  Whereas MRI produces anatomic information about the brain, functional MRI
(fMRI) tells us about neural activity within the brain, including how various
regions communicate with each other. The full chorus of conversations within
the brain is summarized elegantly in the adjacency matrix. Although
information-rich, adjacency matrices typically provide little in the way of
intuition. Whereas trained radiologists viewing anatomic MRI can readily
distinguish between different kinds of brain cancer, a similar determination
using adjacency matrices would exceed any expert's grasp. Artificial
intelligence (AI) in radiology usually analyzes anatomic imaging, providing
assistance to radiologists. For non-intuitive data types such as adjacency
matrices, AI moves beyond the role of helpful assistant, emerging as
indispensible. We sought here to show that AI can learn to discern between two
important brain tumor types, high-grade glioma (HGG) and low-grade glioma
(LGG), based on adjacency matrices. We trained a convolutional neural networks
(CNN) with the method of deep neuroevolution (DNE), because of the latter's
recent promising results; DNE has produced remarkably accurate CNNs even when
relying on small and noisy training sets, or performing nuanced tasks. After
training on just 30 adjacency matrices, our CNN could tell HGG apart from LGG
with perfect testing set accuracy. Saliency maps revealed that the network
learned highly sophisticated and complex features to achieve its success.
Hence, we have shown that it is possible for AI to recognize brain tumor type
from functional connectivity. In future work, we will apply DNE to other noisy
and somewhat cryptic forms of medical data, including further explorations with
fMRI.
