Neural Networks of Colored Sequence Synesthesia

Abstract

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.

Resting state functional MRI reveals abnormal network connectivity in neurofibromatosis 1

“…The primary benefit of stability selection is that it retains only the most stable edges in the network, therefore eliminating the need for “hard” thresholding of the network (i.e., all values less than 0.3 would be arbitrarily discarded). This approach results in a sparse network whose remaining edges represent direct connections between nodes [Narayan and Allen, ; Ryali et al, ; Tomson et al, ]. To estimate a network for each group, all NF1 time‐series were whitened and then concatenated into a single time‐series.…”

“…This procedure allows us make the following exemplar statement: “Subject 1 has an edge connecting regions 4 and 5 in 81 of the 100 total resamplings (i.e., 81% of the time).” As the number of subjects is relatively small (<100), we model the probability of observing an edge using a beta‐binomial distribution and construct a corresponding two‐sample random effects test statistic [Crowder, ; Liang and Hanfelt, ; Narayan et al, ]. These test statistics do not follow known distributions, so we perform a basic permutation test, permuting subject labels to determine whether any significant difference is due to chance or actual group assignment [Good, ; Tomson et al, ]. We also test whether the total number of edges (after R3 sparsification) differed between groups.…”

“…One of the primary goals of rs‐fcMRI is to quantify the functional relationship between multiple brain regions (nodes) by constructing a network of significant relationships between regions (edges). Once the subject‐ and group‐level network variance is accounted for [Narayan and Allen, ], a network graph is estimated and local properties such as edge strength can be compared between groups [Tomson et al, ]. Further application of graph theoretical techniques are applied to the network to characterize the global organization and structure of the resulting network [Bullmore and Sporns, ; Rubinov and Sporns, ; van den Heuvel and Sporns, ].…”

Developing Synaesthesia

“…There are two other common forms of synesthesia that can be mind-driven and that are variants of the already mentioned timeunit synesthesia: the first is known as colored sequence synesthesia in which names of time units, such as days of the week and months of the year, are colored differently from their graphemic constituents Tomson et al, 2013), and the second is known as spatial sequence synesthesia in which ordinal categories involving numbers, time and alphabet elicit experience of spatial forms .…”

Synaesthesia in Chinese characters: The role of radical function and position

“…Around 80-90% of known synaesthesias involve colour triggered by language ; for example, in grapheme-colour synaesthesia, sensations of colour are triggered by letters or digits. During the last decade, the neural basis of synaesthesia has been examined in both functional imaging studies (e.g., Aleman, Rutten, Sitskoorn, Dautzenberg, & Ramsey, 2001;Hubbard, Arman, Ramachandran, & Boynton, 2005;Nunn et al, 2002;Sperling, Prvulovic, Linden, Singer, & Stirn, 2006;Tomson et al, 2013) and in structural imaging studies (e.g., Hanggi, Beeli, Clechslin, & Jande, 2008;Hupé, Bordier, & Dojat, 2012;Rouw & Scholte, 2007;Weiss & Fink, 2009). Together these show that synaesthetic experiences are characterised by atypical patterns of brain activity when compared with non-synaesthetes, and differences in white matter and grey matter structure.…”