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Brain Processes During Expert Cognitive-Motor Performance: The Impact of Mental Stress and Emotion Regulation
Published in Steven Kornguth, Rebecca Steinberg, Michael D. Matthews, Neurocognitive and Physiological Factors During High-Tempo Operations, 2018
Bradley D. Hatfield, Amy J. Haufler
Significant advancements in our understanding of the role of cortical and subcortical processes, and their linkage, in the regulation of emotion have occurred over the last decade (Davidson 2002) with the elucidation that frontal EEG recordings offer a powerful index of one’s emotional state. Neurobiological and imaging work (Adolphs, et al. 1995, Davidson 2002, and Phan et al. 2002) has extended our understanding of the neural circuitry of anxiety consisting of the deep subcortical structures of the limbic system, particularly the two amygdalae structures that are essential to the orchestration of arousal-related processes throughout the brain and body, like the fight or flight response. The limbic system is a major center for emotion formation and processing, learning, and memory. The limbic system consists of the cingulate gyrus, parahippocampal gyrus, dentate gyrus, hippocampi (left and right) and amygdalae (left and right), which are represented bilaterally. The hippocampi are involved in memory storage and formation as well as complex cognitive processing, while the amygdalae are associated with forming complex emotional responses, particularly involving fear and aggression. The limbic structures are also connected with other major structures such as the cortex, hypothalamus, thalamus, and basal ganglia (Pinel 2000). Importantly, this circuitry is largely influenced by the frontal regions of the brain that can serve to inhibit or regulate fear (Northoff et al. 2004).
Introduction
Published in Limiao Deng, Cognitive and Neural Modelling for Visual Information Representation and Memorization, 2022
In terms of semantic memory, the most famous one is Rumelhart model proposed by Rogers and McClelland61. The purpose of the Rumelhart model is to activate the correct set of attributes when probed with an item and association. One of the main features of the model is that it can be extended to new stimuli based on the similarity between the stimuli learned. In the fields of anatomy, physiology, and cognitive neuroscience, there are also a lot of research on human brain memory activities and mechanisms. Many years of research have proved that the Medial temporal lobe (MTL) is an important structure which plays a key role in memory. The research results from Burwell62, Witter et al.63 showed that the main MTL regions of humans, monkeys, and rodents have functional structures for memory processing. MTL region can be divided into the perinasal cortex, the parahippocampal gyrus, the entorhinal cortex, and the hippocampus. Most of the inputs to the perinatal cortex come from areas that process “What” information about object properties, while most of the inputs to the parahippocampal gyrus come from areas that process “Where” information about space. Subsequently, the “What” and “Where” information flows remain largely separate, with information flows from the perinasal cortex mainly going to the lateral entorhinal cortex, while information flows from the parahippocampal gyrus into the entorhinal cortex, Where the “What” and “Where” information flows converge. The output of hippocampal processing involves the return of feedback connections from the hippocampus to the entorhinal cortex, then through the perinasal and parahippocampal gyrus, and finally to the neocortex64. Based on this, the researchers propose a variety of memory models based on the anatomical physiological structure of the human brain.
Improving Alzheimer’s disease classification by performing data fusion with vascular dementia and stroke data
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2021
Zoran Bosnić, Brankica Bratić, Mirjana Ivanović, Marija Semnic, Iztok Oder, Vladimir Kurbalija, Tijana Vujanić Stankov, Vojislava Bugarski Ignjatović
Besides aiming to increase the classification accuracy, Y. Y. Zhang et al. (2015) also managed to detect 30 brain regions that are related to the disease. Hinrichs et al. (2009) proposed the use of the Linear Programming Boosting for predicting Alzheimer’s disease by using MRI. Additional analysis of voxels selected during training phase revealed that they were mostly concentrated in hippocampus and parahippocampal gyrus which have been previously associated with Alzheimer’s disease.