Number of types of synaesthesia can be predicted by structural and functional neuroimaging data.

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract; 'above chance' prediction is stated but no formal significance thresholds or permutation test results are provided

• Effect Size: Not reported in abstract; magnitude of prediction (e.g., R², cross-validated accuracy) is unavailable for assessment

• Primary Outcome: Several of 13 pre-specified brain-based biomarkers (functional MRI hub connectivity measures and structural MRI intracortical myelination among others) predicted above-chance the number of synaesthesia types in individuals with synaesthesia, using elastic net regression

• Nnt Or Sensitivity: Not applicable (basic science neuroimaging prediction study); no sensitivity, specificity, NNT, or hazard ratio reported

• Confidence Interval: Not reported in abstract

Clinical Application

Structural and functional MRI are available in tertiary Australian centres but are not indicated for synaesthesia assessment in routine clinical practice. The elastic net predictive model requires validation before any clinical translation. Cost, accessibility, and the absence of a clinical decision pathway make neuroimaging-based synaesthesia profiling unfeasible outside research settings. Synaesthesia is not currently recognised as a clinical disorder requiring diagnosis or treatment under Australian Medicare Benefits Schedule (MBS) neuroimaging indications, RACGP clinical guidelines, or TGA-approved diagnostic frameworks. There is no PBS-listed intervention for synaesthesia. This study has no immediate implications for Australian clinical practice. It may, however, inform future research into neurodevelopmental conditions with overlapping profiles (e.g., autism spectrum disorder, ADHD) where synaesthesia co-occurs at elevated rates, and where neuroimaging biomarkers are under active investigation. Australian researchers in cognitive neuroscience and neuropsychology at institutions such as the University of Melbourne, Macquarie University, and the Australian National University may find this work relevant to ongoing synaesthesia and perceptual neuroscience programmes. Adults with confirmed synaesthesia, particularly those with multiple synaesthesia types. No current clinical application for diagnosis, prognosis, or treatment selection. Findings are relevant to researchers studying neurodevelopmental perceptual variation and the neuroscience of individual differences.

Abstract

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.

References

1. 1.Fine, C., & Ward, J. (2026). Number of types of synaesthesia can be predicted by structural and functional neuroimaging data. Neuropsychologia. https://doi.org/10.1016/j.neuropsychologia.2026.109468