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Evidence for a close relationship between conscious effort and anterior cingulate cortex activity
International Journal of Psychophysiology 56 (2005) 65 – 80 www.elsevier.com/locate/ijpsycho
Evidence for a close relationship between conscious effort and anterior cingulate cortex activity Christoph Mulert*, Elisabeth Menzinger, Gregor Leicht, Oliver Pogarell, Ulrich Hegerl Department of Psychiatry, Nugbaumstrage 7, LMU, Munich 80336 Mu¨nchen, Germany Received 22 May 2004; received in revised form 28 September 2004; accepted 7 October 2004 Available online 11 November 2004
Abstract The function of the anterior cingulate cortex (ACC) has been discussed in the last years in the context of conflict monitoring and error detection. In addition, ACC activity has been described in the context of bconscious effortQ. Recent neurophysiological and neuroimaging studies have described a negative correlation between ACC activity and reaction times in simple or choice reaction time experiments. One suggested explanation for this finding has been that there is a relationship between effort and ACC activity. The present ERP-LORETA study of healthy volunteers (n=35) was intended to directly investigate this relationship. In this experiment, three conditions were investigated: condition I was a choice reaction task with the instruction to stay relaxed during the task (relaxed condition), condition II was the same choice reaction task with the instruction to press the respective button as fast and correct as possible (effort condition). Condition III was just listening to the tones without button press (control condition). Subjects had to score directly after each experimental run on a visual analogue scale the amount of effort they have actually spent. The subjects showed significantly shorter reaction times during the high effort condition in comparison to the relaxed condition, as well as increased N1 amplitudes and increased ACC activity. In a subgroup analysis, this effect was present only in subjects who were (according to their self-ratings) following the instructions closely. These results provide direct evidence for a close relationship between conscious effort and ACC activity and suggest the usefulness of the applied effort-self-rating. D 2004 Elsevier B.V. All rights reserved. Keywords: Effort; Motivation; Event-related potential; ERP; LORETA; ACC
1. Introduction
* Corresponding author. Tel.: +49 89 5160 3392; fax: +49 89 5160 5542. E-mail address: [email protected] (C. Mulert). 0167-8760/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpsycho.2004.10.002
The function of the anterior cingulate cortex (ACC) has been discussed in the last years primarily in the context of conflict monitoring and error detection (Carter et al., 1998; Bush et al., 2000).
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In addition, ACC activity has been described in the context of bconscious effortQ in bcomplex effortful tasks that presumably cannot be performed without conscious guidanceQ (Dehaene and Naccache, 2001). Accordingly, ACC activity has been discussed in the context of battention to our own actionsQ (Frith, 2002). Recent neurophysiological and neuroimaging studies describing a negative correlation between ACC activity and reaction times in simple or choice reaction time experiments might be of interest in this context. For example, Naito et al. (2000) could show in a PET study in nine healthy volunteers using a simple reaction task in three modalities (auditory, somatosensory or visual signals) that several brain regions became activated, including the supplementary motor area, the dorsal premotor zone, the primary motor cortex and the anterior cingulate cortex. Only fields in the anterior cingulate cortex, rostral to the cingulate motor area, showed consistently significant negative correlation with mean reaction time in all three tasks (Naito et al., 2000). Similar results have been found using ERP based source localisation. Using a choice reaction task, healthy volunteers showed significantly shorter reaction times, higher N1 amplitudes and higher ACC activity during the N1 timeframe as assessed by Low Resolution Electromagnetic Tomography (LORETA) than patients with schizophrenia (Mulert et al., 2001). This finding has been replicated in a new group of patients, using slightly modified recording and in the analysis with both LORETA and equivalent dipole source (Gallinat et al., 2002). In addition, the constellation of short reaction times and high ACC activity versus prolonged reactions times and low ACC activity could be described again in a large sample of healthy volunteers (n=254). Here, two subgroups were selected based on their reaction times, either very fast or very slow. Again, subjects with short reaction times showed significantly increased N1 amplitudes and increased ACC activity during the N1 timeframe in comparison to healthy subjects with prolonged reaction times (Mulert et al., 2003). Accordingly, a negative correlation between reaction times and ACC activity has been described again in a choice reaction time experiment using event-related fMRI (Winterer et al., 2002). While a negative correlation between ACC activity and reaction times is the common finding in reaction
time tasks as assessed with fMRI, PET or eventrelated potentials, things are different in interference/ conflict tasks like the Stroop task. The Stroop task involves naming of the word of the colour of ink of a word that can be congruent (i.e., match the colour of the ink it is printed in; e.g., the word bREDQ in red ink) or incongruent (i.e., mismatch of the word with the colour of ink the word is printed in; e.g., the word bREDQ in blue ink). The typical finding is increased ACC activity and prolonged reaction times in the incongruent condition. This can be described in terms of task difficulty or, more specific, with a conflict between two aspects of information/response competition. Therefore Stroop task results are seen as clear support for the conflict monitoring hypothesis (Kerns et al., 2004). Interestingly, however, is a recent finding that in subliminal conflict tasks no ACC activity is detectable but only in a situation of a conscious conflict. This has been interpreted against a role of the ACC in conflict monitoring and towards a role in conscious monitoring (Dehaene et al., 2003). As an explanation of the abovementioned ACC findings during reaction time experiments, a possible role of mental effort of the subjects during the task was suggested, even if so far no direct prove of this hypothesis could be provided. Obviously, several lines of evidence suggest that activation of the ACC might be related to mental effort: Some support for this hypothesis is provided already by the reaction time study from Wilkinson and Morlock (1967) demonstrating that motivation in a reaction task can increase the N1 amplitude. In this study with 10 healthy subjects, 3 different conditions have been used: a low incentive condition, a high incentive condition and a control condition with no response required. Increasing incentive was associated with significant increase of the N1 amplitude and significant improvement of performance (reaction times). Given the recent results of an electrical generator of the N1-potential within the ACC (Mulert et al., 2001, 2003; Gallinat et al., 2002), these findings might be interpreted at least in parts as a result of increased ACC activity in the incentive conditions, where ban intense effort was made to perform well for a short spellQ (Wilkinson, 1967). More direct evidence comes from lesion studies: Patients with bilateral lesions in the ACC or the medial frontal cortex often show akinetic mutism,
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with profound apathy and complete indifference to their circumstances. These patients appear awake but only rarely and sparely take action (Mega and Cummings, 1997). The loss of spontaneous action is related to the involvement of the supplementary motor cortex and the skeletomotor effector region of the ACC. If these regions are spared, patients show a loss of motivation to engage in a task. Patients lack bcognitive motivationQ (Laplane et al., 1981). Interestingly, patients who recover from an anterior cingulate/SMA infarction describe their former state as a loss of will (Damasio and Van Hoesen, 1983). Patients with small lesions in the anterior cingulate cortex after surgery for pain relief report that the pain is still present but it does not bother them as much as before. Furthermore, the spontaneity of their behaviour is reduced (Cohen et al., 1999). In mental disorders like schizophrenia, symptoms such as apathy and reduced interest may occur. In a large study of drug naRve patients with schizophrenia (n=39), negative symptoms (PANSS negative scores) correlated negatively with the degree of regional cerebral blood flow in the ACC (Ashton et al., 2000). Disturbed function of the ACC is a common finding in schizophrenia using PET, fMRI or EEG (Dolan et al., 1995; Carter et al., 2001; Mulert et al., 2001). In animal studies with rats, a profound change in effort-based decision making has been described as a consequence of a lesion in the medial frontal cortex. Before lesion-surgery, the animals chose to climb a barrier to acquire a large reward on the majority of trials. After surgery, the lesion group selected a low effort-low reward behaviour (Walton et al., 2002). In the present study, 35 healthy subjects have been investigated. The study was intended to directly investigate the relationship between conscious effort and ACC function. In this experiment, three conditions were used: condition I was a choice reaction task with the instruction to stay relaxed during the task (relaxed condition), condition II was the same choice reaction task with the instruction to press the respective button as fast and correct as possible (effort condition). Condition III was just listening to the tones (control condition). Subjects had to score directly after each experimental run on a visual analogue scale the amount of effort they have actually spent.
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Our hypothesis was that subjects following the instructions closely, would show significant shorter reaction times during the effort condition than during the relaxed condition as well as an increased amplitude and that in a source localisation approach this N1-effect would be associated with an increased activity of the anterior cingulate cortex.
2. Materials and methods 2.1. Subjects Thirty-five healthy volunteers (age: 20–46, mean 29, 18 female, 17 male) with no history of neurological or psychiatric disturbance or reduced hearing were recruited from an academic environment and paid for their participation. The study was approved by the local ethics committee of the Ludwig-Maximilians-Universit7t of Munich and written informed consent was obtained from each subject. 2.2. Paradigm The experimental task was a choice reaction paradigm as used previously (Mulert et al., 2001, 2003): 60 tones of different pitches (50%: 800 Hz and 50%: 1300 Hz) were presented by earphones at 85 dB SPL with pseudo-randomized sequence and interstimulus intervals (ISI: 2.5–7.5 s). In the experimental runs, the subjects had to press one of two buttons. The two buttons were assigned in advance to the high and low tone, respectively. The low tone had to be responded by pressing the left button with the left hand and the high tone by pressing the right button with the right hand. Three runs were made: one with the instruction to stay relaxed during the task (relaxed condition), one with the instruction to press the respective button as fast and as precisely as possible (effort condition) and one control run with no action required at all (control condition). Thee three runs were performed in a fixed order: first the control run, second the relaxed run and finally the effort run. The reason for this fixed order was that the effort run was expected to be exhausting with unspecific vigilance reduction to be expected afterwards. Between the runs, only a short break of about 1–2 min was made. Each run took
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exactly 5 min. As a consequence of our notcounterbalanced run, a possible unspecific vigilance effect would be expected to reduce N1 amplitudes during our final condition, the effort run. Auditory stimuli were generated on a Personal Computer using the BrainStim software package (Brain Products, Munich). 2.3. Visual analogue scale Since not all subjects could be expected to strictly follow the instruction either to stay relaxed or to spend maximal effort, we asked the subjects to score the effort they have actually spent during the task directly after each experimental run on a visual analogue scale, which was specifically designed for this purpose. On the visual analogue, the subjects could report the effort they have spent between 1 (low effort) and 5 (high effort). Subjects did report their effort value with a ball-pen on the self-assessment sheet (marking with a cross). 2.4. EEG/ERP Recording took place in a sound-attenuated and electrically shielded room. Subjects were seated with open eyes in a slightly reclined chair with a head rest and were asked to look at the wall 2 m in front of them. The EEG was recorded with 27 electrodes referred to Cz (recording apparatus: Neuroscan Synamps) using an electrode cap. The electrodes were positioned according to the International 10/20 system with the additional electrodes FC1, FC2, FC5, FC6, CP5, CP6, P09, P010. Fpz served as ground. Data were collected with a sampling rate of 1000 Hz and an analogous bandpass filter (0.16–200 Hz). Offline, an additional 70 Hz low-pass filter has been used. The 200-ms pre-stimulus and 600-ms poststimulus periods were evaluated for all sweeps. After rereferencing to common average reference, for artifact suppression, an amplitude criterium has been used (+70 AV) involving all channels at any time point during the averaging period. After artifact elimination and baseline correction (using 100-ms pre-stimulus), averaged ERP wave-shapes were computed. Only wave-shapes based on at least 30 averages were accepted. All data sets did meet these quality criteria.
2.5. Parametrization Peaks and latencies at scalp electrodes: According to earlier suggestions (Mulert et al., 2001), the timeframe 60–160-ms post-stimulus was used for further analysis. The N1 component was defined as the most negative value during this timeframe at Cz. Amplitude and latency values of the were detected semi-automatically using the Brain Vision AnalyzerSoftware Version 1.04. 2.6. LORETA LORETA assumes that the smoothest of all activity distributions is most plausible (bsmoothness assumptionQ) and, therefore, a particular current density distribution is found (Pascual-Marqui et al., 1994). This fundamental assumption of LORETA directly relies on the neurophysiological observation of coherent firing of neighbouring cortical neurons during stimulus processing (Llinas, 1988; Gray et al., 1989; Silva et al., 1991) and therefore can be seen as a physiologically based constraint. However, this coherent firing has been described on the level of cortical columns, which have a much smaller diameter than the voxels used in the LORETA software; the empirical basis for coherent firing in the millimetre range is not strong enough to fully accept this constraint as a physiological one, even if it might help to produce useful results. The characteristic feature of the resulting solution is its relatively low spatial resolution, which is a direct consequence of the smoothness constraint. Specifically, the solution produces a bblurred-localizedQ image of a point source, conserving the location of maximal activity, but with a certain degree of dispersion. It should be emphasized that this solution will typically produce a bblurredlocalizedQ image of arbitrary distributions due to the principle of superposition. However, some distributions of point sources may superpose in such a way that they actually cancel out on the scalp and therefore can not be correctly localised by any method. The version of LORETA used in the present study used the digitized Talairach atlas (Talairach and Tournoux, 1988) available as digitized MRI from the Brain Imaging Center, Montreal Neurologic Institute, estimating the current source density (AA/mm2) distribution for either single timepoints or epochs of brain
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electric activity on a dense grid of 2394 voxels at 7mm spatial resolution (Pascual-Marqui et al., 1999). The solution space (the three-dimensional space where the inverse EEG problem is solved) was restricted to the gray matter and hippocampus in the Talairach atlas (anatomically based constraint). Localisation with regard to spherical and realistic head geometry was done using EEG electrode coordinates reported by Towle et al. (1993). A voxel was labeled as gray matter if it met the following three conditions: its probability of being gray matter was higher than that of being white matter, its probability of being gray matter was higher than that of being cerebrospinal fluid, and its probability of being gray matter was higher than 33% (Pascual-Marqui et al., 1999). LORETA has been widely used in the last years in order to localize electrical generators of scalp EEG data (Mulert et al., 2001, 2004b; Pizzagalli et al., 2001; Gallinat et al., 2002; Park et al., 2002). ROI analysis: Region of interest (ROI) analyses were performed for the auditory cortex (AuC), as defined before (Mulert et al., 2002). Additionally, a region of interest, including the supplementary motor cortex and the dorsal anterior cingulate cortex (based on forty-eight 7-mm3 voxels) was done. This ACCSMA-ROI covered a region extending in Talairach space from x: 3 to 4, y: 32 to 10, z: 29 to 57. For the ROI analysis, we used the bROI-ExtracterQ-tool provided by Marco Congedo (http://www.irisa.fr/ siames/GENS/ mcongedo/MC_Software.html), which averages the current source density values in the specified voxels.
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Briefly, between-group comparisons of the LORETA current density distribution were performed using a non-parametric statistical analysis based on voxel-by-voxel t-tests (Holmes et al., 1996; Nichols and Holmes, 2002). These calculations were based on the current source density values of each subject, which were calculated using the averaged event-related potential data (using all recording channels).
3. Results 3.1. Self rating The mean effort self rating value was 2.66 (S.D.: 0.80) for the relaxed condition and 4.77 (S.D.: 0.42) during the effort condition (T= 14.4, pb0.001). Females scored higher in both conditions (3.06 and 4.89) than males (2.24 and 4.65) and the mean difference in the scores between the two runs was slightly higher in males (2.41 and 1.83, T= 2.07, p=0.047). Since in the relaxed condition a low self rating score of 1 or 2 was expected and in the effort condition a high score of 4 or 5 if a subject was following the instructions, we selected a subgroup of subjects with an effort score difference between the two conditions of 3 or 4 (high effort increase group, HEI, n=12) and a subgroup of subjects with an effort score difference of 2 or less (low effort increase group, LEI, n=23). 3.2. Task performance
2.7. Statistics Statistical comparisons between the conditions were performed as paired t-tests using the SPSSsoftware package (11.5). The relationship between the effort values and the N1 amplitudes as well as the relationship between the LORETA-ROI values and the effort scores was analyzed by means of Spearman’s rank correlation coefficient. Correlations between reaction times and amplitude values or current source density values were analyzed using Pearson’s correlation. All tests were performed with a two-sided Pb0.05. Statistical analysis with LORETA is described in more detail elsewhere (Mulert et al., 2001).
For the whole group, there was a significant difference between the mean reaction time during the relaxed condition in comparison to the effort condition (589F99 and 400F72 ms, T=9.49, pb0.001). The mean reaction time improvements (reaction times in the relaxed run minus reaction times in the effort run) were significantly more pronounced in the HEI group in comparison to the LEI (278F141 and 143F71 ms, T= 3.78, p=0.001). The mean number of errors during the relaxed condition was 4.1F5.2 and 1.5F1.6 ms during the effort condition (T=3.1, pb0.01) for the whole group. The mean accuracy improvement (number of errors in the relaxed run minus number of errors in the effort
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run) of the HEI group was 5.6F6.6 and 1.1F3.2 in the LEI group (T= 2.72, p=0.01). 3.3. Event-related activity For the whole group, the auditory evoked N1 potentials measured at Cz were significantly higher in the effort condition (9.72+4.55 AV) than in the relaxed condition (8.40F3.77 AV, T= 4.70, pb0.001) or in the control condition (8.38F3.78 AV, T= 3.7, p=0.001). In the subgroup analysis, only subjects of the HEI group showed significant increased N1 amplitudes in the effort condition in comparison to the control condition (11.60F4.29 vs. 8.49F3.63 AV, T= 5.44, pb0.001, see Fig. 1), but not subjects of the LEI group (8.73F4.46 vs. 8.32F3.93 AV, T= 1.31, p=0.20). Subjects of the HEI group showed a trend towards a more pronounced N1 amplitude increase (N1 amplitude in the effort condition minus N1 amplitude in the relaxed condition) than subjects of the LEI group (T= 1.96, p=0.058, see Fig. 2). No significant difference or trend was found between the subgroups for the relaxed condition or the control condition (HEI: 9.56F3.67 vs. LEI: 7.80F3.75 AV and HEI 8.49F3.63 vs. LEI 8.32F3.93 AV).
