Neuroimmune activation is associated with neurological outcome in anoxic and traumatic coma.

The pathophysiological underpinnings of critically disrupted brain connectomes resulting in coma are poorly understood. Inflammation is potentially an important but still undervalued factor. Here, we present a first-in-human prospective study using the 18-kDa translocator protein (TSPO) radioligand 18F-DPA714 for PET imaging to allow in vivo neuroimmune activation quantification in patients with coma (n = 17) following either anoxia or traumatic brain injuries in comparison with age- and sex-matched controls. Our findings yielded novel evidence of an early inflammatory component predominantly located within key cortical and subcortical brain structures that are putatively implicated in consciousness emergence and maintenance after severe brain injury (i.e. mesocircuit and frontoparietal networks). We observed that traumatic and anoxic patients with coma have distinct neuroimmune activation profiles, both in terms of intensity and spatial distribution. Finally, we demonstrated that both the total amount and specific distribution of PET-measurable neuroinflammation within the brain mesocircuit were associated with the patient's recovery potential. We suggest that our results can be developed for use both as a new neuroprognostication tool and as a promising biometric to guide future clinical trials targeting glial activity very early after severe brain injury.

Glutamatergic synapse in autism: a complex story for a complex disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder whose pathophysiological mechanisms are still unclear. Hypotheses suggest a role for glutamate dysfunctions in ASD development, but clinical studies investigating brain and peripheral glutamate levels showed heterogenous results leading to hypo- and hyper-glutamatergic hypotheses of ASD. Recently, studies proposed the implication of elevated mGluR5 densities in brain areas in the pathophysiology of ASD. Thus, our objective was to characterize glutamate dysfunctions in adult subjects with ASD by quantifying (1) glutamate levels in the cingulate cortex and periphery using proton magnetic resonance spectroscopy and metabolomics, and (2) mGluR5 brain density in this population and in a validated animal model of ASD (prenatal exposure to valproate) at developmental stages corresponding to childhood and adolescence in humans using positron emission tomography. No modifications in cingulate Glu levels were observed between individuals with ASD and controls further supporting the difficulty to evaluate modifications in excitatory transmission using spectroscopy in this population, and the complexity of its glutamate-related changes. Our imaging results showed an overall increased density in mGluR5 in adults with ASD, that was only observed mostly subcortically in adolescent male rats prenatally exposed to valproic acid, and not detected in the stage corresponding to childhood in the same animals. This suggest that clinical changes in mGluR5 density could reflect the adaptation of the glutamatergic dysfunctions occurring earlier rather than being key to the pathophysiology of ASD.

Quantitative analysis of the formation of nucleoprotein complexes between HIV-1 Gag protein and genomic RNA using transmission electron microscopy.

In HIV, the polyprotein precursor Gag orchestrates the formation of the viral capsid. In the current view of this viral assembly, Gag forms low-order oligomers that bind to the viral genomic RNA triggering the formation of high-ordered ribonucleoprotein complexes. However, this assembly model was established using biochemical or imaging methods that do not describe the cellular location hosting Gag-gRNA complex nor distinguish gRNA packaging in single particles. Here, we studied the intracellular localization of these complexes by electron microscopy and monitored the distances between the two partners by morphometric analysis of gold beads specifically labeling Gag and gRNA. We found that formation of these viral clusters occurred shortly after the nuclear export of the gRNA. During their transport to the plasma membrane, the distance between Gag and gRNA decreases together with an increase of gRNA packaging. Point mutations in the zinc finger patterns of the nucleocapsid domain of Gag caused an increase in the distance between Gag and gRNA as well as a sharp decrease of gRNA packaged into virions. Finally, we show that removal of stem loop 1 of the 5'-untranslated region does not interfere with gRNA packaging, whereas combined with the removal of stem loop 3 is sufficient to decrease but not abolish Gag-gRNA cluster formation and gRNA packaging. In conclusion, this morphometric analysis of Gag-gRNA cluster formation sheds new light on HIV-1 assembly that can be used to describe at nanoscale resolution other viral assembly steps involving RNA or protein-protein interactions.

Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation.

