Behavioral and Brain Functions 

About: Behavioral and Brain Functions is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Cognition & Attention deficit hyperactivity disorder. It has an ISSN identifier of 1744-9081. It is also open access. Over the lifetime, 690 publications have been published receiving 30340 citations. The journal is also known as: Behav. Brain Funct. & BBF.

Dopamine signals for reward value and risk: basic and recent data

Abstract: Previous lesion, electrical self-stimulation and drug addiction studies suggest that the midbrain dopamine systems are parts of the reward system of the brain. This review provides an updated overview about the basic signals of dopamine neurons to environmental stimuli. The described experiments used standard behavioral and neurophysiological methods to record the activity of single dopamine neurons in awake monkeys during specific behavioral tasks. Dopamine neurons show phasic activations to external stimuli. The signal reflects reward, physical salience, risk and punishment, in descending order of fractions of responding neurons. Expected reward value is a key decision variable for economic choices. The reward response codes reward value, probability and their summed product, expected value. The neurons code reward value as it differs from prediction, thus fulfilling the basic requirement for a bidirectional prediction error teaching signal postulated by learning theory. This response is scaled in units of standard deviation. By contrast, relatively few dopamine neurons show the phasic activation following punishers and conditioned aversive stimuli, suggesting a lack of relationship of the reward response to general attention and arousal. Large proportions of dopamine neurons are also activated by intense, physically salient stimuli. This response is enhanced when the stimuli are novel; it appears to be distinct from the reward value signal. Dopamine neurons show also unspecific activations to non-rewarding stimuli that are possibly due to generalization by similar stimuli and pseudoconditioning by primary rewards. These activations are shorter than reward responses and are often followed by depression of activity. A separate, slower dopamine signal informs about risk, another important decision variable. The prediction error response occurs only with reward; it is scaled by the risk of predicted reward. Neurophysiological studies reveal phasic dopamine signals that transmit information related predominantly but not exclusively to reward. Although not being entirely homogeneous, the dopamine signal is more restricted and stereotyped than neuronal activity in most other brain structures involved in goal directed behavior.

Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals

Abstract: Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (< 10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.

The face-specific N170 component is modulated by emotional facial expression

Abstract: According to the traditional two-stage model of face processing, the face-specific N170 event-related potential (ERP) is linked to structural encoding of face stimuli, whereas later ERP components are thought to reflect processing of facial affect. This view has recently been challenged by reports of N170 modulations by emotional facial expression. This study examines the time-course and topography of the influence of emotional expression on the N170 response to faces. Dense-array ERPs were recorded in response to a set (n = 16) of fear and neutral faces. Stimuli were normalized on dimensions of shape, size and luminance contrast distribution. To minimize task effects related to facial or emotional processing, facial stimuli were irrelevant to a primary task of learning associative pairings between a subsequently presented visual character and a spoken word. N170 to faces showed a strong modulation by emotional facial expression. A split half analysis demonstrates that this effect was significant both early and late in the experiment and was therefore not associated with only the initial exposures of these stimuli, demonstrating a form of robustness against habituation. The effect of emotional modulation of the N170 to faces did not show significant interaction with the gender of the face stimulus, or hemisphere of recording sites. Subtracting the fear versus neutral topography provided a topography that itself was highly similar to the face N170. The face N170 response can be influenced by emotional expressions contained within facial stimuli. The topography of this effect is consistent with the notion that fear stimuli exaggerates the N170 response itself. This finding stands in contrast to previous models suggesting that N170 processes linked to structural analysis of faces precede analysis of emotional expression, and instead may reflect early top-down modulation from neural systems involved in rapid emotional processing.

Gender differences in mathematics anxiety and the relation to mathematics performance while controlling for test anxiety

Abstract: Background Mathematics anxiety (MA), a state of discomfort associated with performing mathematical tasks, is thought to affect a notable proportion of the school age population. Some research has indicated that MA negatively affects mathematics performance and that girls may report higher levels of MA than boys. On the other hand some research has indicated that boys’ mathematics performance is more negatively affected by MA than girls’ performance is. The aim of the current study was to measure girls’ and boys’ mathematics performance as well as their levels of MA while controlling for test anxiety (TA) a construct related to MA but which is typically not controlled for in MA studies.

Methylphenidate improves prefrontal cortical cognitive function through α2 adrenoceptor and dopamine D1 receptor actions: Relevance to therapeutic effects in Attention Deficit Hyperactivity Disorder

Abstract: BACKGROUND: Methylphenidate (MPH) is the classic treatment for Attention Deficit Hyperactivity Disorder (ADHD), yet the mechanisms underlying its therapeutic actions remain unclear. Recent studies have identified an oral, MPH dose regimen which when given to rats produces drug plasma levels similar to those measured in humans. The current study examined the effects of these low, orally-administered doses of MPH in rats performing a delayed alternation task dependent on prefrontal cortex (PFC), a brain region that is dysfunctional in ADHD, and is highly sensitive to levels of catecholamines. The receptor mechanisms underlying the enhancing effects of MPH were explored by challenging the MPH response with the noradrenergic alpha2 adrenoceptor antagonist, idazoxan, and the dopamine D1 antagonist, SCH23390. RESULTS: MPH produced an inverted U dose response whereby moderate doses (1.0-2.0 mg/kg, p.o.) significantly improved delayed alternation performance, while higher doses (2.0-3.0 mg/kg, p.o.) produced perseverative errors in many animals. The enhancing effects of MPH were blocked by co-administration of either the alpha2 adrenoceptor antagonist, idazoxan, or the dopamine D1 antagonist, SCH23390, in doses that had no effect on their own. CONCLUSION: The administration of low, oral doses of MPH to rats has effects on PFC cognitive function similar to those seen in humans and patients with ADHD. The rat can thus be used as a model for examination of neural mechanisms underlying the therapeutic effects of MPH on executive functions in humans. The efficacy of idazoxan and SCH23390 in reversing the beneficial effects of MPH indicate that both noradrenergic alpha2 adrenoceptor and dopamine D1 receptor stimulation contribute to cognitive-enhancing effects of MPH.