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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.)

Purpose: To evaluate whether an anatomic dry shoulder Dexter training model surgical simulator would be effective in augmenting orthopaedic residents' skills for arthroscopic rotator cuff repair., Methods: Nine residents, enrolled for this prospective study, watched a video presentation about transosseus equivalent (TOE) double-row, rotator cuff repair technique, repeating the aforementioned technique twice on the simulator. The time to complete all steps to final repair, as well as the quality of the final repair, were measured., Results: The data for repair completion time showed a statistically significant difference in the total duration time of performing the procedure, when comparing the timing in tests 1 and 2, demonstrating a reduced operative time. The qualitative data collected show an increase in the final score in 100% (9 out of 9) of the participants between the first and second tests, with an improvement in the average score of 16.4% (3.22 points with a standard deviation of 2.64). Hence, there is a statistically significant difference ( P  = .006) between the total scores obtained by the participants at the end of tests 1 and 2, presenting an improvement of the quality score, shifting from the first (average score 19.7) to the second (average score 22.9) test. Conclusion: The dry simulator may be a valuable device to improve arthroscopic technical skills. This improvement was observed both in the resulting reduced operative time required to carry out the surgical procedure and the increased quality score (established to evaluate the quality of the required tasks)., Clinical Relevance: In order to provide as many training opportunities as possible, an arthroscopy simulator could be used to supplement clinical arthroscopy skills training for orthopaedic residents., (© 2022 The Authors.)

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'.

Purpose: To evaluate the accuracy of rigid coregistration between multiparametric magnetic resonance (mpMR) and computed tomography (CT) images for radiotherapy of prostate bed cancer recurrence., Materials and Method: Fifty-three patients (59 nodules) accrued in a prospective study on salvage radiotherapy for prostatic bed recurrence were suitable for the analysis. Patients underwent a pre radiotherapy mpMR exam and a planning CT in the same treatment position and with control of organ filling. The site of recurrence was delineated on mpMR images and contours transferred on planning CT images using both rigid and deformable registrations. Coregistrations were evaluated by mathematical operators that quantify deformation (Jacobian determinant and vector curl) and similarity indices (Dice and Jaccard coefficients). Dose coverage was evaluated., Results: Deformable registration did not change volumes, (p = 0.92 MW test). The Jacobian coefficient and the vector curl revealed no important image deformations. Dice and Jaccard coefficients indicated dislocation of the nodule volumes. Dislocation magnitude was d = (5.6 ± 3.1) mm. Organ filling was not correlated with deformation or dislocation. Volumes were covered by the 95% isodose in 96% of cases when rigid registration was performed versus 75% of cases when deformed., Conclusions: Rigid image coregistration is sufficiently accurate in this setting. The results indicate that the deformable registration tends to shrink the voxels and to dislocate the ROI, the adopted expansion for the recurrence volume adequately accounts for the observed deformation and dislocation, provided that organ filling is controlled., (Copyright © 2021 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

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.

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%).

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.)

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.)

Introduction: Different surgical techniques (open and arthroscopic) have been described for the treatment of post-traumatic recurrent anterior instability. The aim of the surgery is to restore when possible, normal shoulder anatomy by repairing the underlying pathology responsible for the instability. Sometimes other surgical techniques are indicated. The purpose of this retrospective study was to investigate the long-term clinical and radiographic results and complications of the open Latarjet procedure after a minimum follow-up of 24 years., Materials and Methods: A retrospective study was performed for 67 patients treated with an open Latarjet procedure in a single center. Forty of these 67 patients returned for follow-up evaluation and clinical/radiological examination during the year 2018, having had a minimum of 24-year follow-up. Clinical outcomes were analyzed using two functional scores, in addition to the ROM and strength assessment. Radiographic evaluation included several views (AP views in neutral, internal and external rotation and a comparative Bernageau view) RESULTS: A total of 40 patients underwent an open Latarjet procedure. All the patients were avaible for follow-up at an average of 25.6 years. Clinically, no patient reported any episode of dislocation at the time of follow-up. The mean Rowe score and the Walch-Duplay score were 84.5 (range 45-100) and 83.5 (range 55-100), respectively. Non-union/fibrous union was reported in 12.5% of cases, partial resorption of the graft was found in 7.5% of cases, while total resorption was found in 5% of cases. Osteoarthritis was identified in 52.5% (21) of the patients., Conclusions: This long-term follow-up study demonstrated that the open Latarjet procedure is a safe and reliable technique for recurrent anterior shoulder instability. The Latarjet procedure provides good long-term stability although associated with a slight limitation in external rotation., Level of Evidence: Level III; retrospective cohort comparison; treatment study.

Introduction: Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures. There are few data about ictal ANS activity alterations induced by PNES in patients with pure PNES (pPNES) compared to PNES with comorbid epilepsy (PNES/ES). We aimed to compare heart rate variability (HRV) parameters and hence autonomic regulation in PNES in epileptic and non-epileptic patients. Methods : We obtained HRV data from video-electroencephalography recordings in 22 patients presenting PNES (11 pPNES and 11 PNES/ES) in awake, and supine states. We calculated HRV parameters in both time and frequency domains including low frequency (LF) power, high frequency power (HF), LF/HF ratio, square root of the mean of the sum of the squares of differences between adjacent R wave intervals (RMSSD) and the standard deviation of all consecutive R wave intervals (SDNN). We also evaluated approximate entropy (ApEn), cardiosympathetic index (CSI), and cardiovagal index (CVI). Four conditions were considered: basal condition (BAS), before PNES (PRE), during PNES (ICT) and after PNES (POST). Results: HRV analysis showed significantly higher ICT LF and LF/HF ratio vs. each condition. We also found higher POST HF vs. PRE and BAS, lower RRI in ICT vs. each condition and PRE vs. BAS. POST RMSSD was significantly higher compared to all other states. ICT CSI was significantly higher compared to all other states, whereas CSI was significantly lower in POST vs. PRE and PRE CVI lower than ICT and higher in POST vs. BAS and PRE. Also, ICT ApEn was lower than in all other states. Higher LF in pPNES vs. PNES/ES was also evident when compared across groups. Significance: A few studies examined HRV alterations in PNES, reporting high sympathetic tone (although less evident than in epileptic seizures). Our data suggest a sympathetic overdrive before and during PNES followed by a post-PNES increase in vagal tone. A sympathovagal imbalance was more evident in pPNES as compared to PNES/ES., (Copyright © 2020 Romigi, Ricciardo Rizzo, Izzi, Guerrisi, Caccamo, Testa, Centonze, Mercuri and Toschi.)

In 2019, approximately 38 million people were living with human immunodeficiency virus (HIV). Combined antiretroviral therapy (cART) has determined a change in the course of HIV infection, transforming it into a chronic condition which results in cumulative exposure to antiretroviral drugs, inflammatory effects and aging. Relatedly, at least one quarter of HIV-infected patients suffer from cognitive, motor and behavioral disorder, globally known as HIV-associated neurocognitive disorders (HAND). In this context, objective, neuroimaging-based biomarkers are therefore highly desirable in order to detect, quantify and monitor HAND in all disease stages. In this study, we employed functional MRI in conjunction with graph-theoretical analysis as well as a newly developed functional brain network disruption index to assess a putative functional reorganization in HIV positive patients. We found that brain function of HIV patients is deeply reorganized as compared to normal controls. Interestingly, the regions in which we found reorganized hubs are integrated into neuronal networks involved in working memory, motor and executive functions often altered in patients with HAND. Overall, our study demonstrates that rs-fMRI combined with advanced graph theoretical analysis and disruption indices is able to detect early, subtle functional changes of brain networks in HIV patients before structural changes become evident.

Primary open angle glaucoma (POAG) is one of the most common causes of permanent blindness in the world. Recent studies have originated the hypothesis that POAG could be considered as a central nervous system pathology which results in secondary visual involvement. The aim of this study is to assess possible structural whole brain connectivity alterations in POAG by combining multi-shell diffusion weighted imaging, multi-shell multi-tissue probabilistic tractography, graph theoretical measures and a newly designed disruption index, which evaluates the global reorganization of brain networks in group-wise comparisons. We found global differences in structural connectivity between Glaucoma patients and controls, as well as in local graph theoretical measures. These changes extended well beyond the primary visual pathway. Furthermore, group-wise and subject-wise disruption indices were found to be statistically different between glaucoma patients and controls, with a positive slope. Overall, our results support the hypothesis of a whole-brain structural reorganization in glaucoma which is specific to structural connectivity, possibly placing this disease within the recently defined groups of brain disconnection syndrome.

Recent reports suggested that even moderate sudden sensorineural hearing loss (SSNHL) can be partly responsible for a loss of gray matter volume in the primary auditory cortex, hence reducing the capacity of the auditory cortical areas to react to sound stimulation. There is also evidence for a plastic reorganization of brain functional networks visible as enhanced local functional connectivity. The aim of this study was to use 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 acute and unilateral sensorineural hearing loss. No statistically significant differences in global or local network measures we found between SSNHL patients and healthy controls. However, when analyzing local metrics through the disruption index k, we found negative values for k which were statistically different from zero both in single subject analysis. Additionally, we found several associations between graph-theoretical metrics and clinical parameters.

Purpose: To evaluate the short-term outcomes of rotator cuff repair in the presence of a greater tuberosity cyst (GTC) using a transosseous repair technique., Methods: This study included patients who underwent arthroscopic rotator cuff tear repair with a transosseous technique and were evaluated clinically and by postoperative magnetic resonance imaging (MRI) after 1 year. The inclusion criteria were based on the results of preoperative MRI and were as follows: patients identified as having a repairable full-thickness rotator cuff tear associated with the presence of cystic changes at the tendon insertion site of the greater tuberosity, defined as a GTC involving the footprint area of the torn tendon (supraspinatus and/or infraspinatus tendons)., Results: We evaluated 25 patients. The mean preoperative and postoperative American Shoulder and Elbow Surgeons scores were 39.48 ( P  = .530) and 84.64 ( P  = .035), respectively; Constant shoulder scores, 38.96 ( P < .005) and 80.28 ( P  = .425), respectively; and University of California-Los Angeles shoulder rating scale scores, 10.6 ( P  = .045) and 29.04 ( P  = .315), respectively. The GTC mapping system was easily adopted in all the MRI examinations independently from the quality of the images. The GTCs were mostly located in the superficial anterolateral section of the humeral head and in both the posterolateral sections (superficial and deep)., Conclusions: Arthroscopic transosseous rotator cuff repair led to significant mid-term improvement and satisfactory subjective outcomes with low complication and failure rates in this study. The GTC mapping system could be useful to evaluate GTCs and to aid surgeons in the choice of the best surgical technique., Level of Evidence: Level IV, therapeutic case series., (© 2020 by the Arthroscopy Association of North America. Published by Elsevier Inc.)