PURPOSE: This study aimed to investigate the impact of several ComBat harmonization strategies, intra-tumoral sub-volume characterization, and automatic segmentations for progression-free survival (PFS) prediction through radiomics modeling for patients with head and neck cancer (HNC) in PET/CT images. METHODS: The HECKTOR MICCAI 2021 challenge set containing PET/CT images and clinical data of 325 oropharynx HNC patients was exploited. A total of 346 IBSI-compliant radiomic features were extracted for each patient's primary tumor volume defined by the reference manual contours. Modeling relied on least absolute shrinkage Cox regression (Lasso-Cox) for feature selection (FS) and Cox proportional-hazards (CoxPH) models were built to predict PFS. Within this methodological framework, 8 different strategies for ComBat harmonization were compared, including before or after FS, in feature groups separately or all features directly, and with center or clustering-determined labels. Features extracted from tumor sub-volume clustering were also investigated for their prognostic additional value. Finally, 3 automatic segmentations (2 threshold-based and a 3D U-Net) were also compared. All results were evaluated with the concordance index (C-index). RESULTS: Radiomics features without harmonization, combined with clinical factors, led to models with C-index values of 0.69 in the testing set. The best version of ComBat harmonization, i.e., after FS, for feature groups separately and relying on clustering-determined labels, achieved a C-index of 0.71. The use of features extracted from tumor sub-volumes further improved the C-index to 0.72. Models that relied on the automatic segmentations yielded close but slightly lower prognostic performance (0.67-0.70) compared to reference contours. CONCLUSION: A standard radiomics pipeline allowed for prediction of PFS in a multicenter HNC cohort. Applying a specific strategy of ComBat harmonization improved the performance. The extraction of intra-tumoral sub-volume features and automatic segmentation could contribute to the improvement and automation of prognosis modeling, respectively.

Neuroinflammation PET imaging of the translocator protein (TSPO) in Alzheimer's disease: An update.

Neuroinflammation is a significant contributor to Alzheimer's disease (AD). Until now, PET imaging of the translocator protein (TSPO) has been widely used to depict the neuroimmune endophenotype of AD. The aim of this review was to provide an update to the results from 2018 and to advance the characterization of the biological basis of TSPO imaging in AD by re-examining TSPO function and expression and the methodological aspects of interest. Although the biological basis of the TSPO PET signal is obviously related to microglia and astrocytes in AD, the observed process remains uncertain and might not be directly related to neuroinflammation. Further studies are required to re-examine the cellular significance underlying a variation in the PET signal in AD.

Differentiated Cells in Prolonged Hypoxia Produce Highly Infectious Native-Like Hepatitis C Virus Particles.

BACKGROUND AND AIMS: Standard hepatitis C virus (HCV) cell-culture models present an altered lipid metabolism and thus produce lipid-poor lipoviral particles (LVPs). These models are thereby weakly adapted to explore the complete natural viral life cycle. APPROACH AND RESULTS: To overcome these limitations, we used an HCV cell-culture model based on both cellular differentiation and sustained hypoxia to better mimic the host-cell environment. The long-term exposure of Huh7.5 cells to DMSO and hypoxia (1% O2 ) significantly enhanced the expression of major differentiation markers and the cellular hypoxia adaptive response by contrast with undifferentiated and normoxic (21% O2 ) standard conditions. Because hepatocyte-like differentiation and hypoxia are key regulators of intracellular lipid metabolism, we characterized the distribution of lipid droplets (LDs) and demonstrated that experimental cells significantly accumulate larger and more numerous LDs relative to standard cell-culture conditions. An immunocapture (IC) and transmission electron microscopy (TEM) method showed that differentiated and hypoxic Huh7.5 cells produced lipoproteins significantly larger than those produced by standard Huh7.5 cell cultures. The experimental cell culture model is permissive to HCV-Japanese fulminant hepatitis (JFH1) infection and produces very-low-buoyant-density LVPs that are 6-fold more infectious than LVPs formed by standard JFH1-infected Huh7.5 cells. Finally, the IC-TEM approach and antibody-neutralization experiments revealed that LVPs were highly lipidated, had a global ultrastructure and a conformation of the envelope glycoprotein complex E1E2 close to that of the ones circulating in infected individuals. CONCLUSIONS: This relevant HCV cell culture model thus mimics the complete native intracellular HCV life cycle and, by extension, can be proposed as a model of choice for studies of other hepatotropic viruses.

