Hierarchy of direction-tuned motion adaptation in human visual cortex

Abstract

Lee HA, Lee SH. Hierarchy of direction-tuned motion adaptation in human visual cortex. J Neurophysiol 107: 2163-2184, 2012. First published January 4, 2012 doi:10.1152/jn.00923.2010.-Prolonged exposure to a single direction of motion alters perception of subsequent static or dynamic stimuli and induces substantial changes in behaviors of motion-sensitive neurons, but the origin of neural adaptation and neural correlates of perceptual consequences of motion adaptation in human brain remain unclear. Using functional magnetic resonance imaging, we measured motion adaptation tuning curves in a fine scale by probing changes in cortical activity after adaptation for a range of directions relative to the adapted direction. We found a clear dichotomy in tuning curve shape: cortical responses in early-tier visual areas reduced at around both the adapted and opposite direction, resulting in a bidirectional tuning curve, whereas response reduction in high-tier areas occurred only at around the adapted direction, resulting in a unidirectional tuning curve. We also found that the psychophysically measured adaptation tuning curves were unidirectional and best matched the cortical adaptation tuning curves in the middle temporal area (MT) and the medial superior temporal area (MST). Our findings are compatible with, but not limited to, an interpretation in which direct impacts of motion adaptation occur in both unidirectional and bidirectional units in early visual areas, but the perceptual consequences of motion adaptation are manifested in the population activity in MT and MST, which may inherit those direct impacts of adaptation from the directionally selective units. visual motion; visual cortex; direction selectivity; functional magnetic resonance imaging; V1; middle temporal area PROLONGED EXPOSURE TO A VISUAL pattern drifting in one direction alters perception of subsequently presented stimuli. Adaptation reduces the visibility of objects moving in the adapted direction (Raymond 1993;Tolhurst 1973), makes static or dynamic patterns appear to drift in the opposite direction (Mather et al. 1998), distorts the apparent speed of a moving stimulus (Stocker and Simoncelli 2009;Thompson 1981), or even shifts the perceived direction of motion away from the adapted direction (Levinson and Sekuler 1976).These profound alterations in perception suggest that adaptation has substantial impacts on the neural systems for processing motion stimuli. Indeed, there have been many attempts at uncovering neural consequences of motion adaptation. Although these studies have demonstrated that adaptation substantially modifies neural activities in retina (Barlow and Hill 1963) or visual areas in the cortex (Hammond et al. 1985;Huk et al. 2001;Petersen et al. 1985;Tolias et al. 2001), it has been quite challenging to single out a neural locus or mechanism for motion adaptation. Given the hierarchical and distributed nature of cortical motion processing, which has been supported by a body of anatomical (Felleman and van Essen 1991; Maunsell a...

Perceptual learning modifies the functional specializations of visual cortical areas

“…In a coherent motion stimulus, the local motion direction of individual dots is the same as the global direction of the stimulus. The specialization of V3A in coherent motion processing might be a result of its greater capacity to process local motion signals than MT+, which is underpinned by its relatively small receptive field sizes and narrow tuning curves for motion direction (27,28). In a noisy motion stimulus, only some dots move in the global direction, whereas others move in random directions and can be treated as noise.…”

Neural Correlates of Induced Motion Perception in the Human Brain

“…(2) These areas may contain perceivedspeed-selective neurons in smaller proportion than hMTϩ does. Although the monkey area V1 contains neurons showing direction-dependent surround suppression (Jones et al, 2001), the proportion of direction-selective neurons is generally smaller than that found in area MT (Hawken et al, 1988), and the difference in the proportion of neurons might affect the results, as argued in a recent report (Lee and Lee, 2012). However, neuroimaging studies have also demonstrated a greater directionselective responses in V3A (Nishida et al, 2003;Ashida et al, 2007) or even in V1 (Huk et al, 2001;Kamitani and Tong, 2006;Ales and Norcia, 2009); hence; limited cellular proportion may not limit the BOLD signal change.…”

fMRI adaptation revisited

“…Murray, Olman, Kersten, 2006; Larsson, Landy, & Heeger, 2006). Three lines of evidence suggest that the bulk of extrastriate adaptation to such simple features originates in V1: first, the tuning of motion adaptation in area MT is very similar to that in V1 (Lee & Lee, 2012), and contrast adaptation in V2 and V3 (but not V4) is nearly identical to that in V1 (Gardner et al, 2005); second, the spatial specificity (i.e. tuning) of direction-selective and orientation-selective adaptation in extrastriate areas is identical to that in V1 (Larsson & Harrison 2015); third, adaptation to motion induces strong motion-selective responses in areas that normally lack such selectivity, including area V4 (Larsson & Harrison 2015).…”

“…Specifically, the ability of fMRI to simultaneously measure correlates of neural activity across multiple brain regions means that fMRI adaptation is particularly suitable to addressing questions about how adaptation cascades across sensory hierarchies, particularly in brain areas that are not easily accessible with direct neuronal recordings or invasive neuronal population measurements, such as LFP or optical imaging. In this context, methods to trace the inheritance of adaptation effects using spatially selective adaptation (Larsson & Harrison, 2015) hold promise, as well as techniques that measure the tuning of adaptation across cortical areas (Lee & Lee, 2012). A full understanding of the relationship between single-neuron and population measurements of adaptation effects will also require concerted effort to conduct parallel and directly comparable experiments with fMRI and direct neuronal recordings.…”