V2 and V5+ activations during coherent motion of dots: An MEG study

www.elsevier.com/locate/ynimg NeuroImage 37 (2007) 1384 – 1395

Timing of V1/V2 and V5+ activations during coherent motion of dots: An MEG study Esther Alonso Prieto, a,b,⁎ Utako B. Barnikol, a,b Ernesto Palmero Soler, a,b Kevin Dolan, a,b Guido Hesselmann, c Hartmut Mohlberg, a Katrin Amunts, a,d,e Karl Zilles, a,d,f Michael Niedeggen, c and Peter A. Tass a,b,d,f,g a

Department of Medicine, INB, Research Center Jülich, Germany Virtual Institute of Neuromodulation, Research Center Jülich, Germany c Institute of Experimental Psychology, Heinrich Heine University Düsseldorf, Germany d Brain Imaging Center West, Jülich, Germany e Department of Psychiatry and Psychotherapy, RWTH Aachen University, Germany f C. and O. Vogt Institute for Brain Research, Heinrich Heine University Düsseldorf, Germany g Department of Stereotactic and Functional Neurosurgery, University of Cologne, Germany b

Received 14 July 2006; revised 20 March 2007; accepted 23 March 2007 Available online 7 June 2007 In order to study the temporal activation course of visual areas V1 and V5 in response to a motion stimulus, a random dots kinematogram paradigm was applied to eight subjects while magnetic fields were recorded using magnetoencephalography (MEG). Sources generating the registered magnetic fields were localized with Magnetic Field Tomography (MFT). Anatomical identification of cytoarchitectonically defined areas V1/V2 and V5 was achieved by means of probabilistic cytoarchitectonic maps. We found that the areas V1/V2 and V5+ (V5 and other adjacent motion sensitive areas) exhibited two main activations peaks at 100–130 ms and at 140–200 ms after motion onset. The first peak found for V1/V2, which corresponds to the visual evoked field (VEF) M1, always preceded the peak found in V5+. Additionally, the V5+ peak was correlated significantly and positively with the second V1/V2 peak. This result supports the idea that the M1 component is generated not only by the visual area V1/V2 (as it is usually proposed), but also by V5+. It reflects a forward connection between both structures, and a feedback projection to V1/V2, which provokes a second activation in V1/V2 around 200 ms. This second V1/V2 activation (corresponding to motion VEF M2) appeared earlier than the second V5+ activation but both peaked simultaneously. This result supports the hypothesis that both areas also generate the M2 component, which reflects a feedback input from V5+ to V1/V2 and a crosstalk between both structures. Our study indicates that during visual motion analysis, V1/V2 and V5+ are activated repeatedly through forward and feedback connections and both contribute to m-VEFs M1 and M2. © 2007 Published by Elsevier Inc. Keywords: Visual motion; V1; V5; m-VEF; Response latency

⁎ Corresponding author. Department of Medicine, INB, Research Center Jülich, Germany. Fax: +49 2461 61 2820. E-mail address: [email protected] (E.A. Prieto). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2007 Published by Elsevier Inc. doi:10.1016/j.neuroimage.2007.03.080

Introduction The visual system consists of different areas which are functionally specialized and interact through networks of reciprocal connections (Zeki and Shipp, 1988; Zeki, 1978). During the analysis of visual motion information, two of those areas are particularly important, although additional areas play also an important role (Bullier, 2001). These areas are V1, placed in the occipital lobe, and V5, the human homologue of monkey MT, placed in the ascending limb of the inferior temporal sulcus (Movshon et al., 1986; Movshon and Newsome, 1996; Tootell et al., 1995; Zeki, 1974). Different functional operations, needed to detect moving stimuli, might arise from the temporal pattern of activations of these two processing nodes which will be the focus of this study. Traditionally, the temporal activation pattern of these structures in humans has been determined by studying motion visual evoked potentials (m-VEPs), measured using electroencephalography, or motion visual evoked fields (m-VEFs), analogous to m-VEPs but measured using magnetoencephalography (Bach and Ullrich, 1994, 1997; Hoffmann et al., 1999; Kuba and Kubova, 1992; Kubova et al., 1990, 1996, 1995). Previous studies have shown that components P1/M1 and N2/M2 might reflect the characteristics of the motion stimulus (Bach and Ullrich, 1994; Kuba and Kubova, 1992; Kubova et al., 1996, 1995; Nakamura and Ohtsuka, 1999; Niedeggen and Wist, 1999). P1/M1 arises approximately at 100– 135 ms after the presentation of a moving stimulus while N2/M2 appears at 150–200 ms post stimuli (Kuba and Kubova, 1992; Kubova et al., 1990, 1995). Because of the spatial restrictions of VEPs/VEFs, it is not clear to what extent V1 and V5 contribute to the generation of P1/M1 and N2/M2, thus limiting conclusions concerned with the timing of

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395

activation of these structures during motion analysis. Several studies, using either dots or gratings patterns, have suggested that V1 is one of the main sources of P1/M1 while V5 is one of the main sources of N2/M2 (Anderson et al., 1996; Bach and Ullrich, 1997; Hollants-Gilhuijs et al., 2000; Holliday et al., 1997; Kuba and Kubova, 1992; Kubova et al., 1996, 1995; Nakamura and Ohtsuka, 1999; Probst et al., 1993; Schellart et al., 2004; Uusitalo et al., 1997). However, it has been also reported that a dipole consistent in origin with V1 can be activated after 200 ms contributing to the N2/M2 component (Anderson et al., 1996). In the same way, a dipole placed in V5 can be activated between 150 and 180 ms contributing to the generation of the P1/M2 component (Anderson et al., 1996). Additionally, reported latencies of these motion components vary across studies. The peak latencies (when peaks reach their highest amplitude) of reconstructed sources placed in V1 have been reported between 82 and 191 ms (Ffytche et al., 1995; Holliday et al., 1997; Nakamura and Ohtsuka, 1999; Schellart et al., 2004) while for reconstructed sources placed in V5 they have been reported between 150 and 262 ms (Ffytche et al., 1995; Maruyama et al., 2002; Nakamura and Ohtsuka, 1999; Schellart et al., 2004). Since most studies agree upon the V1 source being activated prior to the V5 source, the hypothesis that motion information is processed in two sequential stages, which begin in lower area V1 and goes to higher area V5, has been favored (Anderson et al., 1996; Nakamura and Ohtsuka, 1999; Probst et al., 1993). However, some studies have reported that V5 can be activated very early, e.g. just 10–20 ms after area V1 (Ahlfors et al., 1999; Tzelepi et al., 2001; Vanni et al., 2004) or before V1 (Ahlfors et al., 1999; Ffytche et al., 1995). Along the same line of thought, studies using intracerebral recordings procedures in animals have shown that the range of the firing latencies of V1 and V5 neurons overlaps, and that the earliest V5 activation can precede the first V1 response (Raiguel et al., 1989). Because the earliest spike trains of V5 have been significantly associated with sharper direction tuning, it has been proposed that this structure might be able to specify the direction of motion 20–50 ms after stimuli presentation (Raiguel et al., 1989, 1999). Taken together, those results suggest that the analysis of motion involves early V5 activations and repeated V1 and V5 activations. The discrepancies between the above-mentioned studies could be partially due to differences in the techniques employed (i.e. intracerebral recordings in monkeys or scalp recordings in humans), the setup of the experiments (i.e. speed and size of the motion pattern) or the analysis of the results (i.e. methods used to localize the generators of the m-VEP). Even if these differences are taken into account, a question remains open: are P1/M1 and N2/ M2 motion components generated each by a single visual area V1 or V5, activated in a sequential manner, or are they generated by both areas activated repeatedly? Magnetoencephalography is a useful tool to answer this question non-invasively because it measures the neural ensemble activity in the millisecond time scale. Thus, it is able to resolve the activation sequence of various cortical structures. Additionally, the localization of anatomical sources underlying magnetic fields depends only weakly on the head conductivity characteristics and, compared to EEG, fewer assumptions are needed to estimate the brain areas that generate the scalp signal (Hamalainen et al., 1993). To determine the anatomical localization of the areas that generate the scalp signal, after the application of the inverse reconstruction procedure accurate anatomical information about