Normal volumetric and T1 relaxation time values at 1.5 T in segmented pediatric brain MRI using a MP2RAGE acquisition.

OBJECTIVES: This study introduced a tailored MP2RAGE-based brain acquisition for a comprehensive assessment of the normal maturing brain. METHODS: Seventy normal patients (35 girls and 35 boys) from 1 to 16 years of age were recruited within a prospective monocentric study conducted from a single University Hospital. Brain MRI examinations were performed at 1.5 T using a 20-channel head coil and an optimized 3D MP2RAGE sequence with a total acquisition time of 6:36 min. Automated 38 region segmentation was performed using the MorphoBox (template registration, bias field correction, brain extraction, and tissue classification) which underwent a major adaptation of three age-group T1-weighted templates. Volumetry and T1 relaxometry reference ranges were established using a logarithmic model and a modified Gompertz growth respectively. RESULTS: Detailed automated brain segmentation and T1 mapping were successful in all patients. Using these data, an age-dependent model of normal brain maturation with respect to changes in volume and T1 relaxometry was established. After an initial rapid increase until 24 months of life, the total intracranial volume was found to converge towards 1400 mL during adolescence. The expected volumes of white matter (WM) and cortical gray matter (GM) showed a similar trend with age. After an initial major decrease, T1 relaxation times were observed to decrease progressively in all brain structures. The T1 drop in the first year of life was more pronounced in WM (from 1000-1100 to 650-700 ms) than in GM structures. CONCLUSION: The 3D MP2RAGE sequence allowed to establish brain volume and T1 relaxation time normative ranges in pediatrics. KEY POINTS: • The 3D MP2RAGE sequence provided a reliable quantitative assessment of brain volumes and T1 relaxation times during childhood. • An age-dependent model of normal brain maturation was established. • The normative ranges enable an objective comparison to a normal cohort, which can be useful to further understand, describe, and identify neurodevelopmental disorders in children.

Quantification of low-content encapsulated active cosmetic ingredients in complex semi-solid formulations by means of attenuated total reflectance-infrared spectroscopy.

Attenuated total reflectance-infrared (ATR-IR) spectroscopy is a robust tool for molecular characterisation of matter. Applied to semi-solid formulations, it enables rapid and reliable data collection without pre-analytical requirements. Based on nano-encapsulated Omegalight®, a skin-lightening active cosmetic ingredient (ACI), incorporated in a hydrogel, it is first demonstrated that, despite the high water content and the chemical complexity of the samples (i.e. number of ingredients), the spectral features of the ACI can be detected and monitored. Secondly, with a total of 105 samples divided into a training set (n = 60) and an unknown set (n = 45) covering a 0.5% w/w-5% w/w concentration range, the study further investigates the quantitative performance of ATR-IR coupled with partial least squares regression (PLSR). Through a step-by-step approach in testing different cross-validation protocols, accuracy (root mean square error of cross-validation (RMSECV)) and linearity between the experimental and predicted concentrations (R2) of ATR-IR are consistently evaluated to be respectively 0.097% (w/w) and 0.995 with a lower LOD = 0.067% (w/w). Subsequently, further evaluation of the accuracy (relative error of the predicted concentration compared with the true value, expressed as %) of the analysis was undertaken with the 45 unknown samples that were defined as unknown and analysed by PLSR. The outcome of the analysis demonstrates the ruggedness and the consistency of the determination performed using the ATR-IR data. With an average relative error of 2.5% w/w and only 5 samples out of 45 blind samples exhibiting a relative error above the 5% threshold, high accuracy quantification of the nano-encapsulated ACI can be unambiguously achieved by means of the label-free and non-destructive technique of ATR-IR spectroscopy. Ultimately, the study demonstrates that the analytical capabilities of ATR-IR hold significant potential for applications in the cosmetics industry, and although the path remains long, the present study is one step further to support validation of the technique, albeit for the specific case of Omegalight®.

Detection of Neuroinflammation in a Rat Model of Subarachnoid Hemorrhage Using [18F]DPA-714 PET Imaging.

