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

Task-related modulation of anterior theta and posterior alpha EEG reflects top-down preparation

<p>Abstract</p> <p>Background</p> <p>Prestimulus EEG alpha activity in humans has been considered to reflect ongoing top-down preparation for the performance of subsequent tasks. Since theta oscillations may be related to poststimulus top-down processing, we investigated whether prestimulus EEG theta activity also reflects top-down cognitive preparation for a stimulus.</p> <p>Results</p> <p>We recorded EEG data from 15 healthy controls performing a color and shape discrimination task, and used the wavelet transformation to investigate the time course and power of oscillatory activity in the signals. We observed a relationship between both anterior theta and posterior alpha power in the prestimulus period and the type of subsequent task.</p> <p>Conclusions</p> <p>Since task-differences were reflected in both theta and alpha activities prior to stimulus onset, both prestimulus theta (particularly around the anterior region) and prestimulus alpha (particularly around the posterior region) activities may reflect prestimulus top-down preparation for the performance of subsequent tasks.</p

Saliency maps for finding changes in visual scenes?

Sudden changes in the environment reliably summon attention. This rapid change detection appears to operate in a similar fashion as pop-out in visual search, the phenomenon that very salient stimuli are directly attended, independently of the number of distracting objects. Pop-out is usually explained by the workings of saliency maps, i.e., map-like representations that code for the conspicuity at each location of the visual field. While past research emphasized similarities between pop-out search and change detection, our study highlights differences between the saliency computations in the two tasks: in contrast to pop-out search, saliency computation in change detection (i) operates independently across different stimulus properties (e.g., color and orientation), and (ii) is little influenced by trial history. These deviations from pop-out search are not due to idiosyncrasies of the stimuli or task design, as evidenced by a replication of standard findings in a comparable visual-search design. To explain these results, we outline a model of change detection involving the computation of feature-difference maps, which explains the known similarities and differences with visual search