Robust memory of face moral values is encoded in the human caudate tail: a simultaneous EEG-fMRI study.

Moral judgements about people based on their actions is a key component that guides social decision making. It is currently unknown how positive or negative moral judgments associated with a person's face are processed and stored in the brain for a long time. Here, we investigate the long-term memory of moral values associated with human faces using simultaneous EEG-fMRI data acquisition. Results show that only a few exposures to morally charged stories of people are enough to form long-term memories a day later for a relatively large number of new faces. Event related potentials (ERPs) showed a significant differentiation of remembered good vs bad faces over centerofrontal electrode sites (value ERP). EEG-informed fMRI analysis revealed a subcortical cluster centered on the left caudate tail (CDt) as a correlate of the face value ERP. Importantly neither this analysis nor a conventional whole-brain analysis revealed any significant coding of face values in cortical areas, in particular the fusiform face area (FFA). Conversely an fMRI-informed EEG source localization using accurate subject-specific EEG head models also revealed activation in the left caudate tail. Nevertheless, the detected caudate tail region was found to be functionally connected to the FFA, suggesting FFA to be the source of face-specific information to CDt. A further psycho-physiological interaction analysis also revealed task-dependent coupling between CDt and dorsomedial prefrontal cortex (dmPFC), a region previously identified as retaining emotional working memories. These results identify CDt as a main site for encoding the long-term value memories of faces in humans suggesting that moral value of faces activates the same subcortical basal ganglia circuitry involved in processing reward value memory for objects in primates.

Federated Learning of Generalized Linear Causal Networks.

Causal discovery, the inference of causal relations among variables from data, is a fundamental problem of science. Nowadays, due to an increased awareness of data privacy concerns, there has been a shift towards distributed data collection, processing and storage. To meet the pressing need for distributed causal discovery, we propose a novel federated DAG learning method called distributed annealing on regularized likelihood score (DARLS) to learn a causal graph from data stored on multiple clients. DARLS simulates an annealing process to search over the space of topological sorts, where the optimal graphical structure compatible with a sort is found by distributed optimization. This distributed optimization relies on multiple rounds of communication between local clients and a central server to estimate the graphical structure. We establish its convergence to the solution obtained by an oracle with access to all the data. To the best of our knowledge, DARLS is the first distributed method for learning causal graphs with such finite-sample oracle guarantees. To establish the consistency of DARLS, we also derive new identifiability results for causal graphs parameterized by generalized linear models, which could be of independent interest. Through extensive simulation studies and a real-world application, we show that DARLS outperforms existing federated learning methods and is comparable to oracle methods on pooled data, demonstrating its great advantages in estimating causal networks from distributed data.

Gustatory Cortex Is Involved in Evidence Accumulation during Food Choice.

Food choice is one of the most fundamental and most frequent value-based decisions for all animals including humans. However, the neural circuitry involved in food-based decisions is only recently being addressed. Given the relatively fast dynamics of decision formation, electroencephalography (EEG)-informed fMRI analysis is highly beneficial for localizing this circuitry in humans. Here, by using the EEG correlates of evidence accumulation in a simultaneously recorded EEG-fMRI dataset, we found a significant role for the right temporal-parietal operculum (PO) and medial insula including gustatory cortex (GC) in binary choice between food items. These activations were uncovered by using the "EEG energy" (power 2 of EEG) as the BOLD regressor and were missed if conventional analysis with the EEG signal itself were to be used, in agreement with theoretical predictions for EEG and BOLD relations. No significant positive correlations were found with higher powers of EEG (powers 3 or 4) pointing to specificity and sufficiency of EEG energy as the main correlate of the BOLD response. This finding extends the role of cortical areas traditionally involved in palatability processing to value-based decision-making and offers the "EEG energy" as a key regressor of BOLD response in simultaneous EEG-fMRI designs.

Is Carpal Tunnel Release an Effective Treatment for Patients with Suspected Concurrent Carpal Tunnel and Pronator Syndrome?

