC) represents a promising translational probe of brain function; however, no convincing demonstration of the presence of distributed, bilateral rsFC networks in the mouse brain has yet been reported. Here we used blood oxygen level dependent (BOLD) and cerebral blood volume (CBV) weighted fMRI to demonstrate the presence of robust and reproducible resting-state networks in the mouse brain. Independent-component analysis (ICA) revealed inter-hemispheric homotopic rsFC networks encompassing several established neuro-anatomical systems of the mouse brain, including limbic, motor and parietal cortex, striatum, thalamus and hippocampus. BOLD and CBV contrast produced consistent networks, with the latter exhibiting a superior anatomical preservation of brain regions close to air-tissue interfaces (e.g. ventral hippocampus). Seed-based analysis confirmed the inter-hemispheric specificity of the correlations observed with ICA and highlighted the presence of distributed antero-posterior networks anatomically homologous to the human salience network (SN) and default-mode network (DMN). Consistent with rsFC investigations in humans, BOLD and CBV-weighted fMRI signals in the DMN-like network exhibited spontaneous anti-correlation with neighbouring fronto-parietal areas. These findings demonstrate the presence of robust distributed intrinsic functional connectivity networks in the mouse brain, and pave the way for the application of rsFC readouts in transgenic models to investigate the biological underpinnings of spontaneous BOLD fMRI fluctuations and their derangement in pathological states.

Antimicrobial peptides design by evolutionary multiobjective optimization.

Antimicrobial peptides (AMPs) are an abundant and wide class of molecules produced by many tissues and cell types in a variety of mammals, plant and animal species. Linear alpha-helical antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particularly successful structural arrangement in innate defense. Recently, AMPs have received increasing attention as potential therapeutic agents, owing to their broad activity spectrum and their reduced tendency to induce resistance. The introduction of non-natural amino acids will be a key requisite in order to contrast host resistance and increase compound's life. In this work, the possibility to design novel AMP sequences with non-natural amino acids was achieved through a flexible computational approach, based on chemophysical profiles of peptide sequences. Quantitative structure-activity relationship (QSAR) descriptors were employed to code each peptide and train two statistical models in order to account for structural and functional properties of alpha-helical amphipathic AMPs. These models were then used as fitness functions for a multi-objective evolutional algorithm, together with a set of constraints for the design of a series of candidate AMPs. Two ab-initio natural peptides were synthesized and experimentally validated for antimicrobial activity, together with a series of control peptides. Furthermore, a well-known Cecropin-Mellitin alpha helical antimicrobial hybrid (CM18) was optimized by shortening its amino acid sequence while maintaining its activity and a peptide with non-natural amino acids was designed and tested, demonstrating the higher activity achievable with artificial residues.

Long-access heroin self-administration induces region specific reduction of grey matter volume and microglia reactivity in the rat.

In opioid use disorder (OUD) patients, a decrease in brain grey matter volume (GMV) has been reported. It is unclear whether this is the consequence of prolonged exposure to opioids or is a predisposing causal factor in OUD development. To investigate this, we conducted a structural MRI longitudinal study in NIH Heterogeneous Stock rats exposed to heroin self-administration and age-matched naïve controls housed in the same controlled environment. Structural MRI scans were acquired before (MRI 1 ) and after (MRI 2 ) a prolonged period of long access heroin self-administration resulting in escalation of drug intake. Heroin intake resulted in reduced GMV in various cortical and sub-cortical brain regions. In drug-naïve controls no difference was found between MRI 1 and MRI 2 . Notably, the degree of GMV reduction in the medial prefrontal cortex (mPFC) and the insula positively correlated with the amount of heroin consumed and the escalation of heroin use. In a preliminary gene expression analysis, we identified a number of transcripts linked to immune response and neuroinflammation. This prompted us to hypothesize a link between changes in microglia homeostasis and loss of GMV. For this reason, we analyzed the number and morphology of microglial cells in the mPFC and insula. The number of neurons and their morphology was also evaluated. The primary motor cortex, where no GMV change was observed, was used as negative control. We found no differences in the number of neurons and microglia cells following heroin. However, in the same regions where reduced GMV was detected, we observed a shift towards a rounder shape and size reduction in microglia, suggestive of their homeostatic change towards a reactive state. Altogether these findings suggest that escalation of heroin intake correlates with loss of GMV in specific brain regions and that this phenomenon is linked to changes in microglial morphology.