Concerning the N1 latencies, the mean latency of the whole group was 100F13 ms for the control condition, 104F13 ms for the relaxed condition and 107F14 ms for the effort condition; for the LEI group, the mean N1 latency was 98F15 ms for the control condition, 103F15 ms for the relaxed condition and 105F14 ms for the effort condition. In the HEI group, the mean N1 latency was 104F8 ms for the control condition, 104F10 ms for the relaxed condition and 113F12 ms for the effort condition. In the statistical analysis, there has been a significant difference between the N1 latencies of the relaxed condition and the effort condition for the whole group (T= 2.77, p=0.01). In the subgroup analysis, there was a significantly increased N1-latency-prolongation in the effort condition (N1-latency in the effort condition minus N1-latency in the relaxed condition) in the HEI group in comparison to the LEI group (T= 2.64, p=0.01). In the correlation analysis, we found a significant correlation between the N1 amplitude values and the subjective effort scores for the effort condition (r=0.47, p=0.004) but not for the relaxed condition (r=0.02, p=0.90). In addition, there was a trend towards a negative correlation between N1 amplitudes
Fig. 1. Grand averages (high effort increase group, n=12) of the N1 potentials at Cz during the relaxed condition, the effort condition and the control condition. The highest amplitude can be found in the effort condition.
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Fig. 2. Grand averages of the N1 potentials at Cz of the high effort increase group (n=12) and the low effort increase group (n=23) during the effort condition. Subjects of the HEI group show a trend towards higher N1 amplitudes than subjects of the LEI group.
and reaction times during the effort condition for the whole group (r= 0.33, p=0.067) but not during the relaxed condition (r=0.17, p=0.32). In the subgroup analysis, the negative correlation during the effort condition was present only in the HEI group (r= 0.68, pb0.05, see Fig. 3) but not in the LEI group (r= 0.03). Accordingly, we found a significant correlation between the subjective effort score increase (effort minus relaxed condition) and both the N1 amplitude increase (N1 amplitude in the effort condition minus N1 amplitude in the relaxed condition, r=0.36, pb0.05, see Fig. 4) and the reaction time improvement (reaction time in the relaxed condition minus reaction time in the effort condition, r=0.57, pb0.001, see Fig. 5).
coordinates x,y,z: 59, 32,15, superior temporal gyrus, Brodmann area 42, see Fig. 6). In the next step, a statistical analysis based on the auditory cortex region of interest (AuC-ROI) current
3.4. LORETA analysis Following earlier suggestions (Mulert et al., 2001), we performed separate LORETA analyses for the early part of the N1 (60–100-ms post-stimulus) and the late part (100–160-ms post-stimulus). For the whole group, the main activity within the early part of the N1 could be found with activations in the auditory cortex for all the three conditions (highest current source density in the same voxel with Talairach
Fig. 3. In the group with high effort increase (HEI) a negative correlation between N1 amplitude at Cz and reaction times in the effort condition was found.
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Fig. 4. The more pronounced the effort increase in the selfassessment, the more pronounced is the N1 amplitude increase between the two runs.
source density values was done. Lowest activity in the auditory cortex region of interest analysis was found in the control condition (mean current source density of 0.00244F0.00103 AA/mm2), higher activity in the relaxed condition (0.00259F0.00087 AA/mm2) and highest values in the effort condition (0.00283F 0.00104 AA/mm2). There was no significant difference between control condition and relaxed condition, but a significant difference between relaxed condition and effort condition could be found (T= 2.66, pb0.05). In the subgroup analysis, no difference (increase of the AUC-ROI current source density values in the effort run in comparison to the relaxed run) could be found in the HEI group in comparison to the LEI group (T= 1.52, p=0.14). In the late part of the N1, during all three conditions highest current source density values could be observed in the dorsal cingulate gyrus/medial frontal gyrus (Talairach coordinates x,y,z: 4, 11,50, see Fig. 7). Differences in the region of interest analysis of the dorsal cingulate gyrus/SMA (ACC/SMA-ROI) could be observed with lowest activations in the control condition (0.00438F0.00333 AA/mm 2), higher values in the relaxed condition (0.00514F 0.00367 AA/mm2) and highest values in the effort condition (0.00650F0.00436 AA/mm2). These differ-
ences between the activation patterns were highly significant (effort condition versus relaxed condition: T= 3.79, p=0.001). In the subgroup analysis, this effect (increase of the ACC/SMA-ROI current source density values in the effort run in comparison to the relaxed run) was significantly more pronounced in the HEI group than in the LEI group (T= 2.53, p=0.016). In the correlation analysis, we found a relationship between the subjective effort increase (effort score in the effort condition minus effort score in the relaxed condition) and the current source density-increase in the ACC-SMA-ROI (ACC-SMA-current source density in the effort condition minus ACC-SMA-ROIcurrent source density in the relaxed condition, r=0.44, pb0.01, see Fig. 8). There was no correlation between the subjective effort increase and the current source density increase in the AuC-ROI (r=0.26, p=0.13). However, there was a significant negative correlation between the AuC-ROI and reaction times during the relaxed condition ( 0.35, p=0.04) but not for the effort condition ( 0.81, p=0.62). Vice versa, there was a negative correlation between the ACC/ SMA-ROI and reaction times during the effort condition ( 0.33, p=0.05), but not for the relaxed condition (0.03, p=0.85).
Fig. 5. The more pronounced the effort increase in the selfassessment, the more pronounced is the reaction time difference between the two runs.
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Fig. 6. Current source density analysis of the whole group (n=35) for the first part of the N1 (60–100 ms) with similar activation patterns in all three conditions within the auditory cortex: (a) control condition, (b) relaxed condition, (c) effort condition.
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Fig. 7. Current source density analysis of the whole group (n=35) for the second part of the N1 (100–160 ms) with activation in the dorsal anterior cingulate/medial frontal gyrus. Highest activity in the ACC/SMA can be found in the effort condition: (a) control condition, (b) relaxed condition, (c) effort condition.
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3,17,29, Brodmann area 24) could be found only in the HEI group (T=5.43, pb0.05, critical T value=4.77, see Fig. 9) but not in the LEI group (highest T value=4.11). Additional activations in the HEI group were detectable in the right prefrontal cortex (middle frontal gyrus, Talairach coordinates x,y,z: 25,10,64, Brodmann area 6, T=5.16) and the right middle temporal gyrus (Talairach coordinates x,y,z: 46, 60,1, T=5.25).
4. Discussion
Fig. 8. The more pronounced the effort increase in the selfassessment, the more pronounced is the ACC-/SMA-ROI-current source density increase between the two runs.
Finally, in a voxelwise comparison between effort condition and control condition performed for the N1peak, a significant increased activity in the dorsal cingulate cortex (Talairach coordinates x ,y,z :
For the whole group, we could describe shorter reactions times, increased N1 amplitudes and increased ACC/medial frontal gyrus activations in the effort condition in comparison to the relaxed condition. In the subgroup analysis, only subjects following to the instructions closely according to their self-ratings (HEI group) were responsible for these effects. Subjects, which documented in the self-ratings that they did not spend increased effort in the effort condition (LEI group), did not show increased N1 amplitudes and in the LORETA analysis there was no significant increase in the ACC at the N1-peak timeframe.
Fig. 9. Statistical voxelwise comparison between effort condition and control condition of the high effort increase group (n=12) at the N1 peak with significant activation in the dorsal anterior cingulate cortex, Brodmann 24 (T=5.43, pb0.05, critical p-value=4.77) and the right prefrontal cortex (middle frontal gyrus, Brodmann 6, T=5.16).
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We could earlier show that in choice reaction tasks short reaction times, increased N1 amplitudes and increased ACC activity are associated as well as prolonged reaction times, low N1 amplitudes and low ACC activity. This could be observed both in healthy subjects and psychiatric patients (Mulert et al., 2001, 2003). An influence of effort to these results was suggested before (Mulert et al., 2003). The present study directly addressed this question and directly proved the influence of effort on reaction times, N1 amplitude and ACC activity. Interestingly, the self-ratings of the subjects corresponded both to their reaction times and their neurophysiological parameters. At this point, it is useful to discuss our findings in the context of different concepts related to ACC function and effort: Main concepts are related to mental effort as indexed by task difficulty, incentive motivation and arousal or physical effort: Mental effort related to task difficulty, which has been described by several authors to be related to increased ACC activity (Barch et al., 1997; Gevins et al., 1997; Numminen et al., 2004) is passive in a sense that the task is changing (whether the subject wants or not) and mental effort is not directly related to volition. This does not seem to fit our approach since in our experiment the paradigm used in the two runs was exactly the same (in terms of numbers of targets, buttons to press, frequency difference of the tones, etc.). However, one also could argue that the high effort run with the instruction to press as fast and as correct as possible is a bspeeded versionQ of the choice reaction experiment. Such a speeded version could be seen as a more difficult task. (Monetary) incentive/reward is also an external change in the conditions that might be closely related to the motivation and therefore the willful (effortful, volitional) acts of a subject. Simply spoken, a high incentive run is seen a high motivation run and increased ACC activity has been described (Shidara and Richmond, 2002; Akitsuki et al., 2003). Our approach does not involve incentive as basis for volition, since the payment of the volunteers was not related to the level of performance. Therefore, the subjective degree of motivation or volition to spend more effort is more variable across subjects. This is the basis for our distinction of a low versus high effort increase group.
In addition, an important relationship has been established in the leas years between ACC activity and arousal level during effortful cognitive and motor behaviour. Especially the studies by Critchley have shown nicely that ACC activity during effortful cognitive and motor behaviour is related to autonomic states of cardiovascular arousal (Critchley et al., 2000, 2003). Unfortunately, since we have not controlled for skin conductance/heartbeat in the present study, we cannot further follow this point with own data at this time. In the present study, we tried to investigate an active and conscious increase of effort in order to improve the performance in the task. Since we did not give any monetary incentive, there was no strong external motivation and the possibility of different internal motivation states as reported by the self-rating scores did exist more obviously and was in fact found in the present study to be correlated to both performance and neurophysiological parameters. Attentional effects on N1 waveform (Hillyard et al., 1973) and a role of the ACC in attention-to-action and evaluating the consequences of actions have been described years ago (Passingham, 1996). In parallel, the relationship of ACC activity to willful acts and volition was also described. Francis Crick proposed that the ACC is bthe seat of the willQ(Crick, 1994). It has been tried to merge these different aspects in terms of bexecutive attentionQ as described by Posner and DiGirolamo (1998). A unifying concept of volition based on decision making, voluntary action initiation and executive control was recently suggested by Zhu (2004). The present study adds some important points to the present discussion of ACC function: Most important, subjects can report the degree of effort they have actually spend during the task and this subjective rating is related to their level of performance and to their N1 amplitude/ACC activity. Therefore, it might be appropriate to use the term bconsciousQ effort here. In addition, our findings are not in line with a strict conflict monitoring hypothesis but more with concepts like the one of Paus suggesting the ACC being an interface between drive, cognition and motor control (Paus, 2001) and involved in the willed control of actions (Mesulam, 1990; Schall et al., 2002) or the idea of the ACC being involved in bconscious monitoringQ (Dehaene et al., 2003).
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At this point, it is important to mention that the ACC is not a single homogenous structure, but contains several distinct areas, each possessing a different pattern of anatomical connections (Mega and Cummings, 1997; Bush et al., 2000). The present result of a relationship between effort and ACC function was found only in the dorsal, supracallosal ACC. Our results do not at all contradict a relationship between the conflict monitoring hypothesis and other parts of the ACC, which are not activated in the present experiment. Interestingly, different brain regions determine reaction times during the different conditions: While reaction times correlated negatively with and only with the activation of the auditory cortex during the relaxed condition, things were different during the effort condition: Here, only the activity pattern of the SMA/ACC region correlated negatively with reaction times, as it has been described earlier (Naito et al., 2000). While this study was intended to describe the relationship between effort and ACC activity, we did also find some increase in the activation of the auditory cortex during the effort condition. This could be explained with early attention effects for the N1 potential in the auditory cortex as described earlier (Hillyard et al., 1973; Naatanen and Picton, 1987). N1 modulation has also been found for attentional orienting in space and an effect in secondary sensory cortex areas (V2) has been demonstrated (Mangun and Hillyard, 1991; Luck and Hillyard, 1995; Luck et al., 1997). Concerning the methods, the effects for N1 amplitudes and reaction times are free of any sophisticated methodology. However, we have also used an ERP based localisation method, LORETA, which has to deal with the so-called inverse problem and is not free of basic assumptions. Recent studies of our and other groups have demonstrated good accordance of fMRI-(BOLD)-localisations and LORETA results in simultaneous or combined EEG/fMRI studies even for more complex activation patterns like the P300-potential, with several overlapping electrical generators, in the range of 1–2 cm (Vitacco et al., 2002; Mulert et al., 2004a). Evidence for a contribution of the ACC during the N1 timeframe comes also from earlier ERP studies using dipole source analysis (Giard et al., 1994).
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In the present study, the strongest activity during the late N1 timeframe was found in the SMA during all three conditions, with weaker activity in the ACC. But in the voxelwise LORETA comparison between the effort and the relaxed condition a significant difference was found in the anterior cingulate cortex (Bordmann area 24) and not in the SMA. This result would also be in better accordance to the literature (Naito et al., 2000). However, taking the low resolution of the ERP source localisation into account, a relationship between effort and the ACC seems most likely, but other techniques with more precise localisation like, e.g., fMRI are needed in order to confirm the present results. One important result of the present study is the fact that the self-ratings of the subjects are the basis of an important distinction between subjects following well and subjects not following well to the instructions. The differences between these groups in reaction times, N1 amplitudes and ACC activity provide evidence that these self-ratings are related to the physiological processes. This result suggests that these self-ratings are a useful tool in the investigation of cognitive brain functions in healthy subjects and probably also in neuropsychiatric patients, where effort and motivation might often be a relevant co-variable: Reduced performance in cognitive tasks like choice reaction paradigms can result from disturbed ACC function associated with reduced cognitive motivation. This is probably the case in schizophrenia: several lines of evidence, including post-mortem anatomical studies, suggest ACC disturbance in this disease (Benes, 2000). However, since different levels of cognitive motivation, associated with different activation levels of the ACC, also exist within the group of healthy subjects, it might well be possible that not every finding of reduced ACC activity in psychiatric patients is related to an underlying brain pathology. ACC hypofunction then may not reflect a primary brain dysfunction but rather may reflect the degree of effort expended. For example, patients may become demoralized and unmotivated as a secondary reaction to their primary illness and circumstances. If so, then this demoralization and lack of motivation should be amenable to treatment, and when treatment leads to improved moral and motivation, then the ACC activity should normalize during cognitive tasks. Another way to
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examine whether ACC hypofunction in a psychiatric group reflects a primary immutable deficit or a secondary deficit subject to improvement with motivational enhancement, the effects of monetary incentive on task performance and ACC activity could be examined in different psychiatric groups. If the monetary incentive increases motivation and produced normal performance with normal ACC function, then it seems reasonable to postulate that the motivational impairment and ACC hypofunction are not primary to the pathophysiology of the particular psychiatric disorder. In conclusion, it is difficult to explain our results in a strict version of the conflict monitoring hypothesis: Both experimental runs have been performed with the same task and so with the same amount of conflict. Differences in the conflict per se therefore can not explain the differences in the activations of the ACC in the present study. Maybe our results could be better explained with a concept of ACC function in the detection or monitoring of (subjective) important information. According to such a concept, subjects could to some degree voluntarily and consciously influence, whether information (stimuli) is important or not. Spending more effort would then be a consequence of a subjective change in the level of importance associated with a stimulus.
Acknowledgment We like to thank Marco Congedo for the development of the bROI ExtracterQ tool and his assistance in the application to event-related potentials. Parts of this work were prepared in the context of Elisabeth Menzinger’s dissertation at the Faculty of Medicine, Ludwig-Maximilians-Universit7t, Munich.
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simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection. NeuroImage 22, 83 – 94. Mulert, C., Pogarell, O., Juckel, G., Rujescu, D., Giegling, I., Rupp, D., Mavrogiorgou, P., Bussfeld, P., Gallinat, J., Moller, H.J., Hegerl, U., 2004b. The neural basis of the P300 potential. Focus on the time-course of the underlying cortical generators. Eur. Arch. Psychiatry Clin. Neurosci. 254, 190 – 198. Naatanen, R., Picton, T., 1987. The N1 wave of the human electric and magnetic response to sound: a review and an analysis of the component structure. Psychophysiology 24, 375 – 425. Naito, E., Kinomura, S., Geyer, S., Kawashima, R., Roland, P.E., Zilles, K., 2000. Fast reaction to different sensory modalities activates common fields in the motor areas, but the anterior cingulate cortex is involved in the speed of reaction. J. Neurophysiol. 83, 1701 – 1709. Nichols, T.E., Holmes, A.P., 2002. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15, 1 – 25. Numminen, J., Schurmann, M., Hiltunen, J., Joensuu, R., Jousmaki, V., Koskinen, S.K., Salmelin, R., Hari, R., 2004. Cortical activation during a spatiotemporal tactile comparison task. NeuroImage 22, 815 – 821. Park, H.J., Kwon, J.S., Youn, T., Pae, J.S., Kim, J.J., Kim, M.S., Ha, K.S., 2002. Statistical parametric mapping of LORETA using high density EEG and individual MRI: application to mismatch negativities in schizophrenia. Hum. Brain Mapp. 17, 168 – 178. Pascual-Marqui, R.D., Michel, C.M., Lehmann, D., 1994. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int. J. Psychophysiol. 18, 49 – 65. Pascual-Marqui, R.D., Lehmann, D., Koenig, T., Kochi, K., Merlo, M.C., Hell, D., Koukkou, M., 1999. Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Res. 90, 169 – 179. Passingham, R.E., 1996. Attention to action. Philos. Trans. R. Soc. Lond., B Biol. Sci. 351, 1473 – 1479. Paus, T., 2001. Primate anterior cingulate cortex: where motor control, drive and cognition interface. Nat. Rev., Neurosci. 2, 417 – 424. Pizzagalli, D., Pascual-Marqui, R.D., Nitschke, J.B., Oakes, T.R., Larson, C.L., Abercrombie, H.C., Schaefer, S.M., Koger, J.V., Benca, R.M., Davidson, R.J., 2001. Anterior cingulate activity as a predictor of degree of treatment response in major depression: evidence from brain electrical tomography analysis. Am. J. Psychiatry 158, 405 – 415. Posner, M.I., DiGirolamo, G.J., 1998. Executive attention: conflict, target detection, and cognitive control. In: Parasuraman, R. (Ed.), The Attentive Brain. A Bradford Book. The MIT Press, Cambridge, London, pp. 401 – 423. Schall, J.D., Stuphorn, V., Brown, J.W., 2002. Monitoring and control of action by the frontal lobes. Neuron 36, 309 – 322. Shidara, M., Richmond, B.J., 2002. Anterior cingulate: single neuronal signals related to degree of reward expectancy. Science 296, 1709 – 1711.
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Evidence for a close relationship between conscious effort and anterior cingulate cortex activity Christoph Mulert*, Elisabeth Menzinger, Gregor Leicht, Oliver Pogarell, Ulrich Hegerl Department of Psychiatry, Nugbaumstrage 7, LMU, Munich 80336 Mu¨nchen, Germany Received 22 May 2004; received in revised form 28 September 2004; accepted 7 October 2004 Available online 11 November 2004
Abstract The function of the anterior cingulate cortex (ACC) has been discussed in the last years in the context of conflict monitoring and error detection. In addition, ACC activity has been described in the context of bconscious effortQ. Recent neurophysiological and neuroimaging studies have described a negative correlation between ACC activity and reaction times in simple or choice reaction time experiments. One suggested explanation for this finding has been that there is a relationship between effort and ACC activity. The present ERP-LORETA study of healthy volunteers (n=35) was intended to directly investigate this relationship. In this experiment, three conditions were investigated: condition I was a choice reaction task with the instruction to stay relaxed during the task (relaxed condition), condition II was the same choice reaction task with the instruction to press the respective button as fast and correct as possible (effort condition). Condition III was just listening to the tones without button press (control condition). Subjects had to score directly after each experimental run on a visual analogue scale the amount of effort they have actually spent. The subjects showed significantly shorter reaction times during the high effort condition in comparison to the relaxed condition, as well as increased N1 amplitudes and increased ACC activity. In a subgroup analysis, this effect was present only in subjects who were (according to their self-ratings) following the instructions closely. These results provide direct evidence for a close relationship between conscious effort and ACC activity and suggest the usefulness of the applied effort-self-rating. D 2004 Elsevier B.V. All rights reserved. Keywords: Effort; Motivation; Event-related potential; ERP; LORETA; ACC
1. Introduction
* Corresponding author. Tel.: +49 89 5160 3392; fax: +49 89 5160 5542. E-mail address: [email protected] (C. Mulert). 0167-8760/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpsycho.2004.10.002
The function of the anterior cingulate cortex (ACC) has been discussed in the last years primarily in the context of conflict monitoring and error detection (Carter et al., 1998; Bush et al., 2000).
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In addition, ACC activity has been described in the context of bconscious effortQ in bcomplex effortful tasks that presumably cannot be performed without conscious guidanceQ (Dehaene and Naccache, 2001). Accordingly, ACC activity has been discussed in the context of battention to our own actionsQ (Frith, 2002). Recent neurophysiological and neuroimaging studies describing a negative correlation between ACC activity and reaction times in simple or choice reaction time experiments might be of interest in this context. For example, Naito et al. (2000) could show in a PET study in nine healthy volunteers using a simple reaction task in three modalities (auditory, somatosensory or visual signals) that several brain regions became activated, including the supplementary motor area, the dorsal premotor zone, the primary motor cortex and the anterior cingulate cortex. Only fields in the anterior cingulate cortex, rostral to the cingulate motor area, showed consistently significant negative correlation with mean reaction time in all three tasks (Naito et al., 2000). Similar results have been found using ERP based source localisation. Using a choice reaction task, healthy volunteers showed significantly shorter reaction times, higher N1 amplitudes and higher ACC activity during the N1 timeframe as assessed by Low Resolution Electromagnetic Tomography (LORETA) than patients with schizophrenia (Mulert et al., 2001). This finding has been replicated in a new group of patients, using slightly modified recording and in the analysis with both LORETA and equivalent dipole source (Gallinat et al., 2002). In addition, the constellation of short reaction times and high ACC activity versus prolonged reactions times and low ACC activity could be described again in a large sample of healthy volunteers (n=254). Here, two subgroups were selected based on their reaction times, either very fast or very slow. Again, subjects with short reaction times showed significantly increased N1 amplitudes and increased ACC activity during the N1 timeframe in comparison to healthy subjects with prolonged reaction times (Mulert et al., 2003). Accordingly, a negative correlation between reaction times and ACC activity has been described again in a choice reaction time experiment using event-related fMRI (Winterer et al., 2002). While a negative correlation between ACC activity and reaction times is the common finding in reaction
time tasks as assessed with fMRI, PET or eventrelated potentials, things are different in interference/ conflict tasks like the Stroop task. The Stroop task involves naming of the word of the colour of ink of a word that can be congruent (i.e., match the colour of the ink it is printed in; e.g., the word bREDQ in red ink) or incongruent (i.e., mismatch of the word with the colour of ink the word is printed in; e.g., the word bREDQ in blue ink). The typical finding is increased ACC activity and prolonged reaction times in the incongruent condition. This can be described in terms of task difficulty or, more specific, with a conflict between two aspects of information/response competition. Therefore Stroop task results are seen as clear support for the conflict monitoring hypothesis (Kerns et al., 2004). Interestingly, however, is a recent finding that in subliminal conflict tasks no ACC activity is detectable but only in a situation of a conscious conflict. This has been interpreted against a role of the ACC in conflict monitoring and towards a role in conscious monitoring (Dehaene et al., 2003). As an explanation of the abovementioned ACC findings during reaction time experiments, a possible role of mental effort of the subjects during the task was suggested, even if so far no direct prove of this hypothesis could be provided. Obviously, several lines of evidence suggest that activation of the ACC might be related to mental effort: Some support for this hypothesis is provided already by the reaction time study from Wilkinson and Morlock (1967) demonstrating that motivation in a reaction task can increase the N1 amplitude. In this study with 10 healthy subjects, 3 different conditions have been used: a low incentive condition, a high incentive condition and a control condition with no response required. Increasing incentive was associated with significant increase of the N1 amplitude and significant improvement of performance (reaction times). Given the recent results of an electrical generator of the N1-potential within the ACC (Mulert et al., 2001, 2003; Gallinat et al., 2002), these findings might be interpreted at least in parts as a result of increased ACC activity in the incentive conditions, where ban intense effort was made to perform well for a short spellQ (Wilkinson, 1967). More direct evidence comes from lesion studies: Patients with bilateral lesions in the ACC or the medial frontal cortex often show akinetic mutism,
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with profound apathy and complete indifference to their circumstances. These patients appear awake but only rarely and sparely take action (Mega and Cummings, 1997). The loss of spontaneous action is related to the involvement of the supplementary motor cortex and the skeletomotor effector region of the ACC. If these regions are spared, patients show a loss of motivation to engage in a task. Patients lack bcognitive motivationQ (Laplane et al., 1981). Interestingly, patients who recover from an anterior cingulate/SMA infarction describe their former state as a loss of will (Damasio and Van Hoesen, 1983). Patients with small lesions in the anterior cingulate cortex after surgery for pain relief report that the pain is still present but it does not bother them as much as before. Furthermore, the spontaneity of their behaviour is reduced (Cohen et al., 1999). In mental disorders like schizophrenia, symptoms such as apathy and reduced interest may occur. In a large study of drug naRve patients with schizophrenia (n=39), negative symptoms (PANSS negative scores) correlated negatively with the degree of regional cerebral blood flow in the ACC (Ashton et al., 2000). Disturbed function of the ACC is a common finding in schizophrenia using PET, fMRI or EEG (Dolan et al., 1995; Carter et al., 2001; Mulert et al., 2001). In animal studies with rats, a profound change in effort-based decision making has been described as a consequence of a lesion in the medial frontal cortex. Before lesion-surgery, the animals chose to climb a barrier to acquire a large reward on the majority of trials. After surgery, the lesion group selected a low effort-low reward behaviour (Walton et al., 2002). In the present study, 35 healthy subjects have been investigated. The study was intended to directly investigate the relationship between conscious effort and ACC function. In this experiment, three conditions were used: condition I was a choice reaction task with the instruction to stay relaxed during the task (relaxed condition), condition II was the same choice reaction task with the instruction to press the respective button as fast and correct as possible (effort condition). Condition III was just listening to the tones (control condition). Subjects had to score directly after each experimental run on a visual analogue scale the amount of effort they have actually spent.
