Fractal dimension of cerebral white matter : A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Patients with cerebral small vessel disease (SVD) frequently show decline in cognitive performance. However, neuroimaging in SVD patients discloses a wide range of brain lesions and alterations so that it is often difficult to understand which of these changes are the most relevant for cognitive decline. It has also become evident that visually-rated alterations do not fully explain the neuroimaging correlates of cognitive decline in SVD. Fractal dimension (FD), a unitless feature of structural complexity that can be computed from high-resolution T1-weighted images, has been recently applied to the neuroimaging evaluation of the human brain. Indeed, white matter (WM) and cortical gray matter (GM) exhibit an inherent structural complexity that can be measured through the FD. In our study, we included 64 patients (mean age \ub1 standard deviation, 74.6 \ub1 6.9, education 7.9 \ub1 4.2 years, 53% males) with SVD and mild cognitive impairment (MCI), and a control group of 24 healthy subjects (mean age \ub1 standard deviation, 72.3 \ub1 4.4 years, 50% males). With the aim of assessing whether the FD values of cerebral WM (WM FD) and cortical GM (GM FD) could be valuable structural predictors of cognitive performance in patients with SVD and MCI, we employed a machine learning strategy based on LASSO (least absolute shrinkage and selection operator) regression applied on a set of standard and advanced neuroimaging features in a nested cross-validation (CV) loop. This approach was aimed at 1) choosing the best predictive models, able to reliably predict the individual neuropsychological scores sensitive to attention and executive dysfunctions (prominent features of subcortical vascular cognitive impairment) and 2) identifying a features ranking according to their importance in the model through the assessment of the out-of-sample error. For each neuropsychological test, using 1000 repetitions of LASSO regression and 5000 random permutations, we found that the statistically significant models were those for the Montreal Cognitive Assessment scores (p-value =.039), Symbol Digit Modalities Test scores (p-value =.039), and Trail Making Test Part A scores (p-value =.025). Significant prediction of these scores was obtained using different sets of neuroimaging features in which the WM FD was the most frequently selected feature. In conclusion, we showed that a machine learning approach could be useful in SVD research field using standard and advanced neuroimaging features. Our study results raise the possibility that FD may represent a consistent feature in predicting cognitive decline in SVD that can complement standard imaging

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS

Optical conductivity of the nonsuperconducting cuprate La(8-x)Sr(x)Cu(8)O(20)

La(8-x)Sr(x)Cu(8)O(20) is a non-superconducting cuprate, which exhibits a doubling of the elementary cell along the c axis. Its optical conductivity sigma (omega) has been first measured here, down to 20 K, in two single crystals with x = 1.56 and x = 2.24. Along c, sigma (omega) shows, in both samples, bands due to strongly bound charges, thus confirming that the cell doubling is due to charge ordering. In the ab plane, in addition to the Drude term one observes an infrared peak at 0.1 eV and a midinfrared band at 0.7 eV. The 0.1 eV peak hardens considerably below 200 K, in correspondence of an anomalous increase in the sample dc resistivity, in agreement with its polaronic origin. This study allows one to establish relevant similarities and differences with respect to the spectrum of the ab plane of the superconducting cuprates.Comment: Revised version submitted to Phys. Rev. B, including the elimination of Fig. 1 and changes to Figs. 4 and

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates

838 Feasibility of diaphragmatic surgery for advanced ovarian cancer: a single institution analysis

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The Impact of Lockdown on Couples' Sex Lives

Background: the aim of this study was to perform an Italian telematics survey analysis on the changes in couples' sex lives during the coronavirus disease 2019 (COVID-19) lockdown. Methods: a multicenter cross sectional study was conducted on people sexually active and in stable relationships for at least 6 months. To evaluate male and female sexual dysfunctions, we used the international index of erectile function (IIEF-15) and the female sexual function index (FSFI), respectively; marital quality and stability were evaluated by the marital adjustment test (items 10-15); to evaluate the severity of anxiety symptoms, we used the Hamilton Anxiety Rating Scale. The effects of the quarantine on couples' relationships was assessed with questions created in-house. Results: we included 2149 participants. The sex lives improved for 49% of participants, particularly those in cohabitation; for 29% it deteriorated, while for 22% of participants it did not change. Women who responded that their sex lives deteriorated had no sexual dysfunction, but they had anxiety, tension, fear, and insomnia. Contrarily, men who reported deteriorating sex lives had erectile dysfunctions and orgasmic disorders. In both genders, being unemployed or smart working, or having sons were risk factors for worsening the couples' sex lives. Conclusion: this study should encourage evaluation of the long-term effects of COVID-19 on the sex lives of couples

