Georgia State University

About: Georgia State University is a education organization based out in Atlanta, Georgia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 13988 authors who have published 35895 publications receiving 1164332 citations. The organization is also known as: GSU & Georgia State.

Septohippocampal acetylcholine: involved in but not necessary for learning and memory?

Abstract: The neurotransmitter acetylcholine (ACh) has been accorded an important role in supporting learning and memory processes in the hippocampus. Cholinergic activity in the hippocampus is correlated with memory, and restoration of ACh in the hippocampus after disruption of the septohippocampal pathway is sufficient to rescue memory. However, selective ablation of cholinergic septohippocampal projections is largely without effect on hippocampal-dependent learning and memory processes. We consider the evidence underlying each of these statements, and the contradictions they pose for understanding the functional role of hippocampal ACh in memory. We suggest that although hippocampal ACh is involved in memory in the intact brain, it is not necessary for many aspects of hippocampal memory function.

The Lesbian Internalized Homophobia Scale: A Rational/Theoretical Approach

Abstract: This article reports the development and psychometric properties of a new scale that measures internalized homophobia in lesbians: the Lesbian Internalized Homophobia Scale (LIHS). This 52-item measure was developed using a rational/theoretical approach of test construction and includes five subscales. Research findings, based on a sample of 303 female participants, supported the reliability and validity of the LIHS in assessing internalized homophobia in lesbians. Implications for research and practice are discussed.

Applications of Deep Learning to MRI Images: A Survey

Abstract: Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tissue contrast, in recent years, Magnetic Resonance Imaging (MRI) has been attracting increasing attention. With the development of deep learning, many innovative deep learning methods have been proposed to improve MRI image processing and analysis performance. The purpose of this article is to provide a comprehensive overview of deep learning-based MRI image processing and analysis. First, a brief introduction of deep learning and imaging modalities of MRI images is given. Then, common deep learning architectures are introduced. Next, deep learning applications of MRI images, such as image detection, image registration, image segmentation, and image classification are discussed. Subsequently, the advantages and weaknesses of several common tools are discussed, and several deep learning tools in the applications of MRI images are presented. Finally, an objective assessment of deep learning in MRI applications is presented, and future developments and trends with regard to deep learning for MRI images are addressed.