Nogo tasks: An event-related functional MRI study

Brain Research Bulletin 77 (2008) 197–205

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Brain Research Bulletin journal homepage: www.elsevier.com/locate/brainresbull

Research report

Executive functions with different motor outputs in somatosensory Go/Nogo tasks: An event-related functional MRI study Hiroki Nakata a,c,∗ , Kiwako Sakamoto a,c,d , Antonio Ferretti a,b , Mauro Gianni Perrucci a,b , Cosimo Del Gratta a,b , Ryusuke Kakigi c,d , Gian Luca Romani a,b a

ITAB-Institute for Advanced Biomedical Technologies, Gabriele D’Annunzio University Foundation, Chieti, Italy Department of Clinical Sciences and Bio-imaging, Gabriele D’Annunzio University, Chieti, Italy Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan d Department of Physiological Sciences, School of Life Sciences, The Graduate University for Advanced Studies, Hayama, Kanagawa, Japan b c

a r t i c l e

i n f o

Article history: Received 5 March 2008 Received in revised form 23 July 2008 Accepted 23 July 2008 Available online 21 August 2008 Keywords: Go/No-go Tactile P300 P3b ERP Count

a b s t r a c t The aim of this event-related functional magnetic resonance imaging (fMRI) study was to investigate and compare executive functions with different motor outputs in somatosensory Go/Nogo tasks: (1) Button press and (2) Count. Go and Nogo stimuli were presented with an even probability. We observed a common network for Movement and Count Go trials in several regions of the brain including the dorsolateral (DLPFC) and ventrolateral prefrontal cortices (VLPFC), supplementary motor area (SMA), posterior parietal cortex (PPC), inferior parietal lobule (IPL), Insula, and superior temporal gyrus (STG). Direct comparison revealed that primary sensorimotor area (SMI), premotor area (PM), and anterior cingulate cortex (ACC) were more activated during Movement than Count Go trials. In contrast, the VLPFC was more activated during Count than Movement Go trials. Our results suggest that there were two neural networks for the supramodal executive function, common and uncommon, depending on the required response mode. © 2008 Elsevier Inc. All rights reserved.

1. Introduction Event-related potentials (ERPs) elicited using Go/Nogo tasks are useful for investigating response execution and inhibition, and several studies have compared P300 component between Go and Nogo trials, which occurs 300–600 ms after stimulation. The difference between Go and Nogo waveforms is frequently described as the ‘Go/Nogo effect’, but care is needed when interpreting this effect on P300, as movement-related cortical activities might differentially overlap Go- and Nogo ERPs and thereby influence the Go/Nogo effect [13]. That is, preparatory activity is present in both Go and Nogo trials, but movement-related activity, which appears before an electromyogram (EMG) is recorded, is clearly present in Go trials, but not in Nogo trials. In particular, motor potential (MP), which consists of a short negative component, appears in the contralateral sensorimotor area [22]. Consequently, it can be speculated that go-P300 is influenced by this overlapping potential, and distorts the true P300 field [29].

∗ Corresponding author at: Department of Integrative Physiology, National Institute for Physiological Sciences, Myodaiji, Okazaki, 444-8585, Japan. Tel.: +81 564 55 7810; fax: +81 564 52 7913. E-mail address: [email protected] (H. Nakata). 0361-9230/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.brainresbull.2008.07.008

To solve this problem and clarify mechanisms of the true go-P300, some previous studies used silent counting in Go trials, instead of the pushing of a button [4,41,49,53]. These studies showed that the Go/Nogo effect on P300 was not clear in the counting condition. Thus, while the mechanisms generating the true go-P300 will be similar between pushing and counting conditions, it is likely that the response mode, pushing or counting, for Go trials influences the Go/Nogo effect on P300. In other words, common neural networks would exist in the generator mechanisms of go-P300 in both pushing and counting conditions, and uncommon networks, such as movement-related activities in the pushing condition, and count-related activities in the counting condition, might influence the waveform of the true go-P300. Previous studies using functional magnetic resonance imaging (fMRI) have shown the neural network associated with Go trials, including the dorsolateral (DLPFC) and ventrolateral prefrontal cortices (VLPFC), supplementary motor area (SMA), primary sensorimotor area (SMI), anterior cingulate cortex (ACC), temporoparietal junction (TPJ), temporal and parietal lobes, and thalamus [24,30,31,33,35,36,63]. Indeed, they used the standard pushing conditions for Go trials, but did not compare this network between pushing and count conditions. In addition, since these studies applied only visual stimulation in their tasks, it is unclear whether the neural network is common to evoked-stimulus