Background: Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly employed to study changes in functional brain connectivity. The recent hypothesis of a brain involvement in primary open angle Glaucoma has sprung interest for neuroimaging studies in this classically ophthalmological pathology. Object: We explored a putative reorganization of functional brain networks in Glaucomatous patients, and evaluated the potential of functional network disruption indices as biomarkers of disease severity in terms of their relationship to clinical variables as well as select retinal layer thicknesses. Methods: Nineteen Glaucoma patients and 16 healthy control subjects (age: 50-76, mean 61.0 ± 8.2 years) underwent rs-fMRI examination at 3T. After preprocessing, rs-fMRI time series were parcellated into 116 regions using the Automated Anatomical Labeling atlas and adjacency matrices were computed based on partial correlations. Graph-theoretical measures of integration, segregation and centrality as well as group-wise and subject-wise disruption index estimates (which use regression of graph-theoretical metrics across subjects to quantify overall network changes) were then generated for all subjects. All subjects also underwent Optical Coherence Tomography (OCT) and visual field index (VFI) quantification. We then examined associations between brain network measures and VFI, as well as thickness of retinal nerve fiber layer (RNFL) and macular ganglion cell layer (MaculaGCL). Results: In Glaucoma, group-wise disruption indices were negative for all graph theoretical metrics. Also, we found statistically significant group-wise differences in subject-wise disruption indexes in all local metrics. Two brain regions serving as hubs in healthy controls were not present in the Glaucoma group. Instead, three hub regions were present in Glaucoma patients but not in controls. We found significant associations between all disruption indices and VFI, RNFL as well as MaculaGCL. The disruption index based on the clustering coefficient yielded the best discriminative power for differentiating Glaucoma patients from healthy controls [Area Under the ROC curve (AUC) 0.91, sensitivity, 100%; specificity, 78.95%]. Conclusions: Our findings support a possible relationship between functional brain changes and disease severity in Glaucoma, as well as alternative explanations for motor and cognitive symptoms in Glaucoma, possibly pointing toward an inclusion of this pathology in the heterogeneous group of disconnection syndromes., (Copyright © 2019 Minosse, Garaci, Martucci, Lanzafame, Di Giuliano, Picchi, Cesareo, Mancino, Guerrisi, Pistolese, Floris, Nucci and Toschi.)

A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.

While Granger Causality(GC)-based approaches have been widely employed in a vast number of problems in network science, the vast majority of GC applications are based on linear multivariate autoregressive (MVAR) models. However, it is well known that real-life system (and biological networks in particular) exhibit notable nonlinear behavior, hence undermining that validity of MVAR-based approaches to estimating GC (MVAR-GC). In this paper, we define a novel approach to estimating nonlinear, directed within-network interactions based on a specific class of recurrent neural networks (RNN) termed echo-state networks (ESN). We reformulate the classical GC framework in terms of ESN-based models for multivariate signals generated by arbitrarily complex networks, and characterize the ability of our ESN-based Granger Causality (ES-GC) to capture nonlinear causal relations by simulating multivariate coupling in a network of nonlinearly interacting, noisy Duffing oscillators operating in a chaotic regime. Synthetic validation shows a net advantage of ES-GC over all other estimators in detecting nonlinear, causal links. We then explore the structure of EC-GC networks in the human brain in functional MRI data from 1003 healthy subjects scanned at rest at 3T, discovering previously unknown between-network interactions. In summary, ES-GC performs significantly better than commonly used and recently developed GC detection tools, making it a superior tool for the analysis of e.g. multivariate biological networks.

Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly employed to study changes in functional brain connectivity. Recently, the hypothesis of a brain involvement in primary open angle glaucoma has sprung interest for neuroimaging studies in this pathology. The purpose of this study is to evaluate a putative reorganization of brain networks in glaucomatous patients through graph-theoretical measures of integration, segregation and centrality by exploiting a multivariate networks association measure and a recently introduced global and local brain network disruption index. Nineteen glaucoma patients and sixteen healthy control subjects (age: 50 - 76, mean 61 years) underwent rs-fMRI examination at 3T. After preprocessing, rs-fMRI time series were parcellated into 116 regions (AAL atlas), adjacency matrices were computed based on partial correlations and graph-theoretical measures of integration, segregation and centrality as well as group-wise and subject-wise disruption index estimates were generated for all subjects. We found that the group-wise disruption index was negative and statistically different from 0 in for all graph theoretical metrics. Additionally, statistically significant group-wise differences in subject-wise disruption indexes were found in all local metrics. The differences in local network measures highlight cerebral reorganization of brain networks in glaucoma patients, supporting the interpretation of glaucoma as central nervous system disease, likely part of the heterogeneous group of recently described disconnection syndromes.

Breast cancer is one of the most common cancer in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide. Recent studies show that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long-term. While the consequences of a false positive diagnosis can be psychologically and socioeconomically burdensome, the result of a false negative diagnosis can be devastating, especially in terms of health detriment. In this context, the false positive and false negative rates commonly achieved by radiologists are extremely arduous to estimate and control, and some authors have estimated figures of up to 20% of total diagnoses or more. Novel ideas in computer-assisted diagnosis have been prompted by the introduction of deep learning techniques in general and of convolutional neural networks (CNN) in particular. In this paper, we design and validate an ad-hoc CNN architecture specialized in breast lesion classification and heuristically explore possible parameter combinations and architecture styles in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve good classification performance on the validation and test set, demonstrating how an ad-hoc, random initialization CNN architecture can provide practical aid in the classification and staging of breast cancer.

High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g., data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signals often present long-range dependencies which commonly require high autoregressive model orders/number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener-Volterra decompositions with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions.