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Our hypothesis was that subjects following the instructions closely, would show significant shorter reaction times during the effort condition than during the relaxed condition as well as an increased amplitude and that in a source localisation approach this N1-effect would be associated with an increased activity of the anterior cingulate cortex.
2. Materials and methods 2.1. Subjects Thirty-five healthy volunteers (age: 20–46, mean 29, 18 female, 17 male) with no history of neurological or psychiatric disturbance or reduced hearing were recruited from an academic environment and paid for their participation. The study was approved by the local ethics committee of the Ludwig-Maximilians-Universit7t of Munich and written informed consent was obtained from each subject. 2.2. Paradigm The experimental task was a choice reaction paradigm as used previously (Mulert et al., 2001, 2003): 60 tones of different pitches (50%: 800 Hz and 50%: 1300 Hz) were presented by earphones at 85 dB SPL with pseudo-randomized sequence and interstimulus intervals (ISI: 2.5–7.5 s). In the experimental runs, the subjects had to press one of two buttons. The two buttons were assigned in advance to the high and low tone, respectively. The low tone had to be responded by pressing the left button with the left hand and the high tone by pressing the right button with the right hand. Three runs were made: one with the instruction to stay relaxed during the task (relaxed condition), one with the instruction to press the respective button as fast and as precisely as possible (effort condition) and one control run with no action required at all (control condition). Thee three runs were performed in a fixed order: first the control run, second the relaxed run and finally the effort run. The reason for this fixed order was that the effort run was expected to be exhausting with unspecific vigilance reduction to be expected afterwards. Between the runs, only a short break of about 1–2 min was made. Each run took
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exactly 5 min. As a consequence of our notcounterbalanced run, a possible unspecific vigilance effect would be expected to reduce N1 amplitudes during our final condition, the effort run. Auditory stimuli were generated on a Personal Computer using the BrainStim software package (Brain Products, Munich). 2.3. Visual analogue scale Since not all subjects could be expected to strictly follow the instruction either to stay relaxed or to spend maximal effort, we asked the subjects to score the effort they have actually spent during the task directly after each experimental run on a visual analogue scale, which was specifically designed for this purpose. On the visual analogue, the subjects could report the effort they have spent between 1 (low effort) and 5 (high effort). Subjects did report their effort value with a ball-pen on the self-assessment sheet (marking with a cross). 2.4. EEG/ERP Recording took place in a sound-attenuated and electrically shielded room. Subjects were seated with open eyes in a slightly reclined chair with a head rest and were asked to look at the wall 2 m in front of them. The EEG was recorded with 27 electrodes referred to Cz (recording apparatus: Neuroscan Synamps) using an electrode cap. The electrodes were positioned according to the International 10/20 system with the additional electrodes FC1, FC2, FC5, FC6, CP5, CP6, P09, P010. Fpz served as ground. Data were collected with a sampling rate of 1000 Hz and an analogous bandpass filter (0.16–200 Hz). Offline, an additional 70 Hz low-pass filter has been used. The 200-ms pre-stimulus and 600-ms poststimulus periods were evaluated for all sweeps. After rereferencing to common average reference, for artifact suppression, an amplitude criterium has been used (+70 AV) involving all channels at any time point during the averaging period. After artifact elimination and baseline correction (using 100-ms pre-stimulus), averaged ERP wave-shapes were computed. Only wave-shapes based on at least 30 averages were accepted. All data sets did meet these quality criteria.
2.5. Parametrization Peaks and latencies at scalp electrodes: According to earlier suggestions (Mulert et al., 2001), the timeframe 60–160-ms post-stimulus was used for further analysis. The N1 component was defined as the most negative value during this timeframe at Cz. Amplitude and latency values of the were detected semi-automatically using the Brain Vision AnalyzerSoftware Version 1.04. 2.6. LORETA LORETA assumes that the smoothest of all activity distributions is most plausible (bsmoothness assumptionQ) and, therefore, a particular current density distribution is found (Pascual-Marqui et al., 1994). This fundamental assumption of LORETA directly relies on the neurophysiological observation of coherent firing of neighbouring cortical neurons during stimulus processing (Llinas, 1988; Gray et al., 1989; Silva et al., 1991) and therefore can be seen as a physiologically based constraint. However, this coherent firing has been described on the level of cortical columns, which have a much smaller diameter than the voxels used in the LORETA software; the empirical basis for coherent firing in the millimetre range is not strong enough to fully accept this constraint as a physiological one, even if it might help to produce useful results. The characteristic feature of the resulting solution is its relatively low spatial resolution, which is a direct consequence of the smoothness constraint. Specifically, the solution produces a bblurred-localizedQ image of a point source, conserving the location of maximal activity, but with a certain degree of dispersion. It should be emphasized that this solution will typically produce a bblurredlocalizedQ image of arbitrary distributions due to the principle of superposition. However, some distributions of point sources may superpose in such a way that they actually cancel out on the scalp and therefore can not be correctly localised by any method. The version of LORETA used in the present study used the digitized Talairach atlas (Talairach and Tournoux, 1988) available as digitized MRI from the Brain Imaging Center, Montreal Neurologic Institute, estimating the current source density (AA/mm2) distribution for either single timepoints or epochs of brain
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electric activity on a dense grid of 2394 voxels at 7mm spatial resolution (Pascual-Marqui et al., 1999). The solution space (the three-dimensional space where the inverse EEG problem is solved) was restricted to the gray matter and hippocampus in the Talairach atlas (anatomically based constraint). Localisation with regard to spherical and realistic head geometry was done using EEG electrode coordinates reported by Towle et al. (1993). A voxel was labeled as gray matter if it met the following three conditions: its probability of being gray matter was higher than that of being white matter, its probability of being gray matter was higher than that of being cerebrospinal fluid, and its probability of being gray matter was higher than 33% (Pascual-Marqui et al., 1999). LORETA has been widely used in the last years in order to localize electrical generators of scalp EEG data (Mulert et al., 2001, 2004b; Pizzagalli et al., 2001; Gallinat et al., 2002; Park et al., 2002). ROI analysis: Region of interest (ROI) analyses were performed for the auditory cortex (AuC), as defined before (Mulert et al., 2002). Additionally, a region of interest, including the supplementary motor cortex and the dorsal anterior cingulate cortex (based on forty-eight 7-mm3 voxels) was done. This ACCSMA-ROI covered a region extending in Talairach space from x: 3 to 4, y: 32 to 10, z: 29 to 57. For the ROI analysis, we used the bROI-ExtracterQ-tool provided by Marco Congedo (http://www.irisa.fr/ siames/GENS/ mcongedo/MC_Software.html), which averages the current source density values in the specified voxels.
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Briefly, between-group comparisons of the LORETA current density distribution were performed using a non-parametric statistical analysis based on voxel-by-voxel t-tests (Holmes et al., 1996; Nichols and Holmes, 2002). These calculations were based on the current source density values of each subject, which were calculated using the averaged event-related potential data (using all recording channels).
3. Results 3.1. Self rating The mean effort self rating value was 2.66 (S.D.: 0.80) for the relaxed condition and 4.77 (S.D.: 0.42) during the effort condition (T= 14.4, pb0.001). Females scored higher in both conditions (3.06 and 4.89) than males (2.24 and 4.65) and the mean difference in the scores between the two runs was slightly higher in males (2.41 and 1.83, T= 2.07, p=0.047). Since in the relaxed condition a low self rating score of 1 or 2 was expected and in the effort condition a high score of 4 or 5 if a subject was following the instructions, we selected a subgroup of subjects with an effort score difference between the two conditions of 3 or 4 (high effort increase group, HEI, n=12) and a subgroup of subjects with an effort score difference of 2 or less (low effort increase group, LEI, n=23). 3.2. Task performance
2.7. Statistics Statistical comparisons between the conditions were performed as paired t-tests using the SPSSsoftware package (11.5). The relationship between the effort values and the N1 amplitudes as well as the relationship between the LORETA-ROI values and the effort scores was analyzed by means of Spearman’s rank correlation coefficient. Correlations between reaction times and amplitude values or current source density values were analyzed using Pearson’s correlation. All tests were performed with a two-sided Pb0.05. Statistical analysis with LORETA is described in more detail elsewhere (Mulert et al., 2001).
