The spatial and temporal dynamics of anticipatory preparation and response inhibition in task-switching

NeuroImage 51 (2010) 432–449

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The spatial and temporal dynamics of anticipatory preparation and response inhibition in task-switching S. Jamadar, M. Hughes, W.R. Fulham, P.T. Michie, F. Karayanidis ⁎ Functional Neuroimaging Laboratory, University of Newcastle, Australia Centre for Brain and Mental Health Research, University of Newcastle, Australia Schizophrenia Research Institute, Darlinghurst, Australia

a r t i c l e

i n f o

Article history: Received 19 November 2008 Revised 20 December 2009 Accepted 25 January 2010 Available online 1 February 2010

a b s t r a c t We investigated ERP and fMRI correlates of anticipatory preparation and response inhibition in a cued taskswitching paradigm with informatively cued, non-informatively cued and no-go trials. Cue-locked ERPs showed evidence for a multicomponent preparation process. An early cue-locked differential positivity was larger for informative vs. non-informative cues and its amplitude correlated with differential activity for informatively vs. non-informatively cued trials in the dorsolateral prefrontal cortex (DLPFC), consistent with a goal activation process. A later differential positivity was larger for informatively cued switch vs. repeat trials and its amplitude correlated with informatively cued switch vs. repeat activity in the posterior parietal cortex (PPC), compatible with a category-response (C-R) rule activation process. No-go trials elicited a frontal P3, whose amplitude was negatively correlated with activity in the ventrolateral prefrontal cortex (VLPFC) and basal ganglia motor network, suggesting that a network responsible for response execution was inhibited in the course of a no-go trial. These findings indicate that anticipatory preparation in taskswitching is comprised of at least two processes: goal activation and C-R rule activation. They also support a functional dissociation between DLPFC and VLPFC, with the former involved in top-down biasing and the latter involved in response inhibition. © 2010 Elsevier Inc. All rights reserved.

Task-switching paradigms require rapid alternation between two or more simple tasks. Reaction time (RT) is longer on trials that require a task switch relative to a task repeat. This switch cost decreases with increasing preparation interval, which is determined by the response-stimulus interval (RSI) in alternating runs paradigms (e.g. Karayanidis et al., 2003; Rogers and Monsell, 1995) and the cuestimulus interval (CSI) in cued trials paradigms (e.g. Meiran, 2000; Nicholson et al., 2005). The reduction in switch cost with increasing preparation interval has been attributed, at least partly, to anticipatory preparation processes (Karayanidis et al., 2003; Koch and Allport, 2006; Meiran et al., 2000; Monsell, 2003; Nicholson et al., 2005; Rogers and Monsell, 1995; Rubinstein et al., 2001). However, even with very long preparation intervals, a residual switch cost remains, suggesting that anticipatory preparation is not sufficient to fully override the behavioral cost of a switch in task. Rogers and Monsell (1995) invoke the concept of a task-set, a set of rules that link and configure perceptual-motor processes specific to that task. In order to achieve flexible goal-directed behavior, task-sets must be dynamically reconfigured as task demands change. This

⁎ Corresponding author. School of Psychology, University of Newcastle, NSW 2308, Australia. Fax: +612 4921 6980. E-mail address: [email protected] (F. Karayanidis). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.01.090

dynamic reconfiguration process may involve shifting attention between perceptual and conceptual elements, retrieving goals and condition-action rules, activation of relevant task-sets and inhibition of irrelevant task-sets (Monsell, 2003). They conceptualize task-set reconfiguration as a two-stage process. Given advance information and sufficient time, anticipatory task-set reconfiguration occurs in the preparation interval and facilitates performance by biasing the system towards the currently relevant task-set, thereby bringing the system into a state of task readiness prior to stimulus onset. Stimulustriggered reconfiguration accounts for the residual switch cost which remains even at long preparation intervals suggesting that the system cannot be fully reconfigured for a task switch before stimulus onset (Rogers & Monsell). Alternatively, anticipatory task-set reconfiguration has been conceptualized as an all-or-none process and the residual switch cost may either occur as a result of occasional reconfiguration failure (De Jong, 2000) or arise from stimulus-driven positive stimulus-response (S-R) priming for repeat trials and negative S-R priming for switch trials (e.g., Wylie and Allport, 2000). Later models have continued to espouse a two-component account of task switching. For example, Rubinstein et al. (2001) argue that dynamic switching between tasks is facilitated by executive control processes that involve two stages, goal-shifting and rule activation. Goal-shifting updates the contents of working memory, actively ‘inserting’ and ‘deleting’ task-goals as necessary. Rule

