Since the HRF evolves much slower and lasts much longer than the NRF, Eq. (2) can be rewritten as Eq. (3). (3)f(r,t)=p(r,t)⋅∫Tss2(r,t)dt+fn(r,t) where p(r,t)=∑n=1Nδ(t−nTISI)h(t). As graphically illustrated in Fig. 4, Eq. (3) suggests that the BOLD signal at an activated voxel can be modeled as the product of a predictor function and a parameter proportional to the time integral of the power of the local event-related synaptic currents plus the noise. This model resembles the GLM used in the conventional fMRI analysis, except that the predictor function is defined as the convolution of the HRF with a train of delta functions (instead of a box-car function) which represent the occurence of discrete stimuli. The model parameter (or regression coefficient) can be estimated by fitting the model to voxel-specific BOLD fMRI time series. The estimated parameter, known as the BOLD effect size, represents the relative strength of the regional BOLD response, since it reflects the ratio between the measured and predicted BOLD time courses (i.e. f (r,t)/p(t)). More importantly, the model shown in Fig 4 gives the BOLD effect size a
