Neural processing of criticism and positive comments from relatives in individuals with schizotypal personality traits

Objectives. High negative expressed emotion by family members towards schizophrenia patients increases the risk of subsequent relapse. The study aimed to determine whether individuals with high schizotypy (HS) and low schizotypy (LS) would differ in activation of brain areas involved in cognitive control when listening to relative criticism

Limbic Tract Integrity Contributes to Pattern Separation Performance Across the Lifespan.

Accurate memory for discrete events is thought to rely on pattern separation to orthogonalize the representations of similar events. Previously, we reported that a behavioral index of pattern separation was correlated with activity in the hippocampus (dentate gyrus, CA3) and with integrity of the perforant path, which provides input to the hippocampus. If the hippocampus operates as part of a broader neural network, however, pattern separation would likely also relate to integrity of limbic tracts (fornix, cingulum bundle, and uncinate fasciculus) that connect the hippocampus to distributed brain regions. In this study, healthy adults (20-89 years) underwent diffusion tensor imaging and completed the Behavioral Pattern Separation Task-Object Version (BPS-O) and Rey Auditory Verbal Learning Test (RAVLT). After controlling for global effects of brain aging, exploratory skeleton-wise and targeted tractography analyses revealed that fornix integrity (fractional anisotropy, mean diffusivity, and radial diffusivity; but not mode) was significantly related to pattern separation (measured using BPS-O and RAVLT tasks), but not to recognition memory. These data suggest that hippocampal disconnection, via individual- and age-related differences in limbic tract integrity, contributes to pattern separation performance. Extending our earlier work, these results also support the notion that pattern separation relies on broad neural networks interconnecting the hippocampus

Exploring The Neural Correlates of Reading Comprehension and Social Cognition Deficits in College Students with ADHD

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity. Symptoms of this disorder have been shown to adversely impact academic and social functioning of those with ADHD. College students with ADHD, compared to their non-ADHD peers, are at increased risk for academic and social difficulties. Given the reading-intensive and socially-driven environment of the college campus, empirical literature examining the reading comprehension and social cognition of college students are wanting. The current investigation utilized the Nelson-Denny Reading Test (NDRT) and Faux Pas Recognition test (FPRT) to assess reading comprehension and social cognition, respectively, in college students with (n = 3) and without ADHD (n = 9). The Short Story Task (SST) was administered during functional magnetic resonance imaging (fMRI) to examine neural correlates of narrative comprehension and theory of mind (ToM) while reading short fictional stories of varying prose complexity. The ADHD and control groups did not differ in IQ, GPA, or scores of NDRT, FPRT, or SST, suggesting that they had comparable academic performance, narrative comprehension, and social cognition. The fMRI analysis of SST showed that the ADHD group demonstrated increased activation in the left anterior cingulate (ACC) and parahippocampal gyrus (PHG) while reading the complex story compared to the simple story. This differential activation was not observed in the CTRL group, suggesting that the ADHD group required more neural resources to process the emotional components of the complex story to achieve the comparable performance on the SST. The ADHD group additionally exhibited lower activation in the narrative comprehension and ToM networks (medial prefrontal cortex, Broca’s area, angular gyri). Collectively, these results indicate that while ADHD and CTRL groups did not differ behaviorally, they exhibit differential neural activation patterns in tasks related to narrative comprehension and social cognition. Further investigations may inform the development of educational and psychosocial interventions to improve academic and social functioning in young adults with ADHD

Symbolic music generation conditioned on continuous-valued emotions

In this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioning a state-of-the-art transformer based on continuous-valued valence and arousal labels. In addition, we provide a new large-scale dataset of symbolic music paired with emotion labels in terms of valence and arousal. We evaluate our approach in a quantitative manner in two ways, first by measuring its note prediction accuracy, and second via a regression task in the valence-arousal plane. Our results demonstrate that our proposed approaches outperform conditioning using control tokens which is representative of the current state of the art.‘la Caixa’’ Foundation under Grant 100010434 and Grant LCF/BQ/DI19/1173003 - FCT—Foundation for Science and Technology, I.P., through the Project MERGE through the National Funds (PIDDAC) through the Portuguese State Budget under Grant PTDC/CCI-COM/3171/2021 - European Social Fund through the Regional Operational Program Centro 2020 Project CISUC under Grant UID/CEC/00326/2020info:eu-repo/semantics/publishedVersio

Protecting Voice Controlled Systems Using Sound Source Identification Based on Acoustic Cues

Over the last few years, a rapidly increasing number of Internet-of-Things (IoT) systems that adopt voice as the primary user input have emerged. These systems have been shown to be vulnerable to various types of voice spoofing attacks. Existing defense techniques can usually only protect from a specific type of attack or require an additional authentication step that involves another device. Such defense strategies are either not strong enough or lower the usability of the system. Based on the fact that legitimate voice commands should only come from humans rather than a playback device, we propose a novel defense strategy that is able to detect the sound source of a voice command based on its acoustic features. The proposed defense strategy does not require any information other than the voice command itself and can protect a system from multiple types of spoofing attacks. Our proof-of-concept experiments verify the feasibility and effectiveness of this defense strategy.Comment: Proceedings of the 27th International Conference on Computer Communications and Networks (ICCCN), Hangzhou, China, July-August 2018. arXiv admin note: text overlap with arXiv:1803.0915

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks