Levodopa Enhances Reward Learning but Impairs Reversal Learning in Parkinson's Disease Patients

A commentary on Differential influence of levodopa on reward-based learning in Parkinson’s disease

Unconventional TV Detection using Mobile Devices

Recent studies show that the TV viewing experience is changing giving the rise of trends like "multi-screen viewing" and "connected viewers". These trends describe TV viewers that use mobile devices (e.g. tablets and smart phones) while watching TV. In this paper, we exploit the context information available from the ubiquitous mobile devices to detect the presence of TVs and track the media being viewed. Our approach leverages the array of sensors available in modern mobile devices, e.g. cameras and microphones, to detect the location of TV sets, their state (ON or OFF), and the channels they are currently tuned to. We present the feasibility of the proposed sensing technique using our implementation on Android phones with different realistic scenarios. Our results show that in a controlled environment a detection accuracy of 0.978 F-measure could be achieved.Comment: 4 pages, 14 figure

Adenosine A2A Receptor Blockade Prevents Rotenone-Induced Motor Impairment in a Rat Model of Parkinsonism

Pharmacological studies implicate the blockade of adenosine receptorsas an effective strategy for reducing Parkinson’s disease (PD) symptoms. The objective of this study is to elucidate the possible protective effects of ZM241385 and 8-cyclopentyl-1, 3-dipropylxanthine, two selective A2A and A1 receptor antagonists, on a rotenone rat model of PD. Rats were split into four groups: vehicle control (1 ml/kg/48 h), rotenone (1.5 mg/kg/48 h, s.c.), ZM241385 (3.3 mg/kg/day, i.p) and 8-cyclopentyl-1, 3-dipropylxanthine (5 mg/kg/day, i.p). After that, animals were subjected to behavioral (stride length and grid walking) and biochemical (measuring concentration of dopamine levels using high performance liquid chromatography, HPLC). In the rotenone group, rats displayed a reduced motor activity and disturbed movement coordination in the behavioral tests and a decreased dopamine concentration as foundby HPLC. The effect of rotenone was partially prevented in the ZM241385 group, but not with 8-cyclopentyl-1,3-dipropylxanthine administration. The administration of ZM241385 improved motor function and movement coordination (partial increase of stride length and partial decrease in the number of foot slips) and an increase in dopamine concentration in the rotenone-injected rats. However, the 8-cyclopentyl-1,3-dipropylxanthine and rotenone groups were not significantly different. These results indicate that selective A2A receptor blockade by ZM241385, but not A1 receptor blockadeby 8-cyclopentyl-1,3-dipropylxanthine, may treat PD motor symptoms. This reinforces the potential use of A2A receptor antagonists as a treatment strategy for PD patients

The Role of User Behaviour in Improving Cyber Security Management

Information security has for long time been a field of study in computer science, software engineering, and information communications technology. The term ‘information security’ has recently been replaced with the more generic term cybersecurity. The goal of this paper is to show that, in addition to computer science studies, behavioural sciences focused on user behaviour can provide key techniques to help increase cyber security and mitigate the impact of attackers’ social engineering and cognitive hacking methods (i.e., spreading false information). Accordingly, in this paper, we identify current research on psychological traits and individual differences among computer system users that explain vulnerabilities to cyber security attacks and crimes. Our review shows that computer system users possess different cognitive capabilities which determine their ability to counter information security threats. We identify gaps in the existing research and provide possible psychological methods to help computer system users comply with security policies and thus increase network and information security

A Synaptic Pruning-Based Spiking Neural Network for Hand-Written Digits Classification

A spiking neural network model inspired by synaptic pruning is developed and trained to extract features of hand-written digits. The network is composed of three spiking neural layers and one output neuron whose firing rate is used for classification. The model detects and collects the geometric features of the images from the Modified National Institute of Standards and Technology database (MNIST). In this work, a novel learning rule is developed to train the network to detect features of different digit classes. For this purpose, randomly initialized synaptic weights between the first and second layers are updated using average firing rates of pre- and postsynaptic neurons. Then, using a neuroscience-inspired mechanism named, “synaptic pruning” and its predefined threshold values, some of the synapses are deleted. Hence, these sparse matrices named, “information channels” are constructed so that they show highly specific patterns for each digit class as connection matrices between the first and second layers. The “information channels” are used in the test phase to assign a digit class to each test image. In addition, the role of feed-back inhibition as well as the connectivity rates of the second and third neural layers are studied. Similar to the abilities of the humans to learn from small training trials, the developed spiking neural network needs a very small dataset for training, compared to the conventional deep learning methods that have shown a very good performance on the MNIST dataset. This work introduces a new class of brain-inspired spiking neural networks to extract the features of complex data images