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sensory modalities, visual, auditory, and somatosensory. Thus, despite a large number of neuroimaging studies, which regions of the brain are responsible for a supramodal executive function remains a matter of debate. Based on previous ERP and fMRI studies, the first aim of the present study was to clarify the executive functions sub-serving a simple Go/Nogo task with different motor outputs. We used eventrelated fMRI during two different types of somatosensory Go/Nogo tasks, Movement and Count. In the Movement Go/Nogo task, the subjects responded by pushing a button with their right index finger for the presentation of a Go stimulus. In the Count Go/Nogo task, the subjects counted the number of Go stimuli silently. Go and Nogo stimuli were presented with an even probability in order to avoid the effect of stimulus probability and differences in response conflict between event types [32,42,43]. We presented these stimuli every 14 s, because the hemodynamic response lasts over a 10–12 s after stimulus onset [5,6]. The second aim of this study was to assume the generator mechanisms of go-P300. The experiment design of the present study could involve neural activities associated with go-P300. Indeed, many ERP studies using Go/Nogo tasks have presented Go and Nogo stimuli at every 1–5 s [14,42], but the present study matched the experimental design with our previous ERP studies, in terms of the same stimulus sequence and probability [41]. Thus, we can investigate the activated brain regions during Go trials using fMRI, and estimate the generator mechanisms of go-P300. The results indicated some commonly activated areas in both the pushing and counting conditions, and other areas specific for each condition. The data for Nogo trials was already published [44]. 2. Experimental procedures 2.1. Subjects Fifteen normal subjects (eight females and seven males; mean age 23.6 years, range 19–32 years) participated in this study. All of them were right-handed according to the Edinburgh Inventory [48]. The subjects did not have a previous history of any neurological or psychiatric disorders. Before the experiment, all subjects were explained the study risks and procedures, and then a written informed consent was obtained. The protocol of the present study was approved by the Institutional Ethics Committee of the Gabriele D’Annunzio University, Chieti, Italy. 2.2. Tasks The subjects performed somatosensory Go/Nogo tasks. The electrical stimuli were a current constant square-wave pulse 0.3 ms in duration. Stimulus intensity was set to the threshold to induce a clear twitch of the fingers. Just before recording the fMRI scan, the experimenter confirmed that the subjects felt no pain or unpleasant sensation subjectively. The left median nerve was used for the Go stimulus at a probability of 0.5, and the left ulnar nerve was used for the Nogo stimulus at a probability of 0.5. The mean intensities applied to the median and ulnar nerves at 4.5 mA and 6.2 mA, respectively. The interstimulus interval was 14 s. The recordings were conducted in two conditions: Movement and Count Go/Nogo tasks. In the Movement Go/Nogo task, the subjects had to respond by pushing a button with their right index finger (contralateral to the stimulated side) as quickly as possible only after the presentation of a Go stimulus. In the Count Go/Nogo task, the subjects had to count the number of Go stimuli silently and report the result after the end of recordings. One condition comprised 80 epochs of stimulation, which included 40 epochs for the median nerve and 40 for the ulnar nerve. The order of conditions was randomized in each subject and counterbalanced across all subjects. 2.3. fMRI data acquisition and analysis All images were acquired using a 1.5 T MR scanner (Magnetom Vision; Siemens, Erlangen, Germany). Blood oxygen level dependent (BOLD) contrast functional images were acquired using T2*-weighted echo planar imaging (EPI) free induction decay (FID) sequences with the following parameters: TR 2000 ms, TE 60 ms, matrix size 64 × 64, FOV 256 mm, in-plane voxel size 4 mm × 4 mm, flip angle 90◦ , slice thickness 6 mm and no gap. For anatomical reference, finally, a high resolution structural volume was acquired at the end of the session via a 3D magnetizationprepared rapid gradient echo (MPRAGE) sequence (TR 9.7 ms, TE 4 ms, sagittal, matrix 256 × 256, FOV 256 mm, slice thickness 1 mm, no gap, in-plane voxel size 1 mm × 1 mm, flip angle 12◦ , slice thickness 1 mm) for each subject.