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activation biases the activation levels of the currently relevant taskset and inhibits the activation level of the irrelevant task-set. Rubinstein et al. argue that goal-shifting is an endogenous control process that can be performed prior to stimulus onset, whereas rule activation is an exogenous control process that is initiated after stimulus identification, thus accounting for the residual switch cost. Event-related potential (ERP) studies support multi-component models of task switching and task-set reconfiguration. ERP waveforms time-locked to the beginning of the preparatory interval (RSI/CSI) show a larger posterior positivity for switch as compared to repeat trials, an effect that has been observed in many paradigms and across stimulus modalities (e.g., Goffaux et al., 2006; Karayanidis et al., 2003; Kieffaber and Hetrick, 2005; Miniussi et al., 2005; Nicholson et al., 2005; Nicholson et al., 2006a,b; Poulsen et al., 2005; Rushworth et al., 2002, 2005; Swainson et al., 2006). While this effect has been replicated across a broad range of studies, it has been inconsistently labeled in the literature. Some refer to it as an increased P3b for switch relative to repeat trials (e.g., Goffaux et al., 2006; Kieffaber and Hetrick, 2005; Poulsen et al., 2005), others call it a late parietal positivity for switch trials (e.g., Astle et al., 2006, 2008; Swainson et al., 2006). We have used the label ‘differential switch-positivity’ or ‘switch-positivity’ (Karayanidis et al., 2003; Nicholson et al., 2005, 2006a,b) because it is superimposed over a positive posterior complex in cued paradigms and over a posterior negative complex in alternating-runs paradigms and has not been empirically shown to be associated with a P3 component. This differential switch-positivity emerges as early as 150 ms after cue onset and varies in duration depending on cue and task parameters. With long preparation intervals, it resolves fully prior to stimulus onset, whereas with short preparation intervals, it can be seen after stimulus onset (Karayanidis et al., 2003; Nicholson et al., 2005, 2006b; but see Jamadar et al., in press). Lavric et al. (2008) showed that a switch positivity with similar time course and cortical sources can precede and partially overlap with the switch negativity in the post-stimulus interval even on trials with long preparation intervals, suggesting that it is not the duration of the preparation interval per se, but whether preparation is completed prior to stimulus onset that determines whether a switch positivity is observed before or after stimulus onset. With semi-specific task cues, this component only develops fully after task-specific information becomes available (Nicholson et al., 2005, 2006b). Some studies have reported that individual differences in switch-positivity amplitude are related to behavioral indices of preparation (Kieffaber and Hetrick, 1995; Lavric et al., 2008; but see Swainson et al., 2006). This pattern of results suggests that the switchpositivity may index anticipatory task-set reconfiguration. Note however that, although this positive deflection is consistently larger for switch trials, in some studies it is also evident in repeat trial waveforms (Goffaux et al., 2006; Karayanidis et al., 2003; Kieffaber and Hetrick, 2005; Nicholson et al., 2005), suggesting that the same processes associated with this positivity may be activated for both trial types albeit to a different degree. Other electrophysiological components have also been found to vary within the CSI as a function of task-switching, however, they are not observed as consistently. For example, an early frontal cue-locked positivity emerging around 150– 200 ms has been reported by Lavric et al. (2008), Astle et al. (2008), Rushworth et al. (2002, 2005) and Tieges et al. (2006) and a later frontal cue-locked switch-negativity has been reported by Astle et al. (2006, 2008), Lavric et al. (2008), Mueller et al. (2007) and Rushworth et al. (2005). Lavric et al. (2008) conducted a PCA on cue-locked ERP waveforms and concluded that the frontal switch-negativity is highly interdependent with the posterior switch-positivity. This suggests that the frontal effect may be the negative portion of a dipolar distribution and the posterior effect may be the positive portion. This could explain why certain studies that do not record/analyze frontalpolar channels may not detect the frontal negativity; a slight anterior shift in the source can also make the negative portion undetectable

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over the scalp. Alternatively, it is possible that the presence or absence of the late frontal switch-negativity (LFN) depends on the reference used in the recording. Many studies that have reported the LFN have used a common average reference (e.g., Astle et al., 2006, 2008; Lavric et al., 2008; Mueller et al., 2007) whereas most studies that employ a linked mastoids reference do not report a LFN (Goffaux et al., 2006; Karayanidis et al., 2003; Nicholson et al., 2005, 2006a,b; Miniussi et al., 2005). However, there are exceptions to this pattern. Rushworth et al. (2002, 2005) reported a significant LFN with linked mastoids reference but only for attentional set switching, whereas Poulsen et al. (2005) and Swainson et al. (2006) used a common average reference and did not find a significant LFN in the cue-locked epoch. Thus further research is required to elucidate the conditions under which the LFN in observed. Stimulus-locked ERPs for switch trials show a smaller positivity than repeat trials especially over central and parietal scalp (e.g., Karayanidis et al., 2003; Kieffaber and Hetrick, 2005; Miniussi et al., 2005; Nicholson et al., 2005; Poulsen et al., 2005). We refer to this as the stimulus-locked differential switch-negativity. At long preparation intervals, the differential switch-negativity emerges as early as 150 ms and peaks around 400–500 ms after stimulus onset (e.g., Goffaux et al., 2006; Karayanidis et al., 2003; Kieffaber and Hetrick, 2005; Nicholson et al., 2005; Poulsen et al., 2005; Rushworth et al., 2002, 2005; Swainson et al., 2006), whereas at short preparation intervals, it is delayed by more than 300 ms and is often preceded by a switch-positivity (e.g., Karayanidis et al., 2003; Nicholson et al., 2005). The cue-locked switch-positivity and stimulus-locked switchnegativity are consistent with multi-component models of task-set reconfiguration (Rogers and Monsell, 1995; Rubinstein et al., 2001) with the former indexing anticipatory task-set reconfiguration processes and the latter reflecting stimulus-triggered processes that complete task-set reconfiguration (Karayanidis et al., 2003). Alternatively, the stimulus-locked switch-negativity may index processes involved in resolving interference arising after stimulus onset. Allport et al. (1994),Allport and Wylie (2000),Waszak et al. (2003), andWylie and Allport (2000) argue that the magnitude of the residual switch cost reflects the combined effects of increased across-task interference on switch trials and facilitation of task-set activation on repeat trials. The switch-negativity may reflect processes affected by greater difficulty of rule implementation and more S-R interference for switch as compared to repeat trials (Nicholson et al., 2005). This is consistent with the finding that the differential switch-negativity is smaller for univalent stimuli that are unequivocally mapped to a single task as compared to bivalent stimuli that possess features relevant for both tasks (Karayanidis et al., 2003). Most recent models of task-switching acknowledge the role of both anticipatory reconfiguration and interference processes on the magnitude of the switch cost (e.g. Koch and Allport, 2006; Meiran, 2000; Meiran et al., 2000). Although most researchers now concede that some form of anticipatory task-set reconfiguration is involved in the reduction of RT switch cost with increasing preparation interval, the exact nature of the processes contributing to this effect remains to be defined. There is some evidence that anticipatory task-set reconfiguration is comprised of a number of sub-processes. Nicholson et al. (2006b) reported evidence for two switch-related positivities. Fully informative switch cues (i.e., switch-to cues) that validly defined the upcoming task were associated with two cue-locked differential switch-positivities that emerged and resolved within the cuestimulus interval. Partially informative cues that signaled an upcoming switch trial but did not define which task-set will be relevant (i.e., switch-away cues that signaled task transition but not task identity) were also associated with two positivities, but one was time locked to cue onset and the other to stimulus onset. The first cue-locked switchpositivity emerged synchronously to that for fully informative cues, but had a much shorter duration. The second switch-positivity was locked to the onset of the stimulus that provided task identity

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information. It was argued that the early switch-positivity may index disengagement or cognitive inhibition of the irrelevant task-set whereas the later switch-positivity that was cue-locked for fully informative cues and target-locked for non-specific switch cues may reflect activation of the relevant task rules (see also Karayanidis et al., 2009). Other studies also show evidence for multiple cue-locked positivities. Using principal components analysis (PCA), Kieffaber and Hetrick (2005) reported that the cue-locked positivity (which they label as a P3b effect) consists of two components: an early component for both switch and repeat trials in mixed task blocks relative to trials in single task blocks (P3b1 effect) and a later component for switch relative to repeat trials in the mixed task block (P3b2 effect). That is, the early component emerged on switch blocks as compared to single task blocks and the second differentiated between switch and repeat trials in the switch block. In a recent study, Jamadar et al. (2009) also identified an early cuelocked positivity that was evoked by fully informative switch or repeat cues but not by non-informative cues (i.e., cues that signaled the timing of an upcoming stimulus but did not specify whether it would require repeating or switching; see also Swainson et al., 2006) and a later cue-locked positivity that differentiated between informative switch and informative repeat cues. It was argued that the early positivity for informative relative to non-informative cues indexes a process of goal activation (e.g., Rubinstein et al., 2001; Koch and Philipp, 2005; Schuch and Koch, 2003) that may include goal setting (i.e., do the letter task) and orienting of selective attention (i.e., attend to letters, ignore numbers). The second switch-specific differential positivity was argued to reflect a process of categoryresponse (C-R) rule activation i.e., the reloading the now relevant task-set rules prior to stimulus onset so as to maximize performance on the upcoming trial. This process was envisaged as being similar to Rubinstein's concept of C-R rule activation with one important difference. While Rubinstein et al. (2001) argued that C-R rule activation occurs after stimulus identification, and therefore after stimulus onset, the timing of this late differential switch-specific positivity suggests that given valid cueing and sufficient time, C-R rule activation can be completed before stimulus onset, and is therefore not necessarily an exogenous process. Note that we adopt the term ‘C-R rule’ over stimulus-response or ‘S-R rule’ to indicate that an informative cue provides information about the identity of the relevant task (e.g., ‘letter’, ‘digit’) rather than the identity of the specific stimulus or stimulus type (e.g., A or odd). This information can lead to activation of C-R rules that bind categories to their responses (e.g. “letter task: left is vowel, right is consonant”) but not the S-R rules that bind a specific stimulus to a specific response (e.g. “A is a vowel, press left”). This process of C-R rule activation is necessary only on switch trials, which, by definition, require activation of a different set of task-specific C-R rules than the previous trial, however, can also be activated on some proportion of repeat trials (e.g., following a lapse in attention). Whether it is activated before or after stimulus onset depends on the information provided by the cue. Thus, goal activation and C-R rule activation are conceptualized as constituent processes of anticipatory task-set reconfiguration within Rogers and Monsell's (1995) framework. This represents a departure from Rubinstein et al.'s (2001) model which posits that C-R rule activation is initiated only after stimulus identification. In Jamadar et al. (2009), we use the term C-R rule implementation to refer to the set of decision- and response-related processes that can only occur after stimulus identification and involve mapping the specific stimulus to a category, as well as selection and activation of the appropriate response (‘A is a vowel, press left’; i.e., those processes subsumed under the S-R rule label referred to above). Even with fully completed anticipatory task-set reconfiguration, C-R rule implementation may require greater activation of conflict monitoring processes or greater

attentional resources for switch than repeat trials, as the latter would invoke less interference from the irrelevant stimulus dimension than switch trials. Functional magnetic resonance imaging (fMRI) studies of taskswitching have suggested that reconfiguration may be subserved by top-down biasing mechanisms (Barber and Carter, 2005; Brass and von Cramon, 2004; Loose et al., 2006; Luks et al., 2002; Ruge et al., 2005; Wylie et al., 2006). Goal-directed behavior across a range of tasks, including task-switching (e.g. Fassbender et al., 2006; Hester et al., 2007; Ruge et al., 2005) involves activation of a dorsolateral prefrontal cortex (DLPFC)/posterior parietal cortex (PPC) network. Recent studies suggest that the DLPFC may be the locus of the attentional top-down biasing signal. This region is involved in taskrelevant cortical amplification during preparation in a number of paradigms, including the task-switching paradigm (Luks et al., 2002; Liston et al., 2006; Sohn et al., 2000), and may achieve goal-directed behavior by biasing task-relevant S-R rules ‘held’ in the PPC (Andersen, 1987; Andersen and Buneo, 2002). This distinction was neatly demonstrated by Bunge et al. (2002) who showed that the DLPFC was exclusively activated by the need to select between possible responses, whereas the PPC was activated by the need to maintain a representation of available responses. A functional dissociation between dorsolateral and ventrolateral aspects of the PFC may also exist. Although both DLPFC and ventrolateral PFC (VLPFC) have been implicated in response inhibition, it is the latter that appears to be critical for this process (Aron et al., 2003, 2004; Aron and Poldrack, 2006; Aron et al., 2007; Aron, 2007; Blasi et al., 2006; Ridderinkhof et al., 2004). VLPFC (often highly right-lateralized) is consistently activated in go/no-go, stop-signal, distractor suppression and negative-priming tasks, as well as in tasks that require cognitive inhibition of working memory retrieval (Aron et al., 2004). A functional dissociation may therefore exist between the DLPFC and VLPFC with the DLPFC linked to both top-down biasing and inhibition and the VLPFC uniquely linked to inhibition. To our knowledge this possible functional dissociation has not been tested within a single paradigm. Electrophysiological and hemodynamic measures provide complementary information: the excellent temporal resolution of ERPs provides insight into the temporal dynamics of an effect, whereas the outstanding spatial resolution of fMRI offers a window into the neuroanatomical correlates of an effect. While each measure possesses unique advantages, the combination of these modalities within a single paradigm can overcome the inherent disadvantages of each and offer an enriched perspective that is unobtainable from each methodology alone (see Dale and Halgren, 2001; Gore et al., 2006). This study applies behavioral, ERP and fMRI methodologies to examine the role of the frontoparietal network and associated cortical and subcortical structures in processes that underlie performance in a combined task-switching/no-go paradigm. In a cued-trials paradigm, participants switched randomly between three equiprobable and randomized conditions presented with a fixed long CSI. Informatively cued trials consisted of an informative task cue followed by a stimulus that was validly mapped to a response. Non-informatively cued trials included a neutral cue that indicated the timing of the upcoming stimulus but not the identity of the relevant task and was followed by a stimulus which both defined the relevant task and was validly mapped to a response. No-go trials included an informative task cue identical to that for informatively cued trials, but followed by a neutral stimulus that was not mapped to any response. The full report of ERP findings can be found in Jamadar et al. (2009). As reported above, cue-locked ERP waveforms resulted in an early differential positivity for informatively relative to non-informatively cued trials that was interpreted as relating to goal activation, followed by a later differential positivity for informatively cued switch as compared to repeat cues that was interpreted as indexing C-R rule activation. This framework will be

