Michael W. Cole
Center for Molecular and Behavioral Neuroscience (CMBN)
Rutgers University – Newark
Understanding neurocognitive computations – such as those disrupted in mental disorders – will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. Brain connectivity clearly has something to do with it, and decades of “connectionist” (and recent “deep learning”) theory suggests connectivity patterns specify distributed neural computations. I will share my laboratory’s efforts to map the human brain’s functional network organization and to determine how that organization shapes distributed cognitive processes and mental health. This has involved identification of “flexible hubs” – cortical regions that adaptively shift their connectivity to implement instructed task procedures. We have recently linked flexible hubs to the regulation of mental health. Also central to these efforts is the estimation of activity flow – the movement of evoked activity through brain network connections. Estimating activity flow quantifies the likely contribution of a network organization (such as one estimated using resting-state functional connectivity) to function-specific activity patterns (such as fMRI responses to cognitive events). I will cover application of activity flow mapping to predict whole-brain activation patterns across a variety of tasks, such as tasks involving working memory and rapid instructed task learning, as well as in the context of pre-clinical Alzheimer’s disease. These developments promise to better integrate theoretical/computational neuroscience and empirical neuroscience with mutual benefits across both fields.