Breast cancer is one of the most common cancers in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide. In this context, recent studies showed that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long term. X-ray mammography is still the instrument of choice in breast cancer screening. In this context, the false-positive and false-negative rates commonly achieved by radiologists are extremely arduous to estimate and control although some authors have estimated figures of up to 20% of total diagnoses or more. The introduction of novel artificial intelligence (AI) technologies applied to the diagnosis and, possibly, prognosis of breast cancer could revolutionize the current status of the management of the breast cancer patient by assisting the radiologist in clinical image interpretation. Lately, a breakthrough in the AI field has been brought about by the introduction of deep learning techniques in general and of convolutional neural networks in particular. Such techniques require no a priori feature space definition from the operator and are able to achieve classification performances which can even surpass human experts. In this paper, we design and validate an ad hoc CNN architecture specialized in breast lesion classification from imaging data only. We explore a total of 260 model architectures in a train-validation-test split in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve an area under the receiver operatic characteristics curve of 0.785 (accuracy 71.19%) on the test set, demonstrating how an ad hoc random initialization architecture can and should be fine tuned to a specific problem, especially in biomedical applications.

The use of Multivariate Granger Causality (MVGC) in estimating directed Blood-Oxygen-Level- Dependant (BOLD) connectivity is still controversial. This is mostly due to the short data Ienghts typically available in func- tional MRI (fMRI) acquisitions, to the very nature of the BOLD acquisition strategy (which yields extremely low signal- to-noise-ratio) and importantly to the fact that neuronal activi- ty is convolved with a slow-varying haemodynamic response function (HRF) which therefore generates a temporal confound which is arduous to account for when basing MVGC estimates on vector autoregressive models (VAR). In this paper, we em- ploy realistic complex network models based on Izhikevich neuronal populations, interlinked by realistic neuronal fiber bundles which exert compounded directed influences and cas- cade into Baloon-model-like neurovascular coupling, to explore and validate the MVGC approach to directed connectivity es- timation in realistic fMRI conditions and in a complex directed network setting. In particular, we show in silico that the top 1 percentile of a BOLD connectivity matrix estimated with MVGC from BOLD data similar to the one provided by the Human Connectome Project (HCP) has a Positive Predictive Value very close to 1, hence corroborating the evidence that the "strongest" connections can be safely studied with this method in fMRI.

While a large body of research has focused on the study of functional brain "connectivity", few investigators have focused on directionality of brain-brain interactions which, in spite of the mostly bidirectional anatomical substrates, cannot be assumed to be symmetrical. We employ a multivariate Granger Causality-based approach to estimating directed in-network interactions and quantify its advantages using extensive realistic synthetic BOLD data simulations to match Human Connectome Project (HCP) data specification. We then apply our framework to resting state functional MRI (rs-fMRI) data provided by the HCP to estimate the directed connectome of the human brain. We show that the functional interactions between parietal and prefrontal cortices commonly observed in rs-fMRI studies are not symmetrical, but consists of directional connectivity from parietal areas to prefrontal cortices rather than vice versa. These effects are localized within the same hemisphere and do not generalize to cross-hemispheric functional interactions. Our data are consistent with neurophysiological evidence that posterior parietal cortices involved in processing and integration of multi-sensory information modulate the function of more anterior prefrontal regions implicated in action control and goal-directed behaviour. The directionality of functional connectivity can provide an additional layer of information in interpreting rs-fMRI studies both in health and disease.

Major surgeries can result in high rates of adverse postoperative events. Reliable prediction of which patient might be at risk for such events may help guide peri- and postoperative care. We show how archiving and mining of intraoperative hemodynamic data in orthotopic liver transplantation (OLT) can aid in the prediction of postoperative 180-day mortality and acute renal failure (ARF), improving upon predictions that rely on preoperative information only. From 101 patient records, we extracted 15 preoperative features from clinical records and 41 features from intraoperative hemodynamic signals. We used logistic regression with leave-one-out cross-validation to predict outcomes, and incorporated methods to limit potential model instabilities from feature multicollinearity. Using only preoperative features, mortality prediction achieved an area under the receiver operating characteristic curve (AUC) of 0.53 (95% CI: 0.44-0.78). By using intraoperative features, performance improved significantly to 0.82 (95% CI: 0.56-0.91, P = 0.001). Similarly, including intraoperative features (AUC = 0.82; 95% CI: 0.66-0.94) in ARF prediction improved performance over preoperative features (AUC = 0.72; 95% CI: 0.50-0.85), though not significantly (P = 0.32). We conclude that inclusion of intraoperative hemodynamic features significantly improves prediction of postoperative events in OLT. Features strongly associated with occurrence of both outcomes included greater intraoperative central venous pressure and greater transfusion volumes.

Recently, the field of functional brain connectivity has shifted its attention on studying how functional connectivity (FC) between remote regions changes over time. It is becoming increasingly evident that the human "connectome" is a dynamical entity whose variations are effected over very short timescales and reflect crucial mechanisms which underline the physiological functioning of the brain. In this study, we employ ad-hoc statistical and surrogate data generation methods to quantify whether and which brain networks displayed dynamic behaviors in a very large sample of healthy subjects provided by the Human Connectome Project (HCP). Our findings provided evidences that there are specific pairs of networks and specific networks within the healthy brain that are more likely to display dynamic behaviors. This new set of findings supports the notion that studying the time-variant connectivity in the brain could reveal useful and important properties about brain functioning in health and disease.

