Whole-brain white matter network reorganization in HIV.

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.

Disruption of structural brain networks in Primary Open Angle Glaucoma.

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.

Dynamical brain connectivity estimation using GARCH models: An application to personality neuroscience.

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.

Dynamic inter-network connectivity in the human brain.

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.

Estimating directed brain-brain and brain-heart connectivity through globally conditioned Granger causality approaches.

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.

Simultaneous estimation of the in-mean and in-variance causal connectomes of the human brain.

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.

Differences in Gaussian diffusion tensor imaging and non-Gaussian diffusion kurtosis imaging model-based estimates of diffusion tensor invariants in the human brain.

PURPOSE: An increasing number of studies have aimed to compare diffusion tensor imaging (DTI)-related parameters [e.g., mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD)] to complementary new indexes [e.g., mean kurtosis (MK)/radial kurtosis (RK)/axial kurtosis (AK)] derived through diffusion kurtosis imaging (DKI) in terms of their discriminative potential about tissue disease-related microstructural alterations. Given that the DTI and DKI models provide conceptually and quantitatively different estimates of the diffusion tensor, which can also depend on fitting routine, the aim of this study was to investigate model- and algorithm-dependent differences in MD/FA/RD/AD and anisotropy mode (MO) estimates in diffusion-weighted imaging of human brain white matter. METHODS: The authors employed (a) data collected from 33 healthy subjects (20-59 yr, F: 15, M: 18) within the Human Connectome Project (HCP) on a customized 3 T scanner, and (b) data from 34 healthy subjects (26-61 yr, F: 5, M: 29) acquired on a clinical 3 T scanner. The DTI model was fitted to b-value =0 and b-value =1000 s/mm(2) data while the DKI model was fitted to data comprising b-value =0, 1000 and 3000/2500 s/mm(2) [for dataset (a)/(b), respectively] through nonlinear and weighted linear least squares algorithms. In addition to MK/RK/AK maps, MD/FA/MO/RD/AD maps were estimated from both models and both algorithms. Using tract-based spatial statistics, the authors tested the null hypothesis of zero difference between the two MD/FA/MO/RD/AD estimates in brain white matter for both datasets and both algorithms. RESULTS: DKI-derived MD/FA/RD/AD and MO estimates were significantly higher and lower, respectively, than corresponding DTI-derived estimates. All voxelwise differences extended over most of the white matter skeleton. Fractional differences between the two estimates [(DKI - DTI)/DTI] of most invariants were seen to vary with the invariant value itself as well as with MK/RK/AK values, indicating substantial anatomical variability of these discrepancies. In the HCP dataset, the median voxelwise percentage differences across the whole white matter skeleton were (nonlinear least squares algorithm) 14.5% (8.2%-23.1%) for MD, 4.3% (1.4%-17.3%) for FA, -5.2% (-48.7% to -0.8%) for MO, 12.5% (6.4%-21.2%) for RD, and 16.1% (9.9%-25.6%) for AD (all ranges computed as 0.01 and 0.99 quantiles). All differences/trends were consistent between the discovery (HCP) and replication (local) datasets and between estimation algorithms. However, the relationships between such trends, estimated diffusion tensor invariants, and kurtosis estimates were impacted by the choice of fitting routine. CONCLUSIONS: Model-dependent differences in the estimation of conventional indexes of MD/FA/MO/RD/AD can be well beyond commonly seen disease-related alterations. While estimating diffusion tensor-derived indexes using the DKI model may be advantageous in terms of mitigating b-value dependence of diffusivity estimates, such estimates should not be referred to as conventional DTI-derived indexes in order to avoid confusion in interpretation as well as multicenter comparisons. In order to assess the potential and advantages of DKI with respect to DTI as well as to standardize diffusion-weighted imaging methods between centers, both conventional DTI-derived indexes and diffusion tensor invariants derived by fitting the non-Gaussian DKI model should be separately estimated and analyzed using the same combination of fitting routines.

Forecasting nanoparticle toxicity using nonlinear predictive regressor learning systems.

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.

Predicting seizures in untreated temporal lobe epilepsy using point-process nonlinear models of heartbeat dynamics.

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.

Physical and dosimetric optimization of laser equipment in dermatology: a preliminary study.

The aim of this preliminary study is to investigate the correlation between clinical set-up at present used in the treatment of specific skin conditions and laser beam absorbed power in the tissue. This study focused on the CO2 and Nd-Yag laser equipment used in the daily clinical practice in the Department of Dermatology of San Gallicano Institute in Rome. Different types of tissue-equivalent material with various water and haemoglobin concentrations were tested to evaluate laser beam attenuation power. In particular, thinly sliced pork loin, of uniform consistency and without fat, was selected for its high content of haemoglobin to mimic human tissues. An optical power meter was used to measure the power or energy of a laser beam. During measurements, the tissue equivalent phantoms were positioned on the detector head and the laser beam was orthogonally oriented. The results of two experimental set-ups are reported here. The dependence of residual power (W) as a function of ex vivo tissue thickness (mm) for different laser output powers was studied. Data were fitted by a parametric logistic equation. These preliminary data allow for more accurately determining the energy fraction released from lasers to the tissues in order to improve clinical outcomes.

Interventricular coupling coefficients in a thick shell model of passive cardiac chamber deformation.

