Disentangling the attention network test: behavioral, event related potentials, and neural source analyses.

BACKGROUND The study of the attentional system remains a challenge for current neuroscience. The "Attention Network Test" (ANT) was designed to study simultaneously three different attentional networks (alerting, orienting, and executive) based in subtraction of different experimental conditions. However, some studies recommend caution with these calculations due to the interactions between the attentional networks. In particular, it is highly relevant that several interpretations about attentional impairment have arisen from these calculations in diverse pathologies. Event related potentials (ERPs) and neural source analysis can be applied to disentangle the relationships between these attentional networks not specifically shown by behavioral measures. RESULTS This study shows that there is a basic level of alerting (tonic alerting) in the no cue (NC) condition, represented by a slow negative trend in the ERP trace prior to the onset of the target stimuli. A progressive increase in the CNV amplitude related to the amount of information provided by the cue conditions is also shown. Neural source analysis reveals specific modulations of the CNV related to a task-related expectancy presented in the NC condition; a late modulation triggered by the central cue (CC) condition and probably representing a generic motor preparation; and an early and late modulation for spatial cue (SC) condition suggesting specific motor and sensory preactivation. Finally, the first component in the information processing of the target stimuli modulated by the interaction between orienting network and the executive system can be represented by N1. CONCLUSIONS The ANT is useful as a paradigm to study specific attentional mechanisms and their interactions. However, calculation of network effects is based in subtractions with non-comparable experimental conditions, as evidenced by the present data, which can induce misinterpretations in the study of the attentional capacity in human subjects.

New insights into pathophysiology of vestibular migraine.

Vestibular migraine (VM) is a common disorder in which genetic, epigenetic, and environmental factors probably contribute to its development. The pathophysiology of VM is unknown; nevertheless in the last few years, several studies are contributing to understand the neurophysiological pathways involved in VM. The current hypotheses are mostly based on the knowledge of migraine itself. The evidence of trigeminal innervation of the labyrinth vessels and the localization of vasoactive neuropeptides in the perivascular afferent terminals of these trigeminal fibers support the involvement of the trigemino-vascular system. The neurogenic inflammation triggered by activation of the trigeminal-vestibulocochlear reflex, with the subsequent inner ear plasma protein extravasation and the release of inflammatory mediators, can contribute to a sustained activation and sensitization of the trigeminal primary afferent neurons explaining VM symptoms. The reciprocal connections between brainstem vestibular nuclei and the structures that modulate trigeminal nociceptive inputs (rostral ventromedial medulla, ventrolateral periaqueductal gray, locus coeruleus, and nucleus raphe magnus) are critical to understand the pathophysiology of VM. Although cortical spreading depression can affect cortical areas involved in processing vestibular information, functional neuroimaging techniques suggest a dysmodulation in the multimodal sensory integration and processing of vestibular and nociceptive information, resulting from a vestibulo-thalamo-cortical dysfunction, as the pathogenic mechanism underlying VM. The elevated prevalence of VM suggests that multiple functional variants may confer a genetic susceptibility leading to a dysregulation of excitatory-inhibitory balance in brain structures involved in the processing of sensory information, vestibular inputs, and pain. The interactions among several functional and structural neural networks could explain the pathogenic mechanisms of VM.