Original contributions Investigating the neural basis for fMRI-based functional connectivity in a blocked design: application to interregional correlations and psycho-physiological interactions Jieun Kim, Barry Horwitz ⁎ Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD 20892, USA Received 25 June 2007; revised 17 October 2007; accepted 18 October 2007 Abstract This paper investigates how well different kinds of fMRI functional connectivity analysis reflect the underlying interregional neural interactions. This is hard to evaluate using real experimental data where such relationships are unknown. Rather, we use a biologically realistic neural model to simulate both neuronal activities and multiregional fMRI data from a blocked design. Because we know how every element in the model is related to every other element, we can compare functional connectivity measurements across different spatial and temporal scales. We focus on (1) psycho-physiological interaction (PPI) analysis, which is a simple brain connectivity method that characterizes the activity in one brain region by the interaction between another region's activity and a psychological factor, and (2) interregional correlation analysis. We investigated the neurobiological underpinnings of PPI using simulated neural activities and fMRI signals generated by a large-scale neural model that performs a visual delayed match-to-sample task. Simulated fMRI data are generated by convolving integrated synaptic activities (ISAs) with a hemodynamic response function. The simulation was done under three task conditions: high-attention, low-attention and a control task (‘passive viewing’). We investigated how biological and scanning parameters affect PPI and compared these with functional connectivity measures obtained using correlation analysis. We performed correlational and PPI analyses with three types of time-series data: ISA, fMRI and deconvolved fMRI (which yields estimated neural signals) obtained using a deconvolution algorithm. The simulated ISA can be considered as the ‘gold standard’ because it represents the underlying neural activity. Our main findings show (1) that evaluating the change in an interregional functional connection using the difference in regression coefficients (as is essentially done in the PPI method) produces results that better reflect the underlying changes in neural interrelationships than does evaluating the functional connectivity difference as a change in correlation coefficient; (2) that using fMRI and deconvolved fMRI data led to similar conclusions in the PPI-based functional connectivity results, and these generally agreed with the nature of the underlying neural interactions; and (3) the functional connectivity correlation measures often led to different conclusions regarding significance for different scanning and hemodynamic parameters, but the significances of the PPI regression parameters were relatively robust. These results highlight the way in which neural modeling can be used to help validate the inferences one can make about functional connectivity based on fMRI data. Published by Elsevier Inc. Keywords: Neural modeling; fMRI; Brain; Hemodynamic response function; Deconvolution 1. Introduction Many neuroimaging studies have attempted to evaluate human brain functional interactions from data obtained by various imaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET) or electro-/magneto-encephalography (EEG/MEG). One type of neural functional interactivity is called functional connectivity [1,2]. Two neural entities are functionally connected if their activities show a strong covariance with each other, but functional connectivity does not necessarily imply either a direct anatomical connection Available online at www.sciencedirect.com Magnetic Resonance Imaging 26 (2008) 583 – 593 ⁎ Corresponding author. Tel.: +1 301 594 7755. E-mail address: horwitzb@nidcd.nih.gov (B. Horwitz). 0730-725X/$ – see front matter. Published by Elsevier Inc. doi:10.1016/j.mri.2007.10.011