Author:
Tan Hao,Rockhill Alexander P.,Lopez Ramos Christian G.,Nerison Caleb,Shafie Beck,Shahin Maryam N.,Fecker Adeline,Ismail Mostafa,Cleary Daniel R.,Collins Kelly L.,Raslan Ahmed M.
Abstract
ABSTRACTThe ability to conceptualize numerical quantities is an essential human trait. According to the “Triple Code Model” (TCM) in numerical cognition, distinct neural substrates encode the processing of visual, auditory, and nonsymbolic numerical representations. While our contemporary understanding of human number cognition has benefited greatly from advances in clinical imaging, limited studies have investigated the intracranial electrophysiological correlates of number processing. In this study, 13 subjects undergoing stereotactic electroencephalography for epilepsy participated in a number recognition task. Drawing upon postulates of the TCM, we presented subjects with numerical stimuli varying in representation type (symbolic vs. non-symbolic) and mode of stimuli delivery (visual vs. auditory). Time-frequency spectrograms were dimensionally reduced with principal component analysis and passed into a linear support vector machine classification algorithm to identify regions associated with number perception compared to inter-trial periods. Across representation formats, the highest classification accuracy was observed in the bilateral parietal lobes. Auditory (spoken and beeps) and visual (Arabic) number formats preferentially engaged the superior temporal cortices and the frontoparietal regions, respectively. The left parietal cortex was found to have the highest classification for number dots. Notably, the putamen exhibited robust classification accuracies in response to numerical stimuli. Analyses of spectral feature maps revealed that non-gamma frequency below 30 Hz held greater than chance classification value and could be potentially used to characterize format specific number representations. Taken together, our findings obtained from intracranial recordings provide further support and expand on the TCM model for numerical cognition.
Publisher
Cold Spring Harbor Laboratory
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