Raw data were analyzed with Statistical Parametric Mapping (SPM2, Wellcome Department of Cognitive Neurology, London, UK) [17–19] implemented in Matlab (Mathworks, Sherborn, Massachusetts, USA). The first three scans of each run were discarded from the analysis due to unsteady magnetization and the remaining 560 volumes per condition (1120 volumes per subject) were analyzed. We initially corrected the differences in slice timing. The effect of head motion was corrected by realigning all scans to the first one. After being coregistered with the T1-weighted structural volume, they were normalized in the functional scans to a standard stereotaxic space (Montreal Neurological Institute (MNI) template). Then, the images were spatially smoothed using an isotropic Gaussian kernel of 8 mm full width at half maximum (FWHM) in the x, y, and z axes. Temporal filters were also applied and low frequency noise and global changes in the signals were removed. Statistical analysis was performed on two levels. First, individual task-related activation was evaluated for each subject using a general linear model. Event-related responses to Go and Nogo stimuli were modeled, and the changes in BOLD signals were estimated at each voxel by modeling the onsets of each trial with a hemodynamic response function (HRF). Vectors were created for each condition using the timing of the correct responses for each stimulus type. Incorrect responses in Go/Nogo tasks were eliminated from analysis. To detect the specific activation for each trial epoch, a statistical analytical design was constructed for individual data. We constituted a statistical parametric map of the t-statistic for the four contrasts, (1) Movement Go, (2) Movement Nogo, (3) Count Go, and (4) Count Nogo. In the present study, we focused on two contrasts, (1) and (3), to clarify a supramodal executive function with different motor outputs in somatosensory Go/Nogo tasks. Then, subject-specific contrast images of parameter estimates were used for secondlevel analysis using a random-effect model [20] to make inferences at a population level. Group analysis (random-effect model) of each Go condition was performed by entering contrast images into one-sample t-tests. The statistical threshold was set at p < 0.05, corrected by the false discovery rate (FDR). Activation clusters with less than 10 voxels were removed. The locations of brain activity were transformed from MNI coordinates into Talairach standard brain coordinates [57]. Conjunction analysis was employed to detect brain regions activated during both Movement Go and Count Go, and the threshold was also set at p = 0.005 uncorrected (spatial extent > 10 voxels). In addition, to compare activations between movement and Count Go conditions, subtraction images were obtained from two contrast images, Movement Go minus Count Go, and Count Go minus Movement Go. To compare activations between Go and Nogo trials in each condition, subtraction images were made from four contrast images, Movement Go minus Movement Nogo, Movement Nogo minus Movement Go, Count Go minus Count Nogo, and Count Nogo minus Count Go. One-sample ttests were performed, and the threshold was set at p = 0.005 uncorrected (spatial extent > 10 voxels).

3. Results 3.1. Behavioral data As a behavioral data, the mean reaction time (RT) in Movement was 452.3 ms (SD 97.6 ms). The error rates for Movement and Count were 2.6% and 1.6%, respectively. There was no significant difference in the rate between conditions (p > 0.05), according to a paired ttest. 3.2. Imaging data In the imaging analysis, being consistent with previous studies, the activities of multiple areas were determined during both Movement and Count Go trials. Areas of the brain related to Movement Go were located in the left DLPFC (Brodmann’s areas: BA 9, 10, and 46), VLPFC (BA 45 and 47), SMA and premotor area (PM) (BA 6), SMI (BA 2), inferior parietal lobule (IPL) (BA 40), insula (BA 13), superior temporal gyrus (STG) (BA 22), TPJ, and anterior cingulate cortex (ACC) (BA 24 and 32). In the right hemisphere, activation was observed in the SMI (BA 4), posterior parietal cortex (PPC) (BA 5 and 7), IPL (BA 40), precuneus (BA 31), TPJ, and ACC (BA 24 and 32) (Fig. 1A, Table 1). Regions activated by Count Go were located in the left DLPFC (BA 9, 10 and 46), VLPFC (BA 44, 45 and 47), SMA (BA 6), PPC (BA 5 and 7), insula (BA 13), IPL (BA 40), and STG (BA 22). The activities in the right hemisphere were in the VLPFC (BA 45 and 47), STG (BA 13), IPL (BA40), and insula (BA 13) (Fig. 1B, Table 2). Fig. 1C shows the hemodynamic responses (HDRs) in MI and VLPFC of both hemispheres, peaking 5 s after the presentation of

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Fig. 1. (A and B) Group activation map showing activated brain regions in Movement Go and Count Go trials. Using the SPM2 template, areas showing an increase in BOLD signal are superimposed on a 3D-rendered standard brain (left and middle panels) and sagittal image (right panel). (C) Time course of HDRs in Movement Go and Count Go. Error bars denote standard error (SE). DLPFC = dorsolateral prefrontal cortex; VLPFC = ventrolateral prefrontal cortex; IPL = inferior parietal lobule; PM = premotor area; SMI = primary sensorimotor area; SMA = supplementary motor area; ACC = anterior cingulate cortex; STG = superior temporal gyrus; PPC = posterior parietal cortex.

the Go stimuli. We analyzed two correlations, between the peak HDRs of MI and RT, and between the peak HDRs of VLPFC and the percent accuracy of the count. As a result, no significant correlative relationship was observed between them, but there were weak correlations on the left MI and RT (r = 0.413, p = 0.126), and the right VLPFC and accuracy (r = −0.494, p = 0.061). These results indicated that subjects with longer RT needed stronger activation of MI, and subjects with lower accuracy required more activities of VLPFC.

