Spatiotemporal consistency of neural responses to repeatedly presented video stimuli accounts for population preferences.

Population preferences for video advertisements vary across short video clips. What underlies these differences? Repeatedly watching a video clip may produce a consistent spatiotemporal pattern of neural activity that is dependent on the individual and the stimulus. Moreover, such consistency may be associated with the degree of engagement and memory of individual viewers.

Cilostazol decreases duration of spreading depolarization and spreading ischemia after aneurysmal subarachnoid hemorrhage.

Objective: Traditionally, angiographic vasospasm (aVS) has been thought to cause delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). However, successful treatment of aVS alone does not result in improved neurological outcome. Therefore, there may be other potential causes of poor neurological outcome, including spreading depolarization (SD).

Reduction of spike generation frequency by cooling in brain slices from rats and from patients with epilepsy.

This study aimed to understand the mechanism by which brain cooling terminates epileptic discharge. Cortical slices were prepared from rat brains (n = 19) and samples from patients with intractable epilepsy that had undergone temporal lobectomy (n = 7). We performed whole cell current clamp recordings at approximately physiological brain temperature (35℃) and at cooler temperatures (25℃ and 15℃).

Effects of focal brain cooling on extracellular concentrations of neurotransmitters in patients with epilepsy.

Objective: Brain hypothermia controls epileptic discharge and reduces extracellular concentrations of glutamate (Glu), an excitatory neurotransmitter. We aimed to determine the effects of focal brain cooling (FBC) on levels of γ-aminobutyric acid (GABA), which is a major inhibitory neurotransmitter. The relationship between Glu or GABA concentrations and the severity of epileptic symptoms was also analyzed.

Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure-Activity Relationship (QSAR) Models.

Graph data are becoming increasingly common in machine learning and data mining, and its application field pervades to bioinformatics and cheminformatics. Accordingly, as a method to extract patterns from graph data, graph mining recently has been studied and developed rapidly. Since the number of patterns in graph data is huge, a central issue is how to efficiently collect informative patterns suitable for subsequent tasks such as classification or regression.