1385

the position and the extent of brain areas of interest is needed. This anatomic information is especially relevant for studies dealing with the visual system because it exhibits a considerable anatomical variability across and intra-subjects (Aine et al., 1995; Amunts et al., 2000; Zilles et al., 2002). This variability has a strong impact on the surface distribution of the visual evoked field and consequently can lead to erroneous localization of the sources and corresponding latencies, especially if the anatomical structures are simultaneously active. In addition, cytoarchitectonic boundaries, which are closely linked to the function of a given cortical area, vary considerably, and are not precisely correlated to macroanatomical landmarks (Amunts et al., 2000, 2002; Amunts and Zilles, 2001; Malikovic et al., 2007; Roland and Zilles, 1998; Zilles et al., 1997, 2002). Consequently, those landmarks cannot be used to identify anatomical structures in a reliable way. Probabilistic cytoarchitectonic maps of anatomic areas of interest can overcome those obstacles because they give information about the probability with which a given voxel belongs to a particular cytoarchitectonic area, thus retaining information on inter-subject variations in brain architecture (Amunts et al., 2000; Amunts and Zilles, 2001; Luppino et al., 1991; Mazziotta et al., 2001a,b), and because the cytoarchitectonic features determine the function of a given cortical area (Bodegard et al., 2000; Roland and Zilles, 1998; Zilles et al., 1995; Nelissen et al., 2005). In short, the objective of this study was to determine the visual cortical sources that generate the M1 and the M2 components and to determine the temporal activation pattern of those structures during motion processing in humans. We had the hypothesis that not only V1 but also V5 should activate repeatedly in response to a motion stimulus so that both areas contribute to m-VEFs M1 and M2. Although V1 and V5 activations should tend to follow a sequential pattern during the first 140 ms, they should follow a simultaneous pattern after that time. To overcome some of the methodological limitations of previous studies, we used an approach that combines MEG and probabilistic cytoarchitectonic maps of visual areas BA17, BA18 (Amunts et al., 2000) and hOC5 (Malikovic et al., 2007) (the cytoarchitectonic names for V1, V2 and V5). Methods Subjects Eight right-handed male subjects (mean age 26.8 ± 4.0 years), with normal or corrected-to-normal vision acuity, were studied. None of the subjects had a previous medical history, nor were there any under pharmacological effects able to interfere with our measurements. They all gave their informed consent after communication of our aims and of the procedure to be used. All methodology was in accordance with the Institutional Committee on Human Research. Stimuli A random dots kinematogram (RDK) paradigm, characterized by 100% motion coherence, was employed because it evokes strong activation in motion-sensitive areas, and reduces the effects of contrast processing in V1 (Maruyama et al., 2002; Nakamura and Ohtsuka, 1999; Niedeggen and Wist, 1999; Probst et al., 1993). The stimulus was generated using a VSG 2/3F graphics board (Cambridge Research Systems Ltd.) and displayed on an

1386

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395

LCD projector (VPL-X600E, Sony, refresh rate 60 Hz, mean brightness 42 cd/m2) placed outside a magnetically shielded room. Inside the room, subjects were comfortably lying while viewing the stimulation which was projected onto a rear-projection screen and presented to both eyes. The stimulus was presented within an aperture of 22 × 22 cm corresponding to an angular size of 40.3° × 40.3° at a 30 cm viewing distance. The dot pattern contained 720 white dots (luminance: 6 cd/m2) equally distributed on a black ground (luminance: 0.7 cd/m2, Michelson contrast: 0.79). The angular size of each dot was 0.16°. A small red square (whose sides were 0.58 cm long, corresponding to 1.1° × 1.1° of angular size) was placed in the center of the pattern. Subjects were instructed to fixate this point during the experiment. Each trial was defined by a static random dot pattern lasting 750 ms. This pattern was released by a coherent motion epoch lasting for another 750 ms. The motion's direction varied randomly from trial to trial, and was defined by one of the four cardinal directions (right, left, above and below) with a speed of 15°/s. The interstimulus interval was 3000 ± 500 ms (to avoid the overlap of subsequent stimulus-locked responses). The entire sequence was repeated 100 times. Procedure MEG data recording and offline processing A whole head MEG equipment was used (Magnes 2500 WH, 4D-Neuroimaging, San Diego, USA) with 148 sensors uniformly distributed in the helmet with a mean spacing of 2.9 cm. The subjects' head position was measured before and after the experiment using five head location coils of the Magnes 2500 system. The largest head displacement encountered in all subjects was 2 mm. The neuromagnetic activity was continuously recorded using a bandwidth from 0.1 to 400 Hz and a sample rate of 1017.25 Hz. Electro-oculogram and electro-cardiogram signals were additionally registered. Afterwards, MEG signals were digitally band pass filtered offline from 0.5 to 200 Hz including a band stop filter at the power line frequency of 50 Hz and its harmonics. To detect and eliminate artifacts induced by the heartbeat and eye blinks, an independent component analysis (ICA) was used (Bell and Sejnowski, 1995; Jahn et al., 1998). Once the independent components were extracted they were visually inspected by a trained technician and the epochs similar to the EOG or ECG signals were excluded before averaging. The appearance of the motion stimulus on the screen was used as a trigger for averaging. To this end, the time the LCD projector took to build up the stimulus on the screen was determined using a fast photo diode (Silicon PN photo diode BPW21R, VISHAY, rise time and decay time of 3 μs, frequency range of highest sensitivity of 420–675 nm maximum at 565 nm, corresponding to the frequency range of human vision) and an oscilloscope. This delay was 13.6 ± 0.1 ms and it was corrected prior to any further analysis by shifting the trigger time. The time window used for averaging ranged from 500 ms prestimulus to 2400 ms post-stimulus. Finally, the MEG signal was band pass filtered from 3 to 45 Hz (2nd order BTI Butterworth filter, 6 dB with band edges: 2.3 and 45.3 Hz, 4D-Neuroimaging, San Diego, USA). After the measurement, MEG sensors position and orientation were co-registered with the magnetic resonance (MR) image of

each subject's brain. The employed procedure has been published previously (Barnikol et al., 2006). Source analysis of MEG data To compute the intracortical distribution of the primary currents from the surface MEG data, the Magnetic Field Tomography (MFT) was used (Ioannides et al., 1990). MFT uses the sensitivity profiles of MEG sensors as an expansion basis for the reconstruction of the primary current density (PCD), and incorporates an a priori Gaussian probability weight function (Ioannides et al., 1990). In this way, it is able to overcome one of the traditional limitations of classical minimum-norm techniques, specifically the tendency to reconstruct sources in the vicinity of the sensors/ electrodes (Hadamschek, 2006). The decay of the weight function and the regularization parameter were determined in a training session with computer-generated data for each subject. Since we were interested in reconstructing the PCD in visual cortical areas, we selected a source space that covers both hemispheres of the occipital lobe of the brain. MFT was applied using the 90 channels closest to this occipital source space. For the reconstruction, a regular grid which contained 2601 points in space defined in the MRI coordinate system was first created. The maximum distance between those points was 10 mm or below, depending on the size of the brain, for each subject. This regular grid was further clipped to the white and grey brain matter and consequently only 1281 voxels were finally located inside the source space. Note that MFT uses one iteration to improve the spatial accuracy. To extract brain areas displaying strong stimulus-lockedactivity in every subject, regions of interest (ROI) were identified. A detailed explanation of the employed procedure has been given previously (Barnikol et al., 2006). In short, ROIs were detected after determination and baseline correction (BC) of the modulus of the PCD (‖J(r,t)‖BC) in each voxel of the source space that covered the occipital part of the brain without being limited to voxels contained in the probabilistic cytoarchitectonic maps. This detection was performed within the first 400 ms after stimulus onset for each time slice. Because the spatial expansion of the reconstructed PCD may considerably differ in size and amount of activation a cut-off threshold of 70% was placed (Barnikol et al., 2006). This means that only voxels associated with at least 70% of the total maximal activity were included in the analysis (see also the section which explains how the overlap calculation was performed). Additionally, those voxels reaching a local spatial maximum were selected if this maximum was stable during at least 10 ms (five slices) or, if it was stable during three slices while in the other two time slices it moved to voxels of the immediate neighborhood. In the last case, two voxels with coordinates rk and rm, which displayed maximal activity during two different time intervals, Tk and Tm, we compared the maximum of (‖J(rk, t)‖BC) and (‖J(rm, t)‖BC) during their corresponding time intervals of maximal activation, Tk and Tm, by calculating J max = max{‖J(rk, t)‖BC; t ∈ Tk} and J max k m = max{‖J(rm, t)‖BC; t ∈ Tm}. Then, if Jmax N Jmax k m , then rk was assigned as the coordinate of that ROI activated during the two time intervals, Tk and Tm, otherwise rm was assigned as the coordinate of that ROI. Observer independent cytoarchitectonic mapping of areas BA17, BA18 and hOC5 Detailed explanation about the construction procedure of these maps has been given by Amunts et al. (2000) and Malikovic et al.

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395

(2007). Briefly, 10 human postmortem brains (five male and five female brains, age between 37 and 85 years) obtained from the body donor program of the Institute of Brain Research, Heinrich Heine University of Düsseldorf, Germany, were histologically processed, and then visual areas of interest were mapped cytoarchitectonically by means of an observer independent method (Amunts et al., 2000; Malikovic et al., 2007; Schleicher et al., 2000, 1999). This method detects statistically significant changes in the lamination pattern. Afterwards, brain volumes were 3D reconstructed and the cytoarchitectonically defined borders were transferred to the respective reconstructed sections of each brain. Brains were aligned to the standard format of the reference brain (T1-weighted single subject template of the Montreal Neurological Institute, MNI) oriented in the plane determined by the anterior– posterior commissures. This map was afterwards transferred from the MNI reference brain to the 3D space of the MR image of each subject of the experiment using linear and non-linear transformations (common voxel size of 1 × 1 × 1 mm3) (Hömke, 2006; Mohlberg et al., 2003). In this way, it was possible to study the relation between microanatomy and function (MFT activation) considering the individual characteristics of in vivo brains (Fig. 1). Overlap calculation The extent of the overlap between the volume of the selected ROI and the volume of the cytoarchitectonically defined areas was determined in order to know whether selected ROIs were placed in the cytoarchitectonically defined areas of interest. To this end, the spatial relationship, e.g. the overlap, between ROIs and probabilistic cytoarchitectonic maps was determined (for details see Barnikol et al., 2006). Because all ROIs were activated more than one time, in a series of sequential activations of the same ROI, we selected the time (tmax) and the location (rmax) of maximal activation of that particular ROI, since at time (tmax) and at location (rmax) the ROI's selectivity is strongest compared to neighboring activations. The same 70% cut-off threshold used for the extractions of the ROIs (see explanation above) was used for the overlap calculation.

