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Report
1Department of Neurology, University Hospital Düsseldorf, 40225 Düsseldorf; 2Godeshoehe Neurological Rehabilitation Centre, 53177 Bonn; and 3Biomedical Research Centre, Heinrich-Heine-University, 40225 Düsseldorf, Germany
Submitted 24 September 2003; accepted in final form 5 December 2003
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ABSTRACT |
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INTRODUCTION |
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), whereas neuropsychological evidence suggests a dependence of motor activity on perceptional goals (Mechsner et al. 2001). For either model, different cerebral circuits can be expected to be engaged during action control and should be demonstrable with functional neuroimaging.
Owing to the fundamental organization principle of the human brain, incoming information (e.g., visual or somatosensory) about each half of the body is projected to the primary sensory representations in the cortex of the contralateral cerebral hemisphere. From there the incoming information is projected to the polysensory parietal cortex, which constructs a coherent body image subserving postural control and goal-directed motor behavior (Clower et al. 1996; Culham and Kanwisher 2001; Galati et al. 2001). Functional neuroimaging studies revealed that action generation shares the same parietal and premotor areas (Grèzes and Decety 2001) as action observation (Buccino et al. 2001) and visual identification of body part orientation (Parsons et al. 1995). Thus action generation and action perception are closely related and built on a coherent body image.
Dissociation of the seen limb movement from the motor program, e.g., when acting through a mirror, causes interferences in movement execution (Lajoie et al. 1992). Although recently a specialized neural system has been identified for the visual perception of the body (Downing et al. 2001; Peigneux et al. 2000), it still has not been settled as to where in the brain the visually perceived body scheme interacts with the internal proprioceptive feedback. Here, we used a visual perturbation experiment to explore the site of interaction. We provide evidence that the inversion of visual feedback (right hand viewed as left hand and vice versa) during a visual-controlled task strongly modifies processing already in the visual cortical areas.
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METHODS |
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Subjects had either to hold their hand static or to perform a finger-thumb opposition movement sequence under visual control. For each hand, four conditions were investigated. In two conditions (movement), the subjects were required to move the thumb and remaining fingers of one hand sequentially at three different opposition distances at a frequency of 1 Hz. In two further conditions (static), the subjects held their hand in a static posture with the thumb and index finger opposing each other. Because the subjects could not see their hand in the MR-scanner directly, we used a video-chain to show the subjects their hand in a normal perspective: The subjects' hand was filmed with a video camera (Leutron-Vision, Berlin) located outside the scanner and back-projected (LCD data projector, Sony VPL-S500E) in real-time onto a transluminant screen with a size of 10°. By means of a software package (Leutron demonstration software), it was also possible to invert the hand on-line horizontally such that the subjects' right hand appeared as their left hand and vice versa. Thus two conditions with normal view and two corresponding conditions with an inverted view were applied.
We used a block design with the four conditions arranged in a pseudo-randomized sequence with a total time of 10 min. The use of the right and left hand were measured separately, and the movement and static conditions were initiated by an acoustic cue. The subjects were instructed to fixate the hand on the screen. As evident from monitoring their eye movements by means of a limbus eyetracking system (Cambridge Research Systems, Rochester, UK), eye movements had a variance of no more than 1° to either side.
Functional imaging was performed on a Siemens Vision 1.5 T MRI scanner (Erlangen, Germany) using standard echo-planar imaging (EPI, TR 4 s, TE 66 ms, flip angle: 90°, voxel size: 3 x 3 x 4.4 mm3) with a standard radio frequency head coil for signal transmission and reception. Twenty-eight consecutive slices oriented parallel to the AC-PC plane were acquired, covering the whole brain. Image analysis was performed using the fMRI analysis software package Brain Voyager 4.9 (Brain Innovation, Maastricht, The Netherlands). The MR images were realigned to correct for head movements between scans. Preprocessing of the volume time courses involved Gaussian spatial smoothing (FWHM = 6 mm), removal of linear trends, and temporal high-pass filtering with a 3-min cut-off to remove slow periodic drifts. The volumes were normalized into Talairach space for multi-subject comparison. With a Gaussian model of the hemodynamic response to generate idealized response functions, which were used as regressors in a multiple regression model, we contrasted epochs for the different conditions. For each hand, we calculated a two-factorial variance analysis with the main factors "inversion" (inverted vs. normal) and "movement" (movement vs. static). Regions of interest were thresholded in the statistical parametric maps at P < 0.01 (corrected for multiple comparisons) and super-imposed on the subjects' high-resolution three-dimensional anatomical images.
To assess the strength of the fMRI signal changes in the visual areas in the two movement conditions, the beta values of the signal responses were analyzed in regions of interest (ROIs) defined to be the activation areas of the main effect "inversion" (cf. Table 1). The beta values are regression parameters resulting from the best fit of the observed activity to the factorial model. They are a measure of the contribution of each condition to the total BOLD signal. For each ROI, the beta values for the two conditions "static" and "movement" were substracted from each other and averaged across both hemispheres.
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RESULTS |
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DISCUSSION |
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). These data show for the first time that the on-line state of the body and of motor behavior gates the incoming information already at the level of the visual cortex.
The brain's ability to modulate activity in early visual areas in a top-down fashion is well established from attentional processes with shifting attention to one hemifield (Heinze et al. 1994) or even one certain location in retinotopic space (Brefczynski and DeYoe 1999; Somers et al. 1999; Tootell et al. 1998). Feedback projections from extrastriate area MT/V5 to striate cortex are critical for awareness of visual motion (Pascual-Leone and Walsh 2001). This modulation seems to be due to fast back-progations from higher-order areas (Hupe et al. 2001; Martinez et al. 1999). Recently, it was shown that activity changes in visual cortex can also occur during crossmodal (i.e., tactile) cueing (Macaluso et al. 2000), indicating that activity in visual areas can be modulated by other mechanisms than visual attention.
In our study, lateralization of activity was accomplished by presenting an inverted image of a body part. As indicated by earlier work of Parsons and co-workers, information about the visual configuration of one hand is accessible by the contralateral hemisphere only (Parsons et al. 1995, 1998). However, there is no lateralized activation when subjects viewed a right or left hand of a person other than one's self (Buccino et al. 2001). We therefore suggest that it is the mismatch between the expectation corresponding to action and the actual visual perception that enhanced attention and thus the activity in the contralateral hemisphere.
The intriguing aspect of this study is that this results in an interhemispheric dissociation, namely between the motor cortex controlling the contralateral hand and the visual cortex processing the inverted image of the hand. The lack of simultaneous activation in parietal and premotor cortical areas during image inversion shows that these sensorimotor processing nodes did not elicit extra activity to control the required action in spite of the inverted feedback image.
In conclusion, our data show that when visual information is crucial for the execution of an action, motor behavior can tune visual cortex in the same fashion as attention does. This is not totally surprising as action represents a powerful way to funnel goal-directed acts into the given contextual circumstances. What is new from our study is that this is accomplished by tuning information processing already at an early stage of visual processing. This further supports the notion that action control heavily relies on perceptual cues (Mechsner et al. 2001). This finding might help to understand neurological rehabilitation procedures based on alterations of the visual feedback (Altschuler et al. 1999).
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ACKNOWLEDGMENTS |
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GRANTS
This work was supported by grants of the Deutsche Forschungsgemein-schaft (SFB 194) and the Forschungskommission der Heinrich-Heine-Universität Düsseldorf.
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FOOTNOTES |
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Address for reprint requests and other correspondence: R. Kleiser, University Hospital Düsseldorf, Moorenstrasse 5, 40225 Düsseldorf, Germany (E-mail: kleiser{at}uni-duesseldorf.de).
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REFERENCES |
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ABSTRACT
INTRODUCTION