It has recently become evident that the functional connectome of the human brain is a dynamical entity whose time evolution carries important information underpinning physiological brain function as well as its disease-related aberrations. While simple sliding window approaches have had some success in estimating dynamical brain connectivity in a functional MRI (fMRI) context, these methods suffer from limitations related to the arbitrary choice of window length and limited time resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models have been employed to generate dynamical covariance models which can be applied to fMRI. Here, we employ a GARCH-based method (dynamic conditional correlation - DCC) to estimate dynamical brain connectivity in the Human Connectome Project (HCP) dataset and study how the dynamic functional connectivity behaviors related to personality as described by the five-factor model. Openness, a trait related to curiosity and creativity, is the only trait associated with significant differences in the amount of time-variability (but not in absolute median connectivity) of several inter-network functional connections in the human brain. The DCC method offers a novel window to extract dynamical information which can aid in elucidating the neurophysiological underpinning of phenomena to which conventional static brain connectivity estimates are insensitive.

While a large body of research has focused on the study of within-brain physiological networks (i.e. brain connectivity) as well as their disease-related aberration, few investigators have focused on estimating the directionality of these brain-brain interaction which, given the complexity of brain networks, should be properly conditioned in order to avoid the high number of false positives commonly encountered when using bivariate approaches to brain connectivity estimation. Additionally, the constituents of a number of brain subnetworks, and in particular of the central autonomic network (CAN), are still not completely determined. In this study we present and validate a global conditioning approach to reconstructing directed networks using complex synthetic networks of nonlinear oscillators. We then employ our framework, along with a probabilistic model for heartbeat generation, to characterize the directed functional connectome of the human brain and to establish which parts of this connectome effect the directed central modulation of peripheral autonomic cardiovascular control. We demonstrate the effectiveness of our conditioning approach and unveil a top-down directed influence of the default mode network on the salience network, which in turn is seen to be the strongest modulator of directed autonomic cardiovascular control.

In recent years, the study of the human connectome (i.e. of statistical relationships between non spatially contiguous neurophysiological events in the human brain) has been enormously fuelled by technological advances in high-field functional magnetic resonance imaging (fMRI) as well as by coordinated world wide data-collection efforts like the Human Connectome Project (HCP). In this context, Granger Causality (GC) approaches have recently been employed to incorporate information about the directionality of the influence exerted by a brain region on another. However, while fluctuations in the Blood Oxygenation Level Dependent (BOLD) signal at rest also contain important information about the physiological processes that underlie neurovascular coupling and associations between disjoint brain regions, so far all connectivity estimation frameworks have focused on central tendencies, hence completely disregarding so-called in-variance causality (i.e. the directed influence of the volatility of one signal on the volatility of another). In this paper, we develop a framework for simultaneous estimation of both in-mean and in-variance causality in complex networks. We validate our approach using synthetic data from complex ensembles of coupled nonlinear oscillators, and successively employ HCP data to provide the very first estimate of the in-variance connectome of the human brain.

Symptoms of temporal lobe epilepsy (TLE) are frequently associated with autonomic dysregulation, whose underlying biological processes are thought to strongly contribute to sudden unexpected death in epilepsy (SUDEP). While abnormal cardiovascular patterns commonly occur during ictal events, putative patterns of autonomic cardiac effects during pre-ictal (PRE) periods (i.e. periods preceding seizures) are still unknown. In this study, we investigated TLE-related heart rate variability (HRV) through instantaneous, nonlinear estimates of cardiovascular oscillations during inter-ictal (INT) and PRE periods. ECG recordings from 12 patients with TLE were processed to extract standard HRV indices, as well as indices of instantaneous HRV complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra) obtained through definition of inhomogeneous point-process nonlinear models, employing Volterra-Laguerre expansions of linear, quadratic, and cubic kernels. Experimental results demonstrate that the best INT vs. PRE classification performance (balanced accuracy: 73.91%) was achieved only when retaining the time-varying, nonlinear, and non-stationary structure of heartbeat dynamical features. The proposed approach opens novel important avenues in predicting ictal events using information gathered from cardiovascular signals exclusively.

Nanoparticle (NP) toxicity is determined by a vast number of topological, sterical, physico-chemical as well as biological properties, rendering a priori evaluation of the effect of NP on biological tissue as arduous as it is necessary and urgent. We aimed at mining the HORIZON 2020 MODENA COST NP cytotoxicity database through nonlinear predictive regressor learning systems in order to assess the power of available NP descriptors and assay characteristics in predicting NP toxicity. Specifically, we assessed the results of cytotoxicity assays performed on 57 NP and trained two different nonlinear regressors (Support Vector Regressors [SVR] with polynomical kernels and Radial Basis Function [RBF] regressors) within a nested-cross validation scheme for parameter optimization to predict toxicity as quantified by EC25, EC50 and slope while using the regressional ReliefF algorithm (RReliefF) for feature selection. Available NP attributes were material, coating, cell type, dispersion protocol, shape, 1st and 2nd dimension, aspect ratio, surface area, zeta potential and size in situ. In most regressor learning systems, after feature selection with the RReliefF algorithm, the correlation between real and estimated toxicity endpoint values increased monotonically with the number of included features, reaching values above 0.90. The best performance was obtained with RBF regressors, and the most informative features in predicting toxicity endpoints were related to nanoparticle structure. These trends did not change significantly between toxicity endpoints. In conclusion, EC25, EC50 and slope can be predicted with high correlation using purely data-driven, machine learning methods in Adenosine triphosphate (ATP)-based NP cytotoxicity assays.

