Getting into sync:Data-driven analyses reveal patterns of neural coupling that distinguish among different social exchanges

In social interactions, each individual's brain drives an action that, in turn, elicits systematic neural responses in their partner that drive a reaction. Consequently, the brain responses of both interactants become temporally contingent upon one another through the actions they generate, and different interaction dynamics will be underpinned by distinct forms of between-brain coupling. In this study, we investigated this by “performing functional magnetic resonance imaging on two individuals simultaneously (dual-fMRI) while they competed or cooperated with one another in a turn-based or concurrent fashion.” To assess whether distinct patterns of neural coupling were associated with these different interactions, we combined two data-driven, model-free analytical techniques: group-independent component analysis and inter-subject correlation. This revealed four distinct patterns of brain responses that were temporally aligned between interactants: one emerged during co-operative exchanges and encompassed brain regions involved in social cognitive processing, such as the temporo-parietal cortex. The other three were associated with competitive exchanges and comprised brain systems implicated in visuo-motor processing and social decision-making, including the cerebellum and anterior cingulate cortex. Interestingly, neural coupling was significantly stronger in concurrent relative to turn-based exchanges. These results demonstrate the utility of data-driven approaches applied to “dual-fMRI” data in elucidating the interpersonal neural processes that give rise to the two-in-one dynamic characterizing social interaction

Impaired Self-Other Distinction and Subcortical Gray-Matter Alterations Characterize Socio-Cognitive Disturbances in Multiple Sclerosis

Introduction: Recent studies of patients with multiple sclerosis (MS) have revealed disturbances in distinct components of social cognition, such as impaired mentalizing and empathy. The present study investigated this socio-cognitive profile in MS patients in more detail, by examining their performance on tasks measuring more fundamental components of social cognition and any associated disruptions to gray-matter volume (GMV). Methods: We compared 43 patients with relapse-remitting MS with 43 age- and sex-matched healthy controls (HCs) on clinical characteristics (depression, fatigue), cognitive processing speed, and three aspects of low-level social cognition; specifically, imitative tendencies, visual perspective taking, and emotion recognition. Using voxel-based morphometry, we then explored relationships between GMV and these clinical and behavioral measures. Results: Patients exhibited significantly slower processing speed, poorer perspective taking, and less imitation compared with HCs. These impairments were related to reduced GMV throughout the putamen, thalami, and anterior insula, predominantly in the left hemisphere. Surprisingly, differences between the groups in emotion recognition were not significant. Conclusion: Less imitation and poorer perspective taking indicate a cognitive self-bias when faced with conflicting self- and other-representations. This suggests that impaired self-other distinction, and an associated subcortical pattern of GM atrophy, might underlie the socio-cognitive disturbances observed in MS

Algorithm Selection Framework for Cyber Attack Detection

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2

Mining association rules for label ranking

Lecture Notes in Computer Science Volume 6635, 2011.Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we continue this line of work by proposing an adaptation of association rules for label ranking based on the APRIORI algorithm. Given that the original APRIORI algorithm does not aim to obtain predictive models, two changes were needed for this achievement. The adaptation essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. Additionally we propose a simple greedy method to select the parameters of the algorithm. We also adapt the method to make a prediction from the possibly con icting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, partial results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.This work was partially supported by project Rank! (PTDC/EIA/81178/2006) from FCT and Palco AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER). We thank the anonymous referees for useful comments

Magnetic record associated with tree ring density: Possible climate proxy

A magnetic signature of tree rings was tested as a potential paleo-climatic indicator. We examined wood from sequoia tree, located in Mountain Home State Forest, California, whose tree ring record spans over the period 600 – 1700 A.D. We measured low and high-field magnetic susceptibility, the natural remanent magnetization (NRM), saturation isothermal remanent magnetization (SIRM), and stability against thermal and alternating field (AF) demagnetization. Magnetic investigation of the 200 mm long sequoia material suggests that magnetic efficiency of natural remanence may be a sensitive paleoclimate indicator because it is substantially higher (in average >1%) during the Medieval Warm Epoch (700–1300 A.D.) than during the Little Ice Age (1300–1850 A.D.) where it is <1%. Diamagnetic behavior has been noted to be prevalent in regions with higher tree ring density. The mineralogical nature of the remanence carrier was not directly detected but maghemite is suggested due to low coercivity and absence of Verwey transition. Tree ring density, along with the wood's magnetic remanence efficiency, records the Little Ice Age (LIA) well documented in Europe. Such a record suggests that the European LIA was a global phenomenon. Magnetic analysis of the thermal stability reveals the blocking temperatures near 200 degree C. This phenomenon suggests that the remanent component in this tree may be thermal in origin and was controlled by local thermal condition

