Abstract
Neurochemistry of human cognition remains uninvestigated because neuroimaging techniques have limited ability to detect neurochemical changes associated with cognitive processing. In recent years investigators have used molecular imaging to develop methods for detection, mapping and measurement of neurotransmitters released acutely during cognitive processing in the live human brain. Most of these methods exploit the competition between endogenous neurotransmitter and a radiolabeled receptor ligand. Because of the competition, the ligand concentration decreasesin the brain areas where neurotransmitter is released endogenously during a task performance. The decreased concentration is detected by measuring the activity of intravenously injected radioligand using a PET camera. The PET data acquired dynamically isapplied to a receptor kinetic model, which estimates kinetic parameter values at multiple time point. Based on these values dopamine released during performance of a task is detected, mapped and measured. By using different ligands, dopamine released inside or outside the striatum can be detected using this technique. The neurotransmitter imaging technique at present can detect acute changes only in dopamine neurotransmission. Since it significantly limits the scope, there is a need to develop methods and ligands for detection of acute changes in the levels of other neurochemicals.
Keywords: Molecular imaging, raclopride, fallypride, neurochemistry of cognition, dopamine, neurotransmitter, neurochemistry, positron emission tomography, ligand, Detection, neuromodulators
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
3