3.3. Conjunction analysis during Movement Go and Count Go Conjunction analysis for common regions activated during Movement Go and Count Go revealed significant activation in the left DLPFC (BA 9), VLPFC (BA 10), SMA/PM (BA 6 and 8), IPL (BA 40), PPC (BA 7), and cuneus (BA 18), and in the right STG (BA 22), MTG (BA 19 and 37), IPL (BA 40), PPC (BA 7), and caudate (Fig. 2 and Table 3).

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Fig. 2. Brain regions commonly activated in Movement Go and Count Go. VLPFC = ventrolateral prefrontal cortex; DLPFC = dorsolateral prefrontal cortex; IPL = inferior parietal lobule; SMA = supplementary motor area; PM = premotor area; PPC = posterior parietal cortex.

3.4. Comparison of the activation between Movement Go and Count Go To identify the difference in HDRs between Movement Go and Count Go, a paired t-test was applied for the peak HDRs of MI and VLPFC. Activities in the left and right MI were significantly stronger during Movement Go than Count Go (p < 0.001 and p < 0.05, respectively). By contrast, activity in the right VLPFC was significantly larger during Count Go than Movement Go (p < 0.01) (Fig. 1C). In a direct comparison of activation during Movement and Count Go trials using the SPM analysis, greater activation during MoveTable 1 Activated regions in Movement Go trials Region

Frontal lobe Middle frontal gyrus Medial frontal gyrus Inferior frontal gyrus

Superior frontal gyrus Precentral gyrus Temporal lobe Superior temporal gyrus Transverse temporal gyrus Parietal lobe Postcentral gyrus

Superior parietal lobule Precuneus Inferior parietal lobule Supramarginal gyrus Occipital lobe Middle occipital gyrus Lingual gyrus Limbic lobe Insula Cingulate gyrus

Anterior cingulate

Side

BA

Talairach coordinates X

Y

Z

Z-score

L L L L L L L L R

6 6 10 45 46 47 9 6 4

−26 0 −42 −48 −50 −42 −38 −44 59

4 14 49 20 38 17 42 −2 −19

38 47 1 14 13 −8 31 41 40

5.33 4.01 3.88 3.98 3.61 4.54 3.98 4.83 4.73

L L R

22 41 41

−55 −52 36

−44 −33 −36

13 10 9

3.91 3.24 3.86

L R R R R L L

2 5 40 7 31 40 40

−55 40 54 28 16 −38 −61

−27 −47 −29 −47 −69 −37 −41

46 59 51 62 27 41 30

4.67 3.37 3.71 4.08 3.58 4.86 3.17

L R

18 18

−28 18

−80 −72

1 −3

4.60 4.87

L L R L R

13 24 24 32 32

−40 −20 18 −10 2

−20 −18 −14 20 32

−6 46 37 45 24

3.43 4.17 3.85 3.94 3.42

BA, Brodmann’s area; L, left hemisphere; R, right hemisphere.

ment was found in the left SMI (BA 3 and 4), PM (BA 6), and IPL (BA 40), and in the right ACC (BA 24) (Fig. 3 and Table 4). Significantly greater activation during Count was found in the right VLPFC (BA 44 and 47) (Fig. 3 and Table 4). 3.5. Comparison of activation between Go and Nogo trials In subtraction analysis, significant activation during Movement Go minus Movement Nogo is indicated in the left SMI (BA 4), STG (BA 22), and insula (BA 13), and in the right DLPFC (BA 10), PM (BA 6 and 8), inferior temporal gyrus (ITG) (BA 19), cingulate cortex (BA 10), parahippocampal gyrus (PG) (BA 36), and putamen (Fig. 4 and Table 5). Regions activated by Movement Nogo minus Movement Go are shown in the left VLPFC (BA 44 and 45), and ACC (BA 32), and in the

Table 2 Activated regions in Count Go trials Region

Side

BA

Talairach coordinates X

Frontal lobe Middle frontal gyrus Inferior frontal gyrus

Superior frontal gyrus Precentral gyrus Temporal lobe Superior temporal gyrus Parietal lobe Postcentral gyrus Superior parietal lobule Inferior parietal lobule Occipital lobe Lingual gyrus Cuneus Limbic lobe Insula

