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There is increasing evidence that resilience in youth may have a neurobiological basis. However, the existing literature lacks a consistent way of operationalizing resilience, often relying on arbitrary judgments or narrow definitions (e.g., not developing PTSD) to classify individuals as resilient. Therefore, this study used data-driven, continuous resilience scores based on adversity and psychopathology to investigate associations between resilience and brain structure in youth. Structural MRI data from 298 youth aged 9-18 years ( M age = 13.51; 51% female) who participated in the European multisite FemNAT-CD study were preprocessed using SPM12 and analyzed using voxel-based morphometry. Resilience scores were derived by regressing data on adversity exposure against current/lifetime psychopathology and quantifying each individual's distance from the regression line. General linear models tested for associations between resilience and gray matter volume (GMV) and examined whether associations between resilience and GMV differed by sex. Resilience was positively correlated with GMV in the right inferior frontal and medial frontal gyri. Sex-by-resilience interactions were observed in the middle temporal and middle frontal gyri. These findings demonstrate that resilience in youth is associated with volume in brain regions implicated in executive functioning, emotion regulation, and attention. Our results also provide evidence for sex differences in the neurobiology of resilience.

Background: The autonomic response to transcutaneous auricular vagus nerve stimulation (taVNS) has been linked to the engagement of brainstem circuitry modulating autonomic outflow. However, the physiological mechanisms supporting such efferent vagal responses are not well understood, particularly in humans., Hypothesis: We present a paradigm for estimating directional brain-heart interactions in response to taVNS. We propose that our approach is able to identify causal links between the activity of brainstem nuclei involved in autonomic control and cardiovagal outflow., Methods: We adopt an approach based on a recent reformulation of Granger causality that includes permutation-based, nonparametric statistics. The method is applied to ultrahigh field (7T) functional magnetic resonance imaging (fMRI) data collected on healthy subjects during taVNS., Results: Our framework identified taVNS-evoked functional brainstem responses with superior sensitivity compared to prior conventional approaches, confirming causal links between taVNS stimulation and fMRI response in the nucleus tractus solitarii (NTS). Furthermore, our causal approach elucidated potential mechanisms by which information is relayed between brainstem nuclei and cardiovagal, i.e., high-frequency heart rate variability, in response to taVNS. Our findings revealed that key brainstem nuclei, known from animal models to be involved in cardiovascular control, exert a causal influence on taVNS-induced cardiovagal outflow in humans., Conclusion: Our causal approach allowed us to noninvasively evaluate directional interactions between fMRI BOLD signals from brainstem nuclei and cardiovagal outflow., Competing Interests: Declaration of competing interest None., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) have been previously used to explore white matter related to human immunodeficiency virus (HIV) infection. While DTI and DKI suffer from low specificity, the Combined Hindered and Restricted Model of Diffusion (CHARMED) provides additional microstructural specificity. We used these three models to evaluate microstructural differences between 35 HIV-positive patients without neurological impairment and 20 healthy controls who underwent diffusion-weighted imaging using three b-values. While significant group effects were found in all diffusion metrics, CHARMED and DKI analyses uncovered wider involvement (80% vs. 20%) of all white matter tracts in HIV infection compared with DTI. In restricted fraction (FR) analysis, we found significant differences in the left corticospinal tract, middle cerebellar peduncle, right inferior cerebellar peduncle, right corticospinal tract, splenium of the corpus callosum, left superior cerebellar peduncle, left superior cerebellar peduncle, pontine crossing tract, left posterior limb of the internal capsule, and left/right medial lemniscus. These are involved in language, motor, equilibrium, behavior, and proprioception, supporting the functional integration that is frequently impaired in HIV-positivity. Additionally, we employed a machine learning algorithm (XGBoost) to discriminate HIV-positive patients from healthy controls using DTI and CHARMED metrics on an ROIwise basis, and unique contributions to this discrimination were examined using Shapley Explanation values. The CHARMED and DKI estimates produced the best performance. Our results suggest that biophysical multishell imaging, combining additional sensitivity and built-in specificity, provides further information about the brain microstructural changes in multimodal areas involved in attentive, emotional and memory networks often impaired in HIV patients., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Multisystem inflammatory syndrome in children (MIS-C) is a postinfectious sequela of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with some clinical features overlapping with Kawasaki disease (KD). Our research group and others have highlighted that the spike protein of SARS-CoV-2 can trigger the activation of human endogenous retroviruses (HERVs), which in turn induces inflammatory and immune reactions, suggesting HERVs as contributing factors in COVID-19 immunopathology. With the aim to identify new factors involved in the processes underlying KD and MIS-C, we analysed the transcriptional levels of HERVs, HERV-related genes, and immune mediators in children during the acute and subacute phases compared with COVID-19 paediatric patients and healthy controls. The results showed higher levels of HERV-W, HERV-K, Syn-1, and ASCT-1/2 in KD, MIS-C, and COV patients, while higher levels of Syn-2 and MFSD2A were found only in MIS-C patients. Moreover, KD and MIS-C shared the dysregulation of several inflammatory and regulatory cytokines. Interestingly, in MIS-C patients, negative correlations have been found between HERV-W and IL-10 and between Syn-2 and IL-10, while positive correlations have been found between HERV-K and IL-10. In addition, HERV-W expression positively correlated with the C-reactive protein. This pilot study supports the role of HERVs in inflammatory diseases, suggesting their interplay with the immune system in this setting. The elevated expression of Syn-2 and MFSD2A seems to be a distinctive trait of MIS-C patients, allowing to distinguish them from KD ones. The understanding of pathological mechanisms can lead to the best available treatment for these two diseases, limiting complications and serious outcomes.

Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic 18 F-Fluorothymidine (FLT) PET scans from BC patients, extending conventional static radiomics methods to the time domain-termed as 'Dynomics'. Radiomic features were extracted from both static and dynamic PET images on lesion and reference tissue masks. The extracted features were used to train an XGBoost model for classifying tumor versus reference tissue and complete versus partial responders to neoadjuvant chemotherapy. The results underscored the superiority of dynamic and static radiomics over standard PET imaging, achieving accuracy of 94% in tumor tissue classification. Notably, in predicting BC prognosis, dynomics delivered the highest performance, achieving accuracy of 86%, thereby outperforming both static radiomics and standard PET data. This study illustrates the enhanced clinical utility of dynomics in yielding more precise and reliable information for BC diagnosis and prognosis, paving the way for improved treatment strategies., Competing Interests: All authors declare that they have no known conflicts of interest in terms of competing financial interests or personal relationships that could have an influence on or are relevant to the work reported in this paper.

Background: Childhood maltreatment is common in youths with conduct disorder (CD), and both CD and maltreatment have been linked to neuroanatomical alterations. Nonetheless, our understanding of the contribution of maltreatment to the neuroanatomical alterations observed in CD remains limited. We tested the applicability of the ecophenotype model to CD, which holds that maltreatment-related psychopathology is (neurobiologically) distinct from psychopathology without maltreatment., Methods: Surface-based morphometry was used to investigate cortical volume, thickness, surface area, and gyrification in a mixed-sex sample of participants with CD (n = 114) and healthy control subjects (HCs) (n = 146), ages 9 to 18 years. Using vertexwise general linear models adjusted for sex, age, total intracranial volume, and site, the control group was compared with the overall CD group and the CD subgroups with (n = 49) versus without (n = 65) maltreatment (assessed by the Children's Bad Experiences interview). These subgroups were also directly compared., Results: The overall CD group showed lower cortical thickness in the right inferior frontal gyrus. CD youths with a history of maltreatment showed more widespread structural alterations relative to HCs, comprising lower thickness, volume, and gyrification in inferior and middle frontal regions. Conversely, CD youths with no history of maltreatment only showed greater left superior temporal gyrus folding relative to HCs. When contrasting the CD subgroups, those with maltreatment displayed lower right superior temporal gyrus volume, right precentral gyrus surface area, and gyrification in frontal, temporal, and parietal regions., Conclusions: Consistent with the ecophenotype model, findings indicated that CD youths with versus without maltreatment differ neurobiologically. This highlights the importance of considering maltreatment history in neuroimaging studies of CD and other disorders., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: Explainable artificial intelligence (XAI) is a technology that can enhance trust in mental state classifications by providing explanations for the reasoning behind artificial intelligence (AI) models outputs, especially for high-dimensional and highly-correlated brain signals. Feature importance and counterfactual explanations are two common approaches to generate these explanations, but both have drawbacks. While feature importance methods, such as shapley additive explanations (SHAP), can be computationally expensive and sensitive to feature correlation, counterfactual explanations only explain a single outcome instead of the entire model., Methods: To overcome these limitations, we propose a new procedure for computing global feature importance that involves aggregating local counterfactual explanations. This approach is specifically tailored to fMRI signals and is based on the hypothesis that instances close to the decision boundary and their counterfactuals mainly differ in the features identified as most important for the downstream classification task. We refer to this proposed feature importance measure as Boundary Crossing Solo Ratio (BoCSoR), since it quantifies the frequency with which a change in each feature in isolation leads to a change in classification outcome, i.e., the crossing of the model's decision boundary., Results and Conclusions: Experimental results on synthetic data and real publicly available fMRI data from the Human Connect project show that the proposed BoCSoR measure is more robust to feature correlation and less computationally expensive than state-of-the-art methods. Additionally, it is equally effective in providing an explanation for the behavior of any AI model for brain signals. These properties are crucial for medical decision support systems, where many different features are often extracted from the same physiological measures and a gold standard is absent. Consequently, computing feature importance may become computationally expensive, and there may be a high probability of mutual correlation among features, leading to unreliable results from state-of-the-art XAI methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Introduction: The functional connectivity patterns in the brain are highly heritable; however, it is unclear how genetic factors influence the directionality of such "information flows." Studying the "directionality" of the brain functional connectivity and assessing how heritability modulates it can improve our understanding of the human connectome., Methods: Here, we investigated the heritability of "directed" functional connections using a state-space formulation of Granger causality (GC), in conjunction with blind deconvolution methods accounting for local variability in the hemodynamic response function. Such GC implementation is ideal to explore the directionality of functional interactions across a large number of networks. Resting-state functional magnetic resonance imaging data were drawn from the Human Connectome Project (total n = 898 participants). To add robustness to our findings, the dataset was randomly split into a "discovery" and a "replication" sample (each with n = 449 participants). The two cohorts were carefully matched in terms of demographic variables and other confounding factors (e.g., education). The effect of shared environment was also modeled., Results: The parieto- and prefronto-cerebellar, parieto-prefrontal, and posterior-cingulate to hippocampus connections showed the highest and most replicable heritability effects with little influence by shared environment. In contrast, shared environmental factors significantly affected the visuo-parietal and sensory-motor directed connectivity., Conclusion: We suggest a robust role of heritability in influencing the directed connectivity of some cortico-subcortical circuits implicated in cognition. Further studies, for example using task-based fMRI and GC, are warranted to confirm the asymmetric effects of genetic factors on the functional connectivity within cognitive networks and their role in supporting executive functions and learning., (© 2022 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Background: Previous studies have reported brain structure abnormalities in conduct disorder (CD), but it is unclear whether these neuroanatomical alterations mediate the effects of familial (genetic and environmental) risk for CD. We investigated brain structure in adolescents with CD and their unaffected relatives (URs) to identify neuroanatomical markers of familial risk for CD., Methods: Forty-one adolescents with CD, 24 URs of CD probands, and 38 healthy controls (aged 12-18), underwent structural magnetic resonance imaging. We performed surface-based morphometry analyses, testing for group differences in cortical volume, thickness, surface area, and folding. We also assessed the volume of key subcortical structures., Results: The CD and UR groups both displayed structural alterations (lower surface area and folding) in left inferior parietal cortex compared with controls. In contrast, CD participants showed lower insula and pars opercularis volume than controls, and lower surface area and folding in these regions than controls and URs. The URs showed greater folding in rostral anterior cingulate and inferior temporal cortex than controls and greater medial orbitofrontal folding than CD participants. The surface area and volume differences were not significant when controlling for attention-deficit/hyperactivity disorder comorbidity. There were no group differences in subcortical volumes., Conclusions: These findings suggest that alterations in inferior parietal cortical structure partly mediate the effects of familial risk for CD. These structural changes merit investigation as candidate endophenotypes for CD. Neuroanatomical changes in medial orbitofrontal and anterior cingulate cortex differentiated between URs and the other groups, potentially reflecting neural mechanisms of resilience to CD.

Purpose: Hemorrhagic transformation (HT) is an independent predictor of unfavorable outcome in acute ischemic stroke (AIS) patients undergoing endovascular thrombectomy (EVT). Its early identification could help tailor AIS management. We hypothesize that machine learning (ML) applied to cone-beam computed tomography (CB-CT), immediately after EVT, improves performance in 24-h HT prediction., Methods: We prospectively enrolled AIS patients undergoing EVT, post-procedural CB-CT, and 24-h non-contrast CT (NCCT). Three raters independently analyzed imaging at four anatomic levels qualitatively and quantitatively selecting a region of interest (ROI) < 5 mm 2 . Each ROI was labeled as "hemorrhagic" or "non-hemorrhagic" depending on 24-h NCCT. For each level of CB-CT, Mean Hounsfield Unit (HU), minimum HU, maximum HU, and signal- and contrast-to-noise ratios were calculated, and the differential HU-ROI value was compared between both hemispheres. The number of anatomic levels affected was computed for lesion volume estimation. ML with the best validation performance for 24-h HT prediction was selected., Results: One hundred seventy-two ROIs from affected hemispheres of 43 patients were extracted. Ninety-two ROIs were classified as unremarkable, whereas 5 as parenchymal contrast staining, 29 as ischemia, 7 as subarachnoid hemorrhages, and 39 as HT. The Bernoulli Naïve Bayes was the best ML classifier with a good performance for 24-h HT prediction (sensitivity = 1.00; specificity = 0.75; accuracy = 0.82), though precision was 0.60., Conclusion: ML demonstrates high-sensitivity but low-accuracy 24-h HT prediction in AIS. The automated CB-CT imaging evaluation resizes sensitivity, specificity, and accuracy rates of visual interpretation reported in the literature so far. A standardized quantitative interpretation of CB-CT may be warranted to overcome the inter-operator variability., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice., Competing Interests: Declaration of interests H.H. is an employee of Eisai and serves as senior associate editor for the Journal Alzheimer’s & Dementia and has not received any fees or honoraria since May 2019. H.H. is inventor of 11 patents and has received no royalties for: In Vitro Multiparameter Determination Method for The Diagnosis and Early Diagnosis of Neurodegenerative Disorders patent no. 8916388; In Vitro Procedure for Diagnosis and Early Diagnosis of Neurodegenerative Diseases patent no. 8298784; Neurodegenerative Markers for Psychiatric Conditions publication no. 20120196300; In Vitro Multiparameter Determination Method for The Diagnosis and Early Diagnosis of Neurodegenerative Disorders publication no. 20100062463; In Vitro Method for The Diagnosis and Early Diagnosis of Neurodegenerative Disorders publication no. 20100035286; In Vitro Procedure for Diagnosis and Early Diagnosis of Neurodegenerative Diseases publication no. 20090263822; In Vitro Method for The Diagnosis of Neurodegenerative Diseases patent no. 7547553; CSF Diagnostic in Vitro Method for Diagnosis of Dementias and Neuroinflammatory Diseases publication no. 20080206797; In Vitro Method for The Diagnosis of Neurodegenerative Diseases publication no. 20080199966; Neurodegenerative Markers for Psychiatric Conditions publication no. 20080131921; Method for diagnosis of dementias and neuroinflammatory diseases based on an increased level of procalcitonin in cerebrospinal fluid: US patent no. 10921330. P.G. is an employee of Eisai Inc. J.C. has provided consultation to AB Science, Acadia, Alkahest, AlphaCognition, ALZPathFinder, Annovis, AriBio, Artery, Avanir, Biogen, Biosplice, Cassava, Cerevel, Clinilabs, Cortexyme, Diadem, EIP Pharma, Eisai, GatehouseBio, GemVax, Genentech, Green Valley, Grifols, Janssen, Karuna, Lexeo, Lilly, Lundbeck, LSP, Merck, NervGen, Novo Nordisk, Oligomerix, Otsuka, PharmacotrophiX, PRODEO, Prothena, ReMYND, Renew, Resverlogix, Roche, Signant Health, Suven, Unlearn AI, Vaxxinity, VigilNeuro pharmaceutical, assessment, and investment companies. N.T. has provided consultation to Eisai. P.M.T. has received a research grant from Biogen, Inc., for work unrelated to this manuscript, and has provided consultation to Kairos Venture Capital, Inc. Y.H. is an employee of Eisai Inc. M.C. is an employee of Eisai Inc. A.V. declares no competing financial interests related to the present article, and his contribution to this article reflects entirely and only his own academic expertise on the matter. A.V. was an employee of Eisai Inc. (Nov 2019–June 2021). A.V. does not receive any fees or honoraria since November 2019. Before November 2019 he had received lecture honoraria from Roche, MagQu LLC, and Servier., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: During the last decade, physical activity (PA) (or "exercise") has been identified as one of the main modifiable factors that influence the development of Alzheimer's disease (AD) pathophysiology. We performed an umbrella review to summarize the evidence on the association between PA/exercise and the risk of developing AD risk, and the effect of exercise interventions on the progression of AD., Methods: A systematic search was performed in PubMed, SportDiscus, Cochrane Library and Web of Science (March 2022) to identify meta-analyses assessing the association between PA and the incidence of AD, and assessing the effect of exercise interventions on patients with AD., Results: Twenty-one studies were included. The results with strongest evidence revealed the positive effects of PA on AD risk. Specifically, meeting the WHO recommendations for PA was associated with a lower risk of AD. They also revealed positive effects of exercise on cognitive function, physical performance, and functional independence., Conclusions: There is strong evidence of a protective effect of regular PA against AD risk; however, the dose-response association remains unclear. Physical exercise seems to improve several dimensions in patients with AD, although research is warranted to elucidate the exercise characteristics that promote the greatest benefits., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

To date, the relationship between central hallmarks of multiple sclerosis (MS), such as white matter (WM)/cortical demyelinated lesions and cortical gray matter atrophy, remains unclear. We investigated the interplay between cortical atrophy and individual lesion-type patterns that have recently emerged as new radiological markers of MS disease progression. We employed a machine learning model to predict mean cortical thinning in whole-brain and single hemispheres in 150 cortical regions using demographic and lesion-related characteristics, evaluated via an ultrahigh field (7 Tesla) MRI. We found that (i) volume and rimless (i.e., without a "rim" of iron-laden immune cells) WM lesions, patient age, and volume of intracortical lesions have the most predictive power; (ii) WM lesions are more important for prediction when their load is small, while cortical lesion load becomes more important as it increases; (iii) WM lesions play a greater role in the progression of atrophy during the latest stages of the disease. Our results highlight the intricacy of MS pathology across the whole brain. In turn, this calls for multivariate statistical analyses and mechanistic modeling techniques to understand the etiopathogenesis of lesions.

Background: Putative pathogenic effects mediated by human endogenous retroviruses (HERVs) in neurological and psychiatric disorders in humans have been extensively described. HERVs may alter the development of the brain by means of several mechanisms, including modulation of gene expression, alteration of DNA stability, and activation of immune system. We recently demonstrated that autistic children and their mothers share high expression levels of some HERVs and cytokines in peripheral blood mononuclear cells (PBMCs) ex vivo, suggesting a close mother-child association in Autism Spectrum Disorder (ASD)., Results: In the present study, PBMCs from autistic children and their parents were exposed to stimulating factors (Interleukin-2/Phytohaemagglutinin) or drugs, as Valproic acid and Efavirenz. The results show that HERVs and cytokines expression can be modulated in vitro by different stimuli in PBMCs from autistic children and their mothers, while no significant changes were found in PBMCs ASD fathers or in controls individuals. In particular, in vitro exposure to interleukin-2/Phytohaemagglutinin or valproic acid induces the expression of several HERVs and cytokines while Efavirenz inhibits them., Conclusion: Herein we show that autistic children and their mothers share an intrinsic responsiveness to in vitro microenvironmental changes in expressing HERVs and pro-inflammatory cytokines. Remarkably, the antiretroviral drug Efavirenz restores the expression of specific HERV families to values similar to those of the controls, also reducing the expression of proinflammatory cytokines but keeping the regulatory ones high. Our findings open new perspectives to study the role of HERVs in the biological mechanisms underlying Autism., (© 2022. The Author(s).)

Maternal infections during pregnancy and the consequent maternal immune activation (MIA) are the major risk factors for autism spectrum disorder (ASD). Epidemiological evidence is corroborated by the preclinical models in which MIA leads to ASD-like behavioral abnormalities and altered neuroinflammatory profiles, with an increase in pro-inflammatory cytokines and microglial markers. In addition to neuroinflammatory response, an abnormal expression of endogenous retroviruses (ERVs) has been identified in neurodevelopmental disorders and have been found to correlate with disease severity. Our aim was to evaluate the transcriptional profile of several ERV families, ERV-related genes, and inflammatory mediators (by RT real-time PCR) in mouse offspring of both sexes, prenatally exposed to polyinosinic:polycytidylic acid (Poly I:C), a synthetic double-stranded RNA molecule targeting TLR-3 that mimics viral maternal infection during pregnancy. We found that prenatal exposure to Poly I:C deregulated the expression of some ERVs and ERV-related genes both in the prefrontal cortex (PFC) and hippocampus, while no changes were detected in the blood. Interestingly, sex-related differences in the expression levels of some ERVs, ERV-related genes, and inflammatory mediators that were higher in females than in males emerged only in PFC. Our findings support the tissue specificity of ERV and ERV-related transcriptional profiles in MIA mice.

Nanoparticles (NPs) are a wide class of materials currently used in several industrial and biomedical applications. Due to their small size (1-100 nm), NPs can easily enter the human body, inducing tissue damage. NP toxicity depends on physical and chemical NP properties (e.g., size, charge and surface area) in ways and magnitudes that are still unknown. We assess the average as well as the individual importance of NP atomic descriptors, along with chemical properties and experimental conditions, in determining cytotoxicity endpoints for several nanomaterials. We employ a multicenter cytotoxicity nanomaterial database (12 different materials with first and second dimensions ranging between 2.70 and 81.2 nm and between 4.10 and 4048 nm, respectively). We develop a regressor model based on extreme gradient boosting with hyperparameter optimization. We employ Shapley additive explanations to obtain good cytotoxicity prediction performance. Model performances are quantified as statistically significant Spearman correlations between the true and predicted values, ranging from 0.5 to 0.7. Our results show that i) size in situ and surface areas larger than 200 nm and 50 m2/g, respectively, ii) primary particles smaller than 20 nm; iii) irregular (i.e., not spherical) shapes and iv) positive Z-potentials contribute the most to the prediction of NP cytotoxicity, especially if lactate dehydrogenase (LDH) assays are employed for short experimental times. These results were moderately stable across toxicity endpoints, although some degree of variability emerged across dose quantification methods, confirming the complexity of nano-bio interactions and the need for large, systematic experimental characterization to reach a safer-by-design approach.