Subarachnoid hemorrhage (SAH) can lead to delayed cerebral ischemia, which increases the rate of morbidity and mortality. The detection of microglial activation may serve as a biomarker for the identification of patients at risk of this deleterious consequence. We assessed this hypothesis in a rat model of SAH in which the exploration of neuroinflammation related to microglial activation was correlated with the degree of bleeding. We used the rat filament model and evaluated (at 48 hours postsurgery) the intensity of neuroinflammation using positron emission tomography (PET) imaging with the 18-kDa translocator protein (TSPO) tracer [(18)F]DPA-714, quantitative autoradiography with [(3)H]PK-11195, and SAH grade by postmortem brain picture. High SAH grades were strongly and positively correlated with in vivo PET imaging of TSPO in the cortex and striatum. In addition, a positive correlation was found in the cortex in TSPO, with densities determined by imaging and autoradiographic approaches. Qualitative immunofluorescence studies indicated that overexpression of TSPO was linked to astrocytic/microglial activation. In this model, PET imaging of TSPO using [(18)F]DPA-714 appeared to be a relevant index of the degree of bleeding, indicating that this imaging method could be used in human patients to improve the management of patients with SAH.

FIBRASCAN: a novel method for 3D white matter tract reconstruction in MR space from cadaveric dissection.

INTRODUCTION: Diffusion tractography relies on complex mathematical models that provide anatomical information indirectly, and it needs to be validated. In humans, up to now, tractography has mainly been validated by qualitative comparison with data obtained from dissection. No quantitative comparison was possible because Magnetic Resonance Imaging (MRI) and dissection data are obtained in different reference spaces, and because fiber tracts are progressively destroyed by dissection. Here, we propose a novel method and software (FIBRASCAN) that allow accurate reconstruction of fiber tracts from dissection in MRI reference space. METHOD: Five human hemispheres, obtained from four formalin-fixed brains were prepared for Klingler's dissection, placed on a holder with fiducial markers, MR scanned, and then dissected to expose the main association tracts. During dissection, we performed iterative acquisitions of the surface and texture of the specimens using a laser scanner and two digital cameras. Each texture was projected onto the corresponding surface and the resulting set of textured surfaces was coregistered thanks to the fiducial holders. The identified association tracts were then interactively segmented on each textured surface and reconstructed from the pile of surface segments. Finally, the reconstructed tracts were coregistered onto ex vivo MRI space thanks to the fiducials. Each critical step of the process was assessed to measure the precision of the method. RESULTS: We reconstructed six fiber tracts (long, anterior and posterior segments of the superior longitudinal fasciculus; Inferior fronto-occipital, Inferior longitudinal and uncinate fasciculi) from cadaveric dissection and ported them into ex vivo MRI reference space. The overall accuracy of the method was of the order of 1mm: surface-to-surface registration=0.138mm (standard deviation (SD)=0.058mm), deformation of the specimen during dissection=0.356mm (SD=0.231mm), and coregistration surface-MRI=0.6mm (SD=0.274mm). The spatial resolution of the method (distance between two consecutive surface acquisitions) was 0.345mm (SD=0.115mm). CONCLUSION: This paper presents the robustness of a novel method, FIBRASCAN, for accurate reconstruction of fiber tracts from dissection in the ex vivo MR reference space. This is a major step toward quantitative comparison of MR tractography with dissection results.

Prenatal exposure to methylphenidate affects the dopamine system and the reactivity to natural reward in adulthood in rats.

BACKGROUND: Methylphenidate (MPH) is a commonly-used medication for the treatment of children with Attention-Deficit/Hyperactivity Disorders (ADHD). However, its prescription to adults with ADHD and narcolepsy raises the question of how the brain is impacted by MPH exposure during pregnancy. The goal of this study was to elucidate the long-term neurobiological consequences of prenatal exposure to MPH using a rat model. METHODS: We focused on the effects of such treatment on the adult dopamine (DA) system and on the reactivity of animals to natural rewards. RESULTS: This study shows that adult male rats prenatally exposed to MPH display elevated expression of presynaptic DA markers in the DA cell bodies and the striatum. Our results also suggest that MPH-treated animals could exhibit increased tonic DA activity in the mesolimbic pathway, altered signal-to-noise ratio after a pharmacological stimulation, and decreased reactivity to the locomotor effects of cocaine. Finally, we demonstrated that MPH rats display a decreased preference and motivation for sucrose. CONCLUSIONS: This is the first preclinical study reporting long-lasting neurobiological alterations of DA networks as well as alterations in motivational behaviors for natural rewards after a prenatal exposure to MPH. These results raise concerns about the possible neurobiological consequences of MPH treatment during pregnancy.

Evaluation of a computational model for mycetoma-causative agents identification.

BACKGROUND: The therapeutic strategy for mycetoma relies heavily on the identification of the causative agents, which are either fungal or bacterial. While histopathological examination of surgical biopsies is currently the most used diagnostic tool, it requires well-trained pathologists, who are lacking in most rural areas where mycetoma is endemic. In this work we propose and evaluate a machine learning approach that semi-automatically analyses histopathological microscopic images of grains and provides a classification of the disease as eumycetoma or actinomycetoma. METHODS: The computational model is based on radiomics and partial least squares. It is assessed on a dataset that includes 890 individual grains collected from 168 patients originating from the Mycetoma Research Centre in Sudan. The dataset contained 94 eumycetoma cases and 74 actinomycetoma cases, with a distribution of the species among the two causative agents that is representative of the Sudanese distribution. RESULTS: The proposed model achieved identification of causative agents with an accuracy of 91.89%, which is comparable to the accuracy of experts from the domain. The method was found to be robust to a small error in the segmentation of the grain and to changes in the acquisition protocol. Among the radiomics features, the homogeneity of mycetoma grain textures was found to be the most discriminative feature for causative agent identification. CONCLUSION: The results presented in this study support that this computational approach could greatly benefit rural areas with limited access to specialized clinical centres and also provide a second opinion for expert pathologists to implement the appropriate therapeutic strategy.

Brain virtual dissection and white matter 3D visualization.

This paper presents an immersive visualization tool that helps anatomists to establish a ground truth for brain white matter fiber bundles. Each step of a progressive anatomical dissection of human brain hemisphere is acquired using a high resolution 3D laser scanner and a photographic device. Each resulting surface is textured with a high resolution image and registered into a common 3D space using fiducial landmarks. Surfaces can be visualized using stereoscopic hardware and are interactively selectable. The tool allows the user to identify specific fiber bundle parts. Extracted fiber bundles are stacked together and rendered in stereoscopy with the corresponding MR volume. Surgeons have validated this tool for creating ground truth in medical imaging with the perspective of validating tractography algorithms.

Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images.

When no arterial input function is available, quantification of dynamic PET images requires a previous step devoted to the extraction of a reference time-activity curve (TAC). Factor analysis is often applied for this purpose. This paper introduces a novel approach that conducts a new kind of nonlinear factor analysis relying on a compartment model, and computes the kinetic parameters of specific binding tissues jointly. To this end, it capitalizes on data-driven parametric imaging methods to provide a physical description of the underlying PET data, directly relating the specific binding with the kinetics of the non-specific binding in the corresponding tissues. This characterization is introduced into the factor analysis formulation to yield a novel nonlinear unmixing model designed for PET image analysis. This model also explicitly introduces global kinetic parameters that allow for a direct estimation of a binding potential that represents the ratio at equilibrium of specifically bound radioligand to the concentration of nondisplaceable radioligand in each non-specific binding tissue. The performance of the method is evaluated on synthetic and real data to demonstrate its potential interest.

Enhancing histopathological image classification of invasive ductal carcinoma using hybrid harmonization techniques.