Transfection efficiency boost of cholesterol-containing lipoplexes.

Most lipid formulations require cholesterol for successful transfection, but the precise reason remains to be more clearly understood. Here, we have studied the effect of cholesterol on the transfection efficiency (TE) of lipoplexes in vitro. Addition of cholesterol to highly effective DC-Chol-DOPE/DNA lipoplexes increases TE, with 40 mol% cholesterol yielding about 10-fold improvement. The transfection mechanisms of cholesterol-containing lipoplexes have been investigated by combining dynamic light scattering, synchrotron small angle X-ray scattering, laser scanning confocal microscopy and transfection efficiency measurements. Our results revealed that cholesterol-containing lipoplexes enter the cells partially by membrane fusion and this mechanism accounts for efficient endosomal escape. We also found evidence that formulations with high cholesterol content are not specifically targeted to metabolic degradation. These studies will contribute to rationally design novel delivery systems with superior transfection efficiency.

Reduced limbic metabolism and fronto-cortical volume in rats vulnerable to alcohol addiction.

Alcohol abuse is associated with long-term reductions in fronto-cortical volume and limbic metabolism. However, an unanswered question in alcohol research is whether these alterations are the sole consequence of chronic alcohol use, or contain heritable contributions reflecting biological propensity toward ethanol addiction. Animal models of genetic predisposition to alcohol dependence can be used to investigate the role of inborn brain abnormalities in the aetiology of alcoholism. Here we used magnetic resonance imaging (MRI) in the Marchigian-Sardinian (msP) alcohol-preferring rats to assess the presence of inherited structural or functional brain alterations. Alcohol-naïve msP (N=22) and control rats (N=26) were subjected to basal cerebral blood volume (bCBV) mapping followed by voxel-based morphometry (VBM) of grey matter and tract-based spatial statistics mapping of white matter fractional anisotropy. msP rats exhibited significantly reduced bCBV, an established marker of resting brain function, in focal cortico-limbic and thalamic areas, together with reduced grey matter volume in the thalamus, ventral tegmental area, insular and cingulate cortex. No statistically significant differences in fractional anisotropy were observed between groups. These findings highlight the presence of inborn grey matter and metabolic abnormalities in alcohol-naïve msP rats, the localization and sign of which are remarkably similar to those mapped in abstinent alcoholics and subjects at high risk for alcohol dependence. Collectively, these results point for a significant role of heritable neurofunctional brain alterations in biological propensity toward ethanol addiction, and support the translational use of advanced imaging methods to describe the circuital determinants of vulnerability to drug addiction.

In vivo distribution and toxicity of PAMAM dendrimers in the central nervous system depend on their surface chemistry.

Dendrimers have been described as one of the most tunable and therefore potentially applicable nanoparticles both for diagnostics and therapy. Recently, in order to realize drug delivery agents, most of the effort has been dedicated to the development of dendrimers that could internalize into the cells and target specific intracellular compartments in vitro and in vivo. Here, we describe cell internalization properties and diffusion of G4 and G4-C12 modified PAMAM dendrimers in primary neuronal cultures and in the CNS of live animals. Confocal imaging on primary neurons reveals that dendrimers are able to cross the cell membrane and reach intracellular localization following endocytosis. Moreover, functionalization of PAMAMs has a dramatic effect on their ability to diffuse in the CNS tissue in vivo and penetrate living neurons as shown by intraparenchymal or intraventricular injections. 100 nM G4-C12 PAMAM dendrimer already induces dramatic apoptotic cell death of neurons in vitro. On the contrary, G4 PAMAM does not induce apoptotic cell death of neural cells in the sub-micromolar range of concentration and induces low microglia activation in brain tissue after a week. Our detailed description of dendrimer distribution patterns in the CNS will facilitate the design of tailored nanomaterials in light of future clinical applications.