Systems biology and systems neurophysiology in particular have recently emerged as powerful tools for a number of key applications in the biomedical sciences. Nevertheless, such models are often based on complex combinations of multiscale (and possibly multiphysics) strategies that require ad hoc computational strategies and pose extremely high computational demands. Recent developments in the field of deep neural networks have demonstrated the possibility of formulating nonlinear, universal approximators to estimate solutions to highly nonlinear and complex problems with significant speed and accuracy advantages in comparison with traditional models. After synthetic data validation, we use so-called physically constrained neural networks (PINN) to simultaneously solve the biologically plausible Hodgkin-Huxley model and infer its parameters and hidden time-courses from real data under both variable and constant current stimulation, demonstrating extremely low variability across spikes and faithful signal reconstruction. The parameter ranges we obtain are also compatible with prior knowledge. We demonstrate that detailed biological knowledge can be provided to a neural network, making it able to fit complex dynamics over both simulated and real data.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been successfully employed to understand the organisation of the human brain. Typically, the brain is parcellated into regions of interest (ROIs) and modelled as a graph where each ROI represents a node and association measures between ROI-specific blood-oxygen-level-dependent (BOLD) time series are edges. Recently, graph neural networks (GNNs) have seen a surge in popularity due to their success in modelling unstructured relational data. The latest developments with GNNs, however, have not yet been fully exploited for the analysis of rs-fMRI data, particularly with regards to its spatio-temporal dynamics. In this paper, we present a novel deep neural network architecture which combines both GNNs and temporal convolutional networks (TCNs) in order to learn from both the spatial and temporal components of rs-fMRI data in an end-to-end fashion. In particular, this corresponds to intra-feature learning (i.e., learning temporal dynamics with TCNs) as well as inter-feature learning (i.e., leveraging interactions between ROI-wise dynamics with GNNs). We evaluate our model with an ablation study using 35,159 samples from the UK Biobank rs-fMRI database, as well as in the smaller Human Connectome Project (HCP) dataset, both in a unimodal and in a multimodal fashion. We also demonstrate that out architecture contains explainability-related features which easily map to realistic neurobiological insights. We suggest that this model could lay the groundwork for future deep learning architectures focused on leveraging the inherently and inextricably spatio-temporal nature of rs-fMRI data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Positron emission tomography (PET) can reveal metabolic activity in a voxelwise manner. PET analysis is commonly performed in a static manner by analyzing the standardized uptake value (SUV) obtained from the plateau region of PET acquisitions. A dynamic PET acquisition can provide a map of the spatiotemporal concentration of the tracer in vivo, hence conveying information about radiotracer delivery to tissue, its interaction with the target and washout. Therefore, tissue-specific biochemical properties are embedded in the shape of time activity curves (TACs), which are generally used for kinetic analysis. Conventionally, TACs are employed along with information about blood plasma activity concentration, i.e., the arterial input function (AIF), and specific compartmental models to obtain a full quantitative analysis of PET data. The main drawback of this approach is the need for invasive procedures requiring arterial blood sample collection during the whole PET scan. In this paper, we address the challenge of improving PET diagnostic accuracy through an alternative approach based on the analysis of time signal intensity patterns. Specifically, we demonstrate the diagnostic potential of tissue TACs provided by dynamic PET acquisition using various deep learning models. Our framework is shown to outperform the discriminative potential of classical SUV analysis, hence paving the way for more accurate PET-based lesion discrimination without additional acquisition time or invasive procedures. Clinical Relevance- The diagnostic accuracy of dynamic PET data exploited by deep-learning based time signal intensity pattern analysis is superior to that of static SUV imaging.

Glaucoma is a leading cause of blindness worldwide. Although intraocular pressure is the main risk factor for glaucoma, several intraocular pressure independent factors have been associated with the risk of developing the disease and its progression. The diagnosis of glaucoma relies on clinical features of the optic nerve, visual field test, and optical coherence tomography. However, the multidisciplinary aspect of the disease suggests that other biomarkers may be useful for the diagnosis, thus underling the importance of novel imaging techniques supporting clinicians. This review analyzes the common pathogenic mechanisms between glaucoma and Alzheimer's disease and the possible novel approaches for diagnosis and follow up., Competing Interests: Conflict of interest statement Nothing declared., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: In patients with relapsed/refractory acute myeloid leukaemia (R/R AML) who received salvage chemotherapy, limited and not updated studies explored the incidence of invasive aspergillosis (IA) and the role of antifungal prophylaxis (AP). The aims of this multicentre retrospective 'SEIFEM 2016-B' study were as follows: (1) to evaluate the current rate and the outcome of proven/probable IA and (2) to assess the efficacy of AP, in a large 'real life' series of patient with R/R AML submitted to salvage chemotherapy., Results: Of 2250 R/R AML patients, a total of 74 cases of IA (5.1%) were recorded as follows: 10 (0.7%) proven and 64 (4.3%) probable. Information about AP were available in 73/74 (99%) patients. Fifty-eight (79%) breakthrough infections occurred, mainly during AP with posaconazole [25 (43%)]. The patients who received AP during salvage chemotherapy showed a benefit from antifungal therapy (AT) than patients who did not received AP [43 (86%) vs 7 (14%); p < .033]. In a multivariate analysis, AP and absence of severe mucositis had a significant favourable effect on overall response rate., Conclusion: Our data demonstrated that the incidence of IA during the salvage chemotherapy is similar to the past. Nevertheless, the attributable mortality rate (AMR) appears to be lower than that previously reported in R/R AML. Further prospective studies should be performed to confirm our preliminary observation and understand and the why a decreased AMR is reported in this setting of high-risk patients., (© 2021 Wiley-VCH GmbH.)

Patient-clinician concordance in behavior and brain activity has been proposed as a potential key mediator of mutual empathy and clinical rapport in the therapeutic encounter. However, the specific elements of patient-clinician communication that may support brain-to-brain concordance and therapeutic alliance are unknown. Here, we investigated how pain-related, directional facial communication between patients and clinicians is associated with brain-to-brain concordance. Patient-clinician dyads interacted in a pain-treatment context, during synchronous assessment of brain activity (fMRI hyperscanning) and online video transfer, enabling face-to-face social interaction. In-scanner videos were used for automated individual facial action unit (AU) time-series extraction. First, an interpretable machine-learning classifier of patients' facial expressions, from an independent fMRI experiment, significantly distinguished moderately painful leg pressure from innocuous pressure stimuli. Next, we estimated neural-network causality of patient-to-clinician directional information flow of facial expressions during clinician-initiated treatment of patients' evoked pain. We identified a leader-follower relationship in which patients predominantly led the facial communication while clinicians responded to patients' expressions. Finally, analyses of dynamic brain-to-brain concordance showed that patients' mid/posterior insular concordance with the clinicians' anterior insula cortex, a region identified in previously published data from this study 1 , was associated with therapeutic alliance, and self-reported and objective (patient-to-clinician-directed causal influence) markers of negative-affect expressivity. These results suggest a role of patient-clinician concordance of the insula, a social-mirroring and salience-processing brain node, in mediating directional dynamics of pain-directed facial communication during therapeutic encounters., (© 2022. The Author(s).)