This study aims to develop a robust pipeline for classifying invasive ductal carcinomas and benign tumors in histopathological images, addressing variability within and between centers. We specifically tackle the challenge of detecting atypical data and variability between common clusters within the same database. Our feature engineering-based pipeline comprises a feature extraction step, followed by multiple harmonization techniques to rectify intra- and inter-center batch effects resulting from image acquisition variability and diverse patient clinical characteristics. These harmonization steps facilitate the construction of more robust and efficient models. We assess the proposed pipeline's performance on two public breast cancer databases, BreaKHIS and IDCDB, utilizing recall, precision, and accuracy metrics. Our pipeline outperforms recent models, achieving 90-95% accuracy in classifying benign and malignant tumors. We demonstrate the advantage of harmonization for classifying patches from different databases. Our top model scored 94.7% for IDCDB and 95.2% for BreaKHis, surpassing existing feature engineering-based models (92.1% for IDCDB and 87.7% for BreaKHIS) and attaining comparable performance to deep learning models. The proposed feature-engineering-based pipeline effectively classifies malignant and benign tumors while addressing variability within and between centers through the incorporation of various harmonization techniques. Our findings reveal that harmonizing variabilities between patches from different batches directly impacts the learning and testing performance of classification models. This pipeline has the potential to enhance breast cancer diagnosis and treatment and may be applicable to other diseases.

Corpus callosum in children with neurodevelopmental delay: MRI standard qualitative assessment versus automatic quantitative analysis.

BACKGROUND: The corpus callosum (CC) is a key brain structure. In children with neurodevelopmental delay, we compared standard qualitative radiological assessments with an automatic quantitative tool. METHODS: We prospectively enrolled 73 children (46 males, 63.0%) with neurodevelopmental delay at single university hospital between September 2020 and September 2022. All of them underwent 1.5-T brain magnetic resonance imaging (MRI) including a magnetization-prepared 2 rapid acquisition gradient echoes - MP2RAGE sequence. Two radiologists blindly reviewed the images to classify qualitatively the CC into normal, hypoplasic, hyperplasic, and/or dysgenetic classes. An automatic tool (QuantiFIRE) was used to provide brain volumetry and T1 relaxometry automatically as well as deviations of those parameters compared with a healthy age-matched cohort. The MRI reference standard for CC volumetry was based on the Garel et al. study. Cohen κ statistics was used for interrater agreement. The radiologists and QuantiFIRE's diagnostic accuracy were compared with the reference standard using the Delong test. RESULTS: The CC was normal in 42 cases (57.5%), hypoplastic in 20 cases (27.4%), and hypertrophic in 11 cases (15.1%). T1 relaxometry values were abnormal in 26 children (35.6%); either abnormally high (18 cases, 24.6%) or low (8 cases, 11.0%). The interrater Cohen κ coefficient was 0.91. The diagnostic accuracy of the QuantiFIRE prototype was higher than that of the radiologists for hypoplastic and normal CC (p = 0.003 for both subgroups, Delong test). CONCLUSIONS: An automated volumetric and relaxometric assessment can assist the evaluation of brain structure such as the CC, particularly in the case of subtle abnormalities. RELEVANCE STATEMENT: Automated brain MRI segmentation combined with statistical comparison to normal volume and T1 relaxometry values can be a useful diagnostic support tool for radiologists. KEY POINTS: • Corpus callosum abnormality detection is challenging but clinically relevant. • Automated quantitative volumetric analysis had a higher diagnostic accuracy than that of visual appreciation of radiologists. • Quantitative T1 relaxometric analysis might help characterizing corpus callosum better.

An ecological animal model of subthreshold depression in adolescence: behavioral and resting state 18F-FDG PET imaging characterization.

The different depressive disorders that exist can take root at adolescence. For instance, some functional and structural changes in several brain regions have been observed from adolescence in subjects that display either high vulnerability to depressive symptoms or subthreshold depression. For instance, adolescents with depressive disorder have been shown to exhibit hyperactivity in hippocampus, amygdala and prefrontal cortex as well as volume reductions in hippocampus and amygdala (prefrontal cortex showing more variable results). However, no animal model of adolescent subthreshold depression has been developed so far. Our objective was to design an animal model of adolescent subthreshold depression and to characterize the neural changes associated to this phenotype. For this purpose, we used adolescent Swiss mice that were evaluated on 4 tests assessing cognitive abilities (Morris water maze), anhedonia (sucrose preference), anxiety (open-field) and stress-coping strategies (forced swim test) at postnatal day (PND) 28-35. In order to identify neural alterations associated to behavioral profiles, we assessed brain resting state metabolic activity in vivo using 18F-FDG PET imaging at PND 37. We selected three profiles of mice distinguished in a composite Z-score computed from performances in the behavioral tests: High, Intermediate and Low Depressive Risk (HDR, IDR and LDR). Compared to both IDR and LDR, HDR mice were characterized by passive stress-coping behaviors, low cognition and high anhedonia and anxiety and were associated with significant changes of 18F-FDG uptakes in several cortical and subcortical areas including prelimbic cortex, infralimbic cortex, nucleus accumbens, amygdala, periaqueductal gray and superior colliculus, all displaying higher metabolic activity, while only the thalamus was associated with lower metabolic activity (compared to IDR). LDR displayed an opposing behavioral phenotype and were associated with significant changes of 18F-FDG uptakes in the dorsal striatum and thalamus that both exhibited markedly lower metabolic activity in LDR. In conclusion, our study revealed changes in metabolic activities that can represent neural signatures for behavioral profiles predicting subthreshold depression at adolescence in a mouse model.