For the whole group, there was a significant difference between the mean reaction time during the relaxed condition in comparison to the effort condition (589F99 and 400F72 ms, T=9.49, pb0.001). The mean reaction time improvements (reaction times in the relaxed run minus reaction times in the effort run) were significantly more pronounced in the HEI group in comparison to the LEI (278F141 and 143F71 ms, T= 3.78, p=0.001). The mean number of errors during the relaxed condition was 4.1F5.2 and 1.5F1.6 ms during the effort condition (T=3.1, pb0.01) for the whole group. The mean accuracy improvement (number of errors in the relaxed run minus number of errors in the effort
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run) of the HEI group was 5.6F6.6 and 1.1F3.2 in the LEI group (T= 2.72, p=0.01). 3.3. Event-related activity For the whole group, the auditory evoked N1 potentials measured at Cz were significantly higher in the effort condition (9.72+4.55 AV) than in the relaxed condition (8.40F3.77 AV, T= 4.70, pb0.001) or in the control condition (8.38F3.78 AV, T= 3.7, p=0.001). In the subgroup analysis, only subjects of the HEI group showed significant increased N1 amplitudes in the effort condition in comparison to the control condition (11.60F4.29 vs. 8.49F3.63 AV, T= 5.44, pb0.001, see Fig. 1), but not subjects of the LEI group (8.73F4.46 vs. 8.32F3.93 AV, T= 1.31, p=0.20). Subjects of the HEI group showed a trend towards a more pronounced N1 amplitude increase (N1 amplitude in the effort condition minus N1 amplitude in the relaxed condition) than subjects of the LEI group (T= 1.96, p=0.058, see Fig. 2). No significant difference or trend was found between the subgroups for the relaxed condition or the control condition (HEI: 9.56F3.67 vs. LEI: 7.80F3.75 AV and HEI 8.49F3.63 vs. LEI 8.32F3.93 AV).
Concerning the N1 latencies, the mean latency of the whole group was 100F13 ms for the control condition, 104F13 ms for the relaxed condition and 107F14 ms for the effort condition; for the LEI group, the mean N1 latency was 98F15 ms for the control condition, 103F15 ms for the relaxed condition and 105F14 ms for the effort condition. In the HEI group, the mean N1 latency was 104F8 ms for the control condition, 104F10 ms for the relaxed condition and 113F12 ms for the effort condition. In the statistical analysis, there has been a significant difference between the N1 latencies of the relaxed condition and the effort condition for the whole group (T= 2.77, p=0.01). In the subgroup analysis, there was a significantly increased N1-latency-prolongation in the effort condition (N1-latency in the effort condition minus N1-latency in the relaxed condition) in the HEI group in comparison to the LEI group (T= 2.64, p=0.01). In the correlation analysis, we found a significant correlation between the N1 amplitude values and the subjective effort scores for the effort condition (r=0.47, p=0.004) but not for the relaxed condition (r=0.02, p=0.90). In addition, there was a trend towards a negative correlation between N1 amplitudes
Fig. 1. Grand averages (high effort increase group, n=12) of the N1 potentials at Cz during the relaxed condition, the effort condition and the control condition. The highest amplitude can be found in the effort condition.
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Fig. 2. Grand averages of the N1 potentials at Cz of the high effort increase group (n=12) and the low effort increase group (n=23) during the effort condition. Subjects of the HEI group show a trend towards higher N1 amplitudes than subjects of the LEI group.
and reaction times during the effort condition for the whole group (r= 0.33, p=0.067) but not during the relaxed condition (r=0.17, p=0.32). In the subgroup analysis, the negative correlation during the effort condition was present only in the HEI group (r= 0.68, pb0.05, see Fig. 3) but not in the LEI group (r= 0.03). Accordingly, we found a significant correlation between the subjective effort score increase (effort minus relaxed condition) and both the N1 amplitude increase (N1 amplitude in the effort condition minus N1 amplitude in the relaxed condition, r=0.36, pb0.05, see Fig. 4) and the reaction time improvement (reaction time in the relaxed condition minus reaction time in the effort condition, r=0.57, pb0.001, see Fig. 5).
coordinates x,y,z: 59, 32,15, superior temporal gyrus, Brodmann area 42, see Fig. 6). In the next step, a statistical analysis based on the auditory cortex region of interest (AuC-ROI) current
3.4. LORETA analysis Following earlier suggestions (Mulert et al., 2001), we performed separate LORETA analyses for the early part of the N1 (60–100-ms post-stimulus) and the late part (100–160-ms post-stimulus). For the whole group, the main activity within the early part of the N1 could be found with activations in the auditory cortex for all the three conditions (highest current source density in the same voxel with Talairach
Fig. 3. In the group with high effort increase (HEI) a negative correlation between N1 amplitude at Cz and reaction times in the effort condition was found.
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Fig. 4. The more pronounced the effort increase in the selfassessment, the more pronounced is the N1 amplitude increase between the two runs.
source density values was done. Lowest activity in the auditory cortex region of interest analysis was found in the control condition (mean current source density of 0.00244F0.00103 AA/mm2), higher activity in the relaxed condition (0.00259F0.00087 AA/mm2) and highest values in the effort condition (0.00283F 0.00104 AA/mm2). There was no significant difference between control condition and relaxed condition, but a significant difference between relaxed condition and effort condition could be found (T= 2.66, pb0.05). In the subgroup analysis, no difference (increase of the AUC-ROI current source density values in the effort run in comparison to the relaxed run) could be found in the HEI group in comparison to the LEI group (T= 1.52, p=0.14). In the late part of the N1, during all three conditions highest current source density values could be observed in the dorsal cingulate gyrus/medial frontal gyrus (Talairach coordinates x,y,z: 4, 11,50, see Fig. 7). Differences in the region of interest analysis of the dorsal cingulate gyrus/SMA (ACC/SMA-ROI) could be observed with lowest activations in the control condition (0.00438F0.00333 AA/mm 2), higher values in the relaxed condition (0.00514F 0.00367 AA/mm2) and highest values in the effort condition (0.00650F0.00436 AA/mm2). These differ-
ences between the activation patterns were highly significant (effort condition versus relaxed condition: T= 3.79, p=0.001). In the subgroup analysis, this effect (increase of the ACC/SMA-ROI current source density values in the effort run in comparison to the relaxed run) was significantly more pronounced in the HEI group than in the LEI group (T= 2.53, p=0.016). In the correlation analysis, we found a relationship between the subjective effort increase (effort score in the effort condition minus effort score in the relaxed condition) and the current source density-increase in the ACC-SMA-ROI (ACC-SMA-current source density in the effort condition minus ACC-SMA-ROIcurrent source density in the relaxed condition, r=0.44, pb0.01, see Fig. 8). There was no correlation between the subjective effort increase and the current source density increase in the AuC-ROI (r=0.26, p=0.13). However, there was a significant negative correlation between the AuC-ROI and reaction times during the relaxed condition ( 0.35, p=0.04) but not for the effort condition ( 0.81, p=0.62). Vice versa, there was a negative correlation between the ACC/ SMA-ROI and reaction times during the effort condition ( 0.33, p=0.05), but not for the relaxed condition (0.03, p=0.85).
Fig. 5. The more pronounced the effort increase in the selfassessment, the more pronounced is the reaction time difference between the two runs.
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Fig. 6. Current source density analysis of the whole group (n=35) for the first part of the N1 (60–100 ms) with similar activation patterns in all three conditions within the auditory cortex: (a) control condition, (b) relaxed condition, (c) effort condition.
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Fig. 7. Current source density analysis of the whole group (n=35) for the second part of the N1 (100–160 ms) with activation in the dorsal anterior cingulate/medial frontal gyrus. Highest activity in the ACC/SMA can be found in the effort condition: (a) control condition, (b) relaxed condition, (c) effort condition.
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3,17,29, Brodmann area 24) could be found only in the HEI group (T=5.43, pb0.05, critical T value=4.77, see Fig. 9) but not in the LEI group (highest T value=4.11). Additional activations in the HEI group were detectable in the right prefrontal cortex (middle frontal gyrus, Talairach coordinates x,y,z: 25,10,64, Brodmann area 6, T=5.16) and the right middle temporal gyrus (Talairach coordinates x,y,z: 46, 60,1, T=5.25).
4. Discussion
Fig. 8. The more pronounced the effort increase in the selfassessment, the more pronounced is the ACC-/SMA-ROI-current source density increase between the two runs.
Finally, in a voxelwise comparison between effort condition and control condition performed for the N1peak, a significant increased activity in the dorsal cingulate cortex (Talairach coordinates x ,y,z :
For the whole group, we could describe shorter reactions times, increased N1 amplitudes and increased ACC/medial frontal gyrus activations in the effort condition in comparison to the relaxed condition. In the subgroup analysis, only subjects following to the instructions closely according to their self-ratings (HEI group) were responsible for these effects. Subjects, which documented in the self-ratings that they did not spend increased effort in the effort condition (LEI group), did not show increased N1 amplitudes and in the LORETA analysis there was no significant increase in the ACC at the N1-peak timeframe.