Y

Z-score

Z

L L L R L R L L L L

9 10 45 45 47 47 13 46 6 44

−51 −32 −50 50 −35 42 −42 −40 −2 −46

25 47 27 22 35 31 29 39 11 12

28 5 0 8 2 −3 2 4 60 7

3.52 3.74 4.34 4.50 4.07 4.66 4.26 3.99 3.67 3.87

L R

22 13

−55 44

−38 −46

13 19

4.18 3.94

L L L R

5 7 40 40

−38 −34 −61 53

−47 −53 −37 −36

61 62 31 52

3.74 3.66 4.03 3.46

R L L

18 18 30

6 −4 −6

−78 −85 −64

−5 12 9

3.38 4.78 3.85

L R

13 13

−28 42

−32 14

15 5

3.50 4.04

H. Nakata et al. / Brain Research Bulletin 77 (2008) 197–205 Table 3 Activated regions in conjunction analysis between Movement Go and Count Go Region

Side

BA

Talairach coordinates X

Frontal lobe Inferior frontal gyrus Middle frontal gyrus Superior frontal gyrus Temporal lobe Superior temporal gyrus Middle temporal gyrus Prietal lobe Inferior parietal lobule Superior parietal lobule Precuneus

Y

Z-score

Table 5 Activated regions in direct comparisons between Movement Go and Movement Nogo Region

Z

201

Side

Talairach coordinates X

L L L L

10 9 6 8

−42 −51 −2 −2

51 23 11 31

3 30 62 44

3.98 3.21 3.39 2.97

R R

22 37

32 51

−47 −52

24 3

2.63 2.86

L R L R R

Occipital lobe Cuneus Middle temporal gyrus

L R

Limbic lobe Caudate

R

40 40 7 7 7

−42 48 −38 32 26

18 19

−44 −42 −55 −54 −45

57 57 56 52 39

3.83 3.27 3.70 2.73 2.87

−6 32

−91 −62

10 10

2.75 2.90

28

−40

15

3.43

(Movement: Go)–(Movement: Nogo) Frontal lobe Medial frontal gyrus R R Precentral gyrus R R

Middle temporal gyrus Inferior occipital gyrus Middle occipital gyrus Limbic lobe Insula Cingulate gyrus Parahippocampal gyrus Letiform nucleus

Parietal lobe Inferior parietal lobule Limbic lobe Caudate Cingulate gyrus

Anterior cingulate

Fig. 3. Direct comparison between Movement Go and Count Go. Red areas indicate greater activation for Movement Go than Count Go trials, and green areas, vice versa. VLPFC = ventrolateral prefrontal cortex; IPL = inferior parietal lobule; PM = premotor area; SMI = primary sensorimotor area.

Table 4 Activated regions in direct comparisons between Movement Go and Count Go Side

(Movement: Go)–(Count: Go) Frontal lobe Middle frontal gyrus L Precentral gyrus L

BA

Talairach coordinates

Z-score

X

Y

Z

6 4

−24 −55

0 −14

41 39

4.04 4.31

Parietal lobe Inferior parietal lobule Postcentral gyrus

L L

40 3

−44 −20

−35 −25

40 47

3.31 3.26

Limbic lobe Cingulate gyrus

R

24

16

8

46

3.54

(Count: Go)–(Movement: Go) Frontal lobe Inferior frontal gyrus R Precentral gyrus R

47 44

42 48

31 16

−2 8

3.24 3.60

Z

26 18 50 57

33 51 −10 −2

41 7 41 6

2.80 3.46 2.86 2.71

19 22 37

44 −55 −44

−54 −6 −59

−1 −1 −11

3.15 3.24 2.92

L R R L R

37 37 19 18 19

−42 46 38 −30 44

−66 −68 −63 −78 −79

−2 −2 12 −1 9

3.16 2.91 3.15 3.09 3.01

L R R R

13 −40 10 6 36 28 Putamen 20

−12 55 −26 16

−3 −3 −14 1

3.08 3.53 3.17 2.93

44 44 45 9

−51 50 −46 36

14 12 14 13

12 14 18 31

2.70 2.74 3.03 3.32

R

40

55

−42

48

3.26

R L R R L

32 31 32 32

10 −2 24 6 −14

7 19 −29 21 36

24 32 38 32 15

3.80 3.07 3.31 3.37 2.93

Temporal lobe Inferior temporal gyrus R Superior temporal gyrus L Fusiform gyurs L Occipital lobe Inferior temporal gyrus

Y

Z-score

8 10 4 6

(Movement: Nogo)–(Movement: Go) Frontal lobe Inferior frontal gyrus L R L Middle frontal gyrus R