Water dispersal and functionalization of hydrophobic iron oxide nanoparticles with lipid-modified poly(amidoamine) dendrimers.

A novel and facile method for water dispersal of hydrophobic iron oxide nanoparticles based on the amphiphilic PAMAM-C12 dendrimer is described. Stable and highly concentrated water dispersions of multifunctional magnetic nanoparticles were obtained with this single-step approach, and showed interesting relaxometric properties for MRI applications. Importantly, this method does not require substitution of the native hydrophobic capping under nonmild reaction conditions, thus preserving the structural and magnetic properties of the nanoparticles, and extending the possibility of conjugation with thermally labile groups.

Inhibition of glycine transporter-1 reduces cue-induced nicotine-seeking, but does not promote extinction of conditioned nicotine cue responding in the rat.

Pharmacological stimulation of N-methyl-D-aspartate receptors (NMDAr) could enhance the outcome of cue-exposure therapy for smoking cessation. NMDAr stimulation can be achieved by increasing pharmacologically the synaptic levels of glycine, a necessary co-agonist. Here, we evaluate the effects of SSR504734, a selective inhibitor of glycine type I transporter (GlyT1) in an extinction-reinstatement procedure inducing robust and lasting nicotine-seeking behavior in rats. Male Wistar rats were trained to associate discriminative stimuli (S(D)s) with the availability of nicotine (0.03 mg/kg/65 µL/2 second/infusion) or sucrose (45-mg pellet) versus non-reward in two-lever operant cages. Reinforced response was followed by cue signaling 20-second time-out (CSs). Once the training criterion was met, rats underwent extinction of lever presses, in the absence of reinforcers, S(D) s and CSs. Re-exposure to nicotine or sucrose S(D+)/CS(+), but not non-reward S(D-)/CS(-), revived responding at the previously reinforced lever. Acute pre-treatment with SSR504734 (10 mg/kg i.p.) reduced nicotine-seeking but not sucrose-seeking behavior without influencing rats' locomotor activity. Sub-chronic treatment (10 mg/kg i.p. for 5 days) during daily exposure to S(D+)/CS(+) reduced nicotine-seeking; however, this effect was transient, with return to S(D+)/CS(+) responding at 72 hours. Full recovery to S(D+)/CS(+) responding was observed after 1 month suggesting that SSR504734 sub-acute treatment did not engage the long-term plasticity mechanisms probably involved in nicotine-seeking. In conclusion, GlyT1-inhibitors might offer a therapeutic opportunity for acute cue-controlled nicotine-seeking, but the lack of persistent effects of the sub-chronic treatment associated with nicotine cues exposure suggests that short-term administration of GlyT1-inhibitor SSR504734 is not sufficient to promote extinction of nicotine-cue conditioned responding.

Advances in Stabilization and Enrichment of Shallow Nitrogen-Vacancy Centers in Diamond for Biosensing and Spin-Polarization Transfer.

Negatively charged nitrogen-vacancy (NV-) centers in diamond have unique magneto-optical properties, such as high fluorescence, single-photon generation, millisecond-long coherence times, and the ability to initialize and read the spin state using purely optical means. This makes NV- centers a powerful sensing tool for a range of applications, including magnetometry, electrometry, and thermometry. Biocompatible NV-rich nanodiamonds find application in cellular microscopy, nanoscopy, and in vivo imaging. NV- centers can also detect electron spins, paramagnetic agents, and nuclear spins. Techniques have been developed to hyperpolarize 14N, 15N, and 13C nuclear spins, which could open up new perspectives in NMR and MRI. However, defects on the diamond surface, such as hydrogen, vacancies, and trapping states, can reduce the stability of NV- in favor of the neutral form (NV0), which lacks the same properties. Laser irradiation can also lead to charge-state switching and a reduction in the number of NV- centers. Efforts have been made to improve stability through diamond substrate doping, proper annealing and surface termination, laser irradiation, and electric or electrochemical tuning of the surface potential. This article discusses advances in the stabilization and enrichment of shallow NV- ensembles, describing strategies for improving the quality of diamond devices for sensing and spin-polarization transfer applications. Selected applications in the field of biosensing are discussed in more depth.