Background: Some patients with a confirmed diagnosis of carpal tunnel syndrome (CTS) on clinical examination and electrodiagnostic testing (EDX) may also have one or more clinical features of pronator syndrome (PS). However, the EDX is negative for PS. We label these patients as suspected concurrent carpal tunnel and pronator syndrome (CCPS). We suspect that this is a presentation of reverse double crush syndrome that occurs when a symptomatic distal compression neuropathy converts an asymptomatic proximal compression into a symptomatic one. We believe both compressions can be relieved by decompressing the median nerve only at the wrist. The aim of our study is to determine whether carpal tunnel release (CTR) is an effective treatment for patients suffering from CCPS. Methods: This is a prospective, cohort study of the outcomes of CTR in two matched groups with 37 patients in each group. Group A included patients with suspected CCPS and group B included patients with isolated CTS. All patients were evaluated pre-operatively and 1 year after surgery using the Boston Carpal Tunnel Questionnaire (BCTQ). At one year, patients were also assessed for residual symptoms and positive provocative tests. Results: A significant improvement in the symptom and functional severity scales (SSS and FSS) of the BCTQ was noted in both groups. The degree of improvement in SSS was similar in both groups; however, group A showed a greater improvement in FSS. This could be attributed to higher pre-operative values in some items of FSS in group A. No patients in either group had residual symptoms severe enough to necessitate further treatment. Conclusion: The outcomes of CTR are similar in patients with isolated CTS and suspected CCPS and a CTR may be sufficient to address symptoms of CTS and PS in patients with CCPS. Level of Evidence: Level II (Therapeutic).

Contamination, probabilistic health risk assessment and quantitative source apportionment of potentially toxic metals (PTMs) in street dust of a highly developed city in north of Iran.

Street dust (SD) are the particulates that primarily originated from Earth's crust and secondary alteration and erosion of natural and anthropogenic materials. The multi-dimensional pollution and health risk assessment of potentially toxic metals (PTMs) in these particles remain unknown in the majority of world urban areas. The elemental concentration, mineralogy, and micro-morphology of street dust were determined by inductively coupled plasma mass spectrometry (ICP-MS), SEM-EDX, XRD, and petrographical observation. Multivariate statistical analysis combined with positive matrix factorization (PMF) and Monte-Carlo simulations were applied to source identification and health risk assessment of PTMs. A severe enrichment of Sb, Cu and Zn and moderate contamination of Sn, Pb, and Cr were observed in the samples particularly in the areas with higher loads of traffic. The results of geochemical indices showed that K, Al, Mn, and V have natural/geogenic origins. While Sb, Pb, Cr, Cu, and Zn showed an enrichment relative to the background values with dominant anthropogenic sources. The results were confirmed by source appointment techniques. The results of deterministic and probabilistic health risk assessment by Monte-Carlo simulations revealed the non-carcinogenic nature of As, Mn, and Pb for children mainly through skin and ingestion routes. It can be concluded that the chemical compound of street dust in Gorgan city is affected by both natural (loess deposits) and anthropogenic sources. Also, children are in the risk of exposure to PTMs in street dust more than adults.

Predictors of Mortality among COVID-19 Patients Admitted to Intensive Care Units: A Single-Center Study in Tehran, Iran.

BACKGROUND: Iran was one of the first countries to become an epicenter of the coronavirus disease 2019 (COVID-19) epidemic. However, there is a dearth of data on the outcomes of COVID-19 and predictors of death in intensive care units (ICUs) in Iran. We collected extensive data from patients admitted to the ICUs of the one of the tertiary referral hospitals in Tehran, Iran, to investigate the predictors of ICU mortality. METHODS: The study population included 290 COVID-19 patients who were consecutively admitted to the ICUs of the Sina hospital from May 5, 2021, to December 6, 2021, a period that included the peak of the epidemic of the delta (δ) variant. Demographic data, history of prior chronic diseases, laboratory data (including markers of inflammation), radiologic data, and medication data were collected. RESULTS: Of the 290 patients admitted to the ICUs, 187 (64.5%) died and 103 (35.5%) survived. One hundred forty-one (141, 48.6%) were men, and the median age (10th percentile, 90th percentile) was 60 (41, 80). Using logistic regression models, older age, history of hypertension, high levels of inflammatory markers, low oxygen saturation, substantial lung involvement in computed tomography (CT) scans, and gravity of the disease as indicated by the WHO 8-point ordinal scale were primary predictors of mortality at ICU. The use of remdesivir and imatinib was associated with a statistically non-significant reduction in mortality. The use of tocilizumab had almost no effect on mortality. CONCLUSION: The findings are consistent with and add to the currently existing international literature. The findings may be used to predict risk of mortality from COVID-19 and provide some guidance on potential treatments.

On the properties of the toxicity index and its statistical efficiency.