1387

In order to decrease the spatial extent of the volume of each cytoarchitectonic area (i.e. the volume obtained as the sum of all voxels which belong to a given cytoarchitectonic map multiplied by the volume of one voxel) and the number of overlapping voxels between adjacent areas (as BA 17 and BA 18), we placed a cut-off threshold for the probability maps as well (Barnikol et al., 2006; Geyer et al., 2000; Larsson et al., 2002). Concerning BA 17/BA 18, this threshold was placed at 50% (it contains voxels in which BA 17/ BA 18 can be found in ≥ 50% of the postmortem brains) while concerning hOC5 it was placed at 30% (it includes voxels where hOC5 can be found in ≥ 30% of those brains) (Barnikol et al., 2006). The volume of the 50% or 30% probability maps corresponds well with the real volumes of the corresponding areas (Barnikol et al., 2006). Furthermore, the probability thresholds chosen enable an optimal selectivity for differentiating between adjacent areas like BA17 and BA18 (Barnikol et al., 2006) and are not too high so that the volume of small cortical areas like hOC5 would become exceedingly small and consequently the number of gaps between anatomically adjacent structures increases (Geyer et al., 2000; Larsson et al., 2002). According to Barnikol et al. (2006), four indices were used to determine the degree of overlap between a ROI and a probabilistic cytoarchitectonic map. i) Overlap between ROIs and probability maps: we determined the overlap between a 70% ROI and a 50% probability map of BA17/BA18 or a 30% probability map of hOC5. To obtain this index, the number of common voxels contained in the ROI and in the probability maps was divided by the number of voxels of the reference volume which was chosen to be the smaller volume of the volume of the 70% ROIs and the 50% or 30% probability maps (Barnikol et al., 2006). ii) Mean voxel to voxel distance between ROI and probability map: it assesses if and to what extent a ROI does not overlap with a given probability map. For this we calculated the distance between each voxel of the ROI and the nearest voxel

Fig. 1. Contour lines represent the probability maps of V1 (in blue), V2 (in yellow) and V5 (in red) mapped inside the MR image of subject 1. Activation sources of the scalp recorded visual neuromagnetic field are also mapped according to a color scale. This color scale represents the MFT activations, that is, the prestimulus baseline corrected modulus of the PCD. ROIs were defined as the particular voxel reflecting the time and the location of the maximal source activation placed inside or in the neighborhood of the probability maps.

1388

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395

of the probability map and averaged over all such distances (Barnikol et al., 2006). iii) Minimal distance between maximal MFT activation and probability map: this measure provides an estimate of the distance between the place of strongest activation and a given probability map. It was determined by calculating the shortest distance between a 50% or 30% probability map and the place of the ROIs maximal activation (Barnikol et al., 2006). iv) Ratio of the anatomically weighted MFT activity in area BA17 to the anatomically weighted MFT activity in BA18: this index assesses the differential activation of BA17 and BA18 (areas which have strongly overlapping probability maps) in a probabilistic manner. We multiplied the MFT activity of a particular voxel by the voxel's anatomical probability of belonging to BA 17 or BA 18 probability map, respectively. Then, the anatomically weighted MFT activity of all voxels of the entire probability map of each area was summed up (the summation ran over the entire probability map of BA 17 or BA 18). The anatomical weighting was normalized guaranteeing that any bias toward an over- or underestimation of one of the areas was avoided (Barnikol et al., 2006). Finally, we determined the ratio of the anatomically weighted activity in BA 17 to the anatomically weighted activity in BA 18 for each detected m-VEP separately (see Results). To calculate the four above-mentioned quantities, the probability maps of BA 17 and BA 18 containing voxels of both hemispheres were used. In contrast, either the right or left probability map of hOC5 was used since in all subjects the hOC5 activation was unilateral (see Results). All indices were determined for each subject and then averaged or statistically evaluated across all subjects. Latencies and normalized activations To analyze the temporal activation patterns of each ROI, we determined the latency of the peaks of the stimulus-locked baseline corrected modulus ‖J(rmax, t)‖BC of the PCD, and their amplitude. Peak latencies (when the peak reached its highest amplitude) were estimated as follows: First, a fit was made for the data in the near vicinity of the peak. Then, the approximate location of the peak was estimated “by eye”. The data from a small region around the peak (±10 ms) were then selected, and a least squares fit to a parabola was made: x(t) = at2 + bt + c + ξ(t). This had the effect of averaging out the small fluctuations due to noise, which was modeled as ξ(t), allowing for a very precise estimate of the location of the peak. The location of the peak was then given by tp = b/a.

Error bars on this estimate were calculated by using the quality of the fit to judge the degree of fluctuation from a smooth curve. Many realizations of the estimated parabola were constructed, each with a different random realization of this noise ξ(t), whose amplitude is simply the standard deviation of the difference between the data and the above parabolic model. For each such realization, the fit was then made again, and new values of the peak location were estimated. This gave a distribution of estimated locations of the peak, which could then be used to place error bars on the original peak location. The latency of the onset of the identified peaks was also extracted. It was taken as the latency exhibited at 10% of the peak amplitude. Although this measure is not the onset of the peak, strictly speaking, it was selected because it is less sensitive to noise in the signal (Schoenfeld et al., 2003). To compare the amplitude of the activations (in relation to the mean level of single run activity) across subjects, we performed a normalization for each ROI separately, by means of a division with the mean single run activity. This was needed because MFT provides the PCD in arbitrary units (Ioannides et al., 1990). To compute this normalization, we first calculated the single run PCD JSR(rmax,t) for each ROI on a supercomputer (IBM cluster), without baseline correction (Tass et al., 2003). The single run PCD reflects a combination of stimulus-locked physiological activity (which is extracted by the averaging procedure), physiological activity that is not stimulus-locked (which is averaged out by the averaging procedure) and noise. Subsequently, the mean single R T run activity 〈‖JSR(rmax, t)‖〉 was determined where hf ðtÞi ¼ T 1 0 f ðt Þdt denotes averaging of f over time, covering the whole experiment with duration T, and including all stimuli. =‖J(rmax,t) Finally, the normalized PCD is given by ‖J(rmax, t)‖NORM BC ‖BC / ‖JSR(rmax, t)‖. This function estimates the ratio of the activity tightly time locked to stimulus onset to both noise and the activity that is not stimulus-locked. Data were finally analyzed by using a Wilcoxon Matched-Pair Test (p b 0.05). Spearman Rank order correlations (p b 0.05) were used to assess the relationship between selected measures. Results Results will be presented in two parts. First, we will consider the overlap values between the selected MFT ROIs and the probabilistic cytoarchitectonic maps, which allow to tell which ROIs were contained in which probabilistic cytoarchitectonic map. Afterwards, we will detail the activation pattern evoked by our stimulus. Note that, to present our findings, we will use the terms V1, V2 and V5 instead of the corresponding cytoarchitectonic terminology.

Table 1 Overlap between ROIs and corresponding probabilistic cytoarchitectonic maps ROIs vs. 1st overlap index: percentage Z probability maps of overlap between ROI and to be compared probability maps ROI V1/V2 vs. map BA 17 ROI V1/V2 vs. map BA 18 ROI V5 vs. map hOC5

32.2 ± 13.2% (SE = 4.7%)

p

2.38 0.02 3.7 ± 2.2 mm (SE = 0.8 mm)

14.3 ± 7.8% (SE = 2.7%) 35.2 ± 30.8% (SE = 10.9%)

2nd overlap index: mean voxel Z to voxel distance between ROI and probability maps

p

1.40 n/s 2.1 ± 2.7 mm (SE = 0.9 mm)

5.2 ± 3.7 mm (SE = 1.3 mm) –

–

2.9 ± 1.7 mm (SE = 0.6 mm)

3rd overlap index: minimal distance Z between maximal ROI activation and probability map

p

1.40 n/s

3 ± 4.1 mm (SE = 1.5 mm) –

–

15.9 ± 15.9 mm (SE = 5.6 mm)

–

–

Statistical comparisons performed for three overlap indices. Comparison included only 70% V1/V2 ROI and 50% V1 or V2 maps and was made using Wilcoxon matched pair test (p b 0.05).

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395 Table 2 Ratio of the anatomically weighted MFT activity in area V1 to the anatomically weighted MFT activity in V2 for each peak Peak

1st peak

2nd peak

Z

p

Ratio of weighted activity (mean ± SD) 1.25 ± 0.17 1.2 ± 0.10 0.7 n/s Standard error estimate 0.06 0.03 – – Probability 0.00002 0.00001 – – Group average of the ratio of the anatomically weighted MFT activity in area V1 to the anatomically weighted MFT activity in V2 for each peak, separately. The standard error of these estimates of the means (“standard error estimate”) and the probability of the estimated mean being indistinguishable from 1 (“probability”) are shown. Additionally, the comparison between both indices for each component is presented (Wilcoxon matched pair test, p b 0.05).

