Visual motion processing and sensory-motor integration for smooth pursuit eye movements.

Relationship between eye acceleration and retinal image velocity during foveal smooth pursuit in man and monkey.

1. We investigated cells in the middle temporal visual area (MT) and the medial superior temporal area (MST) that discharged during smooth pursuit of a dim target in an otherwise dark room. For each of these pursuit cells we determined whether the response during pursuit originated from visual stimulation of the retina by the pursuit target or from an extraretinal input related to the pursuit movement itself. We distinguished between these alternatives by removing the visual motion stimulus during pursuit either by blinking off the visual target briefly or by stabilizing the target on the retina. 2. In the foveal representation of MT (MTf), we found that pursuit cells usually decreased their rate of discharge during a blink or during stabilization of the visual target. The pursuit response of these cells depends on visual stimulation of the retina by the pursuit target. 3. In a dorsal-medial region of MST (MSTd), cells continued to respond during pursuit despite a blink or stabilization of the pursuit target. The pursuit response of these cells is dependent on an extraretinal input. 4. In a lateral-anterior region of MST (MST1), we found both types of pursuit cells; some, like those in MTf, were dependent on visual inputs whereas others, like those in MSTd, received an extraretinal input. 5. We observed a relationship between pursuit responses and passive visual responses. MST cells whose pursuit responses were attributable to extraretinal inputs tended to respond preferentially to large-field random-dot patterns. Some cells that preferred small spots also had an extraretinal input. 6. For 92% of the pursuit cells we studied, the pursuit response began after onset of the pursuit eye movement. A visual response preceding onset of the eye movement could be observed in many of these cells if the initial motion of the target occurred within the visual receptive field of the cell and in its preferred direction. In contrast to the pursuit response, however, this visual response was not dependent on execution of the pursuit movement. 7. For the remaining 8% of the pursuit cells, the pursuit discharge began before initiation of the pursuit eye movement. This occurred even though the initial motion of the target was outside the receptive field as mapped during fixation trials. Our data suggest, however, that such responses may be attributable to an expansion of the receptive field that accompanies enhanced visual responses.(ABSTRACT TRUNCATED AT 400 WORDS)

The visual system is continually inundated with information received by the eyes. Only a fraction of this information appears to reach visual awareness. This process of selection is one of the functions ascribed to visual attention. Although many studies have investigated the role of attention in shaping neuronal representations in cortical areas, few have focused on attentional modulation of neuronal signals related to visual motion. We recorded from 89 direction-selective neurons in middle temporal (MT) and medial superior temporal (MST) visual cortical areas of two macaque monkeys using identical sensory stimulation under various attentional conditions. Neural responses in both areas were greatly influenced by attention. When attention was directed to a stimulus inside the receptive field of a neuron, responses in MT and MST were enhanced an average of 20 and 40% compared with a condition in which attention was directed outside the receptive field. Even stronger average enhancements (70% in MT and 100% in MST) were observed when attention was switched from a stimulus moving in the nonpreferred direction inside the receptive field to another stimulus in the receptive field that was moving in the preferred direction. These findings show that attention modulates motion processing from stages early in the dorsal visual pathway by selectively enhancing the representation of attended stimuli and simultaneously reducing the influence of unattended stimuli.

The neural basis of smooth pursuit eye movements in the rhesus monkey brain

Many experimental measurements support the hypothesis that the middle temporal visual area (MT) of the rhesus monkey has a central role in processing visual motion. Most of these studies were performed using luminance stimuli, leaving open the question of how color information is used during motion processing. We investigated the specific question of how S-cone signals, an important source of color information, interact with L,M-cone signals, the dominant source of luminance information. In MT, S-cone-initiated signals combine synergistically with L,M-cone (luminance) signals over most of the stimulus range, regardless of whether the stimuli are added or subtracted. A quantitative analysis of the responses to the combination of S- and L,M-cone signals shows that for a significant minority of cells, these S-cone signals are carried to MT by a color-opponent ("blue-yellow") pathway, such that in certain limited contrast ranges, a small amount of S- and L,M-cone cancellation is observed. Both S- and L,M-cone responses are direction-selective, suggesting that MT processes a wide range of motion signals, including those carried by luminance and color. To investigate this possibility further, we measured MT responses while monkeys discriminated the direction of motion of luminance and S-cone-initiated gratings. The sensitivity of single MT neurons and the correlation between trial-to-trial variations in single neuron firing and perception are similar for S- and L,M-cone stimuli, further supporting a role for MT in processing chromatic motion.

Categorization is an essential cognitive and perceptual process for decision-making and recognition. The posterior parietal cortex, particularly the lateral intraparietal (LIP) area has been suggested to transform visual feature encoding into abstract categorical representations. By contrast, areas closer to sensory input, such as the middle temporal (MT) area, encode stimulus features but not more abstract categorical information during categorization tasks. Here, we compare the contributions of the medial superior temporal (MST) and LIP areas in category computation by recording neuronal activity in both areas from two male rhesus macaques trained to perform a visual motion categorization task. MST is a core motion-processing region interconnected with MT and is often considered an intermediate processing stage between MT and LIP. We show that MST exhibits robust decision-correlated motion category encoding and working memory encoding similar to LIP, suggesting that MST plays a substantial role in cognitive computation, extending beyond its widely recognized role in visual motion processing.SIGNIFICANCE STATEMENT Categorization requires assigning incoming sensory stimuli into behaviorally relevant groups. Previous work found that parietal area LIP shows a strong encoding of the learned category membership of visual motion stimuli, while visual area MT shows strong direction tuning but not category tuning during a motion direction categorization task. Here we show that the medial superior temporal (MST) area, a visual motion-processing region interconnected with both LIP and MT, shows strong visual category encoding similar to that observed in LIP. This suggests that MST plays a greater role in abstract cognitive functions, extending beyond its well known role in visual motion processing.

We studied how object speed is reconstructed from the responses of motion-selective cells for the generation of a behavior that is tightly linked to the speed of visual motion. In theory, the speed of an object could be estimated either from the speed tuning of the active population of motion-selective cells or from the rate of displacement of activation across the cortical map of visual space. We measured the pursuit eye movements evoked by stimuli containing two conflicting motion components: a local component designed to excite motion-selective cells with a particular speed tuning and a displacement component designed to excite cells with a sequence of spatial receptive fields. Pursuit eye movements were driven primarily by the local-motion component and were affected to only a small degree by the rate of target displacement across visual space. Extracellular single-unit recordings using the same stimuli revealed that the responses of cells in the middle temporal visual area (MT) depended primarily on the local-motion component but were influenced by the displacement component to the same degree as were pursuit eye movements. We conclude that the initiation of pursuit is consistent with a reconstruction of target speed based on the speed tuning of the active population of MT cells.

The origins and terminations of the amygdaloid connections with the modality-specific visual cortical areas TEa (anterior TE area), TEp (posterior TE area), TEO, V4, V2, MST (medial superior temporal visual area), MT (middle temporal visual area), and V1 were studied in macaques. These were compared with the amygdaloid connections of a vision-related polysensory area TG by making cortical injections of horseradish peroxidase (HRP) and incubating the sections with tetramethylbenzidine (TMB) as the chromogen. Both areas TEa and TEp receive a major projection from the lateral basal nucleus and a minor one from the accessory basal nucleus of the amygdaloid complex, whereas these areas send a major projection to the lateral nucleus and a minor one to the lateral basal nucleus. Areas TEO, V4, V2, MST, MT, and V1 receive projections only from the lateral basal nucleus; none of them project to any amygdaloid nucleus. Thus, the amygdalofugal projections are more widespread and more complex than the amygdalopetal projections. These findings indicate that the connections between the amygdaloid nuclei and the visual areas are generally nonreciprocal and underlie the importance of a feedback mechanism from the amygdala to the visual cortical areas in visual information processing. There appears to be a caudorostral (occipitotemporal) gradient in the distribution and density of the amygdaloid projections, which become progressively more widespread and heavier among the progressively more rostral visual areas (from area V1 to area TEa). The amygdaloid connections with area TG are distinctly different from the connections with the visual areas. Area TG is reciprocally connected mainly with the periamygdaloid cortex, and with the lateral, accessory basal, and medial basal nuclei of the amygdala as well.

BackgroundPatients with bilateral vestibular failure (BVF) suffer from oscillopsia during head movements. This is secondary to the loss of the vestibulo-ocular reflex which is responsible for stabilising retinal images during...

Recovery from auditory and visual neglect after optokinetic stimulation with pursuit eye movements – Transient modulation and enduring treatment effects

Both perceptual and motor systems must decode visual information from the distributed activity of large populations of cortical neurons. We have sought a common framework for understanding decoding strategies for visually guided movement and perception by asking whether the strong motion aftereffects seen in the perceptual domain lead to similar expressions in motor output. We found that motion adaptation indeed has strong sequelae in the direction and speed of smooth pursuit eye movements. After adaptation with a stimulus that moves in a given direction for 7 sec, the direction of pursuit is repelled from the direction of pursuit targets that move within 90 degrees of the adapting direction. The speed of pursuit decreases for targets that move at the direction and speed of the adapting stimulus and is repelled from the adapting speed in the sense that the decrease either becomes greater or smaller (eventually turning to an increase) when tracking targets move slower or faster than the adapting speed. The effects of adaptation are spatially specific and fixed to the retinal location of the adapting stimulus. The magnitude of adaptation of pursuit speed and direction is uncorrelated, suggesting that the two parameters are decoded independently. Computer simulation of motion adaptation in the middle temporal visual area (MT) shows that vector-averaging decoding of the population response in MT can account for the effects of adaptation on the direction of pursuit. Our results suggest a unified framework for thinking, in terms of population decoding, about motion adaptation for both perception and action.