Region

BA

right DLPFC (BA 9), VLPFC (BA 44), IPL (BA 40), caudate, and ACC (BA 31 and 32) (Fig. 4 and Table 5). Enhanced activation during Count Nogo minus Count Go was yielded in the left DLPFC (BA 10), orbitofrontal cortex (BA 11), PM (BA 6 and 8), STG (BA 22 and 38), middle temporal gyrus (MTG) (BA 21, 22 and 37) and transverse temporal gyrus (BA 42), insula (BA 13), ACC (BA 24, 31 and 32), PG, caudate, cuneus (BA 18 and 31), and in the right DLPFC (BA 9), SMA (BA 6), SMI (BA 2 and 4), STG (BA 39), MTG (BA 21), IPL (BA 40), TPJ, ACC (BA 24), and PG (BA 36). There were no significant brain areas during Count Go minus Count Nogo, when the threshold was set at p = 0.005 uncorrected (spatial extent > 10 voxels) (Fig. 4 and Table 6). 4. Discussion The present study investigated neural networks associated with a supramodal executive function sub-serving two kinds of somatosensory Go/Nogo task, using event-related fMRI. We observed a common network for Movement and Count Go trials in several regions of the brain including the DLPFC, VLPFC, SMA, PPC, IPL, Insula, and STG. A direct comparison revealed that the SMI, PM, IPL, and ACC were more activated during Movement than Count Go trials. In contrast, the VLPFC was more activated during Count than Movement Go trials.

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Fig. 4. (Upper figure) Direct comparison between Movement Go and Movement Nogo. Red areas indicate greater activation for Movement Nogo than Movement Go trials, and green areas, vice versa. (Lower figure) Direct comparison between Count Go and Count Nogo. Red areas indicate greater activation for Count Nogo than Count Go trials. There were no greater activations in Count Go than Count Nogo. VLPFC = ventrolateral prefrontal cortex; IPL = inferior parietal lobule; SMI = primary sensorimotor area; STG = superior temporal gyrus; ACC = anterior cingulate cortex; DLPFC = dorsolateral prefrontal cortex; PM = premotor area; PPC = posterior parietal cortex; MTG = middle temporal gyrus.

4.1. Neural network underlying a supramodal executive function with different motor outputs Previous studies using visual Go/Nogo tasks showed that neural networks of Movement Go involved several regions, such as the DLPFC, VLPFC, SMA, SMI, ACC, TPJ, temporal and parietal lobes, and thalamus [24,30,31,33,35,36,63]. Based on these neuroimaging studies, we assumed that such neural networks would not be dependent on sensory modalities, if the response execution process is truly performed. However, after a thorough literature search, we could not identify any studies examining somatosensory fMRI. As a result, the present study confirmed the recruitment of this neural network, even if somatosensory Go/Nogo tasks were used. Thus, the present study indicated that the neural network relating to Movement Go is not dependent on sensory modalities but reflects common neural activities specific to Go trials. Interestingly, beyond confirming the importance of sensory modalities, since the recruitment of neural activities was also found in Count Go trials, this network would be related to supramodal motor cortical information processing in Go/Nogo conditions. To support the data of activated regions in each task, we performed conjunction analysis for overlapping activation during Movement Go and Count Go. The result showed significant activation in DLPFC, VLPFC, SMA/PM, IPL, PPC, STG, and MTG (Fig. 2 and Table 3). However, a difference between our findings and previous visual Go/Nogo tasks was the neural activities in DLPFC and VLPFC. In fact, Konishi et al. [31] showed Go-dominant activities in PFC, but the number of subjects showing significance was only two and four subjects out of five subjects in the right and left hemispheres, respectively. Furthermore, they did not find such activities in the subsequently published data [30], and there were no significant

activations of DLPFC and VLPFC in the other studies mentioned above. Although it is difficult to clarify why we clearly detected these activations in our somatosensory Go/Nogo tasks, our result was the first study to identify a clear activation of DLPFC and VLPFC during Go trials. To our knowledge, only Mostofsky et al. [39] have used visual movement and Count Go/Nogo tasks in a rapid sequence where stimuli were presented every 1.5 s. They found that Movement Go trials elicited activity in the SMI, SMA, and cerebellum, and Count Go trials, in the SMI, SMA, cerebellum, thalamus, and middle occipital gyrus. They did not detect the activation of other typical regions, but suggested that activation associated with the presentation of Go stimuli was fairly consistent across both tasks. As for the MI data, we analyzed correlations between the peak HDRs of MI and RT in both hemispheres. As a result, no significant correlative relationship was observed between them, but there were weak positive correlations of RT in the left MI (r = 0.413, p = 0.126), which is the hemisphere contralateral to the responding hand, thus indicating that subjects with longer RT needed stronger activation of left MI. This result was inconsistent with several functional neuroimaging studies showing significant negative association between the RT and BOLD in MI [37,47]. We considered two possible hypotheses for this discrepancy. The first hypothesis is the difference in the required tasks. Both Oguz et al. [47] and Mohamed et al. [37] investigated the correlation between RT during a simple reaction time and MI activity, while we showed it between RT during somatosensory Go/Nogo tasks and MI activity. Moreover, the recording periods in their studies were 6 min, whereas our recording period was 20 min in each task. Consequently, the task difficulty and their experimental design were clearly different from the present study. The second hypothesis is the ‘neural economy’ in task performing and motor skill learning. Some previous studies