Overlap between MFT ROIs and probabilistic cytoarchitectonic maps Four indices were determined to characterize the spatial relationship between ROIs and probability maps (see Methods):

i) Overlap between ROIs and probability maps (Table 1): the percentage of overlap between a 70% ROI close to V1/V2 with a 50% probability map of V1 was higher than the overlap between the same ROI and the probability map of V2. The difference between both quantities reached statistical significance (p b 0.05). The 70% ROI identified in V5 also showed a pronounced overlap with the 30% probability map of V5. ii) Mean voxel to voxel distance between ROIs and probability maps (Table 1): the mean value of this index calculated across subjects was smaller for the ROIs placed in V1 than for those placed in V2, but the difference did not reach statistical significance. The mean voxel to voxel distance between V5 and the corresponding ROI is also small, despite the fact that V5 is considerably smaller than V1 and V2 (Barnikol et al., 2006). iii) Minimal distance between maximal MFT activation and probability map (Table 1): the distance between the location

1389

of strongest MFT activation and the corresponding probability map averaged across subjects was smaller for V1 than for V2 although the difference was not statistically significant. V5 values were higher than those of V1 and V2. iv) Ratio of the anatomically weighted MFT activity in area V1 to the anatomically weighted MFT activity in V2 for each component: Table 2 shows the mean and standard deviation of this index for each peak identified on V1/V2. Additionally, it shows the probability of the estimated mean being indistinguishable from one. This was computed by dividing the difference between the estimated means and one by the standard error (SE) and comparing to a normal distribution with zero mean and unit variance (Hogg and Tanis, 2001). Results showed that V1 activations were significantly stronger than V2 activations for all peaks (Table 2). Additionally, we compared the ratios obtained for each peak. It was found that the difference was not statistically significant (Table 2). Taken together, those results showed that ROIs were closer to the V1 probability map than to the V2 probability map (indices i and iv); even the parts of the ROIs that do not overlap with the probability maps are close to them considering the grid spacing (index ii). Additionally, our outcome also demonstrated that the distance between the coordinates of the maximum of the MFT ROIs and the map is small compared to the grid spacing (index iii). Consequently, the overlap analysis allowed to consider that the ROIs extracted on the base of the MFT activations were anatomically placed in the cytoarchitectonic areas V1/V2 and V5 (see Discussion). We did not find any other areas besides V1/V2 and V5 that were consistently activated across subjects. Analysis of the visual evoked magnetic response Visual evoked fields exhibited three main peaks over sensors depicted in Fig. 2. A similar three-phasic complex was found in the PCD curve of visual area V5 while a bi-phasic complex was found in V1/V2 (Fig. 3). The highest amplitude of those peaks as well as their onset latencies (when the peaks reached the 10% of their

Fig. 2. Four representative sensors of subject 1 are presented. The identified m-VEFs are pointed out and their latencies are indicated. Thick lines indicate motion onset while thin lines indicate motion offset. The head drafted above shows the position of those sensors.

1390

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395

Fig. 3. Temporal course of the cross trial averaged PCD extracted for ROIs placed in V1/V2 and V5+, for subject 1. Each peak was indicated according to the nomenclature used in the text.

amplitude) and their peak latencies (when they reached the highest amplitude) will be detailed and compared in the following paragraphs. The first peak exhibited over occipital and temporal sensors appeared between 60 and 70 ms (mean latency ± standard deviation = 63 ± 11 ms, SE = 5 ms) and its maximum amplitude was reached between 70 and 94 ms (mean latency ± standard deviation = 76 ± 13 ms, SE = 5 ms). It was identified in three out of eight subjects and labeled M90. Within a homologous time window, a first activation peak in V5 area was found. It appeared in three subjects between 60 and 72 ms (mean latency = 64 ± 7 ms, SE = 4 ms) while its maximum amplitude was reached in a range from 72 to 89 ms (mean latency = 79 ± 9 ms, SE = 5 ms). For the sake of simplicity, it was named M90-V5. Visual evoked fields displayed a second peak over sensors whose onset latency was in a time range from 80 to 107 ms (mean latency ± standard deviation = 91 ± 11 ms, SE = 5 ms) and its highest amplitude was attained between 90 and 150 ms (mean latency ± standard deviation = 138 ± 10 ms, SE = 7 ms). It was detected in six out of eight subjects and because of its latency, it was labeled as M1. A peak similar in latency was displayed by V1/V2. It was found in six out of eight subjects, and appeared between 57 and 107 ms (mean latency = 81 ± 20 ms, SE = 8 ms), and was maximal between 82 and 130 ms (mean latency = 101 ± 18 ms, SE = 7 ms). From now on, this peak will be referred to as M1-V1/V2. Similarly, a V5 activation appeared in all subjects between 89 and 157 ms (mean latency = 128 ± 25 ms, SE = 9 ms), with the highest amplitude between 119 and 180 ms (mean latency = 151 ± 19 ms, SE = 7 ms). For the purposes of this discussion, this V5 peak was labeled M1-V5. The last visual evoked component found on occipital and temporal sensors started between 140 and 170 ms (mean latency = 142 ± 18 ms, SE = 7 ms) and achieved a maximal amplitude between 180 and 230 ms (mean latency ± standard deviation = 196 ± 4 ms, SE = 8 ms). It was reliably found in each subject and named M2. An activation similar in latency was found on the temporal course of V1/V2 activation. It appeared for all

subjects in a time window between 120 and 195 ms (mean latency = 145 ± 26 ms, SE = 9 ms), and presented its maximum amplitude between 131 and 223 ms (mean latency = 175 ± 32 ms, SE = 11 ms). It was labeled M2-V1/V2. Regarding V5, a similar peak emerged for all subjects between 172 and 210 ms (mean latency = 188 ± 14 ms, SE = 6 ms), and presented the maximum amplitude values between 189 and 227 ms (mean latency = 206 ± 15 ms, SE = 6 ms). Again, for the purposes of the discussion below, it will be referred to as M2-V5. All V5 activations were clearly lateralized. In four subjects, the major activation was in the right hemisphere while in the remaining four the major activation was in the left hemisphere. The latency and amplitude values of all observed PCD peaks were compared in order to determine the pattern of activation of V1/V2 and V5 during the analysis of our stimulus and to determine the possible contributions of both areas to m-VEFs identified on the scalp. For the three subjects which showed the M90-V5 component, its onset latency as well as its peak latency preceded the onset and peak latency of the M1-V1/V2 component but because of the small number of subjects presenting this early V5 peak, statistical comparisons were not possible. The remaining activation peaks of the areas under study presented a sequential temporal pattern which was consistently found for all the subjects who participated in the study. Thus, the onset latency of V1/V2 activation precedes the onset of V5 activation for all subjects (even those who presented a V5 activation at about 90 ms) and was significantly and positively correlated (Spearman Rank order correlations, r = 0.83, p b 0.05). Thus, an increase in latency in one is usually accompanied by an increase in the other, and vice-versa (Siegel and Castellan, 1988). Their peak latencies were significantly different (Wilcoxon Matched Pairs Test, Z = 2.20, p = 0.03) with the V1/V2 latency being also smaller than V5 latency for all subjects. Concerning their amplitudes, there were no differences (mean amplitude V1/ V2 = 0.75 ± 0.93, SE = 0.37; mean amplitude V5 = 0.71 ± 0.54, SE = 0.19). The onset latencies of M2–V1/V2 and M2-V5 peaks were significantly different (Wilcoxon Matched Pairs Test, Z = 1.99, p = 0.04), so the onset of V1/V2 was earlier than the onset of V5. In contrast, there were no statistically significant differences between their peak latencies and amplitudes (mean amplitude V1/V2 = 1.21 ± 0.78, SE = 0.28; mean amplitude V5 = 1.55 ± 0.52, SE = 0.21). This pattern of results was consistently found across all subjects. Interestingly, the onset of this M2-V1/V2 peak was significantly and positively correlated also with the onset of the M1-V5 peak (Spearman Rank order correlations, r = 0.81, p b 0.05). Regarding the contribution that those peaks could have made to the m-VEF found on the scalp, the M90-V5 peak corresponds well with the latency of the M90 component found on sensors. In contrast, the first V1/V2 peak (M1-V1/V2) covered a wider time window far beyond this early component. This fact allows to say that our early m-VEF implied a major contribution from V5 while probably a small, if any, contribution from V1/V2. The latency of the M1 component found on the scalp was compatible with the M1-V1/V2 peak and with the M1-V5 peak. Similarly, the scalp component named M2 exhibited latencies comparable to the latencies exhibited by the last peaks found on V1/V2 and V5, respectively. Since we did not find additional areas consistently activated across subjects, our results support the hypothesis that both areas had contributed to these m-VEFs.

