ABSTRACT
In this chapter, the authors reviews how neuroimaging models have used predictive models to ask new questions and uncover brand new aspects of organization in cognition. They provide an overview of up-to-date information on how these approaches can be used for brain disorders. The authors discuss the usage of various tasks, such as Iowa gambling and moral judgment, as well as working memory with the usage of functional near-infrared spectroscopy. Neuroimaging applications using machine learning in regard to identifying biomarkers that are functional within the prefrontal cortex of individuals with game addiction and traumatic brain injuries are discussed and presented. Deep learning, a branch of machine learning, has gained pre-eminence over the classic machine learning framework. As a special type of artificial neural network, it depends on networks of simple units that form multiple layers to generate high level representations of increasing input. Cognitive neuroimaging probes the nervous system underlying human cognition. The brain images are complex and noisy data.