H. Nakata et al. / Brain Research Bulletin 77 (2008) 197–205 Table 6 Activated regions in direct comparisons between Count Go and Count Nogo Region

(Count: Nogo)–(Count: Go) Frontal lobe Middle frontal gyrus

Side BA

Talairach coordinates X

Y

RT and MI activity; that is, neural economy may be driven in VLPFC during high performers. Z-score

4.2. Different neural activities between Movement Go and Count Go

Z

L L L L R R R L R L R

6 8 10 11 9 6 9 5 5 6 4

−32 −30 −38 −40 40 10 44 −12 22 −55 22

5 20 45 44 25 −1 4 −34 −38 −3 −23

59 45 14 −7 32 59 31 50 50 13 51

3.47 3.23 3.82 2.97 2.95 3.65 3.31 4.12 3.40 4.15 3.79

L L R L L L R L

22 38 39 21 22 37 21 42

−46 −36 46 −65 −50 −53 61 −59

−27 5 −48 −24 −39 −64 −50 −19

0 −17 6 −4 0 7 8 10

2.97 3.14 4.00 3.16 3.22 3.08 3.39 3.17

Parietal lobe Postcentral gyrus Inferior parietal lobule Angular gyrus Precuneus Supramarginal gyrus

R R R R R

2 40 39 19 40

53 61 32 30 48

−18 −34 −57 −62 −43

27 24 34 42 33

3.96 3.21 2.67 3.01 3.02

Occipital lobe Cuneus Precuneus

L L

18 31

−12 −8

−71 −63

20 25

2.77 2.69

L L R L R R L L R

13 24 24 31 32 29

−40 −12 20 −10 16 2 −20 −28 28

4 15 6 −33 34 −42 12 −20 −32

−2 31 46 35 17 11 14 −11 −12

3.62 3.10 4.07 4.26 2.92 4.08 3.57 4.40 4.09

Medial frontal gyrus Inferior frontal gyrus Paracentral lobule Precentral gyrus Temporal lobe Superior temporal gyrus

Middle temporal gyrus

Transverse temporal gyrus

Limbic lobe Insula Cingulate gyrus

Anterior cingulate Posterior cingulate Caudate Parahippocampal gyrus

36

203

recording movement-related cortical potentials (MRCPs) demonstrated that the amplitude was significantly smaller in athletes than non-athletes [10,28]. They interpreted this result as neural economy relating to the motor execution process generated from movement-related cortical areas. Floyer-Lea and Matthews [15,16] using fMRI also revealed that short-term motor learning was associated with decreases in activity of MI. Taking these studies into consideration, our results might be linked to a form of neural economy and/or motor skill learning in subjects with shorter RT. In addition, we speculated that these phenomena might be able to explain why there was no significant activity of MI contralateral to the responding hand (left hemisphere), while there was only a significant response in the contralateral SI (BA 2) (Table 1). However, since a direct statistical comparison of neural activation using SPM analysis showed that MI activity was significantly larger during Movement Go than Count Go (Fig. 3 and Table 4), MI activity plays an important role in Movement Go. In addition, we detected weak correlations between the peak HDRs of VLPFC and the percent accuracy of the count. These results indicated that high performers with higher accuracy did not need greater activation of VLPFC to perform Count Go/Nogo tasks precisely, compared to subjects with lower accuracy. We inferred that this result might indicate a similar effect to the correlation between