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395

Discussion Our results can be summarized as follows. First, the analysis of overlaps indicated that the selected ROIs are placed in neuroanatomical regions that can be described as V1/V2, and V5 along with adjacent motion sensitive areas (in the following, we will refer to this cluster of areas as V5+), as it will be discussed below. Second, different activity peaks were found in the temporal course of V1/ V2 and V5+ PCD. Activity found before 180 ms tended to be ordered sequentially. The M1-V1/V2 component was earlier than the M1-V5+ component. Additionally, the onset latencies of this V1/V2 activations were positively correlated with the onset latencies of the V5+ activations (meaning that an early onset of the M2–V1/V2 component corresponds to an early onset of the M1-V5+ component and vice versa) while their peak latencies were significantly different. The following activation, after 200 ms, also appeared earlier in V1/V2 than in V5+ but both areas reached their highest amplitude simultaneously. In addition, the onset of this M2-V1/V2 component was positively correlated with the onset of the M1-V5+ component. The implications of those results for the organization of visual areas V1/V2 and V5+ during motion analysis will be discussed in the next paragraphs. Anatomical identification of active areas The goodness of fit of the overlapping analysis needs to be interpreted taking into account the grid spacing, the anatomical variability of the cytoarchitectonic areas as well as the localization error of the MFT. Here, the MFT grid spacing was 10 mm or less depending on the size of the brain. Because the shortest distances and the mean voxel to voxel distance between MFT ROIs close to V1/V2 and the probability map of V1 or V2 were smaller than 10 mm, we considered our values acceptable. The remaining indices have confirmed this interpretation, and additionally, allow us to conclude that the ROIs were more distant from the V2 probability map than from the V1 map because all values were higher for V2 than for V1. Note that the ratio of the anatomically weighted MFT activity in V1 to the anatomically weighted MFT activity in V2 for each component showed that there was a significantly stronger activation of V1. Nevertheless, our results do not represent an unambiguous discrimination between V1 and V2. Two reasons might explain this fact. First, the intrinsic anatomical variability of areas V1, V2 and V5 provokes that the same brain point might belong to a certain area with some amount of probability and to another area with a different amount of probability. This fact might provoke that even when the MFT ideally reconstructs the exact borders of an area of interest, a smaller group mean cross overlap with nearby areas might occur (Barnikol et al., 2006). On the other hand, the localization error of the MFT can also influence the overlap values. The localization error of this inverse technique for a signal to noise ratio of 50% and for superficial sources is between 10 and 13 mm (Hadamschek, 2006). This means that the “true” activated area can be displaced by about 10–13 mm from the point where it was actually reconstructed. Future studies might benefit from an inverse technique with a better spatial resolution (see e.g. Palmero-Soler et al., 2005, 2007). The second aspect points to the fact that it could have been possible that V1 and V2 were really co-activated during the experiment. Indeed, the motion pathway receives its input principally from the magnocellular fibers which project to layer

1391

4Cα of V1, which in turn projects to layer 4β (Fitzpatrick et al., 1985; Levitt et al., 1994a,b; Lund and Boothe, 1975). From there, projections pass either directly to V5 or indirectly via the thick cytochrome oxidase stripes of V2 (Bullier, 2001; Clark et al., 1992; Livingstone and Hubel, 1987; Maunsell and Newsome, 1987; Maunsell and van Essen, 1983a,b; Movshon and Newsome, 1996; Ungerleider and Desimone, 1986). Furthermore, it has been demonstrated that thick stripes of V2 have a proportion of direction-selective cells close to 40% (Felleman and Van Essen, 1987; Gegenfurtner et al., 1997; Levitt et al., 1994a,b). It is not surprising, then, that monkey studies (Galletti et al., 1988; Nowak et al., 1995, 1999; Orban et al., 1986) as well as human studies (Smith et al., 1998) have reported activations of V2 in response to motion stimuli. Future studies should be directed to better differentiate between activations of single areas or co-activations of several structures. To this end, for example, an inverse technique with a better spatial resolution (see e.g. Palmero-Soler et al., 2005, 2007) or an inverse technique which estimates the probability that some area or combinations of areas produce the scalp data by including anatomical constraints (such as cytoarchitectonic information) and by penalizing areas that receive less support from the scalp data (Trujillo-Barreto et al., 2004) can be very useful. Those techniques will constrain the reconstructed PCD and will decrease the amount of ghost sources which obscure the interpretation of the results. The overlapping indices obtained for area V5 were smaller than the grid spacing in the case of the mean voxel-to-voxel distance between MFT ROIs and corresponding probability maps. Additionally, the percentage of overlap was high. Nevertheless, the minimal distance between selected ROIs and corresponding probabilistic maps was higher than 10 mm. To appreciate this value, one should take into account the small size of the V5 area. Only for comparison, note that in the MNI reference brain the compound volume of right and left 30% probability map of V5 together is more than 10 times smaller than the volume of the 50% probability map of V1 or V2 (Barnikol et al., 2006). This fact, together with the size of our grid spacing would have accounted for those results. However, the most probable explanation could be that not only V5 but also surrounding areas were active. V5 is surrounded by other motion-sensitive regions (Dukelow et al., 2001) which can be activated by dynamic stimuli (Nakamura and Ohtsuka, 1999; Shipp et al., 1991; Watson et al., 1993). This motion-sensitive cluster of structures had been referred to as V5+ and includes mainly V5 and the medial superior temporal area (MST) (Dukelow et al., 2001). In addition, V5 has strong projections to MST (Desimone and Ungerleider, 1986; Maunsell and van Essen, 1983a,b; Ungerleider et al., 1984) and it has been proposed that V5 is primarily related with the analysis of basic elements of motion whereas area MST is related with the analysis of higher order motion elements (Dukelow et al., 2001). Timing of the activation of the anatomical sources of m-VEFs M1 and M2 Two and three main activation peaks were found in the time course of the PCD of V1/V2 and V5+, respectively. The first V5+ peak was probably related to a fast but weak input arriving to V5+ through its connections with the superior colliculus and pulvinar (Standage and Benevento, 1983) or with the LGN (Fries, 1981; Yukie and Iwai, 1981). Such a fast track of visual processing has already been discussed for motion stimuli (Beckers and Zeki, 1995; Ffytche et al., 1995, 1996; Schoenfeld et al., 2002). However, our

1392

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395

data only partially support this hypothesis, because we found this early activation in only 3 out of 8 subjects. Therefore, this finding should be considered carefully. We think that the inconsistent appearance of this peak is due to the fact that our stimulus was not designed to specifically elicit early V5 activations because we were interested in the standard visual response to the motion stimulation (P1/M1 and N2/M2 components) (Kuba and Kubova, 1992; Kubova et al., 1990). Studies focused on the early V5 activations employed experimental methods or experimental manipulations specifically designed to enhance the early V5 activity and to decrease the activity in early sensory areas such as V1 and V2. For example, Ffytche et al. (1995) used MEG and EEG co-registration, together with a stimulus which was smaller (30° × 20° in their study in contrast to 40.3° × 40.3° in ours) and faster ( N 22°/s in their study in contrast to 15°/seg in ours) than our stimulus. Additionally, his stimulus was presented during a shorter period of time (200 ms in his study in contrast to the 750 ms used in our study) (Ffytche et al., 1995). The authors of the abovementioned experiment concluded that the signal may arrive in V5 before V1 when this kind of high velocity stimulation is employed (Ffytche et al., 1995). In another study, Schoenfeld et al. (2002) designed stimulus sequences able to induce sustained refractoriness in V1 (Schoenfeld et al., 2002). Such precaution is needed in order to elicit this component consistently across subjects because it can be easily overlapped by the strong activation of V1/V2 (Schoenfeld et al., 2002), and because it could imply a decrease in the activity in area V5+ due to the absence of the striate input. Indeed, it has been reported that a minor decline (about 20% decline) in V5+ activity occurred when the striate activity was decreased (Schoenfeld et al., 2002). Additionally, it has been also demonstrated that although 50–60% of V5+ neurons still fire after V1 inactivation, their firing is much weaker (Girard et al., 1992). The remaining peaks were reliably detected and had the highest amplitudes. This is in line with previous studies which showed that evoked motion responses appear between 100 and 200 ms after stimulus onset (Bach and Ullrich, 1994; Hoffmann et al., 1999; Kuba and Kubova, 1992; Kubova et al., 1990, 1995). It also agrees with unit recordings studies which demonstrate that there is an increase in V1 and V5 cells firing at about 90–170 ms (Raiguel et al., 1989). The V1/V2 and V5+ activations around 100 ms exhibited a sequential relation even in those subjects who also exhibited a V5+ peak about 80–90 ms. This result is consistent with the notion that these activations reflected a feedforward input from V1/V2 to V5+ through the magnocellular (M) pathway (Bullier, 2001; Maunsell and Newsome, 1987; Movshon and Newsome, 1996). It is well established that cells of area V1 are selectively responsive to motion direction, so all directions are coded by the assembly of neurons (Hubel and Wiesel, 1968). It has been proposed that those direction-selective cells act as local filters that analyze the constituent features of moving objects presented within the limits of their receptive fields (Adelson and Movshon, 1982; Emerson et al., 1992). The result of this processing could be outputted to V5+ analyzers. Cells in area V5+ are selective for both the direction and the speed of motion objects (Maunsell and van Essen, 1983a,b; Rodman and Albright, 1989), and its receptive fields cover a bigger area of the visual space (Maunsell and van Essen, 1983a,b; Movshon and Newsome, 1996; Raiguel et al., 1997). Consequently, this area is able to detect and integrate local motion signals taking into account the information provided by the striate cortex (but not only by this area as the anatomical pattern of connections

