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Recent resilience research has increasingly emphasized the importance of focusing on investigating the protective factors in mentally healthy populations, complementing the traditional focus on psychopathology. Social support has emerged as a crucial element within the complex interplay of individual and socio-environmental factors that shape resilience. However, the neural underpinnings of the relationship between social support and resilience, particularly in healthy subjects, remain largely unexplored. With advances in neuroimaging techniques, such as ultra-high field MRI at 7T and beyond, researchers can more effectively investigate the neural mechanisms underlying these factors. Thus, our study employed ultra-high field rs-fMRI to explore how social support moderates the relationship between psychological resilience and functional connectivity in a healthy cohort. We hypothesized that enhanced social support would amplify resilience-associated connectivity within neural circuits essential for emotional regulation, cognitive processing, and adaptive problem-solving, signifying a synergistic interaction where strong social networks bolster the neural underpinnings of resilience. (n = 30). Through seed-based functional connectivity analyses and interaction analysis, we aimed to uncover the neural correlates at the interplay of social support and resilience. Our findings indicate that perceived social support significantly (p<0.001) alters functional connectivity in the right and left FP, PCC, and left hippocampus, affirming the pivotal roles of these regions in the brain's resilience network. Moreover, we identified significant moderation effects of social support across various brain regions, each showing unique connectivity patterns. Specifically, the right FP demonstrated a significant interaction effect where high social support levels were linked to increased connectivity with regions involved in socio-cognitive processing, while low social support showed opposite effects. Similar patterns by social support levels were observed in the left FP, with connectivity changes in clusters associated with emotional regulation and cognitive functions. The PCC's connectivity was distinctly influenced by support levels, elucidating its role in emotional and social cognition. Interestingly, the connectivity of the left hippocampus was not significantly impacted by social support levels, indicating a unique pattern within this region. These insights highlight the importance of high social support levels in enhancing the neural foundations of resilience and fostering adaptive neurological responses to environmental challenges. [ABSTRACT FROM AUTHOR]

The glutamate and γ‐aminobutyric acid neuroreceptor subtypes mGluR5 and GABAA are hypothesized to be involved in the development of a variety of psychiatric diseases. However, detailed information relating to their in vivo distribution is generally unavailable. Maps of such distributions could potentially aid clinical studies by providing a reference for the normal distribution of neuroreceptors and may also be useful as covariates in advanced functional magnetic resonance imaging (MR) studies. In this study, we propose a comprehensive processing pipeline for the construction of standard space, in vivo distributions of non‐displaceable binding potential (BPND), and total distribution volume (VT) based on simultaneously acquired bolus‐infusion positron emission tomography (PET) and MR data. The pipeline was applied to [11C]ABP688‐PET/MR (13 healthy male non‐smokers, 26.6 ± 7.0 years) and [11C]Flumazenil‐PET/MR (10 healthy males, 25.8 ± 3.0 years) data. Activity concentration templates, as well as VT and BPND atlases of mGluR5 and GABAA, were generated from these data. The maps were validated by assessing the percent error δ from warped space to native space in a selection of brain regions. We verified that the average δABP = 3.0 ± 1.0% and δFMZ = 3.8 ± 1.4% were lower than the expected variabilities σ of the tracers (σABP = 4.0%–16.0%, σFMZ = 3.9%–9.5%). An evaluation of PET‐to‐PET registrations based on the new maps showed higher registration accuracy compared to registrations based on the commonly used [15O]H2O‐template distributed with SPM12. Thus, we conclude that the resulting maps can be used for further research and the proposed pipeline is a viable tool for the construction of standardized PET data distributions. [ABSTRACT FROM AUTHOR]

