Influence of Stimulant Medication and Response Speed on Lateralization of Movement-Related Potentials in Attention-Deficit/Hyperactivity Disorder

Hyperactivity is one of the core symptoms in attention deficit hyperactivity disorder (ADHD). However, it remains unclear in which way the motor system itself and its development are affected by the disorder. Movement-related potentials (MRP) can separate different stages of movement execution, from the programming of a movement to motor post-processing and memory traces. Pre-movement MRP are absent or positive during early childhood and display a developmental increase of negativity. We examined the influences of response-speed, an indicator of the level of attention, and stimulant medication on lateralized MRP in 16 children with combined type ADHD compared to 20 matched healthy controls. We detected a significantly diminished lateralisation of MRP over the pre-motor and primary motor cortex during movement execution (initial motor potential peak, iMP) in patients with ADHD. Fast reactions (indicating increased visuo-motor attention) led to increased lateralized negativity during movement execution only in healthy controls, while in children with ADHD faster reaction times were associated with more positive amplitudes. Even though stimulant medication had some effect on attenuating group differences in lateralized MRP, this effect was insufficient to normalize lateralized iMP amplitudes.A reduced focal (lateralized) motor cortex activation during the command to muscle contraction points towards an immature motor system and a maturation delay of the (pre-) motor cortex in children with ADHD. A delayed maturation of the neuronal circuitry, which involves primary motor cortex, may contribute to ADHD pathophysiology

Electrophysiological correlates of attention networks in childhood and early adulthood

Attention has been related to functions of alerting, orienting, and executive control, which are associated with distinct brain networks. This study aimed at understanding the neural mechanisms underlying the development of attention functions during childhood. A total of 46 healthy 4–13-year-old children and 15 adults performed an adapted version of the Attention Network Task (ANT) while brain activation was registered with a high-density EEG system. Performance of the ANT revealed changes in the efficiency of attention networks across ages. While no differences were observed on the alerting score, both orienting and executive attention scores showed a more protracted developmental curve. Further, age-related differences in brain activity were mostly observed in early ERP components. Young children had poorer early processing of warning cues compared to 10–13-year-olds and adults, as shown by an immature auditory-evoked potential complex elicited by warning tones. Also, 4–6-year-olds exhibited a poorer processing of orienting cues as indexed by lack of modulation of the N1. Finally, flanker congruency produced earlier modulation of ERPs amplitude with age. Flanker congruency effects were delayed and more anteriorly distributed for young children, compared to adults who showed a clear modulation of the N2 in fronto-parietal channels. Additionally, interactions among attention networks were examined. Both alerting and orienting conditions modulated the effectiveness of conflict processing by the executive attention network. The Orienting×Executive networks interactions was only observed after about age 7. Results are informative of the neural correlates of the development of attention networks in childhood

Partage d'expériences en matière de recueil de savoirs traditionnels et d'étude des activités biologiques de plantes liées à ces savoirs

International audienc

Partage d'expériences en matière de recueil de savoirs traditionnels et d'étude des activités biologiques de plantes liées à ces savoirs

International audienc

Local differential privacy for regret minimization in reinforcement learning

Reinforcement learning algorithms are widely used in domains where it is desirable to provide a personalized service. In these domains it is common that user data contains sensitive information that needs to be protected from third parties. Motivated by this, we study privacy in the context of finite-horizon Markov Decision Processes (MDPs) by requiring information to be obfuscated on the user side. We formulate this notion of privacy for RL by leveraging the local differential privacy (LDP) framework. We establish a lower bound for regret minimization in finite-horizon MDPs with LDP guarantees which shows that guaranteeing privacy has a multiplicative effect on the regret. This result shows that while LDP is an appealing notion of privacy, it makes the learning problem significantly more complex. Finally, we present an optimistic algorithm that simultaneously satisfies ε-LDP requirements, and achieves √ K/ε regret in any finite-horizon MDP after K episodes, matching the lower bound dependency on the number of episodes K

Quantitative Analysis of Dynamic Fault Trees Based on the Coupling of Structure Functions and Monte Carlo Simulation

International audienceThis paper focuses on the quantitative analysis of Dynamic Fault Trees (DFTs) by means of Monte Carlo simulation. In a previous article, we defined an algebraic framework allowing to determine the structure function of DFTs. We exploit this structure function and the minimal cut sequences that it allows to determine, to know the failure mode configuration of the system, which is an input of Monte Carlo simulation. We show that the results obtained are in good accordance with theoretical results and that some results, such as importance measures and sensitivity indexes, are not provided by common quantitative analysis and yet interesting. We finally illustrate our approach on a DFT example from the literature