Anthony Stigliani

Repetition of a stimulus typically leads to a reduction in neural response. This adaptation effect, sometimes known as repetition suppression or neural priming, can be observed both in individual neurons as well as fMRI voxels containing hundreds of thousands of neurons. When measured with fMRI, this repetition-related reduction in neural activity is known as fMRI-Adaptation (fMRI-A) and can be used to make inferences about the nature of neuronal representations and their sensitivity to various stimulus transformations (e.g., Grill-Spector et al. 1999). In fMRI-A designs, identical stimuli are presented repeatedly to participants, which is often accompanied by attenuation of fMRI signal relative to presentation of different stimuli. Then fMRI response to repetition of stimuli varied along one dimension is examined. If the neural representation in a given region is sensitive to changes in that dimension, the signal will recover to non-repeated levels, but if the representation is tolerant to changes in that dimension, the signal will remain adapted.

fMRI-A is a flexible method that has been applied to wide variety of topics, but some experimental designs are better suited for studying particular questions than others. The purpose of this wiki page is to describe the necessary components of fMRI-A designs as well as the advantages of different types of experimental designs.

Background

Figure 1. Response of an example IT neuron to first the presentation of stimulus A (left), the first presentation of stimulus B (center), and repeated presentation of stimulus A (right) (courtesy of Grill-Spector et al. 2006).

Repetition-related reductions in neural firing were first observed in electrophysiological recordings of neurons in macaque inferior temporal (IT) cortex (e.g., Li et al., 1993). Instead of representing a general reduction in neural response when any two stimuli are shown in quick succession, response attenuation is only observed only when the same stimulus is repeated (see Figure 1). This raises the questions of what counts as the same stimulus and what counts as a different stimulus, but this to a large extent this depends on the particular question you want to address.

In studies of object perception, individual items are often construed as specific object exemplars or faces (e.g., Grill-Spector et al., 1999), but fMRI-A has also observed in left inferior frontal cortex when object category is repeated (Vuilleumier et al. 2002). Outside of this domain, fMRI-A has also been used to address a range of topics from the orientation tuning of neurons in visual cortex (Fang et al., 2005) to the representation of numerosity in parietal cortex (Piazza et al., 2004). The definition of an item varies greatly across these studies, but the power of this approach lies in its ability examine how different transformations affect the neural representation of individual items or stimuli as defined by the experimenter.

As illustrated in Figure 2, several neural mechanisms have been proposed to explain fMRI-A including general neural fatigue, the sharpening of neural representations, and response facilitation resulting in reduced processing time (Grill-Spector et al., 2006), but recent evidence suggests that different mechanisms may operate across different brain regions and time scales (Weiner et al., 2010). In the fatigue model, all neurons that are responsive to a given stimulus show a reduction in firing rate when the stimulus is repeated. Alternatively, the sharpening model posits that fMRI-A reflects a sparser pattern of activation with repeated exposure to a stimulus, and the facilitation model holds that repetition leads to faster neural processing that results in reduced fMRI response. While these models implicitly assume that fMRI-A reflects some reduction in spiking rates, the inputs and outputs to a region both contribute to the BOLD signal measured there, and it is important to consider that some repetition-related response reductions could be inherited from earlier regions (Logothetis, 2008).

Figure 2. Schematic depictions of different models proposed to explain fMRI-A (courtesy of Grill-Spector et al., 2006).

Measuring fMRI-A

In order to use repetition-related response reductions to make inferences about the selectivity of a neuron or population of neurons involved in representing a stimulus, three measurements must be made:

1. Neural response when an identical stimulus is repeated
2. Neural response when different stimuli are presented
3. Neural response when a stimulus is repeated but varied along one dimension

Neural responses to the first two conditions must be measured in order to define a dynamic range within which to compare response to the third condition.

References

Fang, F., Murray, S.O., Kersten, D., & He, S. (2005). Orientation-tuned fMRI adaptation in human visual cortex. Journal of Neurophysiology, 94(6), 4188-4195.
Grill-Spector, K., Kushnir, T., Edelman, S., Avidan, G., Itzchak, Y., & Malach, R. (1999). Differential processing of objects under various viewing conditions in the human lateral occipital complex. Neuron, 24(1), 187-203.
Grill-Spector, K., Henson, R., & Martin, A. (2006). Repetition and the brain: neural models of stimulus-specific effects. Trends in Cognitive Science, 10(1), 14-23.
Li, L., Miller, E.K., & Desimone, R. (1993). The representation of stimulus familiarity in anterior inferior temporal cortex. Journal of Neurophysiology, 69(9), 1918-1929.
Logothetis, N.K. (2008). What we can do what we cannot do with fMRI. Nature, 453(7179), 869-878.
Piazza, M., Izard, V., Pinel, P., Le Bihan, D., & Dehaene, S. (2004). Tuning curves for approximate numerosity in the human intraparietal sulcus. Neuron, 44(3), 547-555.
Vuilleumier, P., Henson, R.N., Driver, J., & Dolan, R.J. (2002). Multiple levels of visual object constancy revealed by event-related fMRI of repetition priming. Nature Neuroscience, 5(2), 491-499.
Weiner, K.S., Sayres, R., Vinberg, J., & Grill-Spector, K. (2010). fMRI-adaptation and category selectivity in human ventral temporal cortex: regional differences across time scales. Journal of Neurophysiology, 103(6), 3349-3365.