Heart auscultation is an inexpensive and fundamental technique to effectively diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel, e.g. in developing countries, many efforts are made worldwide to propose computational tools for detecting abnormalities in heart sounds. The large heterogeneity of achievable data quality and devices, the variety of possible heart pathologies, and a generally poor signal-to-noise ratio make this problem very challenging. We present an accurate classification strategy for diagnosing heart sounds based on (1) automatic heart phase segmentation, (2) state-of-the art filters drawn from the field of speech synthesis (mel-frequency cepstral representation) and (3) an ad hoc multi-branch, multi-instance artificial neural network based on convolutional layers and fully connected neuronal ensembles which separately learns from each heart phase hence implicitly leveraging their different physiological significance. We demonstrate that it is possible to train our architecture to reach very high performances, e.g. an area under the curve of 0.87 or a sensitivity of 0.97. Our machine-learning-based tool could be employed for heartsound classification, especially as a screening tool in a variety of situations including telemedicine applications. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

While Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour, hence undermining the validity of MVAR-based GC (MVAR-GC). Most nonlinear GC estimators only cater for additive nonlinearities or, alternatively, are based on recurrent neural networks or long short-term memory networks, which present considerable training difficulties and tailoring needs. We reformulate the GC framework in terms of echo-state networks-based models for arbitrarily complex networks, and characterize its ability to capture nonlinear causal relations in a network of noisy Duffing oscillators, showing a net advantage of echo state GC (ES-GC) in detecting nonlinear, causal links. We then explore the structure of ES-GC networks in the human brain employing functional MRI data from 1003 healthy subjects drawn from the human connectome project, demonstrating the existence of previously unknown directed within-brain interactions. In addition, we examine joint brain-heart signals in 15 subjects where we explore directed interaction between brain networks and central vagal cardiac control in order to investigate the so-called central autonomic network in a causal manner. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Recent developments in computational physiology have successfully exploited advanced signal processing and artificial intelligence tools for predicting or uncovering characteristic features of physiological and pathological states in humans. While these advanced tools have demonstrated excellent diagnostic capabilities, the high complexity of these computational 'black boxes' may severely limit scientific inference, especially in terms of biological insight about both physiology and pathological aberrations. This theme issue highlights current challenges and opportunities of advanced computational tools for processing dynamical data reflecting autonomic nervous system dynamics, with a specific focus on cardiovascular control physiology and pathology. This includes the development and adaptation of complex signal processing methods, multivariate cardiovascular models, multiscale and nonlinear models for central-peripheral dynamics, as well as deep and transfer learning algorithms applied to large datasets. The width of this perspective highlights the issues of specificity in heartbeat-related features and supports the need for an imminent transition from the black-box paradigm to explainable and personalized clinical models in cardiovascular research. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Purpose: To evaluate if thulium laser vapoenucleation of the prostate (ThuVEP) is equally safe and effective in a selected cohort of elderly patients when compared to "younger" patients., Materials and Methods: We performed a retrospective analysis of consecutive patients who underwent ThuVEP between September 2018 and February 2020. After application of the inclusion/exclusion criteria, patients were stratified according to the 75 years-old cut-off point suggested by the WHO. Group A included patients ≤75 years-old; Group B included patients >75 years-old. Preoperative assessment included urological consultation, prostate specific antigen (PSA), International Prostate Symptom Score (IPSS) and quality of life index, transrectal ultrasound to estimate prostate volume (PVol), and uroflowmetry to assess preoperative Qmax, Qave and post-void residual volume (PVR). Perioperative and postoperative data were analyzed during 3-month follow-up., Results: After propensity-score analysis, 51 versus 51 patients were 1:1 matched according to PVol, PSA, Qmax, IPSS and QoL. Patients were comparable at baseline excluding age (65 (IQR 59-70) versus 79 (IQR 77-82) years, Group A versus B, respectively, P-value <.001). No differences were found in terms of hemoglobin drop, complications rate, catheterization time and length of hospital stay. Group A (younger) patients had more significant improvement in 30-days absolute Qmax, Qave and ΔQmax. At 90-days follow-up, the differences between the groups disappeared. Within the 90-days follow-up, no significant differences were found in the readmission rate, with no need of reinterventions., Conclusions: In our hands, even in elderly patients affected by BPH, ThuVEP appears to be a safe and effective treatment option., (Copyright © 2021 AEU. Published by Elsevier España, S.L.U. All rights reserved.)

Multiple Sclerosis (MS) is the most common cause, (after trauma) of neurological disability in young adults in Western countries. While several Magnetic Resonance Imaging (MRI) studies have demonstrated a strong association between the presence of cortical grey matter atrophy and the progression of neurological impairment in MS patients, the neurobiological substrates of cortical atrophy in MS, and in particular its relationship with white matter (WM) and cortical lesions, remain unknown. The aim of this study was to investigate the interplay between cortical atrophy and different types of lesions at Ultra-High Field (UHF) 7 T MRI, including cortical lesions and lesions with a susceptibility rim (a feature which histopathological studies have associated with impaired remyelination and progressive tissue destruction). We combined lesion characterization with a recent machine learning (ML) framework which includes explainability, and we were able to predict cortical atrophy in MS from a handful of lesion-related features extracted from 7 T MR imaging. This highlights not only the importance of UHF MRI for accurately evaluating intracortical and rim lesion load, but also the differential contributions that these types of lesions may bring to determine disease evolution and severity. Also, we found that a small subset of features [WM lesion volume (not considering rim lesions), patient age and WM lesion count (not considering rim lesions), intracortical lesion volume] carried most of the prediction power. Interestingly, an almost opposite pattern emerged when contrasting cortical with WM lesion load: WM lesion load is most important when it is small, whereas cortical lesion load behaves in the opposite way.Clinical Relevance- Our results suggest that disconnection and axonal degeneration due to WM lesions and local cortical demyelination are the main factors determining cortical thinning. These findings further elucidate the complexity of MS pathology across the whole brain and the need for both statistical and mechanistic approaches to understanding the etiopathogenesis of lesions.

Heart auscultation is an inexpensive and fundamental technique to effectively to diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel e.g. in developing countries, a large body of research is attempting to develop automated, computational tools for detecting abnormalities in heart sounds. The large heterogeneity of achievable data quality and devices, the variety o possible heart pathologies, and a generally poor signal-to-noise ratio make this problem extremely challenging. We present an accurate classification strategy for diagnosing heart sounds based on 1) automatic heart phase segmentation, 2) state-of-the art filters drawn from the filed of speech synthesis (mel-frequency cepstral representation), and 3) an ad-hoc multi-branch, multi-instance artificial neural network based on convolutional layers and fully connected neuronal ensembles which separately learns from each heart phase, hence leveraging their different physiological significance. We demonstrate that it is possible to train our architecture to reach very high performances, e.g. an AUC of 0.87 or a sensitivity of 0.97. Our machine-learning-based tool could be employed for heart sound classification, especially as a screening tool in a variety of situations including telemedicine applications.

The human immunodeficiency virus (HIV) causes an infectious disease with a high viral tropism toward CD4 T-lymphocytes and macrophage. Since the advent of combined antiretroviral therapy (CART), the number of opportunistic infectious disease has diminished, turning HIV into a chronic condition. Nevertheless, HIV-infected patients suffer from several life-long symptoms, including the HIV-associated neurocognitive disorder (HAND), whose biological substrates remain unclear. HAND includes a range of cognitive impairments which have a huge impact on daily patient life. The aim of this study was to examine putative structural brain network changes in HIV-infected patient to test whether diffusion-imaging-related biomarkers could be used to discover and characterize subtle neurological alterations in HIV infection. To this end, we employed multi-shell, multi-tissue constrained spherical deconvolution in conjunction with probabilistic tractography and graph-theoretical analyses. We found several statistically significant effects in both local (right postcentral gyrus, right precuneus, right inferior parietal lobule, right transverse temporal gyrus, right inferior temporal gyrus, right putamen and right pallidum) and global graph-theoretical measures (global clustering coefficient, global efficiency and transitivity). Our study highlights a global and local reorganization of the structural connectome which support the possible application of graph theory to detect subtle alteration of brain regions in HIV patients.Clinical Relevance-Brain measures able to detect subtle alteration in HIV patients could also be used in e.g. evaluating therapeutic responses, hence empowering clinical trials.

Background and Aims: Several studies demonstrated a role of active chronic inflammatory infiltrate in carotid plaques progression suggesting a possible link between cardiovascular risk factors and inflammation-related plaque instability. The aim of this study is therefore to evaluate the possible effects of cardiovascular risk factors on in situ expression of proinflammatory markers associated with carotid plaque instability., Methods and Results: A tissue microarray containing carotid plaques from 36 symptomatic (major stroke or transient ischemic attack) and 37 asymptomatic patients was built. Serial sections were employed to evaluate the expression of some inflammatory markers by immunohistochemistry [CD3, CD4a, CD8, CD20, CD86, CD163, interleukin (IL)-2, IL-6, IL-17]. Immunohistochemical data were analyzed to study the possible associations between in situ expression of inflammatory biomarker and the main cardiovascular risk factors. Our data demonstrated that plaque instability is associated with the high in situ expression of some cytokines, such as IL-2, IL-6, IL-17. Besides the female sex, none of the risk factors analyzed showed a significant association between the in situ expression of these markers and unstable plaques. A significant increase of IL-6-positive and IL-17-positive cells was observed in unstable atheromatous plaques of female patients, as compared with unstable plaques of male patients., Conclusions: Plaque destabilization is certainly correlated with the presence of the major cardiovascular risk factors, however, our results showed that, with the exception of sex, their action in the evolutive process of plaque instability seems rather nonspecific, favoring a general release of proinflammatory cytokines., Competing Interests: The authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Diffusion tensor imaging (DTI) has been used to explore changes in the brain of subjects with human immunodeficiency virus (HIV) infection. However, DTI notoriously suffers from low specificity. Neurite orientation dispersion and density imaging (NODDI) is a compartmental model able to provide specific microstructural information with additional sensitivity/specificity. In this study we use both the NODDI and the DTI models to evaluate microstructural differences between 35 HIV-positive patients and 20 healthy controls. Diffusion-weighted imaging was acquired using three b-values (0, 1000 and 2500 s/mm 2 ). Both DTI and NODDI models were fitted to the data, obtaining estimates for fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), neurite density index (NDI) and orientation dispersion index (ODI), after which we performed group comparisons using Tract-based spatial statistics (TBSS). While significant group effects were found in in FA, MD, RD, AD and NDI, NDI analysis uncovered a much wider involvement of brain tissue in HIV infection as compared to DTI. In region-of interest (ROI)-based analysis, NDI estimates from the right corticospinal tract produced excellent performance in discriminating the two groups (AUC = 0.974, sensitivity = 90%; specificity =97%).

Alzheimer's disease (AD) is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in biological alterations and disease spatial-temporal progression. Human in-vivo and post-mortem studies point out a failure of multi-level biological networks underlying AD pathophysiology, including proteostasis (amyloid-β and tau), synaptic homeostasis, inflammatory and immune responses, lipid and energy metabolism, oxidative stress. Therefore, a holistic, systems-level approach is needed to fully capture AD multi-faceted pathophysiology. Omics sciences - genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics - embedded in the systems biology (SB) theoretical and computational framework can generate explainable readouts describing the entire biological continuum of a disease. Such path in Neurology is encouraged by the promising results of omics sciences and SB approaches in Oncology, where stage-driven pathway-based therapies have been developed in line with the precision medicine paradigm. Multi-omics data integrated in SB network approaches will help detect and chart AD upstream pathomechanistic alterations and downstream molecular effects occurring in preclinical stages. Finally, integrating omics and neuroimaging data - i.e., neuroimaging-omics - will identify multi-dimensional biological signatures essential to track the clinical-biological trajectories, at the subpopulation or even individual level., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Recent studies suggest that even moderate sudden sensorineural hearing loss (SSNHL) causes reduction of gray matter volume in the primary auditory cortex, diminishing its ability to react to sound stimulation, as well as reorganization of functional brain networks. We employed resting-state functional MRI (rs-fMRI), in conjunction with graph-theoretical analysis and a newly developed functional "disruption index," to study whole-brain as well as local functional changes in patients with unilateral SSNHL. We also assessed the potential of graph-theoretical measures as biomarkers of disease, in terms of their relationship to clinically relevant audiological parameters. Eight patients with moderate or severe unilateral SSNHL and 15 healthy controls were included in this prospective pilot study. All patients underwent rs-fMRI to study potential changes in brain connectivity. From rs-fMRI data, global and local graph-theoretical measures, disruption index, and audiological examinations were estimated. Mann-Whitney U tests were used to study the differences between SSNHL patients and healthy controls. Associations between brain metrics and clinical variables were studied using multiple linear regressions, and the presence or absence of brain network hubs was assessed using Fisher's exact test. No statistically significant differences between SSNHL patients and healthy controls were found in global or local network measures. However, when analyzing brain networks through the disruption index, we found a brain-wide functional network reorganization (p < 0.001 as compared with controls), whose extent was associated with clinical impairment (p < 0.05). We also observed several functional hubs in SSNHL patients that were not present in healthy controls and vice versa. Our results demonstrate a brain involvement in SSNHL patients, not detectable using conventional graph-theoretical analysis, which may yield subtle disease clues and possibly aid in monitoring disease progression in clinical trials., (© 2021 John Wiley & Sons, Ltd.)

Background: Neuroticism is linked to mood disorders and Alzheimer's disease, but fewer studies have tested the prospective association with Parkinson's disease (PD)., Objectives: To examine the association between neuroticism and risk of PD in a large cohort and a meta-analysis of prospective cohort studies., Methods: Participants from the UK Biobank (N = 490,755) completed a neuroticism scale in 2006-2010. Incident PD was ascertained using electronic health records or death records up to 2018. The systematic search and meta-analysis followed the MOOSE guidelines., Results: During 11.91 years of follow-up (mean = 8.88 years; 4,360,105 person-years) 1142 incident PD cases were identified. Neuroticism was associated with higher risk of incident PD, both as continuous (HR = 1.28; 95% CI: 1.21-1.36) and categorical variable (top vs. bottom quartiles: HR = 1.88; 95% CI: 1.60-2.22). The association remained significant after accounting for age, sex, smoking, physical activity, anxiety, and depressed mood, and after excluding cases that occurred within the first 5 years of follow-up. The associations were similar for women and men and across levels of socioeconomic status. Random-effect meta-analysis of four prospective studies (N = 548,284) found neuroticism associated with increased risk of incident PD (HR = 1.82; 95% CI: 1.59-2.08; P = 7.31 -19 ). There was no evidence of heterogeneity across studies with follow-ups ranging from one to four decades., Conclusion: The results from the large UK Biobank and meta-analysis of prospective studies indicate that neuroticism is consistently associated with a higher risk of incident PD. © 2021 International Parkinson and Movement Disorder Society., (© 2021 International Parkinson and Movement Disorder Society.)

Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are usually performed through different imaging modalities such as mammography, magnetic resonance imaging and ultrasound. However, mammography and ultrasound-imaging techniques have limited sensitivity and specificity both in identifying lesions and in differentiating malign from benign lesions, especially in presence of dense breast parenchyma. Due to the higher resolution of magnetic resonance images, MRI represents the method with the higher specificity and sensitivity among all the available tools, in both lesions' identification and diagnosis. However, especially for diagnosis, even MRI has limitations that are only partially solved if combined with mammography. Unfortunately, due to the limits of all these imaging tools, in order to have a certain diagnosis, patients often receive painful and costly bioptics procedures. In this context, several computational approaches have been developed to increase sensitivity, while maintaining the same specificity, in BC diagnosis and screening. Amongst these, radiomics has been increasingly gaining ground in oncology to improve cancer diagnosis, prognosis and treatment. Radiomics derives multiple quantitative features from single or multiple medical imaging modalities, highlighting image traits which are not visible to the naked eye and hence significantly augmenting the discriminatory and predictive potential of medical imaging. This review article aims to summarize the state of the art in radiomics-based BC research. The dominating evidence extracted from the literature points towards a high potential of radiomics in disentangling malignant from benign breast lesions, classifying BC types and grades and also in predicting treatment response and recurrence risk. In the era of personalized medicine, radiomics has the potential to improve diagnosis, prognosis, prediction, monitoring, image-based intervention, and assessment of therapeutic response in BC., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular image processing and interpretation. This has prompted a wave of disruptingly innovative applications in medical imaging, where DL strategies have the potential to vastly outperform human experts. This is particularly relevant in the context of histopathology, where whole slide imaging (WSI) of stained tissue in conjuction with DL algorithms for their interpretation, selection and cancer staging are beginning to play an ever increasing role in supporting human operators in visual assessments. This has the potential to reduce everyday workload as well as to increase precision and reproducibility across observers, centers, staining techniques and even pathologies. In this paper we introduce the most common DL architectures used in image analysis, with a focus on histopathological image analysis in general and in breast histology in particular. We briefly review how, state-of-art DL architectures compare to human performance on across a number of critical tasks such as mitotic count, tubules analysis and nuclear pleomorphism analysis. Also, the development of DL algorithms specialized to pathology images have been enormously fueled by a number of world-wide challenges based on large, multicentric image databases which are now publicly available. In turn, this has allowed most recent efforts to shift more and more towards semi-supervised learning methods, which provide greater flexibility and applicability. We also review all major repositories of manually labelled pathology images in breast cancer and provide an in-depth discussion of the challenges specific to training DL architectures to interpret WSI data, as well as a review of the state-of-the-art methods for interpretation of images generated from immunohistochemical analysis of breast lesions. We finally discuss the future challenges and opportunities which the adoption of DL paradigms is most likely to pose in the field of pathology for breast cancer detection, diagnosis, staging and prognosis. This review is intended as a comprehensive stepping stone into the field of modern computational pathology for a transdisciplinary readership across technical and medical disciplines., (Copyright © 2020. Published by Elsevier Ltd.)

