Features in the primordial spectrum: new constraints from WMAP7+ACT data and prospects for Planck

We update the constraints on possible features in the primordial inflationary density perturbation spectrum by using the latest data from the WMAP7 and ACT Cosmic Microwave Background experiments. The inclusion of new data significantly improves the constraints with respect to older work, especially to smaller angular scales. While we found no clear statistical evidence in the data for extensions to the simplest, featureless, inflationary model, models with a step provide a significantly better fit than standard featureless power-law spectra. We show that the possibility of a step in the inflationary potential like the one preferred by current data will soon be tested by the forthcoming temperature and polarization data from the Planck satellite mission.Comment: V2: 8 pages, 8 figures. Minor changes. Two figures and references added. Matches version published in Phys. Rev.

"What is hidden behind the mask?" Facial emotion recognition at the time of COVID-19 pandemic in cognitively normal multiple sclerosis patients

Social cognition deficits have been described in people with multiple sclerosis (PwMS), even in absence of a global cognitive impairment, affecting predominantly the ability to adequately process emotions from human faces. The COVID-19 pandemic has forced people to wear face masks that might interfere with facial emotion recognition. Therefore, in the present study, we aimed at investigating the ability of emotion recognition in PwMS from faces wearing masks. We enrolled a total of 42 cognitively normal relapsing-remitting PwMS and a matched group of 20 healthy controls (HCs). Participants underwent a facial emotion recognition task in which they had to recognize from faces wearing or not surgical masks which of the six basic emotions (happiness, anger, fear, sadness, surprise, disgust) was presented. Results showed that face masks negatively affected emotion recognition in all participants (p < 0.001); in particular, PwMS showed a global worse accuracy than HCs (p = 0.005), mainly driven by the "no masked" (p = 0.021) than the "masked" (p = 0.064) condition. Considering individual emotions, PwMS showed a selective impairment in the recognition of fear, compared with HCs, in both the conditions investigated ("masked": p = 0.023; "no masked": p = 0.016). Face masks affected negatively also response times (p < 0.001); in particular, PwMS were globally hastier than HCs (p = 0.024), especially in the "masked" condition (p = 0.013). Furthermore, a detailed characterization of the performance of PwMS and HCs in terms of accuracy and response speed was proposed. Results from the present study showed the effect of face masks on the ability to process facial emotions in PwMS, compared with HCs. Healthcare professionals working with PwMS at the time of the COVID-19 outbreak should take into consideration this effect in their clinical practice. Implications in the everyday life of PwMS are also discussed

Lost in classification: lower cognitive functioning in apparently cognitive normal newly diagnosed RRMS patients

Cognitive functioning in multiple sclerosis (MS) patients is usually related to the classic, dichotomic classification of impaired vs. unimpaired cognition. However, this approach is far from mirroring the real efficiency of cognitive functioning. Applying a different approach in which cognitive functioning is considered as a continuous variable, we aimed at showing that even newly diagnosed relapsing-remitting MS (RRMS) patients might suffer from reduced cognitive functioning with respect to a matched group of neurologically healthy controls (HCs), even if they were classified as having no cognitive impairment (CI). Fifty newly diagnosed RRMS patients and 36 HCs were tested with an extensive battery of neuropsychological tests. By using Z-scores applied to the whole group of RRMS and HCs together, a measure of cognitive functioning (Z-score index) was calculated. Among the 50 RRMS patients tested, 36 were classified as cognitively normal (CN). Even though classified as CN, RRMS patients performed worse than HCs at a global level (p = 0.004) and, more specifically, in the domains of memory (p = 0.005) and executive functioning (p = 0.006). These results highlight that reduced cognitive functioning can be present early in the disease course, even in patients without an evident CI. The current classification criteria of CI in MS should be considered with caution

A novel prognostic score to assess the risk of progression in relapsing-remitting multiple sclerosis patients

