Functional Near Infrared Spectroscopy (fNIRS) is a brain imaging technique that is well-suited for use in young children, making it particularly useful for investigating the neural bases of the development of executive functions. In the present study, children (ages 4-10) underwent fNIRS while completing response inhibition and working memory tasks. While both tasks were associated with increases in oxyhemoglobin and decreases in deoxyhemoglobin, we found that strength of activation increased with age and with improvements in task performance. These findings support the relation between emerging executive functions and maturation of the prefrontal cortex.

The Iowa Gambling Task (IGT) provides a framework to evaluate an individual decision-making process through a simulated card game where the risks and rewards vary by the decks chosen. Participants are expected to understand the logic behind the allocation of gains and losses over the course of the test and adapt their pattern of choices accordingly. This review explores the scientific work on studying problem gambling via the IGT while employing neuroimaging techniques. We first concentrate on the historical evolution of the IGT as a mechanism for studying gamblers’ behavioral patterns. Our research will also discuss the prefrontal cortex as this region of the brain is most affected by changes in behavioral patterns. In this review, we describe a number of features that may be useful in investigating decision-making patterns that lead to gambling addiction. We discuss the evidence base to date including experiments involving gambling behavior in different groups of participants (e.g., males and females, adults and minors, patients and controls) and alterations to experiment conditions that provide more thorough understanding of thought patterns in potential gamblers. We conclude that psychological testing combined with functional imaging provide powerful tools to further examine the relationships between functional impairment of the brain and a person’s ability to objectively anticipate the end results of their decisions.

BackgroundWe have explored the potential prefrontal hemodynamic biomarkers to characterize subjects with Traumatic Brain Injury (TBI) by employing the multivariate machine learning approach and introducing a novel task‐related hemodynamic response detection followed by a heuristic search for optimum set of hemodynamic features. To achieve this goal, the hemodynamic response from a group of 31 healthy controls and 30 chronic TBI subjects were recorded as they performed a complexity task.MethodsTo determine the optimum hemodynamic features, we considered 11 features and their combinations in characterizing TBI subjects. We investigated the significance of the features by utilizing a machine learning classification algorithm to score all the possible combinations of features according to their predictive power.Results and ConclusionsThe identified optimum feature elements resulted in classification accuracy, sensitivity, and specificity of 85%, 85%, and 84%, respectively. Classification improvement was achieved for TBI subject classification through feature combination. It signified the major advantage of the multivariate analysis over the commonly used univariate analysis suggesting that the features that are individually irrelevant in characterizing the data may become relevant when used in combination. We also conducted a spatio‐temporal classification to identify regions within the prefrontal cortex (PFC) that contribute in distinguishing between TBI and healthy subjects. As expected, Brodmann areas (BA) 10 within the PFC were isolated as the region that healthy subjects (unlike subjects with TBI), showed major hemodynamic activity in response to the High Complexity task. Overall, our results indicate that identified temporal and spatio‐temporal features from PFC's hemodynamic activity are promising biomarkers in classifying subjects with TBI.