It has been suggested that synaesthetes have a distinct neurocognitive profile with a broad variety of cognitive and behavioural differences. Recent studies have shown that people with more types (relative to fewer types) of synaesthesia are easier to classify using machine learning of questionnaires and cognitive test data. This suggests a spectrum within synaesthesia, despite synaesthesia itself being typically defined in categorical terms. This study uses the same basic approach applied to 13 brain-based biomarkers. These have previously been shown to distinguish synaesthetes from controls, but it is not known whether they explain heterogeneity amongst synaesthetes. Using machine learning methods (elastic net regression), we were able to find several biomarkers that predict above chance the number of types of synaesthesia. These include both functional MRI (the extent to which brain regions act as hubs) and structural MRI (e.g., intracortical myelination) measures. This is the first project that explores whether it's possible to predict the breadth of synaesthesia from brain-based measures.
Keywords: Brain-based biomarkers; Machine learning; Synaesthesia.
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