of V5+ demonstrates) (Movshon et al., 1986; Movshon and Newsome, 1996; Wurtz and Kandel, 2000). Additionally, the M1-V5+ peak was significantly and positively correlated with the second activation in area V1/V2 (M2-V1/V2 peak). This result suggests the idea that the V5+ activation around 100 ms also implies a back projection to V1/V2, which may provoke a second activation in the striate cortex around 200 ms. Indeed, it has been hypothesized that later activations (N100 ms) might reflect the inputs arriving to a visual structure, processing inside that structure as well as feedback projections from higher areas to lower ones (Zeki and Shipp, 1988). Consequently, it is not surprising that the V5+ activation at about 100 ms implies feedforward as well as feedback processing. The importance of feedback inputs to V1/V2 for visual perception and for the conscious awareness of dynamic stimulus has been demonstrated in monkeys (Bullier et al., 2001; Hupe et al., 1998; Rockland and Knutson, 2000) as well as in humans (Lamme, 2001; PascualLeone and Walsh, 2001). Last peaks of both studied areas around 200 ms also displayed a complex temporal relation. Thus, the onset latency of M2-V1/V2 peak was significantly earlier than the onset latency of M2-V5+ peak but both structures reached their maximal amplitude simultaneously. Our view is that the late stage of motion processing is supported not only by a feedback from V5+ to V1/V2 but also by a simultaneous crosstalk between both areas. A second explanation is also consistent with our results. The subcortical input from the lateral geniculate nucleus in response to an ongoing stimulation that lasted 750 ms could have been driving the cortical response. If this were the case, repeated activations of V1/V2 and V5+ can also be expected. Detecting the subcortical generators of the electric/magnetic fields observed over the scalp has represented one of the traditional limitations of inverse imaging techniques (Trujillo-Barreto et al., 2004). These techniques usually tend to underestimate deep generators in favor of cortical ones (Trujillo-Barreto et al., 2004). Although the MFT method uses a Gaussian weighting to overcome this difficulty (Ioannides et al., 1990; Hadamschek, 2006), it has been previously demonstrated that the localization error increases proportionally to the depth of the sources (Hadamschek, 2006). These facts may explain why we did not find reliable subcortical activations across subjects and may point to the fact that one should be more cautious to interpret source localization studies of ERPs according to feedforward– feedback relationships between cortical areas. In line with previous studies, we also obtained a lateralized activation in V5+: four subjects presented the largest activation in right V5+ and the remaining four in left V5+. This result points out the fact that the dominant hemisphere for visual motion analysis can vary inter-individually. This conclusion is supported by previous VEP studies which have shown that the N2 component exhibits a right-hemispheric dominance in the 60% of 80 tested subjects, and a left-hemispheric dominance in the 20% of the subjects (Kubova et al., 1990). Moreover, area V5 of each hemisphere receives inputs from both the ipsilateral (Tootell et al., 1995) and contralateral visual field (Tootell et al., 1995; Van Essen et al., 1982) and the receptive fields of V5 cells extend up to 20° into the ipsilateral visual field of the macaque monkey (Gattass and Gross, 1981; Tootell et al., 1995). This means that area V5 in both hemispheres has some degree of representation of both visual fields. This might explain why here a unilateral activation was sufficient to process full field stimulation (stimulus size 22 × 22 cm corresponding to an angular size of 20.13° × 20.13°).

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395

The bilateral activation foci obtained in Positron Emission Tomography (PET) studies (Cheng et al., 1995; Watson et al., 1993; Zeki et al., 1991) do not necessarily contradict the asymmetric activation in V5+ because the group averaging procedure applied in those studies probably leveled out asymmetric activations. Finally, we would like to point out that the latencies of the V1/ V2 and V5+ peaks correspond to the latencies of motion components and N2/M2 reported in this and previous studies on motion processing (Bach and Ullrich, 1994, 1997; Kuba and Kubova, 1992; Kubova et al., 1990; Niedeggen and Wist, 1999). This result supports the idea that both structures can contribute to the generation of these evoked potentials. The absence of statistical differences between the amplitude of V1/V2 and V5+ peaks demonstrated that their contribution is of the same order of magnitude. This finding confirmed the results of previous studies, in which the brain source of the P1 component was localized in V1, and the source of the N2 was localized in V5 (Anderson et al., 1996; Maier et al., 1987; Probst et al., 1993). Our study extends those previous findings in two ways. First, we demonstrated that both areas can contribute to both transient components, and second, we obtained that the M1 component implies feed forward inputs from V1/V2 to V5 as well as feedback inputs. At the time of the M2 component feedback, processing within V1/V2 and crosstalk between both areas is predominant. It is likely that this pattern was identified here because of the combination of neuromagnetic and cytoarchitectonic methods. Concluding, our MEG results indicate that after the presentation of visual motion stimuli, two nodes (V1/V2 and V5+) of the visual pathway, which are organized according to levels of increasing complexity, are activated repeatedly through feedforward and feedback connections. Acknowledgments Our heartfelt thanks go to all subjects for their patience to participate in the experiment. Also to B.S. M. Majtanik for fruitful discussions. Finally yet importantly, we would like to thank all animals who participated in the studies cited by us. References Adelson, E., Movshon, J.A., 1982. Phenomenal coherence of moving visual patterns. Nature 300, 523–525. Ahlfors, S.P., Simpson, G.V., Dale, A.M., Belliveau, J.W., Liu, A.K., Korvenoja, A., Virtanen, J., Huotilainen, M., Tootell, R.B., Aronen, H.J., Ilmoniemi, R.J., 1999. Spatiotemporal activity of a cortical network for processing visual motion revealed by MEG and fMRI. J. Neurophysiol. 82, 2545–2555. Aine, C.J., Supek, S., George, J.S., 1995. Temporal dynamics of visualevoked neuromagnetic sources: effects of stimulus parameters and selective attention. Int. J. Neurosci. 80, 79–104. Amunts, K., Zilles, K., 2001. Advances in cytoarchitectonic mapping of the human cerebral cortex. Neuroimaging Clin. N. Am. 11, 151–169 (vii). Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T., Zilles, K., 2000. Brodmann's areas 17 and 18 brought into stereotaxic space-where and how variable? NeuroImage 11, 66–84. Amunts, K., Schleicher, A., Zilles, K., 2002. Architectonic mapping of the human cerebral cortex. In: Schüz, A., Miller, R. (Eds.), Cortical Areas: Unity and Diversity. Taylor and Francis, New York, pp. 29–52. Anderson, S., Holliday, I., Singh, K.G.H., 1996. Localization and functional

1393

analysis of human cortical area V5 using magneto-encephalography. Proc. R. Soc. Lond., B 263, 423–431. Bach, M., Ullrich, D., 1994. Motion adaptation governs the shape of motionevoked cortical potentials. Vision Res. 34, 1541–1547. Bach, M., Ullrich, D., 1997. Contrast dependency of motion-onset and pattern-reversal VEPs: interaction of stimulus type, recording site and response component. Vision Res. 37, 1845–1849. Barnikol, U.B., Amunts, K., Dammers, J., Mohlberg, H., Fieseler, T., Malikovic, A., Zilles, K., Niedeggen, M., Tass, P.A., 2006. Pattern reversal visual evoked responses of V1/V2 and V5/MT as revealed by MEG combined with probabilistic cytoarchitectonic maps. NeuroImage 31, 86–108. Beckers, G., Zeki, S., 1995. The consequences of inactivating areas V1 and V5 on visual motion perception. Brain 118 (Pt. 1), 49–60. Bell, A.J., Sejnowski, T.J., 1995. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159. Bodegard, A., Geyer, S., Naito, E., Zilles, K., Roland, P.E., 2000. Somatosensory areas in man activated by moving stimuli: cytoarchitectonic mapping and PET. NeuroReport 11, 187–191. Bullier, J., 2001. Integrated model of visual processing. Brain Res. Brain Res. Rev. 36, 96–107. Bullier, J., Hupe, J.M., James, A.C., Girard, P., 2001. The role of feedback connections in shaping the responses of visual cortical neurons. Prog. Brain Res. 134, 193–204. Cheng, K., Fujita, H., Kanno, I., Miura, S., Tanaka, K., 1995. Human cortical regions activated by wide-field visual motion: an H2(15)O PET study. J. Neurophysiol. 74, 413–427. Clark, V.P., Courchesne, E., Grafe, M., 1992. In vivo myeloarchitectonic analysis of human striate and extrastriate cortex using magnetic resonance imaging. Cereb. Cortex 2, 417–424. Desimone, R., Ungerleider, L.G., 1986. Multiple visual areas in the caudal superior temporal sulcus of the macaque. J. Comp. Neurol. 248, 164–189. Dukelow, S.P., DeSouza, J.F., Culham, J.C., van den Berg, A.V., Menon, R.S., Vilis, T., 2001. Distinguishing subregions of the human MT+ complex using visual fields and pursuit eye movements. J. Neurophysiol. 86, 1991–2000. Emerson, R.C., Bergen, J.R., Adelson, E.H., 1992. Directionally selective complex cells and the computation of motion energy in cat visual cortex. Vision Res. 32, 203–218. Felleman, D.J., Van Essen, D.C., 1987. Receptive field properties of neurons in area V3 of macaque monkey extrastriate cortex. J. Neurophysiol. 57, 889–920. Ffytche, D.H., Guy, C.N., Zeki, S., 1995. The parallel visual motion inputs into areas V1 and V5 of human cerebral cortex. Brain 118 (Pt. 6), 1375–1394. Ffytche, D.H., Guy, C.N., Zeki, S., 1996. Motion specific responses from a blind hemifield. Brain 119 (Pt 6), 1971–1982. Fitzpatrick, D., Lund, J.S., Blasdel, G.G., 1985. Intrinsic connections of macaque striate cortex: afferent and efferent connections of lamina 4C. J. Neurosci. 5, 3329–3349. Fries, W., 1981. The projection from the lateral geniculate nucleus to the prestriate cortex of the macaque monkey. Proc. R. Soc. Lond., B Biol. Sci. 213, 73–86. Galletti, C., Battaglini, P.P., Aicardi, G., 1988. ‘Real-motion’ cells in visual area V2 of behaving macaque monkeys. Exp. Brain Res. 69, 279–288. Gattass, R., Gross, C.G., 1981. Visual topography of striate projection zone (MT) in posterior superior temporal sulcus of the macaque. J. Neurophysiol. 46, 621–638. Gegenfurtner, K.R., Kiper, D.C., Levitt, J.B., 1997. Functional properties of neurons in macaque area V3. J. Neurophysiol. 77, 1906–1923. Geyer, S., Schormann, T., Mohlberg, H., Zilles, K., 2000. Areas 3a, 3b, and 1 of human primary somatosensory cortex. Part 2. Spatial normalization to standard anatomical space. NeuroImage 11, 684–696. Girard, P., Salin, P.A., Bullier, J., 1992. Response selectivity of neurons in area MT of the macaque monkey during reversible inactivation of area V1. J. Neurophysiol. 67, 1437–1446. Hadamschek, V., 2006. Brain stimulation techniques with the aid of