Introduction Oculomotor behavioral metrics change according to time awake and circadian phase following a distinct pattern of impairment. Acute sleep deprivation (ASD) causes large decreases in pursuit initiation and steady-state gain, and a compensatory increase in saccadic rate without any systematic change in saccadic size. It also causes large deficits in visual processing of direction and speed, and impaired saccade dynamics. Such deterioration likely reflects changes in both higher cortical and brainstem function, explaining in-part how sleep loss and circadian misalignment affect cognition. It is unclear how oculomotor behavior might change according to chronic sleep restriction (CSR). Here, we measured the same eye-movement metrics during CSR. Methods Twelve healthy participants (6 females) kept a fixed wake-time sleep-wake schedule, at home for four weeks (weeks 1 and 3 = 9h time in bed (TIB); weeks 2 and 4 randomized to 5h or 9h TIB; actigraphy confirmed). Following weeks two and four, participants completed a 13-hour laboratory visit under dim light (<15 lux), where they maintained a semi-recumbent posture and were provided with hourly isocaloric snacks. A visual tracking task was performed hourly to assess pursuit and saccadic responses and visual motion processing. Performance metrics were computed using MATLAB, including pursuit gain (eye speed/target speed), the rate and amplitude of catch-up saccades, and the accuracy and precision of direction and speed processing. Results As expected, we found a small but significant (t(11)=-2.17, p<0.03) reduction in pursuit gain (mean+/-SEM: -0.028 +/-0.013 with a large (t(11)=2.96, p<0.01) increase in saccadic rate (0.37 +/-0.13 Hz). However, surprisingly, we found a significant (t(11)=-2.52, p<0.03) decrease in the amplitude of catch-up saccades (-0.15 +/-0.06 deg). The only systematic alteration to visual motion processing was a small reduction in horizontal-vertical asymmetry, which was previously observed with ASD. Conclusion A week of CSR to 5h is associated with only mild impairment in smooth pursuit eye movements with little impact on visual motion processing. However, CSR caused a maladaptive decrease in saccade amplitude that was not observed during ASD. Eye-movement metrics reveal differential neurological effects of CSR versus ASD. Support (if any) Force Health Protection Program, ONR (SAA2402925-1), NASA Human Research Program, and agreement NNX17AE07A

Smooth pursuit eye movements provide instantaneous feedback on visual motion processing through sensory-motor integration. Scalp electroencephalogram (EEG) signals can be a useful metric for exploring their dynamic mechanisms that require high temporal precision. However, elucidating the sensory-motor transitions that occur in different regions of the brain without directly recording the underlying neuronal activity remains challenging. In this study, we utilized recurrent neural networks to model smooth pursuit eye movements on a visual target comprised of random dot motion.

Pursuit eye movements correlate with perceived motion in both velocity and direction, even without retinal motion. Cortical cells in the monkey medial temporal region generate signals for initiating pursuit eye movements and respond to retinal motion for perception. However, recent studies suggest multiple motion processes, fast and slow, even for low-level motion. Here we investigated whether the relationship with pursuit eye movements is different for fast and slow motion processes, using a motion aftereffect technique with superimposed low- and high-spatial-frequency gratings. A previous study showed that the low- and high-spatial-frequency gratings adapt the fast and slow motion processes, respectively, and that a static test probes the slow motion process and a flicker test probes the fast motion process (Shioiri & Matsumiya, 2009). In the present study, an adaptation stimulus was composed of two gratings with different spatial frequencies and orientations but the same temporal frequency, moving in the orthogonal direction of ±45° from the vertical. We measured the directions of perceived motion and pursuit eye movements to a test stimulus presented after motion adaptation with changing relative contrasts of the two adapting gratings. Pursuit eye movements were observed in the same direction as that of the motion aftereffects, independent of the relative contrasts of the two adapting gratings, for both the static and flicker tests. These results suggest that pursuit eye movements and perception share motion signals in both slow and fast motion processes.

We sought to better understand the neural representation of visual motion acceleration. A straightforward estimation of acceleration would involve calculating the rate of change of velocity, which itself would be calculated from change in position over time. As it is well-established that neurons in area MT encode velocity, the brain could thus indirectly estimate acceleration by calculating the change in MT’s velocity representations. An alternate mechanism, however, could operate more rapidly and directly. In this case, the brain could exploit interactions between MT’s standard motion encoding and idiosyncratic temporal dynamics of neural responses. Such direct decoding would thus exploit nonlinearities usually ignored in studies of MT coding. We tested between these two theories by measuring from ensembles of MT neurons while two male awake fixating macaques viewed linearly accelerating motion stimuli. Direct decoding of acceleration from MT was possible on faster time scales, and could be done with higher fidelity, than indirect decoding. Distinct motion acceleration information could thus be efficiently read out from rich and heterogeneous MT ensemble responses, regardless of the mechanisms that give rise to various forms of motion tuning that it exhibits. A similar analysis of activity in the medial superior temporal area (MST) did not suggest this later stage of motion processing has a more refined acceleration representation. Together, these results suggest that the brain may opportunistically exploit nonlinear idiosyncrasies of neural responses to efficiently extract behaviorally relevant information on fast time scales, instead of performing explicit calculation of some variables.