Cancer clinical trials typically generate detailed patient toxicity data. The most common measure used to summarize patient toxicity is the maximum grade among all toxicities and it does not fully represent the toxicity burden experienced by patients. In this article, we study the mathematical and statistical properties of the toxicity index (TI), in an effort to address this deficiency. We introduce a total ordering, (T-rank), that allows us to fully rank the patients according to how frequently they exhibit toxicities, and show that TI is the only measure that preserves the T-rank among its competitors. Moreover, we propose a Poisson-Limit model for sparse toxicity data. Under this model, we develop a general two-sample test, which can be applied to any summary measure for detecting differences among two population of toxicity data. We derive the asymptotic power function of this class as well as the asymptotic relative efficiency (ARE) of the members of the class. We evaluate the ARE formula empirically and show that if the data are drawn from a random Poisson-Limit model, the TI is more efficient, with high probability, than the maximum and the average summary measures. Finally, we evaluate our method on clinical trial toxicity data and show that TI has a higher power in detecting the differences in toxicity profile among treatments. The results of this article can be applied beyond toxicity modeling, to any problem where one observes a sparse array of scores on subjects and a ranking based on extreme scores is desirable.

Optimizing Regularized Cholesky Score for Order-Based Learning of Bayesian Networks.

Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs (DAGs). We propose a novel structure learning method, annealing on regularized Cholesky score (ARCS), to search over topological sorts, or permutations of nodes, for a high-scoring Bayesian network. Our scoring function is derived from regularizing Gaussian DAG likelihood, and its optimization gives an alternative formulation of the sparse Cholesky factorization problem from a statistical viewpoint. We combine simulated annealing over permutation space with a fast proximal gradient algorithm, operating on triangular matrices of edge coefficients, to compute the score of any permutation. Combined, the two approaches allow us to quickly and effectively search over the space of DAGs without the need to verify the acyclicity constraint or to enumerate possible parent sets given a candidate topological sort. The annealing aspect of the optimization is able to consistently improve the accuracy of DAGs learned by greedy and deterministic search algorithms. In addition, we develop several techniques to facilitate the structure learning, including pre-annealing data-driven tuning parameter selection and post-annealing constraint-based structure refinement. Through extensive numerical comparisons, we show that ARCS outperformed existing methods by a substantial margin, demonstrating its great advantage in structure learning of Bayesian networks from both observational and experimental data. We also establish the consistency of our scoring function in estimating topological sorts and DAG structures in the large-sample limit. Source code of ARCS is available at https://github.com/yeqiaoling/arcs_bn.

Ellipse Recovery From Blurred Binary Images.

In this paper, we address the problem of ellipse recovery from blurred shape images. A shape image is a binary-valued (0/1) image in continuous-domain that represents one or multiple shapes. In general, the shapes can also be overlapping. We assume to observe the shape image through finitely many blurred samples, where the 2D blurring kernel is assumed to be known. The samples might also be noisy. Our goal is to detect and locate ellipses within the shape image. Our approach is based on representing an ellipse as the zero-level-set of a bivariate polynomial of degree 2. Indeed, similar to the theory of finite rate of innovation (FRI), we establish a set of linear equations (annihilation filter) between the image moments and the coefficients of the bivariate polynomial. For a single ellipse, we show that the image can be perfectly recovered from only 6 image moments (improving the bound in [Fatemi et al., 2016]). For multiple ellipses, instead of searching for a polynomial of higher degree, we locally search for single ellipses and apply a pooling technique to detect the ellipse. As we always search for a polynomial of degree 2, this approach is more robust against additive noise compared to the strategy of searching for a polynomial of higher degree (detecting multiple ellipses at the same time). Besides, this approach has the advantage of detecting ellipses even when they intersect and some parts of the boundaries are lost. Simulation results using both synthetic and real world images (red blood cells) confirm superiority of the performance of the proposed method against the existing techniques.

Brain Networks Sensitive to Object Novelty, Value, and Their Combination.

Novel and valuable objects are motivationally attractive for animals including primates. However, little is known about how novelty and value processing is organized across the brain. We used fMRI in macaques to map brain responses to visual fractal patterns varying in either novelty or value dimensions and compared the results with the structure of functionally connected brain networks determined at rest. The results show that different brain networks possess unique combinations of novelty and value coding. One network identified in the ventral temporal cortex preferentially encoded object novelty, whereas another in the parietal cortex encoded the learned value. A third network, broadly composed of temporal and prefrontal areas (TP network), along with functionally connected portions of the striatum, amygdala, and claustrum, encoded both dimensions with similar activation dynamics. Our results support the emergence of a common currency signal in the TP network that may underlie the common attitudes toward novel and valuable objects.