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used in the current study which examines the relationship between the behavioral and ERP data presented by Jamadar et al. (2009) and BOLD-fMRI data recorded with the same paradigm in a subset of the participants in a separate testing session. We will also use a multiple regression analysis similar to that employed by Forstmann, van den Wildenberg, and Ridderinkhof (2008) to examine the relationship of BOLD signal to behavioral and ERP indices of multiple sub-component processes of task-switching. These correlational analyses take advantage of individual variability in parameters that quantify theoretical cognitive constructs and model the BOLD signal representing these component processes. Correlations between ERP and fMRI data may be used to add a temporal dimension to regional fMRI activation data, whereas correlations between behavioral and fMRI data can inform us on the nature of processes that directly affect the magnitude of behavioral effects, such as motoric processing and top-down influences on behavior. Forstmann et al. (2008) employed a theory-driven approach to examine the neural mechanisms underpinning individual differences in response capture and response inhibition in the Simon Task. RT distribution parameters related to the effects of interest were entered as covariates in the fMRI general linear model (GLM) and suggested that activation in the right inferior frontal cortex covaried with response inhibition and activation in the pre-SMA covaried with response capture. Analogous approaches have been taken in the stop-signal task (Aron et al. 2007; Aron and Poldrack, 2006; Chao et al., 2009) and in reward processing tasks (Haruno and Kawato, 2006). We will adopt a similar approach here to test hypotheses about the brain regions associated with individual differences in behavioral and ERP indices of processes involved in task-switching. We have previously argued that the magnitude of the RT switch cost in the current paradigm is proportional to the ease of response implementation (Jamadar et al., 2009). Thus, we expect the RT switch cost to be related to activity in premotor areas that subserve goaldirected motor response generation. Further, the RT difference between informative and non-informative trials should be reflected in areas necessary for anticipatory preparation. As this is an RT based measure, it is plausible that activity specifically related to the preparation of motor sets may be implicated. Informatively and non-informatively cued trials involve the same final outcome but differ in that informatively cued trials provide an opportunity for anticipatory goal activation and C-R rule activation before stimulus onset whereas non-informatively cued trials do not. Given our hypothesis that the early cue-locked differential positivity indexes goal activation and prior evidence that DLPFC is associated with the top-down biasing signal in goal-directed behavior, we expect that the amplitude of the early cue-locked differential positivity will be positively correlated with differential fMRI activation in the DLPFC. Furthermore, given the role of the PPC in maintaining task rules and our hypothesis that late cue-locked differential positivity indexes C-R rule activation, we expect that the amplitude of the late cue-locked differential positivity will be positively correlated with differential activation in the PPC. Informatively cued and no-go trials are identical during the cuestimulus interval and both elicit cue-locked ERP waveforms associated with anticipatory processing. However, whereas informatively cued trials require implementation of the activated C-R rule after stimulus onset, the stimuli presented on no-go trials are not mapped to either a left or right hand response. Hence, the C-R rule activated during the cue-stimulus interval cannot be implemented. Given the DLPFC/VLPFC distinction discussed above, we expect that the DLPFC will be activated in both conditions, whereas the VLPFC will be activated only on no-go trials. No-go stimuli elicit a frontocentral P3 that is argued to represent inhibition processes (Falkenstein et al., 2002; Smith et al., 2006, 2008). We expect that no-go P3 amplitude will be positively correlated with fMRI activity in the VLPFC.

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Materials and methods Participants Eighteen of the original sample of twenty-four young adults whose data are reported in Jamadar et al. (2009) participated in both EEG and fMRI sessions (mean age 25 ± 7 years, 11 female). All were right handed, had no prior exposure to the current paradigm and gave written informed consent. Tasks and stimuli The task was programmed in Presentation (Neurobehavioral Systems). A square box was outlined in gray (100 × 100 pixels) and presented against a black background on a computer monitor for the duration of the experiment. Each trial began with the presentation of an informative or non-informative cue (fixation cross 40 × 40 pixels), which remained on the screen for the entire cue-stimulus interval of 700 ms and was replaced by the stimulus. Non-informative cues were presented in gray whereas informative cues were presented in one of four ‘hot’ (red, pink, orange, burgundy) and four ‘cold’ (dark blue, green, sky blue, turquoise) colors that were mapped to one of the two tasks. Cue color was never repeated on successive trials to eliminate any confounding effect of cue identity repetition (Logan and Bundesen, 2003; note, however, that mediator cue effects could still be operating i.e., hot/cold or letter/digit). On informatively cued and no-go trials, an informative cue was presented in one of the above colors, and the stimulus was white. As the stimulus replaced the cue and carried no task information, participants were required to use every informative cue to prepare for the upcoming stimulus (Verbruggen et al., 2007). On non-informatively cued trials, the non-informative cue was replaced by a stimulus in one of the above colors. As one third of non-informatively cued trials were followed by another non-informatively cued trial, it could be argued that repetition of the gray fixation cross could have led to a cue-repetition benefit (Logan and Bundesen, 2003) for these non-informative/noninformative sequences as compared to informative/non-informative and no-go/non-informative sequences.1 However, recall that the noninformative cue carried no task-relevant information. Furthermore, the cue-locked ERP waveforms did not differ between these three sequences (Jamadar et al., 2009), suggesting that there was no cuerepetition benefit in non-informative/non-informative sequences. On informatively cued and non-informatively cued trials, the stimulus was a letter-digit pair (such as ‘A4’) presented in 12pt SimSun font (viewed from approximately 90 cm; visual angle 1°). The position of the letter and digit in the pair was randomized between trials (i.e. the stimulus could be ‘A4’ or ‘4A’). On each trial, participants responded to either the letter or the digit. For the letter task, participants classified the letter as vowel (A, E, I, U) or consonant (G, K, M, R). For the digit task, they classified the digit as odd (3, 5, 7, 9) or even (2, 4, 6, 8). Participants used their left and right index fingers to respond. Stimulus-response mapping and cue-task mapping were counterbalanced across participants. Only incongruent character pairs (letter–digit combinations) were presented, that is the task-irrelevant character was always mapped to a response with the other hand (e.g., A4: even and vowel mapped to the different hands). On no-go trials, the stimulus was two non-alphanumeric characters that were not mapped to either task and were selected from a set of four exemplars (#, $, %, &). Thus, an overt response was required on informatively cued and non-informatively cued trials, but not on no-go trials. The stimulus onset asynchrony (SOA; cue to cue) varied between 2.5 and 7 s in an exponential distribution, with a mean of 3.5 s. Each trial began with the cue that was presented for 700 ms and replaced

