Novel deep learning method may help predict cognitive function

Northwestern investigators have developed a deep learning-based method that can predict cognitive function capacity based on brain shape and structure, detailed in a study published in Scientific Reports. The method, which uses graph convolutional neural networks (gCNNs), may also reveal new insights into the relationship between brain morphology and different cognitive functions as well as the decline of brain function. "When we apply the rich capabilities of CNNs to graph representation of the brain, we can explore the brain as an image in a previously unexplored way," said S. Kathleen Bandt, MD, assistant professor of Neurological Surgery and a co-author of the study. Understanding how the relationship between brain structure and cognitive function changes throughout the life course has remained elusive. However, previous work suggests that fluid intelligence--the ability to problem solve and think and reason abstractly--depends heavily on two regions of the brain: the prefrontal cortex and parietal cortex, both of which are involved in decision-making and sensory perception, among other functions.