Manganese-enhanced magnetic resonance imaging reveals light-induced brain asymmetry in embryo.

The idea that sensory stimulation to the embryo (in utero or in ovo) may be crucial for brain development is widespread. Unfortunately, up to now evidence was only indirect because mapping of embryonic brain activity in vivo is challenging. Here, we applied for the first time manganese enhanced magnetic resonance imaging (MEMRI), a functional imaging method, to the eggs of domestic chicks. We revealed light-induced brain asymmetry by comparing embryonic brain activity in vivo of eggs that were stimulated by light or maintained in the darkness. Our protocol paves the way to investigation of the effects of a variety of sensory stimulations on brain activity in embryo.

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.

OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power and sensitivity. Deep learning-based AI relies on training data sets, which should be sufficiently large and diverse to effectively adjust network parameters, avoid biases, and enable generalization of the outcome. However, large sets of diagnostic images acquired at doses of CA outside the standard-of-care are not commonly available. Here, we propose a method to generate synthetic data sets to train an "AI agent" designed to amplify the effects of CAs in magnetic resonance (MR) images. The method was fine-tuned and validated in a preclinical study in a murine model of brain glioma, and extended to a large, retrospective clinical human data set. MATERIALS AND METHODS: A physical model was applied to simulate different levels of MR contrast from a gadolinium-based CA. The simulated data were used to train a neural network that predicts image contrast at higher doses. A preclinical MR study at multiple CA doses in a rat model of glioma was performed to tune model parameters and to assess fidelity of the virtual contrast images against ground-truth MR and histological data. Two different scanners (3 T and 7 T, respectively) were used to assess the effects of field strength. The approach was then applied to a retrospective clinical study comprising 1990 examinations in patients affected by a variety of brain diseases, including glioma, multiple sclerosis, and metastatic cancer. Images were evaluated in terms of contrast-to-noise ratio and lesion-to-brain ratio, and qualitative scores. RESULTS: In the preclinical study, virtual double-dose images showed high degrees of similarity to experimental double-dose images for both peak signal-to-noise ratio and structural similarity index (29.49 dB and 0.914 dB at 7 T, respectively, and 31.32 dB and 0.942 dB at 3 T) and significant improvement over standard contrast dose (ie, 0.1 mmol Gd/kg) images at both field strengths. In the clinical study, contrast-to-noise ratio and lesion-to-brain ratio increased by an average 155% and 34% in virtual contrast images compared with standard-dose images. Blind scoring of AI-enhanced images by 2 neuroradiologists showed significantly better sensitivity to small brain lesions compared with standard-dose images (4.46/5 vs 3.51/5). CONCLUSIONS: Synthetic data generated by a physical model of contrast enhancement provided effective training for a deep learning model for contrast amplification. Contrast above that attainable at standard doses of gadolinium-based CA can be generated through this approach, with significant advantages in the detection of small low-enhancing brain lesions.

Rotating-polarization CARS microscopy: combining chemical and molecular orientation sensitivity.

Coherent Anti-Stokes Raman Spectroscopy (CARS) is a non-linear process in which the energy difference of a pair of incoming photons matches the energy of the vibrational mode of a molecular bond of interest. This phonon population is coherently probed by a third photon and anti-Stokes radiation is emitted. Here a novel approach to CARS microscopy is presented yielding the intensity of the anti-Stokes emission, the directionality the molecular bonds of interest, and their average orientation. Myelinated axons in fixed mouse-brain slices have been imaged by RP-CARS. We were able to detect the local average direction of the acylic chains of membrane phospholipids and their spatial anisotropy. This novel method may impact the study of healthy brain circuitry as well as demyelinating diseases or other pathological states associated with altered neural connectivity.