Background and Purpose: To investigate the reorganization of the central nervous system provided by resting state-functional MRI (rs-fMRI), graph-theoretical analysis, and a newly developed functional brain network disruption index in patients with human immunodeficiency virus (HIV) infection., Methods: Forty HIV-positive patients without neurological impairment and 20 age- and sex-matched healthy controls underwent rs-fMRI at 3T; blood sampling was obtained the same day to evaluate biochemical variables (absolute, relative, and nadir CD4 T-lymphocytes value and plasmatic HIV-RNA). From fMRI data, disruption indices, as well as global and local graph theoretical measures, were estimated and examined for group differences (HIV vs. controls) as well as for associations with biochemical variables (HIV only). Finally, all data (global and local graph-theoretical measures, disruption indices, and biochemical variables) were tested for putative differences across three patient groups based on the duration of combined antiretroviral therapy (cART)., Results: Brain function of HIV patients appeared to be deeply reorganized as compared to normal controls. The disruption index showed significant negative association with relative CD4 values, and a positive significant association between plasmatic HIV-RNA and local graph-theoretical metrics in the left lingual gyrus and the right lobule IV and V of right cerebellar hemisphere was also observed. Finally, a differential distribution of HIV clinical biomarkers and several brain metrics was observed across cART duration groups., Conclusion: Our study demonstrates that rs-fMRI combined with advanced graph theoretical analysis and disruption indices is able to detect early and subtle functional changes of brain networks in HIV patients., (© 2021 American Society of Neuroimaging.)

In multiple sclerosis, individual lesion-type patterns on magnetic resonance imaging might be valuable for predicting clinical outcome and monitoring treatment effects. Neuropathological and imaging studies consistently show that cortical lesions contribute to disease progression. The presence of chronic active white matter lesions harbouring a paramagnetic rim on susceptibility-weighted magnetic resonance imaging has also been associated with an aggressive form of multiple sclerosis. It is, however, still uncertain how these two types of lesions relate to each other, or which one plays a greater role in disability progression. In this prospective, longitudinal study in 100 multiple sclerosis patients (74 relapsing-remitting, 26 secondary progressive), we used ultra-high field 7-T susceptibility imaging to characterize cortical and rim lesion presence and evolution. Clinical evaluations were obtained over a mean period of 3.2 years in 71 patients, 46 of which had a follow-up magnetic resonance imaging. At baseline, cortical and rim lesions were identified in 96% and 63% of patients, respectively. Rim lesion prevalence was similar across disease stages. Patients with rim lesions had higher cortical and overall white matter lesion load than subjects without rim lesions ( P  =   0.018-0.05). Altogether, cortical lesions increased by both count and volume (P = 0.004) over time, while rim lesions expanded their volume ( P  =   0.023) whilst lacking new rim lesions; rimless white matter lesions increased their count but decreased their volume ( P  =   0.016). We used a modern machine learning algorithm based on extreme gradient boosting techniques to assess the cumulative power as well as the individual importance of cortical and rim lesion types in predicting disease stage and disability progression, alongside with more traditional imaging markers. The most influential imaging features that discriminated between multiple sclerosis stages (area under the curve±standard deviation = 0.82 ± 0.08) included, as expected, the normalized white matter and thalamic volume, white matter lesion volume, but also leukocortical lesion volume. Subarachnoid cerebrospinal fluid and leukocortical lesion volumes, along with rim lesion volume were the most important predictors of Expanded Disability Status Scale progression (area under the curve±standard deviation = 0.69 ± 0.12). Taken together, these results indicate that while cortical lesions are extremely frequent in multiple sclerosis, rim lesion development occurs only in a subset of patients. Both, however, persist over time and relate to disease progression. Their combined assessment is needed to improve the ability of identifying multiple sclerosis patients at risk of progressing disease., (© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

This study examines whether neuroticism is differentially associated with risk of incident Alzheimer's disease (AD), vascular dementia (VD), and frontotemporal dementia (FTD) using a prospective study design. Participants from the UK Biobank (N = 401,422) completed a self-report neuroticism scale in 2006-2010 and incident all-cause dementia, AD, VD, and FTD were ascertained using electronic health records or death records up to 2018. During an average follow-up of 8.8 years (3,566,123 person-years), there were 1798 incident of all-cause dementia, 675 AD, 376 VD, and 81 FTD. Accounting for age and sex, compared to individuals in the low quartile, individuals in the top quartile of neuroticism had higher risk of all-cause dementia (HR = 1.70; 95% CI: 1.49-1.93), AD (HR = 1.42; 1.15-1.75), VD (HR = 1.73; 1.30-2.29), but not FTD (HR = 0.89; 0.49-1.63). The associations with AD and VD were attenuated but remained significant after further accounting for education, household income, deprivation index, diabetes, hypertension, stroke, heart attack, ever smoker, physical activity, obesity, hemoglobin A1c, C-reactive protein, and low-density lipoprotein. The associations were not moderated by socioeconomic status. The findings were consistent in analyses that excluded cases that occurred within the first 5 years of follow-up. In conclusion, neuroticism is a robust predictor of incident AD and VD, but not FTD. This pattern suggests that the affective symptoms that distinguish dementia types may partly reflect premorbid differences in trait neuroticism., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Abstract: Using positron emission tomography, we recently demonstrated elevated brain levels of the 18 kDa translocator protein (TSPO), a glial activation marker, in chronic low back pain (cLBP) patients, compared to healthy controls (HCs). Here, we first sought to replicate the original findings in an independent cohort (15 cLBP, 37.8 ± 12.5 y/o; 18 HC, 48.2 ± 12.8 y/o). We then trained random forest machine learning algorithms based on TSPO imaging features combining discovery and replication cohorts (totaling 25 cLBP, 42.4 ± 13.2 y/o; 27 HC, 48.9 ± 12.6 y/o), to explore whether image features other than the mean contain meaningful information that might contribute to the discrimination of cLBP patients and HC. Feature importance was ranked using SHapley Additive exPlanations values, and the classification performance (in terms of area under the curve values) of classifiers containing only the mean, other features, or all features was compared using the DeLong test. Both region-of-interest and voxelwise analyses replicated the original observation of thalamic TSPO signal elevations in cLBP patients compared to HC (P < 0.05). The random forest-based analyses revealed that although the mean is a discriminating feature, other features demonstrate similar level of importance, including the maximum, kurtosis, and entropy. Our observations suggest that thalamic neuroinflammatory signal is a reproducible and discriminating feature for cLBP, further supporting a role for glial activation in human cLBP, and the exploration of neuroinflammation as a therapeutic target for chronic pain. This work further shows that TSPO signal contains a richness of information that the simple mean might fail to capture completely., (Copyright © 2020 International Association for the Study of Pain.)