Classical multivariate approaches based on Granger causality (GC) which estimate functional connectivity in the brain are almost exclusively based on autoregressive models. Nevertheless, information available from past samples is limited due to both signal autocorrelation and necessarily low model orders. Consequently, multiple time-scales interactions are usually unaccounted for. To overcome these limitations, in this study we propose the use of discrete-time orthogonal Laguerre basis functions within a Wiener-Volterra decomposition of the BOLD signals to perform effective GC assessments of brain functional connectivity. We validate our method in synthetic noisy oscillator networks, and analyze experimental fMRI data from 30 healthy subjects publicly available within the Human Connectome Project (HCP). Synthetic results demonstrate that our Laguerre-Volterra based GC estimates outperform classical approaches in terms of accuracy in detecting true causal links while rejecting false causal links in complex nonlinear networks. Human data analysis shows for the first time that the default mode network modulates both the salience network as well as fronto-temporal circuits in a causal fashion.

Diffusion tensor imaging (DTI) provides exquisite sensitivity to structural and microstructural characteristics of brain tissue, and is routinely employed in advanced neuroimaging applications. DTI is commonly performed using intrinsically noisy echo-planar imaging techniques and poses high demands both on scanner performance and on in-scanner subject time, which in turn is directly related to the number of diffusion-weighting direction one requires. While DTI-derived indices such as fractional anisotropy (FA), diffusion tensor trace and anisotropy mode have proven extremely useful in characterizing disease-related aberrations, their estimation is commonly performed using fitting routines that do not properly take into account MRI noise distribution. In this paper, we present a distribution-aware maximum likelihood tensor estimation framework which also allows, for the first time, separate local noise estimation in both diffusion weighted and reference images. We validate our framework using multiple water phantom diffusion weighted acquisitions, and demonstrate its feasibility in human data. We then employ our framework within Monte Carlo simulations to show how the minimum achievable uncertainty attainable in DTI depends on signal-to-noise ratio (SNR) and number of diffusion gradient directions, demonstrating that these dependencies could be recast into simple power laws which may serve as guidelines for application-specific DTI protocol design.

The causal, directed interactions between brain regions at rest (brain-brain networks) and between resting-state brain activity and autonomic nervous system (ANS) outflow (brain-heart links) have not been completely elucidated. We collected 7 T resting-state functional magnetic resonance imaging (fMRI) data with simultaneous respiration and heartbeat recordings in nine healthy volunteers to investigate (i) the causal interactions between cortical and subcortical brain regions at rest and (ii) the causal interactions between resting-state brain activity and the ANS as quantified through a probabilistic, point-process-based heartbeat model which generates dynamical estimates for sympathetic and parasympathetic activity as well as sympathovagal balance. Given the high amount of information shared between brain-derived signals, we compared the results of traditional bivariate Granger causality (GC) with a globally conditioned approach which evaluated the additional influence of each brain region on the causal target while factoring out effects concomitantly mediated by other brain regions. The bivariate approach resulted in a large number of possibly spurious causal brain-brain links, while, using the globally conditioned approach, we demonstrated the existence of significant selective causal links between cortical/subcortical brain regions and sympathetic and parasympathetic modulation as well as sympathovagal balance. In particular, we demonstrated a causal role of the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions in modulating the so-called central autonomic network (CAN). In summary, we show that, provided proper conditioning is employed to eliminate spurious causalities, ultra-high-field functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain-brain and brain-heart interactions reflecting central modulation of ANS outflow., (© 2016 The Author(s).)

Objective: To compare heart rate variability (HRV) parameters in newly diagnosed and untreated temporal lobe epilepsy (TLE) between the interictal, preictal, ictal, and postictal states., Methods: HRV parameters were extracted from single-lead electrocardiography data collected during video-electroencephalography (EEG) recordings from 14 patients with newly diagnosed TLE in a resting, awake, and supine state. HRV parameters in the time and frequency domains included low frequency (LF), high frequency (HF), standard deviation of all consecutive R wave intervals (SDNN), and square root of the mean of the sum of the squares of differences between adjacent R wave intervals (RMSSD). Cardiovagal index (CVI), cardiosympathetic index (CSI), and approximate entropy (ApEn) were also studied., Results: Frequency domain analysis showed significantly higher preictal, ictal, and postictal LF/HF ratio compared to the interictal state. Similarly, the LF component increased progressively and was significantly higher during the ictal state compared to interictal and preictal states. RR interval values were lower in the ictal state compared to basal and preictal states and in the postictal state compared to the preictal state. Interictal RMSSD was significantly higher compared to all other states, and ictal SDNN was significantly higher compared to all other states. Ictal CSI was significantly higher compared to preictal and interictal states, whereas preictal CVI was lower than in basal and ictal states. In addition, ictal ApEn was significantly lower than interictal and preictal ApEn. Interictal CVI was lower in left TLE compared to right TLE. In addition, in left TLE, ictal CVI was higher than interictal CVI, whereas in right TLE, CVI was lower in the preictal state compared to all other states., Significance: Our data suggest an ictal sympathetic overdrive with partial recovery in the postictal state. Higher sympathetic tone and vagal tone imbalance may induce early autonomic dysfunction and increase cardiovascular risk in patients affected by TLE., (Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.)