1394

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395

nonlinear delayed neurofeedback and MEG inverse methods. Ph.D. Dissertation, Berlin University, Germany. Hamalainen, M., Hari, R., Ilmoniemi, R.J., Knuutila, J., Lounasmaa, O.V., 1993. Magneto-encephalography theory, instrumentation, and applications to non-invasive studies of the working human brain. Rev. Mod. Phys. 65, 413–497. Hoffmann, M., Dorn, T.J., Bach, M., 1999. Time course of motion adaptation: motion-onset visual evoked potentials and subjective estimates. Vision Res. 39, 437–444. Hogg, R.V., Tanis, E.A., 2001. Probability and Statistical Inference, 6th ed. Prentice-Hall, New Jersey. Hollants-Gilhuijs, M.A., De Munck, J.C., Kubova, Z., van Royen, E., Spekreijse, H., 2000. The development of hemispheric asymmetry in human motion VEPs. Vision Res. 40, 1–11. Holliday, I.E., Anderson, S.J., Harding, G.F., 1997. Magnetoencephalographic evidence for non-geniculostriate visual input to human cortical area V5. Neuropsychologia 35, 1139–1146. Hömke, L., 2006. A multigrid method for anisotrophic PDE's in elastic image registration. Numer. Linear Algebra Appl. 13, 215–229. Hubel, D.H., Wiesel, T.N., 1968. Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195, 215–243. Hupe, J.M., James, A.C., Payne, B.R., Lomber, S.G., Girard, P., Bullier, J., 1998. Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons. Nature 394, 784–787. Ioannides, A.A., Bolton, J.P.R., Clarke, C.J.S., 1990. Continuous probabilistic solution to the biomagnetic inverse problem. Inverse Probl. 6, 523–542. Jahn, O., Cichocki, A., Ioannides, A.A., Amari, S., 1998. Identification and elimination of artifacts from MEG signals using efficient independent component analysis. Proceedings of the 11th International Conference on Biomagnetism, Sendai, pp. 224–227. Kuba, M., Kubova, Z., 1992. Visual evoked potentials specific for motion onset. Doc. Ophthalmol. 80, 83–89. Kubova, Z., Kuba, M., Hubacek, J., Vit, F., 1990. Properties of visual evoked potentials to onset of movement on a television screen. Doc. Ophthalmol. 75, 67–72. Kubova, Z., Kuba, M., Spekreijse, H., Blakemore, C., 1995. Contrast dependence of motion-onset and pattern-reversal evoked potentials. Vision Res. 35, 197–205. Kubova, Z., Kuba, M., Juran, J., Blakemore, C., 1996. Is the motion system relatively spared in amblyopia? Evidence from cortical evoked responses. Vision Res. 36, 181–190. Lamme, V.A., 2001. Blindsight: the role of feedforward and feedback corticocortical connections. Acta Psychol. (Amst.) 107, 209–228. Larsson, J., Amunts, K., Gulyas, B., Malikovic, A., Zilles, K., Roland, P.E., 2002. Perceptual segregation of overlapping shapes activates posterior extrastriate visual cortex in man. Exp. Brain Res. 143, 1–10. Levitt, J.B., Kiper, D.C., Movshon, J.A., 1994a. Receptive fields and functional architecture of macaque V2. J. Neurophysiol. 71, 2517–2542. Levitt, J.B., Yoshioka, T., Lund, J.S., 1994b. Intrinsic cortical connections in macaque visual area V2: evidence for interaction between different functional streams. J. Comp. Neurol. 342, 551–570. Livingstone, M.S., Hubel, D.H., 1987. Connections between layer 4B of area 17 and the thick cytochrome oxidase stripes of area 18 in the squirrel monkey. J. Neurosci. 7, 3371–3377. Lund, J.S., Boothe, R.G., 1975. Interlaminar connections and pyramidal neuron organisation in the visual cortex, area 17, of the macaque monkey. J. Comp. Neurol. 159, 305–344. Luppino, G., Matelli, M., Camarda, R.M., Gallese, V., Rizzolatti, G., 1991. Multiple representations of body movements in mesial area 6 and the adjacent cingulate cortex: an intracortical microstimulation study in the macaque monkey. J. Comp. Neurol. 311, 463–482. Maier, J., Dagnelie, G., Spekreijse, H., van Dijk, B.W., 1987. Principal components analysis for source localization of VEPs in man. Vision Res. 27, 165–177. Malikovic, A., Amunts, K., Schleicher, A., Mohlberg, H., Eickhoff, S.B., Wilms, M., Palomero-Gallagher, N., Armstrong, E., Zilles, K., 2007.

Cytoarchitectonic analysis of the human extrastriate cortex in the region of V5/MT+: a probabilistic, stereotaxic map of area hOc5. Cereb. Cortex 17, 562–574. Maruyama, K., Kaneoke, Y., Watanabe, K., Kakigi, R., 2002. Human cortical responses to coherent and incoherent motion as measured by magnetoencephalography. Neurosci. Res. 44, 195–205. Maunsell, J.H., Newsome, W.T., 1987. Visual processing in monkey extrastriate cortex. Annu. Rev. Neurosci. 10, 363–401. Maunsell, J.H., van Essen, D.C., 1983a. The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey. J. Neurosci. 3, 2563–2586. Maunsell, J.H., van Essen, D.C., 1983b. Functional properties of neurons in middle temporal visual area of the macaque monkey. I. Selectivity for stimulus direction, speed, and orientation. J. Neurophysiol. 49, 1127–1147. Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., Woods, R., Paus, T., Simpson, G., Pike, B., Holmes, C., Collins, L., Thompson, P., MacDonald, D., Iacoboni, M., Schormann, T., Amunts, K., PalomeroGallagher, N., Geyer, S., Parsons, L., Narr, K., Kabani, N., Le Goualher, G., Boomsma, D., Cannon, T., Kawashima, R., Mazoyer, B., 2001a. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R Soc. Lond., B Biol. Sci. 356, 1293–1322. Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., Woods, R., Paus, T., Simpson, G., Pike, B., Holmes, C., Collins, L., Thompson, P., MacDonald, D., Iacoboni, M., Schormann, T., Amunts, K., PalomeroGallagher, N., Geyer, S., Parsons, L., Narr, K., Kabani, N., Le Goualher, G., Feidler, J., Smith, K., Boomsma, D., Hulshoff Pol, H., Cannon, T., Kawashima, R., Mazoyer, B., 2001b. A four-dimensional probabilistic atlas of the human brain. J. Am. Med. Inform. Assoc. 8, 401–430. Mohlberg, H., Lerch, J., Amunts, K., Evans, A.C., Zilles, K., 2003. Probabilistic cytoarchitectonic maps transformed into MNI space. Proceedings of the 9th International Conference on Functional Mapping of the Human Brain, New York. Neuroimage, 19 (2). Available on CD ROM. Movshon, J.A., Newsome, W.T., 1996. Visual response properties of striate cortical neurons projecting to area MT in macaque monkeys. J. Neurosci. 16, 7733–7741. Movshon, J.A., Adelson, E.H., Gizzi, M.S., Newsome, W.T., 1986. The analysis of moving visual patterns. Exp. Brain Res. 11, 117–152. Nakamura, Y., Ohtsuka, K., 1999. Topographical analysis of motiontriggered visual-evoked potentials in man. Jpn. J. Ophthalmol. 43, 36–43. Nelissen, K., Luppino, G., Vanduffel, W., Rizzolatti, G., Orban, G., 2005. Observing others: multiple action representation in the frontal lobe. Science 310, 332–336. Niedeggen, M., Wist, E.R., 1999. Characteristics of visual evoked potentials generated by motion coherence onset. Brain Res. Cogn. Brain Res. 8, 95–105. Nowak, L.G., Munk, M.H., Girard, P., Bullier, J., 1995. Visual latencies in areas V1 and V2 of the macaque monkey. Vis. Neurosci. 12, 371–384. Nowak, L.G., Munk, M.H., James, A.C., Girard, P., Bullier, J., 1999. Crosscorrelation study of the temporal interactions between areas V1 and V2 of the macaque monkey. J. Neurophysiol. 81, 1057–1074. Orban, G.A., Kennedy, H., Bullier, J., 1986. Velocity sensitivity and direction selectivity of neurons in areas V1 and V2 of the monkey: influence of eccentricity. J. Neurophysiol. 56, 462–480. Palmero-Soler, E., Hadamschek, V., Dolan, K., Dammers, J., Tass, P.A., 2005. A comparison of the LORETA method in the presence of noise with different prior functions. Proceedings of the 11th International Conference on Functional Mapping of the Human Brain, Toronto, Canada. Neuroimage, 26 (1). Available on CD ROM. Palmero-Soler, E., Dolan, K., Hadamschek, V., Tass, P.A., 2007. swLORETA: a novel approach to robust source localization and synchronization tomography. Phys. Med. Biol. 52, 1–18. Pascual-Leone, A., Walsh, V., 2001. Fast backprojections from the motion to

