(Un)crowding modulates recurrent connectivity in the visual cortex

Introduction: In crowding, neighboring elements impede the perception of a target. Surprisingly, increasing the number of neighboring elements can decrease crowding, i.e., lead to uncrowding (Manassi et al., 2015). Few neuroimaging studies have explored the neural correlates of crowding (Bi et al., 2009; Anderson et al., 2012; Millin et al., 2013; Chen et al., 2014; Chicherov et al., 2014; Ronconi et al., 2016). The resulting evidence about the neural locus of crowding has been equivocal, but crowding is consistently found to attenuate the BOLD response. Here, we used fMRI to investigate the effects of uncrowding on the BOLD response and effective connectivity in the visual cortex. We expected that the percent BOLD signal change (PSC) would best reflect behavioral differences related to (un)crowding in the brain areas driving the crowding effect. We used dynamic causal modelling (DCM; Friston, 2003) to investigate whether (un)crowding is rather a top-down, bottom-up or recurrent processing phenomenon. In accordance with the notion that crowding depends on grouping, we hypothesized that PSC in higher visual areas would better reflect (un)crowding and that effective connectivity would reflect the involvement of top-down processing in uncrowding. Methods: The fMRI experiment consisted of seven conditions: (1) target only (eight circular oblique target gratings surrounding a central fixation dot, tilted either clockwise (CW) or counterclockwise (CCW), (2) 2-flanker (each of the 8 targets was flanked by an inside and outside vertical grating), (3) 4-flanker (one inside and three outside flanker gratings), (4) annulus-flanker (inside and outside flankers connected into annuli), and (5-7) control conditions corresponding to conditions (2-4) with targets removed. Ten participants were asked to indicate whether target gratings were tilted CW or CCW by pushing one of two buttons or to push a button randomly if targets were not present. We calculated the PSC for each participant, each target region of interest (tg-ROI; target-activated areas in V1–V4 and LOC) and each of the conditions of interest and then correlated PSC with response accuracy. We estimated the effective connectivity between tg-ROIs using bilinear DCM and constructed a group-level parametric empirical Bayesian (PEB) model (Friston et al., 2016) over all the participants. After conducting a search over nested models and computing a Bayesian model average (BMA), we compared input-modulatory connectivity (DCM.B matrix) between crowding conditions in terms of excitatory and inhibitory bottom-up and top-down connectivity. Results: Target discrimination (accuracy) was highest in the single target condition, followed by annulus-flanker, 4-flanker and 2-flanker, respectively. A linear mixed model of PSC revealed significant main effects of condition (F(3,171) = 100.06, p