Abstract
INTRODUCTION. Neural signals exhibit a wide variety of temporal characteristics, but in human brain studies it is difficult to derive the neural response time courses for local cortical regions of interest. A biophysically-based forward optimization procedure for the BOLD fMRI waveforms was constrained by a plausible parametrized model of local neural population responses. This paradigm allowed us to determine the temporal dynamics of the local neural populations during the various phases of the mental calculation process within the sequence of processing regions in the intra-parietal sulcus (IPS), which is well known to be involved in this visuo-cognitive activity. METHODS. BOLD responses were measured throughout the human brain using a 3T scanner with a 1 s sampling rate and a jittered event-related design for stimuli consisting of temporally sequenced numeric equations together with a trial solution and error feedback about response correctness. The responses to the visual number presentations in each component were fit by a model with three waveform parameters for the neural response and three for the BOLD response, plus an overall scaling parameter. RESULTS. The pattern of response dynamics differentiated bilateral areas corresponding to the angular gyrus and IPS regions 1-5 along the intraparietal sulcus. While the responses to the initial number and operator presentations were typically brief throughout retinotopic cortex, the angular gyrus showed prolonged responses that could support the number memory. Typically, IPS1-4 showed strong involvement in the calculation phase, while IPS5 was predominantly active during evaluation and response selection for the trial solution. CONCLUSION. This novel optimization technique allows estimation of the neural signal dynamics underlying the BOLD waveforms in an algebraic processing task, allowing the differentiation of distinct functional components and their causal roles in the information flow among cortical response areas lying along the IPS.
NSF Supported by NSF grant # 0846229.