The processing of first- and second-order motion in human visual cortex assessed by functional magnetic resonance imaging (fMRI)

Abstract

We have examined the activity levels produced in various areas of the human occipital cortex in response to various motion stimuli using functional magnetic resonance imaging (fMRI) methods. In addition to standard luminance-defined (first-order) motion, three types of second-order motion were used. The areas examined were the motion area V5 (MT) and the following areas that were delineated using retinotopic mapping procedures: V1, V2, V3, VP, V3A, and a new area that we refer to as V3B. Area V5 is strongly activated by second-order as well as by first-order motion. This activation is highly motion-specific. Areas V1 and V2 give good responses to all motion stimuli, but the activity seems to be related primarily to the local spatial and temporal structure in the image rather than to motion processing. Area V3 and its ventral counterpart VP also respond well to all our stimuli and show a slightly greater degree of motion specificity than do V1 and V2. Unlike V1 and V2, the response in V3 and VP is significantly greater for second-order motion than for first-order motion. This trend is evident, but less marked, in V3A and V3B and absent in V5. The results are consistent with the hypothesis that first-order motion sensitivity arises in V1, that second-order motion is first represented explicitly in V3 and VP, and that V5 (and perhaps also V3A and V3B) is involved in further processing of motion information, including the integration of motion signals of the two types.

Fig. 1.

Examples of the visual images used in the study.a, One frame from an animation sequence in which the contrast of a sample of 2-D noise is sinusoidally modulated along the radius. The phase of the sinusoid changes smoothly over time to produce expanding or contracting second-order motion. In the experiments, the mean luminance was the same in regions of high and low contrast (luminance distortions may have been introduced by the printing process). b, Similar to a but a case in which the noise is luminance-modulated and the amplitude of the noise remains constant to give first-order motion. c, Ahemifield checkerboard used for retinotopic mapping. The checks reverse polarity at a rate of 8 Hz to give a high-contrast stimulus that is broadband in both spatial and temporal frequency. The flickering hemifield rotates slowly about the central fixationpoint. d, A checkerboardwedge that flickers and rotates in the same way as thehemifield in c does.

Fig. 2.

Space–time plots illustrating the various types of motion stimuli used in the experiments. Each plot represents a section along the radius of the circular grating in the original image (shown horizontally) seen at successive points in time (represented vertically). a, Contrast-modulated, two-dimensional dynamic noise (2ndDyn). Eachframe consists of 2-D noise the contrast of which is sinusoidally modulated. On each update (every 30 msec), the noise sample is replaced by a new one, and the contrast modulation moves a short distance to the left, giving smoothleftward motion over time. b, Contrast-modulated, two-dimensional, high-pass-filtered static noise (2ndFilt). In this case, the carrier is again 2-D noise, but this time the noise is filtered to remove the lowest spatial frequencies, and the noise sample remains the same over time. Again, the contrast envelope drifts smoothly to the left. c, Flicker-frequency-modulated two-dimensional noise (2ndFlick). Eachframe consists of binary, two-dimensional noise of uniform contrast, and no spatial structure is visible within it. Over time, the noise sample is replaced in some areas but not in others to form a frequency-defined grating. The boundaries of the regions in which the noise is dynamic drift smoothly leftward over time. d, Luminance-modulated, two-dimensional dynamic noise (1stDynLow). Each frame consists of 2-D noise the luminance of which is sinusoidally modulated with an amplitude calculated to give similar visibility to the contrast modulation shown in a. On each update, the noise sample is replaced, and the luminance modulation moves to the left.e, Luminance-modulated, two-dimensional, high-pass-filtered static noise (1stFiltLow). The noise is the same as that in b, and the luminance is modulated to give similar visibility to the contrast modulation in b.f, Luminance-modulated, two-dimensional, high-pass-filtered static noise (1stFiltHigh). The noise is the same as that in e except that the amplitude of the luminance modulation is much greater.

Fig. 3.

Sample temporal activation waveforms. Each plot shows (solid line) the percentage change in signal, averaged across a number of voxels in one region of interest, as a function of time. The periods during which a visual stimulus (2ndFlick) was present are shown by black bars; during the intervening periods, the screen was blank. Also shown (dashed line) is the theoretical waveform used for correlation; this is a square wave that has been smoothed and retarded in phase (see text) and has arbitrary amplitude. Results are shown for four different visual areas in the same subject.

Fig. 4.

Top. Maps of the posterior cortex of three subjects obtained by simulating flattening of the gray matter.a–c, The left hemisphere is shown in all cases; similar results were obtained in the right hemispheres. Overlaid on the map is a pseudocolor representation of the phase of the fundamental component of the activation time course elicited by a rotating, flickering checkerboard (see Materials and Methods). Thecolors reflect visual field position (seekey in a) and show a smooth progression through the visual field within each visual area, with a reversal of the direction of change at the boundaries. Estimates of the locations of various boundaries are indicated. The dotted white line shows the approximate location of the fundus of the calcarine sulcus. The approximate position of the occipital pole is marked with a star.

Fig. 7.

Normalized activation levels elicited by seven visual stimuli in each of two visual areas of the cortex:V1 (top) and V2(bottom). In both cases, data are pooled across upper and lower visual field representations. The data are averaged across 10 hemispheres (V1) or 8 hemispheres (V2) from five individuals. The three second-order motion stimuli are shadedblack; responses to the first-order stimuli are shown inwhite. Error bars show ±1 SEM.

Fig. 8.

Normalized activation levels elicited by seven visual stimuli in each of two visual areas: V3(top) and VP (bottom). The data are averaged across 9 hemispheres (V3) or 10 hemispheres (VP) from five individuals. Error bars show ±1 SE.

Fig. 9.

Normalized activation levels elicited by seven visual stimuli in each of two visual areas: V3A(top) and V3B (bottom). The data are averaged across six hemispheres (V3A) or eight hemispheres (V3B) from five individuals. Error bars show ±1 SE.

Fig. 10.

Normalized activation levels elicited by seven visual stimuli in area V5. The data are averaged across nine hemispheres from five individuals. Error bars show ±1 SE.

Fig. 11.

Motion specificity of the various visual regions studied. The ratio of the activation produced by each of three second-order motion stimuli to that produced by images that are identical except that the grating is stationary are shown separately for each region. The regions are arranged in increasing order (left to right) of motion specificity.