Purchase this article with an account.
Ivan Alvarez, Benjamin De Haas, Chris A. Clark, Geraint Rees, D. Samuel Schwarzkopf; Optimal stimulation for population receptive field mapping in human fMRI. Journal of Vision 2013;13(9):31. doi: 10.1167/13.9.31.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Population receptive field (pRF) mapping is a model-based approach to estimating the visual field position tuning of neuronal populations. While pRF methods are better predictors of visual field maps than conventional phase-encoded methods (Dumoulin & Wandell, 2008; Zuiderbann et al., 2012), the optimal stimulation paradigm for producing visual field maps and effective pRF size estimation remains unclear.
Visual stimuli: dynamic, high-contrast stimulus within bars in either 1) linear or 2) logarithmic configurations (bar width scaled with eccentricity) drifting along cardinal and oblique directions. Each aperture comprised 560 volumes. A 'ridge' stimulus consisting of frequency-dissociated wedge and ring sections of the stimulus was also presented for 280 volumes.
Scan parameters: Three neurotypical adults underwent fMRI at 3T (TR= 2.55, voxel= 2.3x2.3x2.3 mm3[/sup]).
Data analysis: pRF predictions with a simple Gaussian model were convolved with an independently-estimated hemodynamic response function and compared to the observed fMRI responses at every surface point for each condition.
1. We observed no difference in visual region delineation between conditions; regions V1-V7, and VO, LO and MT complexes were identified in all subjects.
2. While the linear and logarithmic conditions produced similar results, the ridge condition produced significantly better model fits than other conditions.
3. Linear bars produced significantly higher pRF size estimates (σ = 4.81, ±1.93) than either its logarithmic counterpart (σ = 3.41, ±1.63, p<.05) or the ridge stimulus (σ= 3.41 ±1.01, p<.01).
These results highlight that pRF parameter estimation is constrained by the stimulus configuration used. Linear bars produced larger sigma estimates and poorer model fits compared with methods that account for cortical magnification. Finally, ridge stimulation allows reliable parameter estimation and accurate model fitting within a much shorter scanning time, half of the time required for bar stimulation.
Meeting abstract presented at VSS 2013
This PDF is available to Subscribers Only