Back to the lab: neuroadaptive technology for cognitive neuroscientists
Neuroadaptive technology is widely recognised for its exciting potential to augment human-computer interaction. Besides these applied uses, neuroadaptive technology provides a powerful technique to address basic research questions within the cognitive neurosciences that have been challenging to tackle with conventional methods.
The classic taxonomy of cognitive processes is based on cognitive psychology theory and was developed largely blind to the functional organization of the brain. Therefore, classic cognitive tasks tend to tap multiple cognitive processes that involve multiple brain networks. Resolving this many-to-many mapping problem between cognitive tasks and brain networks is practically intractable with standard functional magnetic resonance imaging (fMRI) methodology as only a small subset of all possible cognitive tasks can be tested. This is problematic, as studying only a fraction from the large space of cognition has resulted in over-specified inferences about functional-anatomical mappings with a misleadingly narrow function being proposed as the definitive role of a network, concealing the broader role a network may play in cognition.
In this talk, I present an alternative approach that resolves these problems by combining real-time fMRI with a branch of machine learning, Bayesian optimization. Neuroadaptive Bayesian optimization is a powerful strategy to efficiently explore many more experimental conditions than is currently possible with standard methodology in a single individual. I will present results from a study where we used this method to understand the unique contributions of two frontoparietal networks in cognition. Our findings deviate from previous meta-analyses and hypothesized functional labels for these networks. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy.
In addition, I touch on the potential of the approach in combination with non-invasive brain stimulation (e.g., tACS) and for accelerated biomarker discovery. I conclude my talk by discussing ethical implications associated with neuroadaptive technology.
