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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.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.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.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.

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.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.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.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.

In this study, we employ a time-varying, probabilistic model of linear and nonlinear heartbeat dynamics to investigate the possibility of detecting subtle autonomic alterations in subjects suffering from amnestic mild cognitive impairment (aMCI) by exploiting heartbeat information alone. aMCI is a frequent form of cognitive dysfunction which increases the risk of culminating in Alzheimer's disease (AD)-related dementia, and previous studies have demonstrated that AD is accompanied by alterations in autonomic function, which in turn have been linked to cognitive performance in non-demented subjects. We compare 13 MCI patients without ouvert dysautonomia to 13 age- and gender-matched healthy controls by feeding an autonomic nervous system-related linear and nonlinear feature set into a classification framework. Our results show a satisfactory classification performance (73% balanced accuracy), which dropped to 65% when excluding cardiovascular nonlinear/complex features. This outcome confirms the presence of subtle autonomic dysfunction in aMCI (a possible prodromal condition to AD), which can only be detected through to the use of our comprehensive modeling strategy which comprises time-varying, nonlinear/complex estimates of heartbeat dynamics.

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.

Objective This study describes changes in heart rate (HR) and HR variability (HRV) related to clinical onset of seizures in nocturnal frontal lobe epilepsy (NFLE) in order to determine whether signs of autonomic activation precede onset of seizure motor manifestations, which was selected as seizure onset (SO). Further, to clarify the nature (epileptic or physiologic) of the changes in autonomic cardiac control presumed to precede SO, time-dependent variations in HR and HRV related to physiological cortical arousals associated with motor activity (phases of transitory activation, PAT) were also investigated. Methods HR and HRV spectral power, quantified by means of wavelet transform, were analyzed in relation to the onset of motor manifestations in 45 NFLE seizures and 45 PAT derived from whole night video-polysomnographic recordings of ten patients and of ten control subjects, respectively. Results Analysis of HRV showed a shift of sympathetic/parasympathetic cardiac control toward a sympathetic predominance in the 10 s immediately preceding SO, while changes in HR were evident only one second before SO. This sympathetic activation was not associated with a sleep-wake transition or changes in respiratory activity, both of which occurred concurrently with SO. Similar changes in HR and HRV were observed in the 10 s before the motor and electroencephalographic onset of PAT. Conclusions Our study demonstrates that a similar autonomic activation precedes the motor manifestations of NFLE seizures and physiological arousal. This autonomic activation could represent part of the arousal response, which could be implicated in the occurrence of both seizure and arousal motor manifestations.