BACKGROUND: At patient-level, the prognostic value of several features that are known to be associated with an increased risk of converting from relapsing remitting (RR) to secondary phase (SP) multiple sclerosis (MS), remain limited.METHODS: Among 262 RRMS patients followed up for ten years, we assessed the probability of developing the SP course based on clinical and conventional and non-conventional magnetic resonance imaging (MRI) parameters at diagnosis and after two years. We used a machine learning method, the Random Survival Forests, to identify, according to their minimal depth (MD), the most predictive factors associated with the risk of SP conversion, which were then combined to compute the Secondary Progressive Risk Score (SP-RiSc).RESULTS: During the observation period, 69 (26%) patients converted to SPMS. The number of cortical lesions (MD=2.47) and age (MD=3.30) at diagnosis, the global cortical thinning (MD = 1.65), the cerebellar cortical volume loss (MD = 2.15) and the cortical lesion load increase (MD=3.15) over the first two years, exerted the greatest predictive effect. Three patients' risk-groups were identified; in the high-risk group, 85% (46 out of 55) of patients entered the SP phase in 7 median years. The SP-RiSc optimal cut-off estimated was 17.7 showing specificity and sensitivity of 87% and 92% respectively, and overall accuracy of 88%.CONCLUSIONS: The SP-RiSc yielded a high performance in identifying MS patients with high probability to develop SPMS, which can help improve management strategies. These findings are the premise of further larger prospective studies to assess its use in clinical settings

Visual-attentional load unveils slowed processing speed in multiple sclerosis patients: a pilot study with a tablet-based videogame

Slowing in information processing speed (IPS) is the key cognitive deficit in multiple sclerosis (MS). Testing IPS in different cognitive load conditions by using computerized tools might reveal initial IPS slowness underestimated by classic paper-and-pencil tests. To investigate the extent to which IPS can be affected by increased task demands, we developed three tasks based on the manipulation of the visual-attentional load, delivered with a home-made, tablet-based videogame. Fifty-one patients with MS (pwMS), classified as having no cognitive impairment in classic paper-and-pencil tests, and 20 healthy controls (HC) underwent the videogame tasks; reaction times (RTs) and accuracy were recorded. A significant reduced performance of pwMS as compared with HC was found on the videogame tasks, with pwMS being on average slower and less accurate than HC. Furthermore, pwMS showed a significantly more pronounced decrement in accuracy as a function of the visual-attentional load, suggesting a higher susceptibility to increased task demands. Significant correlations among the Symbol Digit Modalities Test (SDMT) and the videogame mean RTs and accuracy were found, providing evidence for the concurrent validity of the videogame as a valid tool to test IPS in pwMS. The high potential that might derive from the adoption of computerized assessment tools in clinical practice should be taken into consideration and investigated further

Modeling the Distribution of New MRI Cortical Lesions in Multiple Sclerosis Longitudinal Studies

Objective: Recent studies have shown the relevance of the cerebral grey matter involvement in multiple sclerosis (MS). The number of new cortical lesions (CLs), detected by specific MRI sequences, has the potential to become a new research outcome in longitudinal MS studies. Aim of this study is to define the statistical model better describing the distribution of new CLs developed over 12 and 24 months in patients with relapsing-remitting (RR) MS. Methods: Four different models were tested (the Poisson, the Negative Binomial, the zero-inflated Poisson and the zeroinflated Negative Binomial) on a group of 191 RRMS patients untreated or treated with 3 different disease modifying therapies. Sample size for clinical trials based on this new outcome measure were estimated by a bootstrap resampling technique. Results: The zero-inflated Poisson model gave the best fit, according to the Akaike criterion to the observed distribution of new CLs developed over 12 and 24 months both in each treatment group and in the whole RRMS patients group adjusting for treatment effect. Conclusions: The sample size calculations based on the zero-inflated Poisson model indicate that randomized clinical trials using this new MRI marker as an outcome are feasible

A soft robot structure with limbless resonant, stick and slip locomotion

We present a smart robot structure that exploits anisotropic friction to achieve stick-slip locomotion. The robot is made out of three components: a plastic beam, a planar dielectric elastomer actuator and four bristle pads with asymmetric rigid metallic bristles. We show that when the robot is electronically activated at increasing frequency, its structure exploits the resonance condition to reach the maximum locomotion speed. The fundamental frequency of the structure is estimated both analytically and numerically, allowing the range of frequencies in which the top locomotion speed was observed during the experiments to be identified. The locomotion speed of the robot as a function of the actuation frequency is estimated with a frequency response analysis performed on a discretised model of the structure, revealing good agreement with the experimental evidence