Fig. 9. Statistical voxelwise comparison between effort condition and control condition of the high effort increase group (n=12) at the N1 peak with significant activation in the dorsal anterior cingulate cortex, Brodmann 24 (T=5.43, pb0.05, critical p-value=4.77) and the right prefrontal cortex (middle frontal gyrus, Brodmann 6, T=5.16).
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We could earlier show that in choice reaction tasks short reaction times, increased N1 amplitudes and increased ACC activity are associated as well as prolonged reaction times, low N1 amplitudes and low ACC activity. This could be observed both in healthy subjects and psychiatric patients (Mulert et al., 2001, 2003). An influence of effort to these results was suggested before (Mulert et al., 2003). The present study directly addressed this question and directly proved the influence of effort on reaction times, N1 amplitude and ACC activity. Interestingly, the self-ratings of the subjects corresponded both to their reaction times and their neurophysiological parameters. At this point, it is useful to discuss our findings in the context of different concepts related to ACC function and effort: Main concepts are related to mental effort as indexed by task difficulty, incentive motivation and arousal or physical effort: Mental effort related to task difficulty, which has been described by several authors to be related to increased ACC activity (Barch et al., 1997; Gevins et al., 1997; Numminen et al., 2004) is passive in a sense that the task is changing (whether the subject wants or not) and mental effort is not directly related to volition. This does not seem to fit our approach since in our experiment the paradigm used in the two runs was exactly the same (in terms of numbers of targets, buttons to press, frequency difference of the tones, etc.). However, one also could argue that the high effort run with the instruction to press as fast and as correct as possible is a bspeeded versionQ of the choice reaction experiment. Such a speeded version could be seen as a more difficult task. (Monetary) incentive/reward is also an external change in the conditions that might be closely related to the motivation and therefore the willful (effortful, volitional) acts of a subject. Simply spoken, a high incentive run is seen a high motivation run and increased ACC activity has been described (Shidara and Richmond, 2002; Akitsuki et al., 2003). Our approach does not involve incentive as basis for volition, since the payment of the volunteers was not related to the level of performance. Therefore, the subjective degree of motivation or volition to spend more effort is more variable across subjects. This is the basis for our distinction of a low versus high effort increase group.
In addition, an important relationship has been established in the leas years between ACC activity and arousal level during effortful cognitive and motor behaviour. Especially the studies by Critchley have shown nicely that ACC activity during effortful cognitive and motor behaviour is related to autonomic states of cardiovascular arousal (Critchley et al., 2000, 2003). Unfortunately, since we have not controlled for skin conductance/heartbeat in the present study, we cannot further follow this point with own data at this time. In the present study, we tried to investigate an active and conscious increase of effort in order to improve the performance in the task. Since we did not give any monetary incentive, there was no strong external motivation and the possibility of different internal motivation states as reported by the self-rating scores did exist more obviously and was in fact found in the present study to be correlated to both performance and neurophysiological parameters. Attentional effects on N1 waveform (Hillyard et al., 1973) and a role of the ACC in attention-to-action and evaluating the consequences of actions have been described years ago (Passingham, 1996). In parallel, the relationship of ACC activity to willful acts and volition was also described. Francis Crick proposed that the ACC is bthe seat of the willQ(Crick, 1994). It has been tried to merge these different aspects in terms of bexecutive attentionQ as described by Posner and DiGirolamo (1998). A unifying concept of volition based on decision making, voluntary action initiation and executive control was recently suggested by Zhu (2004). The present study adds some important points to the present discussion of ACC function: Most important, subjects can report the degree of effort they have actually spend during the task and this subjective rating is related to their level of performance and to their N1 amplitude/ACC activity. Therefore, it might be appropriate to use the term bconsciousQ effort here. In addition, our findings are not in line with a strict conflict monitoring hypothesis but more with concepts like the one of Paus suggesting the ACC being an interface between drive, cognition and motor control (Paus, 2001) and involved in the willed control of actions (Mesulam, 1990; Schall et al., 2002) or the idea of the ACC being involved in bconscious monitoringQ (Dehaene et al., 2003).
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At this point, it is important to mention that the ACC is not a single homogenous structure, but contains several distinct areas, each possessing a different pattern of anatomical connections (Mega and Cummings, 1997; Bush et al., 2000). The present result of a relationship between effort and ACC function was found only in the dorsal, supracallosal ACC. Our results do not at all contradict a relationship between the conflict monitoring hypothesis and other parts of the ACC, which are not activated in the present experiment. Interestingly, different brain regions determine reaction times during the different conditions: While reaction times correlated negatively with and only with the activation of the auditory cortex during the relaxed condition, things were different during the effort condition: Here, only the activity pattern of the SMA/ACC region correlated negatively with reaction times, as it has been described earlier (Naito et al., 2000). While this study was intended to describe the relationship between effort and ACC activity, we did also find some increase in the activation of the auditory cortex during the effort condition. This could be explained with early attention effects for the N1 potential in the auditory cortex as described earlier (Hillyard et al., 1973; Naatanen and Picton, 1987). N1 modulation has also been found for attentional orienting in space and an effect in secondary sensory cortex areas (V2) has been demonstrated (Mangun and Hillyard, 1991; Luck and Hillyard, 1995; Luck et al., 1997). Concerning the methods, the effects for N1 amplitudes and reaction times are free of any sophisticated methodology. However, we have also used an ERP based localisation method, LORETA, which has to deal with the so-called inverse problem and is not free of basic assumptions. Recent studies of our and other groups have demonstrated good accordance of fMRI-(BOLD)-localisations and LORETA results in simultaneous or combined EEG/fMRI studies even for more complex activation patterns like the P300-potential, with several overlapping electrical generators, in the range of 1–2 cm (Vitacco et al., 2002; Mulert et al., 2004a). Evidence for a contribution of the ACC during the N1 timeframe comes also from earlier ERP studies using dipole source analysis (Giard et al., 1994).
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In the present study, the strongest activity during the late N1 timeframe was found in the SMA during all three conditions, with weaker activity in the ACC. But in the voxelwise LORETA comparison between the effort and the relaxed condition a significant difference was found in the anterior cingulate cortex (Bordmann area 24) and not in the SMA. This result would also be in better accordance to the literature (Naito et al., 2000). However, taking the low resolution of the ERP source localisation into account, a relationship between effort and the ACC seems most likely, but other techniques with more precise localisation like, e.g., fMRI are needed in order to confirm the present results. One important result of the present study is the fact that the self-ratings of the subjects are the basis of an important distinction between subjects following well and subjects not following well to the instructions. The differences between these groups in reaction times, N1 amplitudes and ACC activity provide evidence that these self-ratings are related to the physiological processes. This result suggests that these self-ratings are a useful tool in the investigation of cognitive brain functions in healthy subjects and probably also in neuropsychiatric patients, where effort and motivation might often be a relevant co-variable: Reduced performance in cognitive tasks like choice reaction paradigms can result from disturbed ACC function associated with reduced cognitive motivation. This is probably the case in schizophrenia: several lines of evidence, including post-mortem anatomical studies, suggest ACC disturbance in this disease (Benes, 2000). However, since different levels of cognitive motivation, associated with different activation levels of the ACC, also exist within the group of healthy subjects, it might well be possible that not every finding of reduced ACC activity in psychiatric patients is related to an underlying brain pathology. ACC hypofunction then may not reflect a primary brain dysfunction but rather may reflect the degree of effort expended. For example, patients may become demoralized and unmotivated as a secondary reaction to their primary illness and circumstances. If so, then this demoralization and lack of motivation should be amenable to treatment, and when treatment leads to improved moral and motivation, then the ACC activity should normalize during cognitive tasks. Another way to
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examine whether ACC hypofunction in a psychiatric group reflects a primary immutable deficit or a secondary deficit subject to improvement with motivational enhancement, the effects of monetary incentive on task performance and ACC activity could be examined in different psychiatric groups. If the monetary incentive increases motivation and produced normal performance with normal ACC function, then it seems reasonable to postulate that the motivational impairment and ACC hypofunction are not primary to the pathophysiology of the particular psychiatric disorder. In conclusion, it is difficult to explain our results in a strict version of the conflict monitoring hypothesis: Both experimental runs have been performed with the same task and so with the same amount of conflict. Differences in the conflict per se therefore can not explain the differences in the activations of the ACC in the present study. Maybe our results could be better explained with a concept of ACC function in the detection or monitoring of (subjective) important information. According to such a concept, subjects could to some degree voluntarily and consciously influence, whether information (stimuli) is important or not. Spending more effort would then be a consequence of a subjective change in the level of importance associated with a stimulus.
Acknowledgment We like to thank Marco Congedo for the development of the bROI ExtracterQ tool and his assistance in the application to event-related potentials. Parts of this work were prepared in the context of Elisabeth Menzinger’s dissertation at the Faculty of Medicine, Ludwig-Maximilians-Universit7t, Munich.
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