Neural Networks of Colored Sequence Synesthesia

Synesthesia is a condition in which normal stimuli can trigger anomalous associations. In this study, we exploit synesthesia to understand how the synesthetic experience can be explained by subtle changes in network properties. Of the many forms of synesthesia, we focus on colored sequence synesthesia, a form in which colors are associated with overlearned sequences, such as numbers and letters (graphemes). Previous studies have characterized synesthesia using resting-state connectivity or stimulus-driven analyses, but it remains unclear how network properties change as synesthetes move from one condition to another. To address this gap, we used functional MRI in humans to identify grapheme-specific brain regions, thereby constructing a functional synesthetic network. We then explored functional connectivity of color and grapheme regions during a synesthesia-inducing fMRI paradigm involving rest, auditory grapheme stimulation, and audiovisual grapheme stimulation. Using Markov networks to represent direct relationships between regions, we found that synesthetes had more connections during rest and auditory conditions. We then expanded the network space to include 90 anatomical regions, revealing that synesthetes tightly cluster in visual regions, whereas controls cluster in parietal and frontal regions. Together, these results suggest that synesthetes have increased connectivity between grapheme and color regions, and that synesthetes use visual regions to a greater extent than controls when presented with dynamic grapheme stimulation. These data suggest that synesthesia is better characterized by studying global network dynamics than by individual properties of a single brain region.