Detecting functional magnetic resonance imaging activation in white matter: Interhemispheric transfer across the corpus callosum

<p>Abstract</p> <p>Background</p> <p>It is generally believed that activation in functional magnetic resonance imaging (fMRI) is restricted to gray matter. Despite this, a number of studies have reported white matter activation, particularly when the corpus callosum is targeted using interhemispheric transfer tasks. These findings suggest that fMRI signals may not be neatly confined to gray matter tissue. In the current experiment, 4 T fMRI was employed to evaluate whether it is possible to detect white matter activation. We used an interhemispheric transfer task modelled after neurological studies of callosal disconnection. It was hypothesized that white matter activation could be detected using fMRI.</p> <p>Results</p> <p>Both group and individual data were considered. At liberal statistical thresholds (p < 0.005, uncorrected), group level activation was detected in the isthmus of the corpus callosum. This region connects the superior parietal cortices, which have been implicated previously in interhemispheric transfer. At the individual level, five of the 24 subjects (21%) had activation clusters that were located primarily within the corpus callosum. Consistent with the group results, the clusters of all five subjects were located in posterior callosal regions. The signal time courses for these clusters were comparable to those observed for task related gray matter activation.</p> <p>Conclusion</p> <p>The findings support the idea that, despite the inherent challenges, fMRI activation can be detected in the corpus callosum at the individual level. Future work is needed to determine whether the detection of this activation can be improved by utilizing higher spatial resolution, optimizing acquisition parameters, and analyzing the data with tissue specific models of the hemodynamic response. The ability to detect white matter fMRI activation expands the scope of basic and clinical brain mapping research, and provides a new approach for understanding brain connectivity.</p

Cytomegalovirus-based vaccine expressing Ebola virus glycoprotein protects nonhuman primates from Ebola virus infection.

Ebolaviruses pose significant public health problems due to their high lethality, unpredictable emergence, and localization to the poorest areas of the world. In addition to implementation of standard public health control procedures, a number of experimental human vaccines are being explored as a further means for outbreak control. Recombinant cytomegalovirus (CMV)-based vectors are a novel vaccine platform that have been shown to induce substantial levels of durable, but primarily T-cell-biased responses against the encoded heterologous target antigen. Herein, we demonstrate the ability of rhesus CMV (RhCMV) expressing Ebola virus (EBOV) glycoprotein (GP) to provide protective immunity to rhesus macaques against lethal EBOV challenge. Surprisingly, vaccination was associated with high levels of GP-specific antibodies, but with no detectable GP-directed cellular immunity

Concepts, utilization, and perspectives on the Dutch Nationwide Trauma registry: a position paper

Over the last decades, the Dutch trauma care have seen major improvements. To assess the performance of the Dutch trauma system, in 2007, the Dutch Nationwide Trauma Registry (DNTR) was established, which developed into rich source of information for quality assessment, quality improvement of the trauma system, and for research purposes. The DNTR is one of the most comprehensive trauma registries in the world as it includes 100% of all trauma patients admitted to the hospital through the emergency department. This inclusive trauma registry has shown its benefit over less inclusive systems; however, it comes with a high workload for high-quality data collection and thus more expenses. The comprehensive prospectively collected data in the DNTR allows multiple types of studies to be performed. Recent changes in legislation allow the DNTR to include the citizen service numbers, which enables new possibilities and eases patient follow-up. However, in order to maximally exploit the possibilities of the DNTR, further development is required, for example, regarding data quality improvement and routine incorporation of health-related quality of life questionnaires. This would improve the quality assessment and scientific output from the DNTR. Finally, the DNTR and all other (European) trauma registries should strive to ensure that the trauma registries are eligible for comparisons between countries and healthcare systems, with the goal to improve trauma patient care worldwide