Involvement of cAMP-Dependent Protein Kinase in the Nucleus Accumbens in Cocaine Versus Social Interaction Reward

Evidence suggests that PKA activity in the nucleus accumbens (NAc) plays an essential role in reward-related learning. In this study, we investigated whether PKA is differentially involved in the expression of learning produced by either natural reinforcers or psychostimulants. For that purpose, we inhibited PKA through a bilateral infusion of Rp-cAMPS, a specific PKA inhibitor, directly into the NAc. The effects of PKA inhibition in the NAc on the expression of concurrent conditioned place preference (CPP) for cocaine (drug) and social interaction (natural reward) in rats were evaluated. We found that PKA inhibition increased the expression of cocaine preference. This effect was not due to altered stress levels or decreased social reward. PKA inhibition did not affect the expression of natural reward as intra-NAc Rp-cAMPS infusion did not affect expression of social preference. When rats were trained to express cocaine or social interaction CPP and tested for eventual persisting preference 7 and 14 days after CPP expression, cocaine preference was persistent, but social preference was abolished after the first test. These results suggest that PKA in the NAc is involved in drug reward learning that might lead to addiction and that only drug, but not natural, reward is persistent.     

Towards an early diagnosis of Alzheimer disease: a precise and parallel image segmentation approach via derived hybrid cross entropy thresholding method

Alzheimer’s disease (AD) is an irreversible and progressive brain disease causing brain degenerative disorder and dementia. An early diagnosis of AD provides the individual an opportunity to participate in clinical trials. Computer Aided Diagnosis (CAD) system in the health care sector has been widely used and plays an important role in detecting such diseases. However, the main challenge of such systems is through identifying the region of interest obtained through precise segmentation. This paper attempts to solve the segmentation issue by developing a precise image segmentation model. The proposed model used a derivation of a hybrid cross entropy thresholding technique for the precise extraction of infected regions. In other words, a novel segmentation methodology has been proposed using the output derivation of both Gamma and Gaussian distributions. Moreover, to tackle the performance and time-consuming problems in digital image segmentation, a parallel boosting methodology has been developed and implemented. Through using the ADNI, OASIS, and MIRIAD benchmark datasets, the experimentation results validate the effectiveness of the proposed model through achieving more than 90% accuracy with 2x times speed improvement compared to other benchmark segmentation methods.