The neural basis of video gaming

Video game playing is a frequent recreational activity. Previous studies have reported an involvement of dopamine-related ventral striatum. However, structural brain correlates of video game playing have not been investigated. On magnetic resonance imaging scans of 154 14-year-olds, we computed voxel-based morphometry to explore differences between frequent and infrequent video game players. Moreover, we assessed the Monetary Incentive Delay (MID) task during functional magnetic resonance imaging and the Cambridge Gambling Task (CGT). We found higher left striatal grey matter volume when comparing frequent against infrequent video game players that was negatively correlated with deliberation time in CGT. Within the same region, we found an activity difference in MID task: frequent compared with infrequent video game players showed enhanced activity during feedback of loss compared with no loss. This activity was likewise negatively correlated with deliberation time. The association of video game playing with higher left ventral striatum volume could reflect altered reward processing and represent adaptive neural plasticity

Preparation of chitosan nanoparticles by TPP ionic gelation combined with spray drying, and the antibacterial activity of chitosan nanoparticles and a chitosan nanoparticle–amoxicillin complex

[[abstract]]Chitosan nanoparticles were prepared from chitosan with various molecular weights by tripolyphosphate (TPP) ionic gelation combined with a spray drying method. The morphologies and characteristics of chitosan nanoparticles were determined by TEM, FE-SEM and from their mean sizes and zeta potentials. The effect of chitosan molecular weight (130, 276, 760 and 1200 cPs) and size of spray dryer nozzle (4.0, 5.5 and 7.0 µm) on mean size, size distribution and zeta potential values of chitosan nanoparticles was investigated. The results showed that the mean size of chitosan nanoparticles was in the range of 166–1230 nm and the zeta potential value ranged from 34.9 to 59 mV, depending on the molecular weight of chitosan and size of the spray dryer nozzles. The lower the molecular weight of chitosan, the smaller the size of the chitosan nanoparticles and the higher the zeta potential. A test for the antibacterial activity of chitosan nanoparticles (only) and a chitosan nanoparticle–amoxicillin complex against Streptococcus pneumoniae was also conducted. The results indicated that a smaller chitosan nanoparticle and higher zeta potential showed higher antibacterial activity. The chitosan nanoparticle–amoxicillin complex resulted in improved antibacterial activity as compared to amoxicillin and chitosan nanopaticles alone. Using a chitosan nanoparticle–amoxicillin complex could reduce by three times the dosage of amoxicillin while still completely inhibiting S. pneumoniae.[[notice]]補正完

Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults

Cognitive training interventions are a promising approach to mitigate cognitive deficits common in aging and, ultimately, to improve functioning in older adults. Baseline neural factors, such as properties of brain networks, may predict training outcomes and can be used to improve the effectiveness of interventions. Here, we investigated the relationship between baseline brain network modularity, a measure of the segregation of brain sub-networks, and training-related gains in cognition in older adults. We found that older adults with more segregated brain sub-networks (i.e., more modular networks) at baseline exhibited greater training improvements in the ability to synthesize complex information. Further, the relationship between modularity and training-related gains was more pronounced in sub-networks mediating "associative" functions compared with those involved in sensory-motor processing. These results suggest that assessments of brain networks can be used as a biomarker to guide the implementation of cognitive interventions and improve outcomes across individuals. More broadly, these findings also suggest that properties of brain networks may capture individual differences in learning and neuroplasticity. Trail Registration: ClinicalTrials.gov, NCT#00977418