Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes.

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation.

The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Multishell diffusion MRI reveals whole-brain white matter changes in HIV.

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.

Neuroanatomical markers of familial risk in adolescents with conduct disorder and their unaffected relatives.

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.

Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study.

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 mm2. 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.

Identifying structural brain markers of resilience to adversity in young people using voxel-based morphometry.

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 (Mage = 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.

Preliminary Evidence of the Differential Expression of Human Endogenous Retroviruses in Kawasaki Disease and SARS-CoV-2-Associated Multisystem Inflammatory Syndrome in Children.

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.

Radiomics in breast cancer classification and prediction.

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.

Deep computational pathology in breast cancer.

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.

Frataxin deficiency in Friedreich's ataxia is associated with reduced levels of HAX-1, a regulator of cardiomyocyte death and survival.

Frataxin deficiency, responsible for Friedreich's ataxia (FRDA), is crucial for cell survival since it critically affects viability of neurons, pancreatic beta cells and cardiomyocytes. In FRDA, the heart is frequently affected with typical manifestation of hypertrophic cardiomyopathy, which can progress to heart failure and cause premature death. A microarray analysis performed on FRDA patient's lymphoblastoid cells stably reconstituted with frataxin, indicated HS-1-associated protein X-1 (HAX-1) as the most significantly upregulated transcript (FC = +2, P < 0.0006). quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) and western blot analysis performed on (I) HEK293 stably transfected with empty vector compared to wild-type frataxin and (II) lymphoblasts from FRDA patients show that low frataxin mRNA and protein expression correspond to reduced levels of HAX-1. Frataxin overexpression and silencing were also performed in the AC16 human cardiomyocyte cell line. HAX-1 protein levels are indeed regulated through frataxin modulation. Moreover, correlation between frataxin and HAX-1 was further evaluated in peripheral blood mononuclear cells (PBMCs) from FRDA patients and from non-related healthy controls. A regression model for frataxin which included HAX-1, group membership and group* HAX-1 interaction revealed that frataxin and HAX-1 are associated both at mRNA and protein levels. Additionally, a linked expression of FXN, HAX-1 and antioxidant defence proteins MnSOD and Nrf2 was observed both in PBMCs and AC16 cardiomyocytes. Our results suggest that HAX-1 could be considered as a potential biomarker of cardiac disease in FRDA and the evaluation of its expression might provide insights into its pathogenesis as well as improving risk stratification strategies.

Modulation of human endogenous retroviruses and cytokines expression in peripheral blood mononuclear cells from autistic children and their parents.

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.

Renal Function, Adherence and Quality of Life Improvement After Conversion From Immediate to Prolonged-Release Tacrolimus in Liver Transplantation: Prospective Ten-Year Follow-Up Study.

Immunosuppression non-adherence is a major cause of graft failure after liver transplantation. The aim of this study was to evaluate practice surrounding conversion from immediate-release to prolonged-release Tacrolimus formulation and to assess patient adherence and quality of life (QoL). One hundred and seven adult liver transplant recipients, receiving immediate-release Tacrolimus for a minimum of 6 months, were converted to prolonged-release formulation, based on a dose ratio of one (1:1). The median follow-up was 120 [IQR, 120-123] months. Tacrolimus dosage and blood level, liver and renal function, lipid and glucose profiles were recorded. In addition, questionnaires were submitted to evaluate adherence and QoL following conversion. No rejection was recorded. The median serum Tacrolimus blood level decreased over 1 month (5.80, [IQR, 2.0-10.8] vs. 3.8 [IQR, 1.4-8.7]; p < 0.0005). Significant improvement in renal function was noted (median GFR was 81.7 [IQR, 43.4-128.6] vs. 73.9 [IQR, 27.1-130.2]; p = 0.0002). At the end of the follow-up, conversion resulted in an overall decrease in non-adherence of 53.3% (p = 0.0001) and an improvement in QoL was reported by 76.2% of patients. Thus, 1:1 conversion from immediate to prolonged-release Tacrolimus is safe, feasible and efficient, avoiding under-therapeutic and toxic peak concentrations, improving renal function, adherence to immunosuppression and overall patient QoL.

Invasive aspergillosis in relapsed/refractory acute myeloid leukaemia patients: Results from SEIFEM 2016-B survey.

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.

Differential Expression of Endogenous Retroviruses and Inflammatory Mediators in Female and Male Offspring in a Mouse Model of Maternal Immune Activation.

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.

Global and local brain connectivity changes associated with sudden unilateral sensorineural hearing loss.

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.

Neuroticism and Risk of Parkinson's Disease: A Meta-Analysis.

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.

Echo state network models for nonlinear Granger causality.

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'.

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'.

A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms.

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'.

Physical Exercise and Alzheimer's Disease: Effects on Pathophysiological Molecular Pathways of the Disease.

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.