Negative mood induction increases choice of heroin versus food pictures in opiate-dependent individuals: Correlation with self-medication coping motives and subjective reactivity

This is the final version. Available from Frontiers Media via the DOI in this record.Acute growth in negative affect is thought to play a major role in triggering relapse in opiate-dependent individuals. Consistent with this view, three lab studies have demonstrated that negative mood induction increases opiate craving in opiate-dependent individuals. The current study sought to confirm these effects with a behavioral measure of heroin seeking, and test whether the effect is associated with self-reported opiate use to cope with negative affect and subjective reactivity to mood induction. Participants were heroin-dependent individuals engaged with treatment services (n = 47) and control participants (n = 25). Heroin users completed a questionnaire assessing reasons for using heroin: negative affect, social pressure, and cued craving. Baseline heroin choice was measured by preference to enlarge heroin versus food thumbnail pictures in two-alternative forced-choice trials. Negative mood was then induced by depressive statements and music before heroin choice was tested again. Subjective reactivity was indexed by negative and positive mood reported at the pre-induction to post-test timepoints. Heroin users chose heroin images more frequently than controls overall (p = .001) and showed a negative mood-induced increase in heroin choice compared to control participants (interaction p < .05). Mood-induced heroin choice was associated with self-reported heroin use to cope with negative affect (p < .05), but not social pressure (p = .39) or cued craving (p = .52), and with subjective mood reactivity (p = .007). These data suggest that acute negative mood is a trigger for heroin seeking in heroin-dependent individuals, and this effect is pronounced in those who report using heroin to cope with negative affect, and those who show greater subjective reactivity to negative triggers. Interventions should seek to target negative coping motives to build resilience to affective triggers for relapse.Alcohol Research U

Dynamic Communications Between GABAA Switch, Local Connectivity, and Synapses During Cortical Development: A Computational Study

Several factors regulate cortical development, such as changes in local connectivity and the influences of dynamical synapses. In this study, we simulated various factors affecting the regulation of neural network activity during cortical development. Previous studies have shown that during early cortical development, the reversal potential of GABAA shifts from depolarizing to hyperpolarizing. Here we provide the first integrative computational model to simulate the combined effects of these factors in a unified framework (building on our prior work: Khalil et al., 2017a,b). In the current study, we extend our model to monitor firing activity in response to the excitatory action of GABAA. Precisely, we created a Spiking Neural Network model that included certain biophysical parameters for lateral connectivity (distance between adjacent neurons) and nearby local connectivity (complex connections involving those between neuronal groups). We simulated different network scenarios (for immature and mature conditions) based on these biophysical parameters. Then, we implemented two forms of Short-term synaptic plasticity (depression and facilitation). Each form has two distinct kinds according to its synaptic time constant value. Finally, in both sets of networks, we compared firing rate activity responses before and after simulating dynamical synapses. Based on simulation results, we found that the modulation effect of dynamical synapses for evaluating and shaping the firing activity of the neural network is strongly dependent on the physiological state of GABAA. Moreover, the STP mechanism acts differently in every network scenario, mirroring the crucial modulating roles of these critical parameters during cortical development. Clinical implications for pathological alterations of GABAergic signaling in neurological and psychiatric disorders are discussed

THE RELATIONSHIP BETWEEN INTRUSIVE COGNITIONS AND DEFENSE MECHANISMS IN HEALTHY AND CLINICAL POPULATIONS

Purpose: to examine the relationship between defense mechanisms and intrusive cognitions in normal healthy individuals and psychiatric patients. Methodology: The study sample consists of a healthy group (n=60; 30 males &amp; 30 females), whereas the clinical group (n=66; 34 males, 32 females) includes patients with major depressive disorder (12 patients, 5 males, 7 females), schizophrenia (31 patients; 14 males, 17 females), obsessive-compulsive disorder (23 patients; 15 males, 8 females). We used several scales to measure the following variables: intrusive cognitions, intrusive memories, and defense mechanisms. Finding: The results show that there is a positive correlation between defense mechanisms and intrusive cognitions in healthy and clinical groups. Intrusive cognitions were more common in the patient than in a healthy group. Furthermore, there was no significant difference between males and females in measures of intrusive thoughts and memories in both groups. Implications: These findings have implications for behavioral treatment. Treatments used for managing posttraumatic stress disorder can also be used for the treatment of a major depressive disorder, OCD, and schizophrenia. Originality: This investigation the relationship between intrusive cognitions and defense mechanisms in healthy and clinical populations and its implication on the cue exposure therapy that can be the treatment of intrusive cognitions and thoughts in with major depressive disorder, OCD, and schizophrenia

Optimization of deep learning features for age-invariant face recognition

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods