1 Wellcome Department of Cognitive Neurology, Institute of Neurology and , 2 Department of Experimental Psychology, University of Oxford, Oxford, UK
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Abstract |
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Introduction |
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The present paper has two purposes. The first is to use functional magnetic resonance imaging (fMRI) to map the areas involved in the sensorimotor transformations elicited by arbitrary visuomotor associations. In particular, a comparison is made between the contribution of the dorsal visual system (parietal cortex) and the ventral visual system (ventral temporal and ventral prefrontal cortex). The second aim is to use event- related fMRI to distinguish between areas with activity that is associated with the presentation of a visual stimulus, areas with movement-related activity and intermediate areas with activity that is related both to the visual stimulus and to the movement. We used brain imaging since it can reveal the whole of a distributed system at the same time, whereas single-unit recording has to be performed in each area separately.
Electrophysiologists studying the premotor cortex have distinguished between signal-, set- and movement-related activity (Weinrich et al., 1984; Kurata and Wise, 1988
; di Pellegrino and Wise, 1991
; Boussaoud and Wise, 1993
; Shen and Alexander, 1997
). They have been able to draw these distinctions by varying the delay between the presentation of an instruction cue (IC) and the cue that triggers movement (TC). This technique made it possible to distinguish phasic neuronal activity time-locked to the IC (signal-related units) or to the TC (movement-related units). It was also possible to record sustained activity occurring during the interval between the two cues (set-related units). It is important to be able to draw these distinctions because signal- and set-related activities are likely to be involved in the selection of responses and preparation for movement, whereas movement- related activity is more likely to be linked with movement execution and somatosensory feedback. Comparisons have been made between these classes of activity in different areas: dorsal premotor and motor cortex (Weinrich et al., 1984
; Kurata and Wise, 1988
; Crammond and Kalaska, 1996
; Shen and Alexander, 1997
), dorsal premotor and ventral prefrontal cortex (di Pellegrino and Wise, 1991
), dorsal premotor and parietal area 5 (Kalaska and Crammond, 1995
), the supplementary motor cortex and motor cortex (Tanji and Kurata, 1985
; Alexander and Crutcher, 1990
; Crutcher and Alexander 1990
), and the striatum and frontal regions (Alexander and Crutcher, 1990
; Crutcher and Alexander, 1990
; Boussaoud and Kermadi, 1997
).
In these electrophysiological experiments the monkeys performed visuomotor conditional tasks with instructed delays. On such a task the instruction cues specify which movement is appropriate, and the trigger cue tells the animal when to respond. In the present paper human subjects also performed a version of the same task, and as in the animal experiments a variable delay was introduced between the presentation of the cue and the trigger cue. This allowed us to use event-related fMRI to differentiate signal-, set- and movement-related activity in the human brain.
By varying the phase between events and image acquisition, we have been able to sample the evoked hæmodynamic response (EHR) at ~1 Hz while scanning the whole brain (Josephs et al., 1997). By using the General Linear model and Gaussian field theory, we have been able to make statistical inferences that are corrected for multiple comparisons (Friston et al., 1995b
). Furthermore, by systematically varying the interval between two events, it has been possible to statistically disambiguate the EHRs evoked by two events close in time within a trial (mean stimulus onset asynchrony = 7 s). A preliminary analysis of these data has been reported in abstract form (Schluter et al., 1997
).
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Materials and Methods |
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We studied three neurologically normal right-handed volunteers (two males, one female, 2430 years of age) after obtaining informed consent. The subjects lay supine in the scanner. Head movements were minimized by an adjustable padded head holder. Visual stimuli were projected on to a screen above the subjects' heads. The visual stimuli (white shapes on a black background) subtended an angle of 20° on the retina. The acoustic stimuli (300 Hz tones) were presented binaurally via plastic tubes inserted into earplugs. The intensity of the auditory stimuli was adjusted to a level that subjects felt was comfortable (without the scanner noise) but easily detected (against the scanner noise). Motor responses were monitored via a keypad with four buttons, positioned on the subject's abdomen. Each finger, excluding the thumb, was positioned over a button. Stimulus presentation and response collection were controlled by computer. The program for presenting the stimuli was synchronized with the scanner through a second computer during the whole experiment.
Task
The subjects were trained to perform a visuomotor conditional task with instructed delays (see Fig. 1). One of four shapes (instruction cue, IC) was presented for 300 ms. Two shapes instructed the subjects to flex the right index finger; the other two shapes instructed the flexion of the right middle finger. After a variable delay (see Experimental Timing) a tone (trigger cue, TC) was presented for 300 ms. The subjects were asked to prepare the response during the delay, but not to tense or move their right hand until the trigger cue. The movement was to be performed as quickly as possible after the trigger cue. The task rules were explained to the subjects during a pre-scanning session, during which they practised the task for 10 min. No feedback on performance was provided to the subjects during scanning.
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Images were acquired using a VISION scanner operating at 2 T (Siemens, Erlangen, Germany). Functional images were acquired using T2*-weighted echo-planar sequence (TE = 40 ms, TR = 6.4 s, 64 parasagittal slices, voxel size 3 x 3 x 3 mm). For each subject, 378 volumes were acquired during the performance of the task. In addition, for each subject three dummy whole brain scans were acquired to ensure steady-state magnetization at the start of the session. Structural images were acquired using a T1 MPRAGE sequence (TE = 4 ms, TR = 9.5 s, TI = 600 ms, voxel size 1 x 1 x 1.5 mm).
Experimental Timing
The crucial aspect of the method relies on the experimental timing. First, the inter-trial interval (ITI, 26.88 s) was chosen so that successive trials started progressively 1.28 s (TR/5) later in the scanning sequence. This mismatch between trial occurrence and volume acquisition allowed a characterization of the EHRs at a finer temporal resolution than the actual TR, while preserving coverage of the whole brain (Josephs et al., 1997).
Second, the long ITI (26.88 s) between two successive instruction cues allowed the EHR to return to baseline between trials and reduced interactions between successive EHRs.
Third, the delays between the IC and the TC were selected from a uniform distribution of intervals (1.2812.8 s in steps of 1.28 s). This variable delay allowed us to partition the EHR model into two com- ponents; one aligned with the IC and the other with the TC. For example, if the scans are considered as aligned to the IC, the responses evoked by the TC will not occur at a fixed time after the IC, but will be distributed at 10 time positions over 11.52 s. In addition, the variation in the delays between the instruction cue and the trigger stimulus ensured that the subjects could not anticipate the occurrence of the trigger cue.
Fourth, the periodicity of the delays over the whole experimental session was minimized. This allowed us to remove the low-frequency noise present in fMRI series (Holmes et al., 1997) up to a period of 268.8 s (ITI x 10). This was achieved by pairing the ten different delays in pairs differing by 6.4 s (i.e. 1.287.68 s) and presenting them in a pseudorandom order over a group of 10 trials (i.e. 1.28, 1.28, 7.68, 1.28, 7.68, 7.68, 7.68, 1.28, 7.68, 1.28).
Image Analysis Preprocessing
The data were analysed with SPM96 (Wellcome Department of Cognitive Neurology, London, UK; see Friston et al., 1995b) in Matlab (Mathworks, Sherborn, MA). All calculations were performed on Sparc computers (Sun Microsystems, Mountain View, CA). Each subject's data were realigned to a scan halfway through the time series. Six parameters (three translations, three rotations) were extracted from the rigid body transformation that minimized the difference between each image and the references (Friston et al., 1995a
). The subjects' heads moved up to a maximum of 1.0° (roll) and 0.5 mm (x axis). The estimated movement parameters were used to realign the time-series and subsequently used as confounds in the statistical analysis (see below). A mean functional image was also computed.
For each subject, the structural T1 image was co-registered to the mean T2*-weighted functional image. The structural image was then spatially normalized into the system of reference of Talairach and Tournoux (1988), using as template a representative brain from the Montreal Neurological Institute series (Evans et al., 1994). Twelve linear parameters (translation, rotation, zoom and shear) were estimated to correct the position and size of the structural images with respect to the template image. Residual differences between each pair of images were corrected using non-linear basis functions (Friston et al., 1995a
). The normalization parameters were subsequently applied to the functional images. The structural image was subsampled to a voxel size of 1 x 1 x 1 mm for display purposes.
Finally, in order to conform to the assumption of multivariate Gaussian distribution of the data, on which SPM is based (Friston et al., 1995b), the functional images were subsampled to a voxel size of 2 x 2 x 2 mm and smoothed with an isotropic Gaussian kernel of 4 mm. Possible temporal autocorrelation of the residuals was also taken into account by smoothing with a Gaussian kernel of 6 s.
Image Analysis Statistical Model and Inference
Ninety trials were analysed for each subject. The EHRs to the IC and TC were modelled independently in the same model, each with a Fourier set of temporal basis functions (up to the 6th harmonic), with the ITI as the fundamental period. For each trial, this period was aligned to the event to which the model was relative. It is important to note that this model allowed us to characterize EHRs without specifying their exact form or timing, i.e. the phase and amplitude of the basis functions. However, we assumed that the observed EHR at each trial is equal to the linear combination of the EHRs to the IC and TC. Since all components of the model are tested simultaneously, the fitted EHR is determined by the best fit of the data (least squares) that a linear combination of the components of the model can provide. Since the model is independently tested at each and every voxel, variations in the EHR function are allowed over the whole brain. The two models, representing responses time locked to the IC or TC, were considered alternately as effects of interest and no interest, in order to distinguish the EHRs associated with either cue.
Low-frequency changes over time, residual head movement-related effects, changes in mean signal over the whole brain and overall differences across subjects were considered as effects of no interest. Low-frequency changes in signal were modelled with a set of seven discrete cosine basis functions. The highest frequency modelled was twice the longest experimental period (10 trials), i.e. 537.6 s (ITI x 10 x 2; see Experimental Timing); the lowest frequency modelled was the whole scanning session. Note that these low-frequency changes are not correlated with the EHR models. Head movement-related effects were modelled using the first-, second- and third-order functions of the movement estimates obtained from the realignment procedure.
The statistical significance of the estimated EHRs was assessed using F-statistics in the context of a multiple regression analysis. The null hypothesis was that the variance explained by the effects of interest was consistent with the residual error, once the variance explained by the effects of no interest was removed. F-ratios for each voxel in the image were computed. SPM{F}s were generated to indicate the spatial distri- bution of significant event-related activations associated with either IC (analysis 1) or TC (analysis 2). Gaussian field theory allowed us to make inferences corrected for the number of non-independent comparisons (Friston et al., 1995b). The effective degrees of freedom of the error term took into account the temporal autocorrelation of the data (Friston et al., 1995c
).
A third set of basis functions was subsequently added to the models described above to test for sustained EHRs occurring during the delay period. The sustained EHRs were modelled with another set of Fourier series temporal basis functions (up to the 6th harmonic), having one ITI as the fundamental period (time locked to a point midway between IC and TC) and an amplitude that was proportional to the length of the delay. The assumption embodied by this model is that the neural activity occurring during the delay period is sustained at constant amplitude. The EHR relative to the delay period is the convolution of this neural activity with an EHR impulse function. We also assumed that the observed EHR at each trial is determined by a linear combination of the EHRs to the IC, the TC and the delay.
The presence of voxels with sustained EHRs was tested in two ways. First, the model relative to sustained activity was used as a covariate of interest and the models time locked to IC and TC as covariates of no interest (analysis 3). Other effects of no interest (low frequency confounds, head movement related effects and global signal changes) were removed as described above. The significant voxels represent sustained EHRs, once the EHRs associated with the IC and the TC have been removed. This is a very conservative test because the model relative to sustained activity is not orthogonal to the models time locked to IC and TC. In addition, the underlying neuronal responses are also correlated, since many set-related cells also show signal-related or movement-related activity. Kurata and Wise (1988) reported that only 35% of set-related cells were exclusively set-related. Second, all three models previously described were used as covariates of interest (analysis 4). The effects of no interest were removed, as in the previous analyses. The significant voxels obtained from this analysis represent EHRs associated with the IC, the TC, the delay period or a combination of these three components. We compared the results of analysis 4 with the results for the other analyses (13) to identify clusters unique to analysis 4. We considered as new those voxels of analysis 4 whose location was at least one smoothing kernel apart from the location of significant voxels in analysis 1 or 2.
This paper reports the results of the group analysis (three subjects). However, in order to verify the anatomical location of the activation foci and to evaluate the consistency of the results across subjects, single subject analyses were also performed. The statistical threshold used in the single subject analyses was F(11.9, 321.7) > 5.20 (analyses 1 and 2), and F(32.7,312.9) > 3.24 (analysis 4). These thresholds correspond to P < 0.05 corrected for multiple comparisons. The same thresholds were used for the group analyses.
Anatomical details of significant signal changes were obtained by superimposing the SPM{F} on both the structural and the mean func- tional images of each subject. The atlas of Duvernoy (1991) was used to identify the relevant landmarks. The timecourse of the EHRs is shown for some significant areas of activation. The signal estimated from each slice has been reordered according to its latency with respect to either IC or TC. Note that this allows one to take into account the delay occurring between the beginning of the acquisition of each volume and the actual time of acquisition of each slice.
The number of subjects was deliberately chosen to be small, given that the group analyses presented only fit and assess fixed effects. The inter-subject variability in task response, i.e. the subject by task inter- actions, is not taken into account in the residual variance. Under these conditions, the inferences are about the effects on these subjects during these scanning sessions, and they do not extend to the population from which the subjects were drawn. In order to appropriately estimate inter-subject variability, it would be necessary to collapse the data over replications within subjects. This would give a single summary image representative of each condition for each subject. However, the analyses used in the present paper make use of multiple regressors for each condition and cannot be collapsed to a unique parameter describing a single condition.
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Results |
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The subjects made no errors during scanning. The response time (RT) data did not show any significant trend over time for subject 2 (RT = 208.6 0.3*time; 195 ± 70 [mean ± standard deviation]) and subject 3 (RT = 235.1 0.6*time; 206 ± 110), whereas for subject 1 it showed a decrease over time (RT = 437.1 1.7*time; 361 ± 91).
SPM
Figure 2 shows the SPM{F}s for analysis 1 (time locked to the IC; Fig. 2A
) and analysis 2 (time locked to the TC; Fig. 2B
). The list of significant activations is presented in Tables 1 and 2
. Note that the activations elicited by the visual instruction cue were confined to occipital, inferior temporal and parietal cortex. The IC evoked activity bilaterally in the calcarine fissure (V1), lingual gyrus, occipitotemporal fissure and caudally within the intraparietal sulcus. The activations elicited by the auditory trigger cue were confined to the frontal, inferior parietal and superior temporal cortex. The TC evoked activity bilaterally in the transverse gyrus, perisylvian temporal cortex, supramarginal gyrus (bilaterally), left superior parietal gyrus, anterior wall of the left central sulcus (MI), left dorsal precentral gyrus (PMd) and mesial convexity of the superior frontal gyrus (SMA). The TC also evoked activity subcortically in the left ventrolateral thalamus and the cerebellar hemispheres.
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Evoked Hæmodynamic Responses
The following section characterizes the EHRs to different task events. The EHRs are only illustrated for some of the areas that are listed in Tables 1 and 2. In order to evaluate the contribution of the sustained component to the overall response, the EHRs for some of these significant regions were plotted selecting the trials with the longest delay (12.8 s) from analysis 4. In this analysis signal-, set- and movement-related activities were considered but not distinguished. Both group and single subject data are presented in order to show whether the group curves are properly representative of the single subject data.
EHRs Associated with the Instruction Cue
Figures 3A (group analysis) and 4A (single subject analyses) show the fitted EHRs for the local maxima in the left calcarine fissure. The EHRs were plotted as estimated from analysis 1 (i.e. response time locked to the IC) and from analysis 2 (i.e. response time locked to the TC). Note the lack of EHRs associated with the TC. When the hæmodynamic responses associated with the IC are aligned to the TC, they become dispersed in time and their sum vacillates around baseline levels.
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Figures 3C (group analysis) and 4C (single subject analyses) show the EHRs for the local maxima on the right caudal intraparietal sulcus. For this area there were significant EHRs associated with the IC. Inspection of the figures also suggests an association with the TC, though the F-value for this association was below the threshold used in this study [F(35.7,967.2) = 2.88].
EHRs Associated with the Trigger Cue
Figures 5A (group analysis) and 6A (single subject analyses) show the fitted EHRs for the local maxima in the left transverse gyrus. The EHRs were significantly associated with the TC but not the IC. Note that the onset of the EHRs is well aligned with the mean occurrence of the TC (7 s). As shown by Table 2
, there are three local maxima in the same general area. These lie lateral to the primary auditory area as defined in the probability map of Penhune et al. (1996), but still in the transverse gyrus (for all three subjects) where several other auditory areas have been localised (Rivier and Clarke, 1997
).
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Comparison of Figure 3 with Figure 4
and of Figure 5
with Figure 6
confirms that the timecourse of the EHRs estimated from the group analysis is consistent across subjects. However, the responses measured over higher-order cortical areas show a higher degree of variability than the responses measured over primary sensory and motor areas.
EHRs Associated with Sustained Activity
Figure 7 shows the fitted EHR for local maxima revealed by analysis 4 but not by the other analyses. As described in Materials and Methods, analysis 4 shows voxels associated with the IC, the TC, the delay or a combination of these three components. A first voxel (18, 6, 70; see Fig. 7A
) was located in the left precentral gyrus; it lays anteriorly and medially to the premotor voxel shown by analysis 2 (see Table 2
), where voxels significantly associated with the trigger cue only were distinguished. In order to evaluate the contribution of the sustained component to the overall response, the EHR for this particular voxel was plotted for the trials with the longest delay (12.8 s). For comparison, the EHR recorded for MI from the same analysis (4) has been plotted for the same set of trials. The EHR for the anterior premotor cortex showed an initial increase after the IC, a sustained response during the delay period and a final response associated with the TC. When averaged across all delays, the signal-related component of this voxel was just below the statistical threshold used [F(35.6,962.3) = 4.35, from analysis 1].
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Figure 8 compares the timecourse of the EHRs for some voxels revealed by analysis 1 and 2. In this figure, the EHRs estimated in analysis 4 have been plotted for the trials with the longest delay (12.8 s). The EHR recorded from MI from the same analysis and the same subset of trials has also been plotted for comparison. Figure 8A
shows the EHR for a local maximum in the right caudal intraparietal sulcus (16, 76, 56; see Table 1
). For this subset of trials, it is possible to appreciate both a signal- and a movement- related component of the response. The movement-related component for the intraparietal voxel peaks later than the response for the motor cortex. A similar pattern of activity (not shown) was present in the left caudal intraparietal sulcus (see Table 1
).
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Figure 8C shows the EHR for a local maximum in the left inferior frontal gyrus (30, 28, 12; see Table 2
). For this subset of trials, the inferior frontal EHR was associated with both the IC and the TC, but there was no clear evidence of set-related activity.
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Discussion |
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In this study we have applied a new experimental design for fMRI that allows us to statistically distinguish the EHR associated with two events that are close in time. This has been achieved by imposing a variable delay period between the two events, i.e. by arranging that the two events are out of phase across trials. We have dissociated sensory from motor components of the neural response and recorded sustained activity across the two cues, even in the absence of any stimulus. A validation of the method used is provided by the demonstration that the EHRs statistically associated with the visual instruction cue were confined to areas known to have visual inputs (Fig. 2A), whereas EHRs associated with the auditory trigger cue were observed in superior temporal auditory cortex (Fig. 2B
) (Schluter et al., 1997
).
Several studies have measured EHRs to single events (Buckner et al., 1996, 1998a
,Buckner et al., b
; Cohen et al., 1997
; McCarthy et al., 1997
; Richter et al., 1997a
,b
; Schacter et al., 1997
; Zarahn et al., 1997
; Buechel et al., 1998
; Courtney et al., 1998
). However, some studies rely on relatively poor spatial or temporal resolution, or on predefined regions of interest. We have been able to preserve a continuous field of view over the whole brain at `high' spatial resolution (27 mm3), while characterizing the EHRs at a finer temporal resolution (1.28 s) than the actual TR. This has been possible by introducing a mismatch between trial occurrence and image acquisition (Josephs et al., 1997
). This technique has allowed us to explore the whole sensorimotor network at once, providing a considerable advance over the region of interest approach used in other imaging studies or, necessarily, in electrophysiological recording.
The method we have used (Josephs et al., 1997; Schluter et al., 1997
) also avoids the use of standard response functions for the EHR (Buckner et al., 1996
, 1998a
,Buckner et al., b
; Cohen et al., 1997
; Schacter et al., 1997
; Zarahn et al., 1997
; Buechel et al., 1998
; Courtney et al., 1998
). Using a set of Fourier series temporal basis functions, we have been able to characterize EHRs without specifying their exact form and timing. Since the shape of the EHR timecourse can depend on both the duration and the amplitude of the stimulus (Vazquez and Noll, 1998
), it is particularly important to avoid the use of predefined responses when investigating cognitive processes of unknown timing and intensity. The overall timecourse of the EHR for primary visual cortex is in line with previous reports (Boynton et al., 1996
; Malonek and Grinvald, 1996
; Cohen, 1997
), with a short latency between the occurrence of the IC and the rise of the EHR. However, because we did not use a standard EHR as a template, the fitted EHRs in different areas could differ in shape, amplitude and timing. For example, a marked post-activation undershoot is present in the motor and premotor areas, but not in some other areas (compare Fig. 5B
with Fig. 3A
). A recent study of Buxton et al. (1998) confirmed the presence of a prolonged post-activation undershoot during the performance of a finger-tapping task. They interpreted and modelled such phenomenon as a mismatch between the time constants of flow and blood volume changes. Also, the onset of the EHR seemed to vary from area to area (compare Fig. 3A
with Fig. 3B
). These different onsets can be due to different neurovascular coupling, since the vasculature of different brain areas can be specialized (Zheng et al., 1991
). However, in the posterior parietal cortex there were both signal-related and movement-related EHRs, the latter peaked after the EHR recorded in MI (Fig. 8A,B
). Delayed EHRs have been previously reported (Schacter et al., 1997
), but our method allows us to discard neurovascular coupling as an explanation of the movement-related response latency in parietal cortex, since the signal-related response is well-timed (Fig. 8A, B
).
There are, of course, limitations with the method used here. The model does not explore the interactions between the EHRs in one trial and the next trial (Friston et al., 1998; Vazquez and Noll, 1998
). However, the long ITI used makes these interactions unlikely. Another limitation is that we chose to rely on a discrete distribution of the temporal mismatches between trial occurrence and image acquisition. This could explain the oscillations present in the fitted EHRs, since an aliasing of low-frequency noise into the higher harmonics of our model could have occurred.
Ventral Visual Areas
The activations associated with the IC, but not the TC, occurred in a network of striate and extrastriate areas. EHRs time locked to the IC were observed in striate cortex, ventral prestriate cortex (lingual gyrus) and the cortex around the occipitotemporal sulcus (Brodmann area 19/37) (Figs 2A and 3). The anatomical location of the occipitotemporal activation may correspond to the area that has been reported to be active during passive recognition of objects (Malach et al., 1995
; Kanwisher et al., 1997
). The activation is probably related to the analysis of the shapes. This region appears to be involved in intermediate stages of visual signal processing, lying between primary visual cortex and higher-order, category-specific areas (Tootell et al., 1996
). The present experiment shows that the activity in the ventral prestriate and inferotemporal areas is signal-related. However, no sustained activity was found in these regions, either when examining activity that was specific for the delay (analysis 3) or when examining activity that was present in any of the task events (analysis 4).
Motor Cortex
In motor cortex the activation was time-locked to the movements (Figs 2B, 5B, 6B and 7). There was no statistically significant EHR associated with the instruction cue. Single-unit activity time- locked to the presentation of an instruction cue has been reported in the motor cortex, but the proportions are much lower than in the premotor areas (Weinrich et al., 1984
; Tanji et al., 1988
). Indeed, there is a decreasing rostrocaudal gradient in the proportion of signal-related cells over the precentral gyrus (Weinrich et al., 1984
; Johnson et al., 1996
). In the present study, the local maximum in motor cortex lay in the central sulcus, near the convexity (see Fig. 5B
); and this is likely to be within area 4a (Geyer et al., 1996
). The primary motor cortex showed no significant set-related activity. Richter et al. (1997a) have reported preparatory activity in motor cortex in 7/9 subjects, though the magnitude of the effect for the group was less than for the dorsal premotor cortex. In monkeys there is a low proportion of set-related cells in area 4, but, as for the signal-related cells, the proportion increases as one proceeds from the central sulcus rostrally (Weinrich et al., 1984
; Johnson et al., 1996
).
Premotor Cortex
There were two local maxima in the dorsal premotor cortex. For the posterior one (38, 4, 60) there was significant movement- related activity (see Figs 5C and 6C). Once this component was removed, the residual signal-related activity was not significant. However, there was also a local maximum more anteriorly (18, 6, 70; see Fig. 7A
), lying in the superior branch of the precentral sulcus. For this voxel, once the movement-related component of the response was removed (analysis 1), the residual signal-related activity was just below the significance threshold. However, though there are many cells in the dorsal premotor cortex with signal-related activity (Weinrich et al., 1984
; Kurata and Wise, 1988
), many also show set-related or movement-related responses as well. When the data were tested for either signal-, set- or movement-related activity, or a combination of these (analysis 4), this voxel showed a complex response. The EHR profile for a subset of trials (12.8 s delay) shows signal-, set-, and movement-related components to the response (see Fig. 7A
). Weinrich et al. (1984) reported that in the dorsal premotor cortex 34% of the cells were set-related; however, Kurata and Wise (1988) reported that, of set-related cells, only 35% were exclusively set-related. We assume that this is one reason why we failed to isolate set-related activity (analysis 3). In a functional imaging study of prefrontal cortex, Zarahn et al. (1997) also failed to find voxels for which the activity was only set-related, though as in the present study they report sustained activity when all the components of the task were considered.
