477 Identifying the Neural Correlates of the Triple Code Model in Numerical Cognition Using Stereotactic Electroencephalography in Epilepsy Patients

INTRODUCTION: The capacity to process numerical quantities is an essential human trait. The “Triple Code Model” (TCM) in numerical cognition, theorizes that different neural substrates encode the processing of visual, auditory, and nonsymbolic numerical representations formats. Limited data exists examining the direct electrophysiological activity involved in numerical cognition. METHODS: Thirteen patients with medically refractory epilepsy that underwent sEEG for the localization of seizures performed a passive numerical recognition task. Subjects were presented with a number quantity from 1 to 9 in one of five different representation formats: two were auditory (spoken numbers, sequential beeps) and three were visual (Arabic numeral, written word, assortment of dots). A total of 2,482 electrode contacts were analyzed. Time-frequency spectrograms were reduced with principal component analysis (PCA) and passed into a linear support vector machine (SVM) classification algorithm to identify neural correlates of number processing. RESULTS: The highest classification accuracy in number processing, irrespective of representation format occurred in the bilateral parietal lobes, right lingual cortex, and bilateral putamen. A total of 407, 776, 854, 475, and 513 contacts had significant classification values during Arabic, beep, spoken, written, and dots, respectively. Bilateral superior temporal cortices demonstrated the best classification value for the processing of auditory formats, whereas visual formats (Arabic and written number) showed greatest involvement in the frontal lobe and inferior precentral gyrus. Classification of dots indicated preferential engagement of the left parietal cortex. Spectral analyses revealed that non-gamma frequency bands held greater than chance classification values to characterize format-specific number representations. CONCLUSIONS: Using a linear SVM classifier with PCA, we examined the Triple Code Model in numerical cognition and identified several neural structures with high classification values involved in numerical processing.