OBJECTIVES

Efficient behavior requires the ability to generalize from previous experiences. This can be achieved by behavioral strategies. We use many behavioral strategies; some strategies have strict S-R associations -stop at the RED light-; others are modifiable -balancing skills of a ballerina that becomes very useful during rock climbing- and serve as abstract strategies that enable solving problems.

Strategy use is common in our behavioral repertoire. A strategy can be defined as a set of computations associated with the act of planning and directing overall operations and movements involved in a task. A behavioral strategy that conscious behaving primates spontaneously adopted in order to maximize their rewards have been well characterized in the literature. These are called "Repeat-Stay"/"Change-Shift" strategy, and were shown to be associated with prefrontal neuronal activity during multi-unit intra-cortical recordings, clearly indicating a special role played by the prefrontal cortex in computing strategy use. It is important to understand how the human brain computes and processes strategies. This study aims at understanding the activation patterns, and neuronal connectivity in the human brain when engaged in tasks that require strategies. We hypothesize that application of strategies to solve tasks would show specifically and significantly increase Blood Oxygenation Dependant (BOLD) signal, particularly the fronto-polar cortex (PFp), ventral and orbitofrontal prefrontal cortex (PFV+o) in the human brains.

STUDY POPULATION

The two experiments described in this protocol may recruit up to 61 (6 for the pilot study) adult healthy volunteers.

DESIGN

The study will consist of functional Magnetic Resonance Imaging (fMRI). The fMRI will consist of two separate experiments: (1) the strategy experiment and (2) the memory control experiment. Data will be analyzed separately for each part of the experiment: Responses to tasks will be collected and this data (response times, accuracy rates) will be searched for statistically significant differences using linear contrasts in an ANOVA model.

The imaging fMRI data will be analyzed for statistically significant functional activations by using an implementation of the General Linear Model (GLM) (R. Turner et al., 1998; K. J. Friston et al., 2005) in Statistical Parametric Mapping (SPM).

OUTCOME MEASURES

We propose to acquire response data (response times, error rates), and functional brain activation data using fMRI. Therefore, we would have two outcome measures.

From the response data we will evaluate statistically significant differences in response times, error rates, learning curves.

From the BOLD fMRI data, the main outcome would be task specific neural activations that would regress with the behavioral tasks in a General Linear Model.

These measures will further our understanding about how the human brains use strategies during complex task performance. This will lay the foundation to our understanding for how we are capable of generalizing our experiences from specific instances. Such knowledge will also improve our understanding of various aspects of movement genesis, and is likely to eventually shed light on various movement disorders including psychogenic movement disorders and chorea among others.