GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology.

During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood. GAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure. In the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p < 0.001), respectively. This work demonstrates that the proposed GAMER MRI might be a useful method to assess the relative importance of MRI measures in neurological diseases with focal pathology. Moreover, the obtained AWs may in fact help to choose the best combination of MR contrasts for a specific classification problem

Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe.

Funder: European and Developing Countries Clinical Trials Partnership (EDCTP); doi: https://doi.org/10.13039/501100001713Funder: MRC Centre for Global Infectious Disease Analysis (MR/R015600/1), jointly funded by the U.K. Medical Research Council (MRC) and the U.K. Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement. Community Jameel. The UK Research and Innovation (MR/V038109/1), the Academy of Medical Sciences Springboard Award (SBF004/1080), The MRC (MR/R015600/1), The BMGF (OPP1197730), Imperial College Healthcare NHS Trust- BRC Funding (RDA02), The Novo Nordisk Young Investigator Award (NNF20OC0059309) and The NIHR Health Protection Research Unit in Modelling Methodology. S. Bhatt thanks Microsoft AI for Health and Amazon AWS for computational credits.Funder: EA FundsFunder: University of Oxford (Oxford University); doi: https://doi.org/10.13039/501100000769Funder: DeepMindFunder: OpenPhilanthropyFunder: UKRI Centre for Doctoral Training in Interactive Artificial Intelligence (EP/S022937/1)Funder: Augustinus Fonden (Augustinus Foundation); doi: https://doi.org/10.13039/501100004954Funder: Knud Højgaards Fond (Knud Højgaard Fund); doi: https://doi.org/10.13039/501100009938Funder: Kai Lange og Gunhild Kai Langes Fond (Kai Lange and Gunhild Kai Lange Foundation); doi: https://doi.org/10.13039/501100008206Funder: Aage og Johanne Louis-Hansens Fond (Aage and Johanne Louis-Hansen Foundation); doi: https://doi.org/10.13039/501100010344Funder: William Demant FoundationFunder: Boehringer Ingelheim Fonds (Stiftung für medizinische Grundlagenforschung); doi: https://doi.org/10.13039/501100001645Funder: Imperial College COVID-19 Research FundFunder: Cancer Research UK (CRUK); doi: https://doi.org/10.13039/501100000289European governments use non-pharmaceutical interventions (NPIs) to control resurging waves of COVID-19. However, they only have outdated estimates for how effective individual NPIs were in the first wave. We estimate the effectiveness of 17 NPIs in Europe's second wave from subnational case and death data by introducing a flexible hierarchical Bayesian transmission model and collecting the largest dataset of NPI implementation dates across Europe. Business closures, educational institution closures, and gathering bans reduced transmission, but reduced it less than they did in the first wave. This difference is likely due to organisational safety measures and individual protective behaviours-such as distancing-which made various areas of public life safer and thereby reduced the effect of closing them. Specifically, we find smaller effects for closing educational institutions, suggesting that stringent safety measures made schools safer compared to the first wave. Second-wave estimates outperform previous estimates at predicting transmission in Europe's third wave

Deconstructing Insight: EEG Correlates of Insightful Problem Solving

Background: Cognitive insight phenomenon lies at the core of numerous discoveries. Behavioral research indicates four salient features of insightful problem solving: (i) mental impasse, followed by (ii) restructuring of the problem representation, which leads to (iii) a deeper understanding of the problem, and finally culminates in (iv) an “Aha!” feeling of suddenness and obviousness of the solution. However, until now no efforts have been made to investigate the neural mechanisms of these constituent features of insight in a unified framework. Methodology/Principal Findings: In an electroencephalographic study using verbal remote associate problems, we identified neural correlates of these four features of insightful problem solving. Hints were provided for unsolved problems or after mental impasse. Subjective ratings of the restructuring process and the feeling of suddenness were obtained on trial-by-trial basis. A negative correlation was found between these two ratings indicating that sudden insightful solutions, where restructuring is a key feature, involve automatic, subconscious recombination of information. Electroencephalogram signals were analyzed in the space×time×frequency domain with a nonparametric cluster randomization test. First, we found strong gamma band responses at parieto-occipital regions which we interpreted as (i) an adjustment of selective attention (leading to a mental impasse or to a correct solution depending on the gamma band power level) and (ii) encoding and retrieval processes for the emergence of spontaneous new solutions. Secondly, we observed an increased upper alpha band response in right temporal regions (suggesting active suppression of weakly activated solution relevant information) for initially unsuccessful trials that after hint presentation led to a correct solution. Finally, for trials with high restructuring, decreased alpha power (suggesting greater cortical excitation) was observed in right prefrontal area. Conclusions/Significance: Our results provide a first account of cognitive insight by dissociating its constituent components and potential neural correlates

Interpretation of light scattering and turbidity measurements in aggregated systems: effect of intra-cluster multiple-light scattering

In this work we studied the effect of intracluster multiple-light scattering on the scattering properties of a population of fractal aggregates. To do so, experimental data of diffusion-limited aggregation for three polystyrene latexes with similar surface properties but different primary particle diameters (equal to 118, 420, and 810 nm) were obtained by static light scattering, and by means of a spectrophotometer. In parallel, a population balance equation (PBE) model, which takes into account the effect of intracluster multiple-light scattering by solving the T-matrix and the mean-field version of T-matrix, was formulated and validated against time evolution of the root mean radius of gyration, , of the zero angle intensity of scattered light, I(0), and of the turbidity, tau. It was found that the mean-field version of the T-matrix theory is able to correctly predict the time evolution of all measured light scattering quantities for all sizes of primary particles without any adjustable parameter. The Structure of the aggregates, characterized by fractal dimension, d(f), was independent of the primary particle size and equal to 1.7, which is in agreement with values found in literature. Since the mean-field version of the T-matrix theory used is rather complicated and requires advanced knowledge of cluster structure (i.e., the particle-particle correlation function), a simplified version of the light scattering model was proposed and tested. It was found that within the range of operating conditions investigated, the simplified version of the light scattering model was able to describe with reasonable accuracy the time evolution of all measured light scattering quantities of the Cluster Mass Distribution (CMD) for all three sizes of primary particles and two values of the laser wavelength