Mechanical interplay between the adjacent ventricles is one of the principal modulators of physiopathological heart function, and the underlying mechanisms of interaction are only partially understood, hence hampering clinically useful interpretation of imaging data. In order to characterize the influence of chamber geometry on ventricular coupling, the ventricles and septum are modeled as portions of ellipsoidal shells, and configuration is derived as a function of pressure gradients by combining shell element equilibrium equations through static boundary conditions applied at the sulcus. Diastolic volume (v) surfaces are calculated as a function of pressure (p), contralateral pressure (clp) and intrathoracic pressure (p ( t )) and match literature data where available. Ventricular interaction is characterized in terms of partial derivatives in v-p-clp-p ( t ) space both under physiological and altered (selectively stiffened walls) conditions. The model allows prediction of diastolic ventricular v-p-clp-p ( t ) interplay in a variety of physiopathological circumstances.

Advanced lung ventilation system (ALVS) with linear respiratory mechanics assumption for waveform optimization of dual-controlled ventilation.

The present paper describes the functional features of an advanced lung ventilation system (ALVS) properly designed for the optimization of conventional dual-controlled ventilation (DCV), i.e. with pressure-controlled ventilation with ensured tidal or minute volume. Considering the particular clinical conditions of patients treated with controlled ventilation the analysis and synthesis of ALVS control have been performed assuming a linear respiratory mechanics. Moreover, new airways pressure waveforms with more physiological shape can be tested on simulators of respiratory system in order to evaluate their clinical application. This is obtained through the implementation of a compensation procedure making the desired airways pressure waveform independent on patient airways resistance and lung compliance variations along with a complete real-time monitoring of respiratory system parameters leading the ventilator setting. The experimental results obtained with a lung simulator agree with the theoretical ones and show that ALVS performance is useful for the research activity aiming at the improvement of both diagnostic evaluation and therapeutic outcome relative to mechanical ventilation treatments.

Bioelectrical impedance measures in different position and vs dual-energy X-ray absorptiometry (DXA).

BACKGROUND: Bioelectrical impedance analysis (BIA) is a safe, low-cost, non-invasive, rapid method for the assessment of body composition. It has therefore a great potential to be employed for epidemiological and clinical studies. However, many devices are available to estimate total body water (TBW), fat-free mass (FFM) and fat mass (FM) by bioelectrical impedance measurements. Moreover, bipedal devices allowing measurements in the only standing position are recently developed. They are easy and practical to use without operator, so a large diffusion can be forecasted in fields as sport and diet programs. Comparison of body composition estimation by a bipedal device with bioimpedance devices currently used, using dual-energy X-ray absorptiometry (DXA) as reference method. METHODS: The study was performed on 18 healthy women volunteers, age 32.0+/-10.7 years divided in two groups at different levels of body fatness. A Xitron 4000 impedance analyser, a BIA-101 RJL System, and the bipedal device Tanita were used for comparison. The measurements were performed in standing and supine position for Xitron and RJL devices. DXA measurements were performed with a total body scanner DPX, Lunar. RESULTS: FM and FFM were not statistically different when measured with Xitron and RJL in comparison with DXA, while these variables were significantly different between Tanita and DXA measurements. No significant difference were found between measurements in the supine and standing position with the Xitron and RJL system. CONCLUSIONS: Our results suggest that FM and FFM evaluated by bipedal device Tanita are significantly different from FM and FFM measured by DXA in both normal and obese population.

A new approach to non-invasive quantitative study of hepatic haemodynamics using radiocolloids in vivo.

A non-invasive radioisotopic method for the study of liver haemodynamics is described. Data collected by means of a computerised gamma camera for about 4 min after intravenous administration of 99Tcm human serum albumin colloids were analysed using a new mathematical model formulated on a physiopathological basis. Several quantities of possible clinical interest were determined, namely parameters related to liver blood flows, intrahepatic shunts, the intrahepatic space of distribution of the tracer, transit times and extraction efficiency. Results are not affected, within certain limits, by the shape of the radioactive bolus and, with the exception of extraction efficiency, they appear to be independent of the size of the radiocolloidal particle. The dose employed (3-4 mCi) is comparable with that used in liver scintigraphy. Results in 19 subjects with normal liver function, 45 patients with liver cirrhosis and 7 patients with focal liver lesions were in good agreement, from a quantitative viewpoint, with known physiopathological data, thus validating this method in comparison with other more traumatic and/or less practical techniques, which provide less complete information on liver haemodynamics. The method proposed appears to be sufficiently accurate, reproducible, safe and practical, and may thus be considered suitable for routine use in the assessment of functional aspects of liver perfusion for clinical purposes.

On the interpretation of the early part of the liver time-activity curve: double tracer experiment.

A double tracer experiment was performed in one normal subject and in one cirrhotic patient to better understand the tracer kinetics in the liver area during the first minute after injection. From a comparison of the time-activity curves obtained in each subject by subsequent iv administration of a nonextracted and an extracted (colloidal) tracer it was possible to distinguish the contribution due to the transport mechanism through intrahepatic and extrahepatic circulation and that due to reticuloendothelial extraction. Three main phases were identified on the curves and an attempt was made to interpret their significance. Results of the study may be of interest for the development of models which could possibly provide more reliable noninvasive quantitation of liver hemodynamics and reticuloendothelial function with the use of one or both types of tracer.

A new automatically controlled electric TAH.

Cardiovascular regulatory mechanisms are implemented by means of a very complex control system involving neural, humoral, and mechanical agents. The state of the art in TAH today reveals that we have passed a stage where we can be satisfied with a 6 or 9 mo survival. Development and implementation of a versatile control theory must be another milestone before a TAH can really serve the prospective patient population requiring such a device. This study reveals the vital role and merits as well as the feasibility of incorporating such a control theory into the device.