Neuromodulation, the localized manipulation of neuronal activity, holds therapeutic promise for neurological/neuropsychiatric diseases. There is currently an unmet need for a non-invasive, safe, personalized and inexpensive method of neuromodulation that can be used to functionally target and manipulate brain networks to achieve therapeutic goals. Our long-term goal is to develop a novel neuromodulatory method based on individually designed sensory stimulation. While there have been notable successes in characterizing the “forward” mapping from sensory stimulus to brain response, harnessing this strategy for neuromodulation requires solution of the inverse problem: designing sensory stimuli that activate pre-specified targets. Using the visual system as a model, we propose building a computational procedure that, given a targeted brain region or network to activate, can synthesize personalized, two-dimensional images that achieve that target. Our central hypothesis is that combining a generative artificial neural network with information about an individual’s brain will enable such a procedure. We plan to create 1) a computational model that can predict brain responses to two-dimensional images and 2) synthetic images that can achieve a predetermined target brain response. We will validate our approach via fMRI experiments in 20 individuals. The next step would be to show repeated presentation of the synthetic stimuli could predictably change the brain’s connectivity network, and finally, to show that these changes could alleviate symptoms or boost recovery. The project is significant in that it could be used to develop a novel, non-invasive, inexpensive and convenient neuromodulatory tool that could lessen the burden of neuropsychiatric disease.