Reconstruction of Binary Shapes from Blurred Images via Hankel-structured Low-rank Matrix Recovery.

With the dominance of digital imaging systems, we are often dealing with discrete-domain samples of an analog image. Due to physical limitations, all imaging devices apply a blurring kernel on the input image before taking samples to form the output pixels. In this paper, we focus on the reconstruction of binary shape images from few blurred samples. This problem has applications in medical imaging, shape processing, and image segmentation. Our method relies on representing the analog shape image in a discrete grid much finer than the sampling grid. We formulate the problem as the recovery of a rank r matrix that is formed by a Hankel structure on the pixels. We further propose efficient ADMM-based algorithms to recover the low-rank matrix in both noiseless and noisy settings. We also analytically investigate the number of required samples for successful recovery in the noiseless case. For this purpose, we study the problem in the random sampling framework, and show that with O(r log4(n1n2)) random samples (where the size of the image is assumed to be n1 x n2) we can guarantee the perfect reconstruction with high probability under mild conditions. We further prove the robustness of the proposed recovery in the noisy setting by showing that the reconstruction error in the noisy case is bounded when the input noise is bounded. Simulation results confirm that our proposed method outperform the conventional total variation minimization in the noiseless settings.

Shapes From Pixels.

Continuous-domain visual signals are usually captured as discrete (digital) images. This operation is not invertible in general, in the sense that the continuous-domain signal cannot be exactly reconstructed based on the discrete image, unless it satisfies certain constraints (e.g., bandlimitedness). In this paper, we study the problem of recovering shape images with smooth boundaries from a set of samples. Thus, the reconstructed image is constrained to regenerate the same samples (consistency), as well as forming a shape (bilevel) image. We initially formulate the reconstruction technique by minimizing the shape perimeter over the set of consistent binary shapes. Next, we relax the non-convex shape constraint to transform the problem into minimizing the total variation over consistent non-negative-valued images. We also introduce a requirement (called reducibility) that guarantees equivalence between the two problems. We illustrate that the reducibility property effectively sets a requirement on the minimum sampling density. We also evaluate the performance of the relaxed alternative in various numerical experiments.

Quality of life among veterans with war-related unilateral lower extremity amputation: a long-term survey in a prosthesis center in Iran.

OBJECTIVE: To determine the factors that have an adverse effect on the long-term health-related quality of life (HRQOL) of veterans who have lost their extremities on the battlefield. DESIGN: Cross-sectional study. SETTING: Tertiary prosthesis center. PARTICIPANTS: One hundred forty-one male Iranian veterans who have sustained unilateral lower extremity amputation during the Iran-Iraq War (1980-1988) were evaluated after an average of 21.6 years (range, 20-27 years) after amputation. INTERVENTION: No intervention. MAIN OUTCOME MEASUREMENTS: Physical and mental HRQOL according to the Short Form-36 (SF-36) Health Survey. A cutoff point to define poor versus good HRQOL was calculated using the first quartile of SF-36 physical and mental component scores. RESULTS: Poor physical HRQOL was positively associated with transfemoral amputation, phantom movement, low back pain, and a lower Barthel Index [odds ratios (ORs): 4.1, 7.8, 9.1, and 0.9, respectively). Poor mental HRQOL was associated with education level lower than high school diploma and the articular pain of the sound leg (OR = 2.9 and 6.5, respectively). Being employed or receiving disability was a factor that had a lower OR to associate with poor mental HRQOL (OR = 0.2). CONCLUSIONS: Alleviation of complaints such as low back pain and articular pain of the sound leg through appropriate medical management, granting facilities for continuing education, and employment are issues that should be considered by authorities and rehabilitative centers to increase HRQOL in amputee veterans.

The incidence of selected congenital malformations during a two-year period in Tehran, Iran.

In this descriptive cross-sectional study carried out from March 2005 to March 2007, 410 congenital malformations were recorded among 61,112 live births in six general hospitals in different districts of Tehran. The overall incidence was 0.6% (0.69% in males and 0.62% in females) and the most affected system was the digestive system (40%), followed by the musculoskeletal system (20.5%), central nervous system (19.5%), genitourinary system (12.2%) and cardiovascular system (3%). It is essential that government organizations educate the population through scheduled preconceptional primary care visits on interventions like diet supplementation and avoidance of teratogenic agents, in order to decrease the rate of congenital malformations.