Purpose: To introduce quantitative rapid gradient-echo (QRAGE), a novel approach for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T 1 , T 2 *, and magnetic susceptibility at ultrahigh field strength., Methods: QRAGE leverages a newly developed multi-echo MPnRAGE sequence, facilitating the acquisition of 171 distinct contrast images across a range of inversion and TE points. To maintain a short acquisition time, we introduce MIRAGE2, a novel model-based reconstruction method that exploits prior knowledge of temporal signal evolution, represented as damped complex exponentials. MIRAGE2 minimizes local Block-Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps. We validate QRAGE through extensive simulations, phantom studies, and in vivo experiments, demonstrating its capability for high-precision imaging., Results: In vivo brain measurements show the promising performance of QRAGE, with test-retest SDs and deviations from reference methods of < 0.8% for water content, < 17 ms for T 1 , and < 0.7 ms for T 2 *. QRAGE achieves whole-brain coverage at a 1-mm isotropic resolution in just 7 min and 15 s, comparable to the acquisition time of an MP2RAGE scan. In addition, QRAGE generates a contrast image akin to the UNI image produced by MP2RAGE., Conclusion: QRAGE is a new, successful approach for simultaneously mapping multiple MR parameters at ultrahigh field., (© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Traditionally, the diagnosis of schizophrenia was based on the psychiatrist's introspective diagnosis through clinical stratification factors and score-scales, which led to heterogeneity and discrepancy in the symptoms and results. However, there are many studies trying to improve and assist in how its diagnosis could be performed. To objectively classify schizophrenia patients it is required to determine quantitative biomarkers of the disease. In this contribution we propose a method based on feature extraction both in magnetic resonance (MR) and Positron Emission Tomography (PET) imaging. A dataset of 34 participants (17 patients and 17 control subjects) were analyzed and 5 different brain regions were studied (frontal cortex, posterior cingulate cortex, temporal cortex, primary auditory cortex and thalamus). Following a radiomics approach, 43 texture features were extracted using five different statistical methods. These features were used for the training of the five different predictive models (Linear SVM, Gaussian SVM, Bagged Tree, KNN and Naive Bayes). The precision results were obtained classifying schizophrenia both in MR images (89% Area Under the Curve (AUC) in the posterior cingulate cortex) and with PET images (82% AUC in the frontal cortex), being Linear SVM and Naive Bayes the classification models with the highest predictive power. Clinical Relevance- The current study establishes a methodology to classify schizophrenia disease based on quantitative biomarkers using MR and PET images. This tool could assist the psychiatrist as an additional criterion for the diagnosis evaluation.

The glutamate and γ-aminobutyric acid neuroreceptor subtypes mGluR 5 and GABA A are hypothesized to be involved in the development of a variety of psychiatric diseases. However, detailed information relating to their in vivo distribution is generally unavailable. Maps of such distributions could potentially aid clinical studies by providing a reference for the normal distribution of neuroreceptors and may also be useful as covariates in advanced functional magnetic resonance imaging (MR) studies. In this study, we propose a comprehensive processing pipeline for the construction of standard space, in vivo distributions of non-displaceable binding potential (BP ND ), and total distribution volume (V T ) based on simultaneously acquired bolus-infusion positron emission tomography (PET) and MR data. The pipeline was applied to [ 11 C]ABP688-PET/MR (13 healthy male non-smokers, 26.6 ± 7.0 years) and [ 11 C]Flumazenil-PET/MR (10 healthy males, 25.8 ± 3.0 years) data. Activity concentration templates, as well as V T and BP ND atlases of mGluR 5 and GABA A , were generated from these data. The maps were validated by assessing the percent error δ from warped space to native space in a selection of brain regions. We verified that the average δ ABP  = 3.0 ± 1.0% and δ FMZ  = 3.8 ± 1.4% were lower than the expected variabilities σ of the tracers (σ ABP  = 4.0%-16.0%, σ FMZ  = 3.9%-9.5%). An evaluation of PET-to-PET registrations based on the new maps showed higher registration accuracy compared to registrations based on the commonly used [ 15 O]H 2 O-template distributed with SPM12. Thus, we conclude that the resulting maps can be used for further research and the proposed pipeline is a viable tool for the construction of standardized PET data distributions., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)