Using ventral striatum-prefrontal cortex (VS-PFC) functional connectivity signals to predict intrinsic motivation: New ideas from a machine learning perspective

Over the past decade, educational neuroscience research has increasingly identified the functional connectivity between the ventral striatum (VS) and the prefrontal cortex (PFC) as a significant biomarker for intrinsic motivation in adolescent students. Despite these findings, there remains a dearth of methods for utilizing such connectivity indices to directly measure intrinsic motivation levels in educational settings. With the aims of informing educational and cognitive neuroscience researchers of intrinsic motivation about new technical ideas that can advance their research, this opinion paper presents an overview of the most important neuroscientific research on intrinsic motivation in human youths together with a new methodological proposal. Crucially, we proposed the use of VS-PFC functional connectivity signals, extracted from functional magnetic resonance imaging (fMRI) data analysis, as predictors of intrinsic motivation through a machine learning (ML)-based linear regression model. By developing a robust linear regression model buttressed by tried-and-tested ML techniques, our method aims to facilitate rapid and precise predictions of intrinsic motivation levels without the need for repeated assessments of intrinsic motivation, thereby saving time and resources in subsequent studies. To elucidate our model, we presented equations showing how regression parameters are computed using the conventional ordinary least squares (OLS) method and the ML-based gradient descent (GD) method, highlighting their differences in the process. Potential technical difficulties concerning the establishment and validation of our ML-based model are also discussed with concrete recommendations on how to resolve them. With the right implementation, we expect our method to benefit longitudinal fMRI studies examining developmental brain and behavioral changes in intrinsic motivation and educational intervention programs that require quick and accurate identification of students’ intrinsic motivation levels. Also noteworthy is that our proposed methodology is not limited to predicting intrinsic motivation alone and can be adapted for other functional connectivity and behavioral variables that may predict different outcome variables. The flexibility of our ML-based regression model will allow researchers to tailor the model by selecting alternative variables to suit their specific research needs.