We have previously found activation in the anterior part of the dorsal premotor cortex in a study which compared performance of a visuomotor conditional task with reaching for and grasping visually presented objects (Toni et al., 1998). The rostrocaudal differences in the extent of signal-, set- and movement-related activity over the precentral gyrus are confirmed by studies in non-human primates (Weinrich et al., 1984
; Johnson et al., 1996
). Recently, Courtney et al. (1998) have reported sustained activity at the junction of the precentral with the superior frontal sulcus. However, they found this for a task with spatial cues and not for a task with non-spatial cues. The present experiment used non-spatial cues, and furthermore the local maximum lay higher. We therefore take the maximum reported in the present study to lie within the premotor cortex rather than in the prefrontal cortex as in the paper by Courtney et al. (1998).
Parietal Cortex
Local maxima with signal- and movement-related activity were found respectively in the caudal intraparietal sulcus (Table 1; Figs 3C, 4C and 8A
) and in the superior parietal gyrus (Table 2
; Fig. 8B
). The former maximum lay in the depth of the intraparietal sulcus, in front of the occipitoparietal fissure. The timecourse of the EHRs for a subset of trials (12.8 s delay) reveals that these parietal areas were active during both stimulus pres- entation and movement execution (Fig. 8A,B
). The experimental design does not allow us to attribute the movement-related activity to a single factor. It could be a combination of motor discharge, sensory reafference and/or corollary discharge from motor areas. Galletti et al. (1997) have reported that in the parieto-occipital fissure (area V6A; Galletti et al., 1996
) there are cells that are visually responsive and cells that are movement- related. For long delays, the movement-related responses appear to be delayed with respect to the motor response (Fig. 8A,B
). Kalaska and Crammond (1992) have reported that changes in cell activity occur earlier in motor cortex than in superior parietal cortex, including MIP; and Ashe and Georgopoulos (1994) estimated a difference for the population to be 120 ms.
The time difference between the peak of the EHR in parietal and motor cortex is much greater than would be suggested by the single-unit data; one reason may be that it is the hæmodynamic response to the neural activity of cell groups and not cell activity that is being measured. However, part of the delay could have a neuronal source, since the BOLD signal could be affected by the time at which the whole neuronal popu- lation of a given voxel shows a response peak, and not only by the onset of the neuronal responses of individual neurons. Studies on a well-characterized neuronal system like the rat somatosensory `barrel' cortex have shown that whereas the onset of cortical responses to whisker stimulation can be <10 ms (Armstrong-James and Fox, 1987), the population potentials peak 100400 ms after the onset of sensory stimulation (Woolsey, CN, 1952
; Woolsey, TA, 1967
; Woolsey et al., 1996
). This time lag could be larger for cognitive processes than for primary sensory responses.
Signal-, set- and movement-related activity has also been reported in the superior parietal cortex of monkeys, including the dorsal bank of the intraparietal sulcus (Crammond and Kalaska, 1989; Kalaska and Crammond, 1995
; Kalaska, 1996
). The timecourse of the EHR measured over the superior parietal gyrus reveals that this area was active not only during movement execution but also after stimulus presentation (Fig. 8B
). In addition, the response appears to extend over part of the delay period.
Ventral Prefrontal Cortex
In the left inferior frontal sulcus there was a local maximum with a movement-related response (Table 2). We cannot exclude the possibility that this response is related to the auditory trigger cue rather than to the movement made. However, the movement- related response in the right inferior frontal sulcus was clearly related to the movement rather than to the trigger cue, since there was preparatory activity before the trigger cue (Fig. 7C
). Movement-related responses have been reported by di Pellegrino and Wise (1991) in the ventral prefrontal cortex. Inspection of Figure 8C
shows that, for the trials with the longest delay (12.8 s), there appears to be a signal-related response. It will be seen that there was no clear evidence of set-related activity in this portion of the inferior frontal sulcus. di Pellegrino and Wise (1991) compared activity in the ventral prefrontal and dorsal premotor cortex, and they report mainly phasic activity in the ventral prefrontal cortex when a cue is presented that specifies the appropriate response. In the ventral prefrontal cortex only 3% of cells showed tonic responses during the delay period, whereas the comparable figure for the dorsal premotor cortex was 34%.