The ACC was activated only in Movement Go, not in Count Go, although some regions were activated in both conditions (Fig. 1A and B and Tables 1 and 2). In aspects of behavioral control, the ACC is associated with error detection and response selection [2,51]. In addition, ERPs and fMRI studies using Go/Nogo tasks reported that the ACC reflects response conflict monitoring [3,12,46]. Therefore, we considered that our results reflected the difference in response conflict monitoring with the different response modes for Go trials. That is, while the execution of the Movement Go/Nogo task is needed for an actual behavioral response such as the pushing of a button, the Count Go/Nogo task might consume less conflict resources because of the absence of behavior. To resolve the difference between Movement Go and Count Go, we made a direct comparison. Greater activation during Movement Go was found in SMI, PM, IPL, and ACC (Fig. 3 and Table 4). It seems reasonable to show the activation of movement-related regions, such as the MI and PM, because the subjects pushed a button in Movement Go, but not in Count Go. As for the activation of SI, it is likely that the hand movement itself induces the somatosensory afferent from the deep receptor and proprioceptive organ to area 3a [38,40]. In contrast, the VLPFC was more activated during Count than Movement Go trials (Fig. 3 and Table 4). There are several possible explanations for the activity in the VLPFC. One explanation is that the activation was elicited by the counting itself. Data from other studies provide evidence that counting processes involved the activities of the VLPFC, which makes a recurrent circuit in neural networks with the parietal cortex [9,45]. The second explanation is memory processing. VLPFC is thought to play an important role in memory encoding and the retrieval of information [50,56]. The count condition is basically a working memory task, and specific information has to be retained across presentations of Go stimuli. In terms of memory processing, the count condition was clearly different from the movement condition. Thus, the activation of VLPFC may be related to the human memory system, including working memory. A third explanation is that the first two possibilities occurred simultaneously and interactively. 4.3. Different neural activities between Go and Nogo trials We compared significant activations between Movement Go and Movement Nogo. Greater activations during Movement Go minus Movement Nogo were identified in the DLPFC, PM, SMI, STG, ITG, insula, PG, and putamen (Fig. 4 and Table 5). Among these regions, SMI and PM are considered to be related to motor activities to push a button. Additionally, we considered that DLPFC, STG, ITG, and insula might be related to target detection processing, which also is needed to perform our task precisely. These areas were consistent with some fMRI and PET studies exploring the responsible regions for target detection of an oddball paradigm [27,34,55,61], including the frontal cortex, temporal gyrus, parietal cortex, and insula. Activated regions found by Movement Nogo minus Movement Go are shown in the DLPFC, VLPFC, ACC, and IPL (Fig. 4 and Table 5). These regions are considered to be related to the neural network of response inhibitory processing, which has been demonstrated by a number of researches analyzing Nogo activities [7,8,21,30,33,52,60]. This notion was explicitly supported by the analysis of greater activation during Count Nogo minus Count

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Go. Activated regions were observed in the DLPFC, SMA, PM, STG, MTG, TPJ, insula, ACC, PG, IPL, and caudate (Fig. 4 and Table 6). However, there were no significant brain areas during Count Go minus Count Nogo (Fig. 4), indicating that Count Nogo was more activated in broad brain regions than Count Go.

4.4. Comparison between the present study and previous electrophysiological studies In our previous study using ERPs, we used the same two types of somatosensory Go/Nogo tasks, Movement and Count [41]. Our previous results showed that the amplitudes at Fz and Cz were almost the same between the Movement and Count Go conditions (Fz, 10.8 ␮V vs. 10.7 ␮V; Cz, 16.7 ␮V vs. 16.3 ␮V, respectively), and there were slight differences at Pz, C3 , and C4 (Pz, 16.9 ␮V vs. 14.1 ␮V; C3 , 14.7 ␮V vs. 13.7 ␮V; C4 , 17.3 ␮V vs. 14.1 ␮V, respectively). Being consistent with the present findings, the previous results indicated the involvement of two neural networks in the generation of goP300, that is, common and uncommon networks depending on the required response mode. Previous studies have investigated the generator of the somatosensory P300 component or P3b elicited in the standard oddball paradigm by using equivalent electrical dipole modeling, intracerebral recordings, and magnetoencephalograhy (MEG) [23,58,62,64]. These studies have provided evidence that somatosensory P3b activity originates from multiple cerebral regions, such as the DLPFC, PM, SMA, SMI, PPC, IPL, TPJ, Insula, medial temporal region, ACC and hippocampal area, some of which are consistent with our somatosensory go-P300. A crucial difference between standard the P3b and our go-P300 is the stimulus probability. The probability that target stimuli will be presented in the standard oddball paradigm is at less than 20%, to link the cognitive processes of context updating, context closure, and eventcategorization [1,11]. On the other hand, the Go and Nogo stimuli in Go/Nogo tasks are generally presented with the same probability to minimize differences in response conflict between event types [25,26,32,42,54,59]. In fact, it is difficult to reveal the difference in networks between the standard P3b and go-P300 in this study, but we speculate that similar regions would be involved in generating both P3b and go-P300.

4.5. Conclusion Here we showed a supramodal executive function with different motor outputs in somatosensory Go/Nogo tasks: Movement and Count. The neural network involved the DLPFC, VLPFC, SMA, PPC, IPL, insula, and STG, and existed for different types of Go/Nogo tasks. However, there were differences of activation intensity between Movement and Count Go trials in the PM, SMI, IPL, ACC, and VLPFC. Our results suggest that there were two neural networks for a supramodal executive function, common and uncommon, depending on the required response mode.

Acknowledgements We would like to thank Ms. T.L. Chen and N. Savini for help in recruiting the subjects, and Dr. D.N. Saito for analyzing the data. This study was supported by grants from the Japan Society for the Promotion of Science for Young Scientists to H.N. Conflict of interest: None declared.

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