Performance evaluation of the IRIS XL-220 PET/CT system, a new camera dedicated to non-human primates.

BACKGROUND: Non-human primates (NHP) are critical in biomedical research to better understand the pathophysiology of diseases and develop new therapies. Based on its translational and longitudinal abilities along with its non-invasiveness, PET/CT systems dedicated to non-human primates can play an important role for future discoveries in medical research. The aim of this study was to evaluate the performance of a new PET/CT system dedicated to NHP imaging, the IRIS XL-220 developed by Inviscan SAS. This was performed based on the National Electrical Manufacturers Association (NEMA) NU 4-2008 standard recommendations (NEMA) to characterize the spatial resolution, the scatter fraction, the sensitivity, the count rate, and the image quality of the system. Besides, the system was evaluated in real conditions with two NHP with 18F-FDG and (-)-[18F]FEOBV which targets the vesicular acetylcholine transporter, and one rat using 18F-FDG. RESULTS: The full width at half maximum obtained with the 3D OSEM algorithm ranged between 0.89 and 2.11 mm in the field of view. Maximum sensitivity in the 400-620 keV and 250-750 keV energy windows were 2.37% (22 cps/kBq) and 2.81% (25 cps/kBq), respectively. The maximum noise equivalent count rate (NEC) for a rat phantom was 82 kcps at 75 MBq and 88 kcps at 75 MBq for energy window of 250-750 and 400-620 keV, respectively. For the monkey phantom, the maximum NEC was 18 kcps at 126 MBq and 19 kcps at 126 MBq for energy window of 250-750 and 400-620 keV, respectively. The IRIS XL provided an excellent quality of images in non-human primates and rats using 18F-FDG. The images acquired using (-)-[18F]FEOBV were consistent with those previously reported in non-human primates. CONCLUSIONS: Taken together, these results showed that the IRIS XL-220 is a high-resolution system well suited for PET/CT imaging in non-human primates.

Persistent neuroinflammation and behavioural deficits after single mild traumatic brain injury.

Despite an apparently silent imaging, some patients with mild traumatic brain injury (TBI) experience cognitive dysfunctions, which may persist chronically. Brain changes responsible for these dysfunctions are unclear and commonly overlooked. It is thus crucial to increase our understanding of the mechanisms linking the initial event to the functional deficits, and to provide objective evidence of brain tissue alterations underpinning these deficits. We first set up a murine model of closed-head controlled cortical impact, which provoked persistent cognitive and sensorimotor deficits, despite no evidence of brain contusion or bleeding on MRI, thus recapitulating features of mild TBI. Molecular MRI for P-selectin, a key adhesion molecule, detected no sign of cerebrovascular inflammation after mild TBI, as confirmed by immunostainings. By contrast, in vivo PET imaging with the TSPO ligand [18F]DPA-714 demonstrated persisting signs of neuroinflammation in the ipsilateral cortex and hippocampus after mild TBI. Interestingly, immunohistochemical analyses confirmed these spatio-temporal profiles, showing a robust parenchymal astrogliosis and microgliosis, at least up to 3 weeks post-injury in both the cortex and hippocampus. In conclusion, we show that even one single mild TBI induces long-term behavioural deficits, associated with a persistent neuro-inflammatory status that can be detected by PET imaging.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.