The use of the central vein sign in the diagnosis of multiple sclerosis: a systematic review and meta-analysis

Background: The central vein sign (CVS) is a radiological feature proposed as a multiple sclerosis (MS) imaging biomarker able to accurately differentiate MS from other white matter diseases of the central nervous system. In this work, we evaluated the pooled proportion of the CVS in brain MS lesions and to estimate the diagnostic performance of CVS to perform a diagnosis of MS and propose an optimal cut-off value. Methods: A systematic search was performed on publicly available databases (PUBMED/MEDLINE and Web of Science) up to 24 August 2020. Analysis of the proportion of white matter MS lesions with a central vein was performed using bivariate random-effect models. A meta-regression analysis was performed and the impact of using particular sequences (such as 3D echo-planar imaging) and post-processing techniques (such as FLAIR*) was investigated. Pooled sensibility and specificity were estimated using bivariate models and meta-regression was performed to address heterogeneity. Inclusion and publication bias were assessed using asymmetry tests and a funnel plot. A hierarchical summary receiver operating curve (HSROC) was used to estimate the summary accuracy in diagnostic performance. The Youden index was employed to estimate the optimal cut-off value using individual patient data. Results: The pooled proportion of lesions showing a CVS in the MS population was 73%. The use of the CVS showed a remarkable diagnostic performance in MS cases, providing a pooled specificity of 92% and a sensitivity of 95%. The optimal cut-off value obtained from the individual patient data pooled together was 40% with excellent accuracy calculated by the area under the ROC (0.946). The 3D-EPI sequences showed both a higher pooled proportion compared to other sequences and explained heterogeneity in the meta-regression analysis of diagnostic performances. The 1.5 Tesla (T) scanners showed a lower (58%) proportion of MS lesions with a CVS compared to both 3T (74%) and 7T (82%). Conclusions: The meta-analysis we have performed shows that the use of the CVS in differentiating MS from other mimicking diseases is encouraged; moreover, the use of dedicated sequences such as 3D-EPI and the high MRI field is beneficial

Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis

Imaging; Multiple sclerosis; Quantitative MRIImatges; Esclerosi múltiple; Ressonància magnètica quantitativaImágenes; Esclerosis múltiple; Resonancia magnética cuantitativaQuantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.C.G. is supported by the Swiss National Science Foundation (SNSF) grant PP00P3_176984, the Stiftung zur Förderung der gastroenterologischen und allgemeinen klinischen Forschung and the EUROSTAR E! 113682 HORIZON2020. F.B. is supported by the National Institute for Health Research biomedical research center at University College London Hospitals. J.W. is supported by the EU Horizon2020 research and innovation grant (FORCE, 668039). D.S.R. is supported by the Intramural Research Program of National Institute of Neurological Disorders and Stroke, National Institutes of Health. A.T.T. is supported by an Medical Research Council grant (MR/S026088/1). S.R. is supported by the Austrian Science Foundation (FWF) grant I-3001. P.S. is supported by the Intramural Research Program of National Institute of Neurological Disorders and Stroke, National Institutes of Health. H.V. is supported by the Dutch multiple sclerosis Research Foundation, ZonMW and HealthHolland

Microstructural MRI Correlates of Cognitive Impairment in Multiple Sclerosis: The Role of Deep Gray Matter

Although cognitive impairment (CI) is frequently observed in people with multiple sclerosis (pwMS), its pathogenesis is still controversial. Conflicting results emerged concerning the role of microstructural gray matter (GM) damage especially when involving the deep GM structures. In this study, we aimed at evaluating whether differences in cortical and deep GM structures between apparently cognitively normal (ACN) and CI pwMS (36 subjects in total) are present, using an extensive set of diffusion MRI (dMRI) indices and conventional morphometry measures. The results revealed increased anisotropy and restriction over several deep GM structures in CI compared with ACN pwMS, while no changes in volume were present in the same areas. Conversely, reduced anisotropy/restriction values were detected in cortical regions, mostly the pericalcarine cortex and precuneus, combined with reduced thickness of the superior frontal gyrus and insula. Most of the dMRI metrics but none of the morphometric indices correlated with the Symbol Digit Modality Test. These results suggest that deep GM microstructural damage can be a strong anatomical substrate of CI in pwMS and might allow identifying pwMS at higher risk of developing CI