In standardized low-resolution brain electromagnetic tomography (SLORETA), the electrical activity recorded by the electroencephalogram (EEG) electrodes, is transformed into a three-dimensional source distribution within the brain. It helps us in understanding the internal details of human brain and its working. It helps us in visualizing and analyzing the activity of the brain electrically with exceptional precision. The EEG recordings along with mathematical algorithms (advanced) help in reconstruction of neural foundational blocks for the recorded brain signals. This transformation is achieved by solving an inverse problem using linear, weighted minimum norm estimation. By solving this inverse problem, SLORETA estimates the locations and strengths of neural sources underlying the measured EEG signals. The accuracy and reliability of sLORETA have been validated through comparisons with other neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET).
Top1. Introduction
The main objective of the chapter described is to help neuroscience and mental health professionals understand the role of emotions in decision-making. The author of the thesis aims to address the fact that while there is increasing evidence that emotions play a critical role in decision-making, there is a lack of a common language and a model that can be used to illustrate the potential neurological pathways involved. Overall, the main objective of this is to provide a comprehensive and integrated understanding of the neuroscience of emotions and decision-making that can be applied in the context of mental health and neuroscience (Ocklenburg et al., 2012; Jancke et al., 2001).
To achieve this objective, the author may review and synthesize existing literature on the topic, as well as conduct their own research. They may also propose a new model that integrates the different pathways and processes involved in emotional decision-making (Bocquillon et al., 2011; Gevins et al., 1997), based on their findings and analysis.
Aims to highlight the importance of emotions in decision-making processes in the context of mental health and neuroscience. While there is a growing body of research that suggests emotions play a crucial role in decision-making, there is a lack of a common language and a clear model that can help professionals understand the potential neurological pathways involved (Ozgoren et al., 2009; Hugdahl, 2005).
To address this issue, the thesis may propose a framework that integrates the current understanding of the neuroscience of emotions and decision-making. This framework may include a common language and terminology that can be used by professionals across different disciplines, such as psychology, psychiatry, and neuroscience.
The framework may also include a model that illustrates the potential neurological pathways involved in decision-making under different emotional states. For example, the model may show how different brain regions are activated when a person is experiencing fear or anger, and how these activations may influence their decision-making processes.
Standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA) (An et al., 2017; Bocquillon et al., 2011) is a neuroimaging technique used to estimate and map electrical brain activity. It's primarily based on electroencephalography (EEG) data. sLORETA uses a standardized head model and electrode coordinates to calculate the likely distribution of neural activity throughout the brain. It operates at lower spatial resolution but offers excellent temporal resolution, making it useful for studying dynamic brain processes and diagnosing neurological and psychiatric disorders (Bayazit et al., 2008; Jancke et al., 2001). sL.ORETA has been widely used in neuro scientific research, including studies on cognitive processes, epilepsy, brain disorders, and neuro feedback training. Its clinical applications range from pre-surgical evaluation in epilepsy patients to investigating neural correlates of psychiatric disorders.
sLORETA utilizes a specific mathematical (Bocquillon et al., 2011; Hugdahl, 2003) and computational approach to achieve this source localization. Here is a simplified overview of the architecture of sLORETA as shown in Figure 1.
Figure 1. Frontal-negative slow oscillation
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