Liver transplantation remains the only curative treatment option for a variety of end-stage liver diseases. Prediction of major adverse events following surgery has traditionally focused on static predictors that are known prior to surgery. The effects of intraoperative management can now be explored due to the archiving of high-resolution monitoring data. We extracted intraoperative hemodynamic trend data of 55 patients undergoing orthotopic liver transplantation (OLT) and computed 12 features from the systolic arterial blood pressure (ABP), cardiac index, central venous pressure (CVP), and stroke volume variation (SVV) signals. Using a logistic regression classifier with a leave-one-out cross-validation procedure, we selected subsets of these features to predict mortality up to 180 days after surgery. Best performance was achieved with a combination of 3 features - median absolute deviation (MAD) of ABP, median CVP, and time spent with SVV <; 10% - reaching an area under the receiver-operating characteristic (or c-statistic) of 0.808. Odds ratios (OR) computed from the coefficients of the multivariate logistic regression model constructed from these features showed that greater time spent with SVV <; 10% (OR = 0.981 min(-1), p = 0.001) and greater MAD of systolic ABP (OR = 0.696 mmHg(-1), p = 0.026) were significantly associated with survival. Adding preoperative measures such as age and serum concentrations of albumin, bilirubin, and creatinine failed to improve performance of the prediction model. These results show that the course of intraoperative hemodynamics can predict 180-day mortality after OLT.

We used 7T fMRI with simultaneous physiological signals acquisitions to investigate the causal interactions from resting state brain activity to autonomic nervous system (ANS) outflow as quantified through a probabilistic heartbeat model. Given the highly redundant nature of brain-derived signals, we compare the results of traditional bivariate Granger Causality (GC) to a globally conditioned approach which evaluates the additional influence of each brain region on ANS activity while factoring out effects concomitantly mediated by other brain regions. The bivariate approach results in an unrealistically large number of spurious causal brain-heart links. In contrast, using the globally conditioned approac, we demonstrate the existence of significant selective causal links between cortical/subcortical brain regions and ANS outflow for sympathetic and parasympathetic modulation as well as sympathovagal balance, with a prominent involvement of frontal, parietal, and cerebellar regions and Sensory Motor, Default Mode, Left and Right executive networks. Provided proper conditioning is employed to eliminate spurious causalities, 7T functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain-heart interactions reflecting central modulation of ANS outflow.

Introduction: Kennedy's disease (KD) is a progressive degenerative disorder affecting lower motor neurons. We investigated the correlation between disease severity and whole brain white matter microstructure, including upper motor neuron tracts, by using diffusion-tensor imaging (DTI) in eight patients with KD in whom disease severity was evaluated using the Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS)., Methods: From DTI acquisitions we obtained maps of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (L1) and radial diffusivities (L2, L3). We then employed tract-based spatial statistics (TBSS) to investigate within-patient correlations of DTI invariants with ALSFRS and disease duration (DD)., Results: We found a significant correlation between low ALSFRS and 1) low FA values in association commissural and projection fibers, and 2) high L3 values in commissural tracts and fronto-parietal white matter. Additionally, we found a significant association between longer DD and 1) low FA in the genu and body of corpus callosum, association fibers and midbrain and 2) high L1 in projection and association tracts., Conclusions: The associations between clinical variables and white matter microstructural changes in areas thought to be spared by the disease process support the hypothesis of a multisystem involvement in the complex pathogenic mechanisms responsible for the clinical disability of these patients., (© The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.)

Aims and Background: To present the Italian state-of-the-art contribution to radiobiology of external beam radiotherapy, brachytherapy, and radionuclide radiotherapy., Methods and Study Design: A survey of the literature was carried out, using PubMed, by some independent researchers of the Italian group of radiobiology. Each paper was reviewed by researchers of centers not comprising its authors. The survey was limited to papers in English published over the last 20 years, written by Italian investigators or in Italian institutions, excluding review articles., Results: A total of 135 papers have been published in journals with an impact factor, with an increase in the number of published papers over time, for external beam radiotherapy rather than radionuclide radiotherapy. The quantity and quality of the papers researched constitutes a proof of the enduring interest in clinical radiobiology among Italian investigators., Conclusions: The survey could be useful to individuate expert partners for an Italian network on clinical radiobiology, addressing future collaborative investigations.

The aim of this study was to identify potential diagnostic markers of Hereditary Spastic Paraplegia (HSP). We investigated the white matter features of spastic gait (SPG)11- and SPG4-linked HSP, using diffusion tensor imaging performed with a 3-Tesla (3T) scanner. We examined four patients with SPG11 mutations, three with SPG4 mutations, and 26 healthy controls. We obtained maps of fractional anisotropy (FA) and mean diffusivity (MD), which we analyzed through both region of interest -based approach and tract-based spatial statistics (TBSS). Compared with healthy controls, SPG11 patients presented increased MD and decreased FA in the semioval centers, frontal and peritrigonal white matter, posterior limb of the internal capsule, and throughout the corpus callosum. Similar alterations were seen in the SPG4 patients at the levels of the semioval centers, the posterior limb of the internal capsule, the left cerebral pedicle, the genu and trunk of the corpus callosum, and the peritrigonal white matter on the left. No MD or FA alterations were observed in the cerebellar white matter. In a direct comparison, white matter alterations were more pronounced and widespread in HSP-SPG11 than in HSP-SPG4 patients. Joint TBSS analysis of all three groups confirmed significant widespread alterations of FA and MD values in the supratentorial white matter. This noninvasive study documented the presence of altered diffusivity in white matter in both forms of HSP, which could represent an important diagnostic marker of HSP. The association of reduced FA and increased MD in this patient population supports the interpretation of HPG as a neurodegenerative disorder.