The Distributed Network
Studies of monkeys reaching for and grasping objects have demonstrated that the visuomotor transformations involved in this task are performed through the inferior parietal and ventral premotor cortex. In the macaque brain, cells in parietal area AIP code information about the shape of objects to be grasped (Sakata et al., 1996), this area sends projections to ventral premotor cortex (PMv) (Matelli et al., 1986
), and cells in this premotor region (area F5) become active when the animal prepares to grasp an object (Murata et al., 1997
). Inactivation of cells in both areas (AIP, PMv) severely impairs the pre-shaping of hand and wrist in preparation for grasping (Gallese et al., 1997
). It might be assumed that all visuomotor transformations involve a circuitry based on the dorsal visual stream.
However, when a monkey reaches for and grasps an object, the visuomotor transformation is non-arbitrary, since there is a spatial correspondence between the location of the object and the target of the reach, and between the shape of the objects and the appropriate shape of the hand. Wise et al. (1996) suggested the term `standard mapping'. There is no such spatial corres- pondence where the visuomotor association is arbitrary. The shape of the cues that the subjects saw in this experiment did not bare any spatial correspondence with the finger to be used. The mapping is `non-standard' (Wise et al. 1996), and the association must be learned or acquired.
The results of the present study suggest that a wide distributed network is involved in visuomotor transformations where the association between the visual cue and the appropriate movement is arbitrary. The posterior parietal cortex was active during the performance of the task. There was activity that was time-locked to the presentation of the visual cue, but the movement-related activity occurred later than the activity in the motor cortex. The contribution of the parietal cortex to the task remains unclear. However, the present study suggests that the arbitrary association of visual shapes with finger-presses involves the ventral visual system, the ventral prefrontal cortex and the anterior part of the dorsal premotor cortex.
There were peaks with signal-related activity in the striate (Fig. 3A) and inferotemporal cortex (Fig. 3B
). Activation in the ventral visual stream has not previously been reported during performance of visuomotor conditional tasks (Deiber et al., 1996
, 1997
; Grafton et al., 1998
; Toni et al., 1998
). In these positron emission tomography studies sensory controls were used in which visual cues were also presented. However, Toni and Passingham (1998) have scanned subjects while they learned a visuomotor conditional task, and have shown learning- related activity in the lingual gyrus, i.e. an increase in activation across four scans during which the subjects learned the task. The authors suggested that this activity could be related to the need to learn the identity of the visual stimuli, since the subject can only make the appropriate movement when the cue has been identified.
There are projections from somatic parietal areas to dorsal premotor cortex, and there are projections from visually receiving areas in parietal cortex (MIP, V6A) to the anterior part of the dorsal premotor cortex (Johnson et al., 1996; M. Matelli et al., submitted). However, no paths have yet been described from the inferotemporal cortex directly to the dorsal premotor cortex (Seltzer and Pandya, 1989
; Distler et al., 1993
; Boussaoud et al., 1995
). The projections from the inferotemporal cortex go to the ventral prefrontal cortex (Webster et al., 1994
; Pandya and Yeterian, 1996
). In the present study two peaks were found in the ventral prefrontal cortex. For one there was both signal- related and movement-related activity (Fig. 8C
), and for the other there appeared to be preparatory activity increasing towards the time of movement (Fig. 7B
). Toni and Passingham (1998) have shown that there is an increase of activity in the ventral prefrontal cortex associated with the learning of a visuomotor conditional task.
It is less clear how the ventral prefrontal cortex communicates with the premotor cortex. In the present study there was a peak in the anterior part of the dorsal premotor cortex for which there appear to be signal-related, set-related and movement-related components (Fig. 7A). In macaques there are projections from the dorsal prefrontal cortex to an anterior part of the dorsal area 6 (including the caudal bank of the superior limb of the arcuate sulcus) (Lu et al. 1994
; Dum and Strick, 1997
), and from the ventral prefrontal cortex to the ventral premotor cortex (Matelli et al., 1986
). However, it is not clear whether the anterior region in the dorsal premotor cortex in the macaque corresponds to the anterior part of the dorsal premotor cortex in which there was signal-related, set-related and movement related activity in the human brain. Thus the connections of the latter area remain to be established for certain.
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Notes |
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Address correspondence to Ivan Toni, Institut für Medizin, Forschungszentrum Jülich, D-52425 Jülich, Germany. Email: i.toni{at}fz-juelich.de.
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