MetaBags: Bagged Meta-Decision Trees for Regression

Ensembles are popular methods for solving practical supervised learning problems. They reduce the risk of having underperforming models in production-grade software. Although critical, methods for learning heterogeneous regression ensembles have not been proposed at large scale, whereas in classical ML literature, stacking, cascading and voting are mostly restricted to classification problems. Regression poses distinct learning challenges that may result in poor performance, even when using well established homogeneous ensemble schemas such as bagging or boosting. In this paper, we introduce MetaBags, a novel, practically useful stacking framework for regression. MetaBags is a meta-learning algorithm that learns a set of meta-decision trees designed to select one base model (i.e. expert) for each query, and focuses on inductive bias reduction. A set of meta-decision trees are learned using different types of meta-features, specially created for this purpose - to then be bagged at meta-level. This procedure is designed to learn a model with a fair bias-variance trade-off, and its improvement over base model performance is correlated with the prediction diversity of different experts on specific input space subregions. The proposed method and meta-features are designed in such a way that they enable good predictive performance even in subregions of space which are not adequately represented in the available training data. An exhaustive empirical testing of the method was performed, evaluating both generalization error and scalability of the approach on synthetic, open and real-world application datasets. The obtained results show that our method significantly outperforms existing state-of-the-art approaches

A survey of the European Reference Network EpiCARE on clinical practice for selected rare epilepsies

Objective: Clinical care of rare and complex epilepsies is challenging, because evidence-based treatment guidelines are scarce, the experience of many physicians is limited, and interdisciplinary treatment of comorbidities is required. The pathomechanisms of rare epilepsies are, however, increasingly understood, which potentially fosters novel targeted therapies. The objectives of our survey were to obtain an overview of the clinical practice in European tertiary epilepsy centers treating patients with 5 arbitrarily selected rare epilepsies and to get an estimate of potentially available patients for future studies. Methods: Members of the European Reference Network for rare and complex epilepsies (EpiCARE) were invited to participate in a web-based survey on clinical practice of patients with Dravet syndrome, tuberous sclerosis complex (TSC), autoimmune encephalitis, and progressive myoclonic epilepsies including Unverricht Lundborg and Unverricht-like diseases. A consensus-based questionnaire was generated for each disease. Results: Twenty-six of 30 invited epilepsy centers participated. Cohorts were present in most responding centers for TSC (87%), Dravet syndrome (85%), and autoimmune encephalitis (71%). Patients with TSC and Dravet syndrome represented the largest cohorts in these centers. The antiseizure drug treatments were rather consistent across the centers especially with regard to Dravet syndrome, infantile spasms in TSC, and Unverricht Lundborg / Unverricht-like disease. Available, widely used targeted therapies included everolimus in TSC and immunosuppressive therapies in autoimmune encephalitis. Screening for comorbidities was routinely done, but specific treatment protocols were lacking in most centers. Significance: The survey summarizes the current clinical practice for selected rare epilepsies in tertiary European epilepsy centers and demonstrates consistency as well as heterogeneity in the treatment, underscoring the need for controlled trials and recommendations. The survey also provides estimates for potential participants of clinical trials recruited via EpiCARE, emphasizing the great potential of Reference Networks for future studies to evaluate new targeted therapies and to identify novel biomarkers

Abnormal Distracter Processing in Adults with Attention-Deficit-Hyperactivity Disorder

Background: Subjects with Attention-Deficit Hyperactivity Disorder (ADHD) are overdistractible by stimuli out of the intended focus of attention. This control deficit could be due to primarily reduced attentional capacities or, e. g., to overshooting orienting to unexpected events. Here, we aimed at identifying disease-related abnormalities of novelty processing and, therefore, studied event-related potentials (ERP) to respective stimuli in adult ADHD patients compared to healthy subjects. Methods: Fifteen unmedicated subjects with ADHD and fifteen matched controls engaged in a visual oddball task (OT) under simultaneous EEG recordings. A target stimulus, upon which a motor response was required, and non-target stimuli, which did not demand a specific reaction, were presented in random order. Target and most non-target stimuli were presented repeatedly, but some non-target stimuli occurred only once (‘novels’). These unique stimuli were either ‘relative novels ’ with which a meaning could be associated, or ‘complete novels’, if no association was available. Results: In frontal recordings, a positive component with a peak latency of some 400 ms became maximal after novels. In healthy subjects, this novelty-P3 (or ‘orienting response’) was of higher magnitude after complete than after relative novels, in contrast to the patients with an undifferentially high frontal responsivity. Instead, ADHD patients tended to smaller centro-parietal P3 responses after target signals and, on a behavioural level, responded slower than controls

Verbal working memory and functional large-scale networks in schizophrenia

The aim of this study was to test whether bilinear and nonlinear effective connectivity (EC) measures of working memory fMRI data can differentiate between patients with schizophrenia (SZ) and healthy controls (HC). We applied bilinear and nonlinear Dynamic Causal Modeling (DCM) for the analysis of verbal working memory in 16 SZ and 21 HC. The connection strengths with nonlinear modulation between the dorsolateral prefrontal cortex (DLPFC) and the ventral tegmental area/substantia nigra (VTA/SN) were evaluated. We used Bayesian Model Selection at the group and family levels to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging was used to assess the connection strengths with nonlinear modulation. The DCM analyses revealed that SZ and HC used different bilinear networks despite comparable behavioral performance. In addition, the connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area showed differences between SZ and HC. The adoption of different functional networks in SZ and HC indicated neurobiological alterations underlying working memory performance, including different connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area. These novel findings may increase our understanding of connectivity in working memory in schizophrenia