E.A. Prieto et al. / NeuroImage 37 (2007) 1384–1395 the primary visual area necessary for visual awareness. Science 292, 510–512. Probst, T., Plendl, H., Paulus, W., Wist, E.R., Scherg, M., 1993. Identification of the visual motion area (area V5) in the human brain by dipole source analysis. Exp. Brain Res. 93, 345–351. Raiguel, S.E., Lagae, L., Gulyas, B., Orban, G.A., 1989. Response latencies of visual cells in macaque areas V1, V2 and V5. Brain Res. 493, 155–159. Raiguel, S., Van Hulle, M.M., Xiao, D.K., Marcar, V.L., Lagae, L., Orban, G.A., 1997. Size and shape of receptive fields in the medial superior temporal area (MST) of the macaque. NeuroReport 8, 2803–2808. Raiguel, S.E., Xiao, D.K., Marcar, V.L., Orban, G.A., 1999. Response latency of macaque area MT/V5 neurons and its relationship to stimulus parameters. J. Neurophysiol. 82, 1944–1956. Rockland, K.S., Knutson, T., 2000. Feedback connections from area MT of the squirrel monkey to areas V1 and V2. J. Comp. Neurol. 425, 345–368. Rodman, H.R., Albright, T.D., 1989. Single-unit analysis of pattern-motion selective properties in the middle temporal visual area (MT). Exp. Brain Res. 75, 53–64. Roland, P.E., Zilles, K., 1998. Structural divisions and functional fields in the human cerebral cortex. Brain Res. Brain Res. Rev. 26, 87–105. Schellart, N.A., Trindade, M.J., Reits, D., Verbunt, J.P., Spekreijse, H., 2004. Temporal and spatial congruence of components of motion-onset evoked responses investigated by whole-head magneto-electroencephalography. Vision Res. 44, 119–134. Schleicher, A., Amunts, K., Geyer, S., Morosan, P., Zilles, K., 1999. Observer-independent method for microstructural parcellation of cerebral cortex: a quantitative approach to cytoarchitectonics. NeuroImage 9, 165–177. Schleicher, A., Amunts, K., Geyer, S., Kowalski, T., Schormann, T., Palomero-Gallagher, N., Zilles, K., 2000. A stereological approach to human cortical architecture: identification and delineation of cortical areas. J. Chem. Neuroanat. 20, 31–47. Schoenfeld, M., Heinze, H., Woldorff, M., 2002. Unmasking motionprocessing activity in human brain area V5/MT+ mediated by pathways that bypass primary visual cortex. NeuroImage 17, 769–779. Schoenfeld, M.A., Woldorff, M., Duzel, E., Scheich, H., Heinze, H.J., Mangun, G.R., 2003. Form-from-motion: MEG evidence for time course and processing sequence. J. Cogn. Neurosci. 15, 157–172. Shipp, S., de Jong, B.M., Zihl, J., Frackowiak, R.S., Zeki, S., 1991. The brain activity related to residual motion in a patient with bilateral lesions of V5. Brain 117, 1023–1038. Siegel, S., Castellan, N.J., 1988. Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill, New York. Smith, A.T., Greenlee, M.W., Singh, K.D., Kraemer, F.M., Hennig, J., 1998. The processing of first- and second-order motion in human visual cortex assessed by functional magnetic resonance imaging (fMRI). J. Neurosci. 18, 3816–3830. Standage, G.P., Benevento, L.A., 1983. The organization of connections between the pulvinar and visual area MT in the macaque monkey. Brain Res. 262, 288–294. Tass, P.A., Fieseler, T., Dammers, J., Dolan, K., Morosan, P., Majtanik, M., Boers, F., Muren, A., Zilles, K., Fink, G.R., 2003. Synchronization tomography: a method for three-dimensional localization of phase synchronized neuronal populations in the human brain using magnetoencephalography. Phys. Rev. Lett. 90, 088101. Tootell, R.B., Reppas, J.B., Kwong, K.K., Malach, R., Born, R.T., Brady, T.J., Rosen, B.R., Belliveau, J.W., 1995. Functional analysis of human

1395

MT and related visual cortical areas using magnetic resonance imaging. J. Neurosci. 15, 3215–3230. Trujillo-Barreto, N.J., Aubert-Vazques, E., Valdes-Sosa, P.A., 2004. Bayesian model averaging in EEG/MEG imaging NeuroImage 21 (4), 1300–1319. Tzelepi, A., Ioannides, A.A., Poghosyan, V., 2001. Early (N70m) neuromagnetic signal topography and striate and extrastriate generators following pattern onset quadrant stimulation. NeuroImage 13, 702–718. Ungerleider, L.G., Desimone, R., 1986. Projections to the superior temporal sulcus from the central and peripheral field representations of V1 and V2. J. Comp. Neurol. 248, 147–163. Ungerleider, L.G., Desimone, R., Galkin, T.W., Mishkin, M., 1984. Subcortical projections of area MT in the macaque. J. Comp. Neurol. 223, 368–386. Uusitalo, M.A., Virsu, V., Salenius, S., Nasanen, R., Hari, R., 1997. Activation of human V5 complex and rolandic regions in association with moving visual stimuli. NeuroImage 5, 241–250. Van Essen, D.C., Newsome, W.T., Bixby, J.L., 1982. The pattern of interhemispheric connections and its relationship to extrastriate visual areas in the macaque monkey. J. Neurosci. 2, 265–283. Vanni, S., Warnking, J., Dojat, M., Delon-Martin, C., Bullier, J., Segebarth, C., 2004. Sequence of pattern onset responses in the human visual areas: an fMRI constrained VEP source analysis. NeuroImage 21, 801–817. Watson, J.D., Myers, R., Frackowiak, R.S., Hajnal, J.V., Woods, R.P., Mazziotta, J.C., Shipp, S., Zeki, S., 1993. Area V5 of the human brain: evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cereb. Cortex 3, 79–94. Wurtz, R., Kandel, E., 2000. Perception of motion, depth and form. In: Kandel, E., Schwartz, J., Jessell, T. (Eds.), Principle of Neural Science. McGraw-Hill, USA, pp. 549–571. Yukie, M., Iwai, E., 1981. Direct projection from the dorsal lateral geniculate nucleus to the prestriate cortex in macaque monkeys. J. Comp. Neurol. 201, 81–97. Zeki, S.M., 1974. Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey. J. Physiol. 236, 549–573. Zeki, S.M., 1978. Functional specialisation in the visual cortex of the rhesus monkey. Nature 274, 423–428. Zeki, S., Shipp, S., 1988. The functional logic of cortical connections. Nature 335, 311–317. Zeki, S., Watson, J.D., Lueck, C.J., Friston, K.J., Kennard, C., Frackowiak, R.S., 1991. A direct demonstration of functional specialization in human visual cortex. J. Neurosci. 11, 641–649. Zilles, K., Schlaug, G., Matelli, M., Luppino, G., Schleicher, A., Qu, M., Dabringhaus, A., Seitz, R., Roland, P.E., 1995. Mapping of human and macaque sensorimotor areas by integrating architectonic, transmitter receptor, MRI and PET data. J. Anat. 187 (Pt. 3), 515–537. Zilles, K., Schleicher, A., Langemann, C., Amunts, K., Morosan, P., Palomero-Gallagher, N., Schormann, T., Mohlberg, H., Buergel, U., Steinmetsz, H., Schlaug, G., Roland, P., 1997. Quantitative analysis of sulci in the human cerebral cortex: development, regional heterogenity, gender difference, asymmetry, intersubject variability and cortical architecture. Hum. Brain Mapp. 5, 218–221. Zilles, K., Schleicher, A., Palomero-Gallagher, N., Amunts, K., 2002. Quantitative analysis of cyto- and receptor architecture of the human brain. In: Mazziotta, J.C., Toga, A. (Eds.), Brain Mapping: The Methods. Academic Press, San Diego, pp. 573–602.