Alzheimer's disease (AD), the most common form of neurodegenerative dementia in adults worldwide, is a multifactorial and heterogeneous disorder characterized by the interaction of genetic and epigenetic factors and the dysregulation of numerous intracellular signaling and cellular/molecular pathways. The introduction of the systems biology framework is revolutionizing the study of complex diseases by allowing the identification and integration of cellular/molecular pathways and networks of interaction. Here, we reviewed the relationship between physical activity and the next pathophysiological processes involved in the risk of developing AD, based on some crucial molecular pathways and biological process dysregulated in AD: (1) Immune system and inflammation; (2) Endothelial function and cerebrovascular insufficiency; (3) Apoptosis and cell death; (4) Intercellular communication; (5) Metabolism, oxidative stress and neurotoxicity; (6) DNA damage and repair; (7) Cytoskeleton and membrane proteins; (8) Synaptic plasticity. Moreover, we highlighted the increasingly relevant role played by advanced neuroimaging technologies, including structural/functional magnetic resonance imaging, diffusion tensor imaging, and arterial spin labelling, in exploring the link between AD and physical exercise. Regular physical exercise seems to have a protective effect against AD by inhibiting different pathophysiological molecular pathways implicated in AD.

A good quality and amount of sleep are fundamental to preserve cognition and affect. New evidence also indicates that poor sleep is detrimental to brain myelination. In this study, we test the hypothesis that sleep quality and/or quantity relate to variability in cognitive and emotional function via the mediating effect of interindividual differences in proxy neuroimaging measures of white matter integrity and intracortical myelination. By employing a demographically and neuropsychologically well-characterized sample of healthy people drawn from the Human Connectome Project (n = 974), we found that quality and amount of sleep were only marginally linked to cognitive performance. In contrast, poor quality and short sleep increased negative affect (i.e. anger, fear, and perceived stress) and reduced life satisfaction and positive emotionality. At the brain level, poorer sleep quality and shorter sleep duration related to lower intracortical myelin in the mid-posterior cingulate cortex (p = 0.038), middle temporal cortex (p = 0.024), and anterior orbitofrontal cortex (OFC, p = 0.034) but did not significantly affect different measures of white matter integrity. Finally, lower intracortical myelin in the OFC mediated the association between poor sleep quality and negative emotionality (p < 0.05). We conclude that intracortical myelination is an important mediator of the negative consequences of poor sleep on affective behavior., (© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society.)

Background: Coronavirus disease 2019 (COVID-19) is characterized by immune-mediated lung injury and complex alterations of the immune system, such as lymphopenia and cytokine storm, that have been associated with adverse outcomes underlining a fundamental role of host response in severe acute respiratory syndrome coronavirus 2 infection and the pathogenesis of the disease. Thymosin alpha 1 (Tα1) is one of the molecules used in the management of COVID-19, because it is known to restore the homeostasis of the immune system during infections and cancer., Methods: In this study, we captured the interconnected biological processes regulated by Tα1 in CD8+ T cells under inflammatory conditions., Results: Genes associated with cytokine signaling and production were upregulated in blood cells from patients with COVID-19, and the ex vivo treatment with Tα1-mitigated cytokine expression, and inhibited lymphocyte activation in a CD8+ T-cell subset specifically., Conclusion: These data suggest the potential role of Tα1 in modulating the immune response homeostasis and the cytokine storm in vivo., (© The Author(s) 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Unlike other sensory systems, the structural connectivity patterns of the human vestibular cortex remain a matter of debate. Based on their functional properties and hypothesized centrality within the vestibular network, the 'core' cortical regions of this network are thought to be areas in the posterior peri-sylvian cortex, in particular the retro-insula (previously named the posterior insular cortex-PIC), and the subregion OP2 of the parietal operculum. To study the vestibular network, structural connectivity matrices from n=974 healthy individuals drawn from the public Human Connectome Project (HCP) repository were estimated using multi-shell diffusion-weighted data followed by probabilistic tractography and spherical-deconvolution informed filtering of tractograms in combination with subject-specific grey-matter parcellations. Weighted graph-theoretical measures, modularity, and 'hubness' of the multimodal vestibular network were then estimated, and a structural lateralization index was defined in order to assess the difference in fiber density of homonym regions in the right and left hemisphere. Differences in connectivity patterns between OP2 and PIC were also estimated. We found that the bilateral intraparietal sulcus, PIC, and to a lesser degree OP2, are key 'hub' regions within the multimodal vestibular network. PIC and OP2 structural connectivity patterns were lateralized to the left hemisphere, while structural connectivity patterns of the posterior peri-sylvian supramarginal and superior temporal gyri were lateralized to the right hemisphere. These lateralization patterns were independent of handedness. We also found that the structural connectivity pattern of PIC is consistent with a key role of PIC in visuo-vestibular processing and that the structural connectivity pattern of OP2 is consistent with integration of mainly vestibular somato-sensory and motor information. These results suggest an analogy between PIC and the simian visual posterior sylvian (VPS) area and OP2 and the simian parieto-insular vestibular cortex (PIVC). Overall, these findings may provide novel insights to the current models of vestibular function, as well as to the understanding of the complexity and lateralized signs of vestibular syndromes., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Since its beginning at the end of 2019, the pandemic spread of the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2) caused more than one million deaths in only nine months. The threat of emerging and re-emerging infectious diseases exists as an imminent threat to human health. It is essential to implement adequate hygiene best practices to break the contagion chain and enhance society preparedness for such critical scenarios and understand the relevance of each disease transmission route. As the unconscious hand-face contact gesture constitutes a potential pathway of contagion, in this paper, the authors present a prototype system based on low-cost depth sensors able to monitor in real-time the attitude towards such a habit. The system records people's behavior to enhance their awareness by providing real-time warnings, providing for statistical reports for designing proper hygiene solutions, and better understanding the role of such route of contagion. A preliminary validation study measured an overall accuracy of 91%. A Cohen's Kappa equal to 0.876 supports rejecting the hypothesis that such accuracy is accidental. Low-cost body tracking technologies can effectively support monitoring compliance with hygiene best practices and training people in real-time. By collecting data and analyzing them with respect to people categories and contagion statistics, it could be possible to understand the importance of this contagion pathway and identify for which people category such a behavioral attitude constitutes a significant risk.

The identification of efficient markers of disease progression and response to possibly effective treatments is a key priority for slowly progressive, rare and neurodegenerative diseases, such as Friedreich's ataxia. Various imaging modalities have documented specific abnormalities in Friedreich's ataxia that could be tracked to provide useful indicators of efficacy in clinical trials. Advanced MRI imaging (diffusion tensor imaging, DTI; functional MRI, fMRI; and resting-state fMRI, rs-fMRI) and retinal imaging (optical coherence tomography, OCT) were tested longitudinally in a small group of Friedreich's ataxia patients participating in an open-label clinical trial testing the safety and the efficacy of 6-month treatment with interferon gamma. While the DTI indices documented the slow progression of fractional anisotropy loss, fMRI and rs-fMRI were significantly modified during and after treatment. The fMRI changes significantly correlated with the Scale for the Assessment and Rating of Ataxia, which is used to monitor clinical response. OCT documented the known thickness reduction of the retinal nerve fiber layer thickness, but there was no change over time. This pilot study provides indications for the potential utility of fMRI and rs-fMRI as ancillary measures in clinical trials for Friedreich's ataxia., (Copyright © 2020 Vavla, Arrigoni, Toschi, Peruzzo, D’Angelo, Gandossini, Russo, Diella, Tirelli, Salati, Rufini, Condo, Testi and Martinuzzi.)