Breast imaging represents a relatively recent and promising field of application of quantitative diffusion-MRI techniques. In view of the importance of guaranteeing and assessing its reliability in clinical as well as research settings, the aim of this study was to specifically characterize how the main MR scanner system-related factors affect quantitative measurements in diffusion-MRI of the breast. In particular, phantom acquisitions were performed on three 1.5 T MR scanner systems by different manufacturers, all equipped with a dedicated multi-channel breast coil as well as acquisition sequences for diffusion-MRI of the breast. We assessed the accuracy, inter-scan and inter-scanner reproducibility of the mean apparent diffusion coefficient measured along the main orthogonal directions () as well as of diffusion-tensor imaging (DTI)-derived mean diffusivity (MD) measurements. Additionally, we estimated spatial non-uniformity of (NU) and MD (NUMD) maps. We showed that the signal-to-noise ratio as well as overall calibration of high strength diffusion gradients system in typical acquisition sequences for diffusion-MRI of the breast varied across MR scanner systems, introducing systematic bias in the measurements of diffusion indices. While and MD values were not appreciably different from each other, they substantially varied across MR scanner systems. The mean of the accuracies of measured and MD was in the range [-2.3%,11.9%], and the mean of the coefficients of variation for and MD measurements across MR scanner systems was 6.8%. The coefficient of variation for repeated measurements of both and MD was < 1%, while NU and NUMD values were <4%. Our results highlight that MR scanner system-related factors can substantially affect quantitative diffusion-MRI of the breast. Therefore, a specific quality control program for assessing and monitoring the performance of MR scanner systems for diffusion-MRI of the breast is highly recommended at every site, especially in multicenter and longitudinal studies.

The aim of this paper is to compare baroreflex sensitivity (BRS) following anesthesia induction via propofol to pre-induction baseline values through a systematic and mathematically robust analysis. Several mathematical methods for BRS quantification were applied to pre-operative and intra-operative data collected from patients undergoing major surgery, in order to track the trend in BRS variations following anesthesia induction, as well as following the onset of mechanical ventilation. Finally, a comparison of BRS trends in chronic hypertensive patients (CH) with respect to non hypertensive (NH) patients was performed. 10 NH and 7 CH patients undergoing major surgery with American Society of Anesthesiologists classification score 2.5 ± 0.5 and 2.6 ± 0.5 respectively, were enrolled in the study. A Granger causality test was carried out to verify the causal relationship between RR interval duration and systolic blood pressure (SBP), and four different mathematical methods were used to estimate the BRS: (1) ratio between autospectra of RR and SBP, (2) transfer function, (3) sequence method and (4) bivariate closed loop model. Three different surgical epochs were considered: baseline, anesthetic procedure and post-intubation. In NH patients, propofol administration caused a decrease in arterial blood pressure (ABP), due to its vasodilatory effects, and a reduction of BRS, while heart rate (HR) remained unaltered with respect to baseline values before induction. A larger decrease in ABP was observed in CH patients when compared to NH patients, whereas HR remained unaltered and BRS was found to be lower than in the NH group at baseline, with no significant changes in the following epochs when compared to baseline. To our knowledge, this is the first study in which the autonomic response to propofol induction in CH and NH patients was compared. The analysis of BRS through a mathematically rigorous procedure in the perioperative period could result in the availability of additional information to guide therapy and anesthesia in uncontrolled hypertensive patients, which are prone to a higher rate of hypotension events occurring during general anesthesia induction.

The majority of studies on fluid responsiveness is focused on volume expansion maneuvers in intensive care unit (ICU), while fewer studies have analyzed the same problem during major surgery. Among them, the results are contrasting. The aim of this work was to compare the performance of different hemodynamic indices in the prediction of cardiac output variations following fast fluid infusion. The study was limited to a particular type of major surgery, i.e. liver transplantation and hepatectomy. Our results showed that pulse pressure variation (PPV) estimated according to the definition, i.e. within single respiratory cycles, and PPV estimated by PiCCO monitor system are coherent and very similar. Moreover, PPV and stroke volume variation (SVV) produced good values of sensitivity and specificity in separating the subjects into responsive and non responsive to maneuvers.

This paper presents the analysis of autonomic nervous system (ANS) control of heart rate (HR) and of cardiac baroreflex sensitivity (BRS) in patients undergoing general anesthesia for major surgery through spectral analysis techniques and with the Granger causality approach that take into account the causal relationships between HR and arterial blood pressure (ABP) variability. Propofol produced a general decrease in ABP due to its vasodilatory effects, a reduction in BRS, while HR remained unaltered with respect to baseline values before induction of anesthesia. The bivariate model suggests that the feedback pathway of cardiac baroreflex could be blunted by propofol induced anesthesia and that the feedforward pathway could be unaffected by anesthesia.

Transcranial Magnetic Stimulation offers enormous potential for noninvasive brain stimulation. While it is known that brain tissue significantly "reshapes" induced field and charge distributions, most modeling investigations to-date have focused on single-subject data with limited generality. Further, the effects of the significant uncertainties which exist in the simulation (i.e. brain conductivity distributions) and stimulation (e.g. coil positioning and orientations) setup have not been quantified. In this study, we construct a high-resolution anisotropic head model in standard ICBM space, which can be used as a population-representative standard for bioelectromagnetic simulations. Further, we employ Monte-Carlo simulations in order to quantify how uncertainties in conductivity values propagate all the way to induced field and currents, demonstrating significant, regionally dependent dispersions in values which are commonly assumed "ground truth". This framework can be leveraged in order to quantify the effect of any type of uncertainty in noninvasive brain stimulation and bears relevance in all applications of TMS, both investigative and therapeutic.