1

We thank an anonymous reviewer for this suggestion.

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by the stimulus. On informatively cued and non-informatively cued trials, the stimulus was removed at response onset. The response was followed 300 ms later by one of two feedback tones (correct: 1000 Hz tone; incorrect: Microsoft Windows ‘dong’). The response-cue interval was determined by the SOA on that particular trial after subtracting the CSI, RT and feedback interval. On no-go trials, a response led to an ‘incorrect’ feedback tone. When a response was correctly withheld, a ‘correct’ feedback tone was presented 300 ms prior to the cue for the subsequent trial. The three conditions (informatively cued, non-informatively cued and no-go) were equiprobable and were presented with overall switch trial probability of 33% (across informatively cued, noninformatively cued and no-go trials) in a pseudorandom sequence that included no more than three consecutive trials of the same trial type (switch, repeat) and no more than four consecutive trials of the same condition (informatively cued, non-informatively cued, no-go). As the informative cue on no-go trials signaled preparation for either a repeat or a switch trial, cue-locked activity for no-go trials could also be classified as an informative ‘switch’ or ‘repeat’ cue type, even though the stimulus was not mapped to either task and no response was elicited. Procedure Participants completed three testing sessions. The first session was a task practice session, and included training initially on each task alone and then in switching between the tasks with the informative cue. The other two sessions included testing with either ERP or fMRI data acquisition. All except one participant completed the ERP testing in the second session that was scheduled no more than a week after session 1. Session 2 began with further practice on the informatively cued condition, and training on the non-informatively cued and no-go conditions. Thus in the second training session, participants were first exposed to each condition (informatively cued, non-informatively cued, no-go) in a blocked design. This was followed by a final run of training with randomized presentation of the three conditions that was identical to the sequences used on fMRI and ERP recording sessions. Across both practice sessions, participants completed a total of 750 training trials before testing. Participants were instructed to respond as quickly as possible while maintaining a high level of accuracy. Following each block, RT and percent accuracy feedback was displayed and participants were encouraged to monitor and improve their performance. EEG session Participants sat upright and visual stimuli were presented on a computer monitor, viewed from approximately 90 cm (visual angle 1°). Each participant completed a total 120 switch and 240 repeat trials within each of the three conditions (informatively cued, non-informatively cued, no-go; total 1080 trials; approx. 60 min recording time). The sequence was divided into six blocks of 180 trials and each block was followed by a brief rest. fMRI session Participants lay supine in the scanner bore, holding a response device in each hand. Visual stimuli were back projected onto a screen positioned approximately 2 m from the scanner bore entrance. Subjects viewed the stimuli through a mirror mounted on the head coil (maximal horizontal and vertical extent on screen: 80 × 30 cm). Tones were delivered binaurally through MRI compatible piezoelectric headphones. Responses and scanner TTL pulses were passed through an in-house developed ‘response box’, which was connected to a laptop computer. Responses, stimulus timing and slice acquisition (TTL pulses) were recorded. Each participant completed a total 60

switch and 120 repeat trials within each of the three conditions (informatively cued, non-informatively cued, no-go; total 540 trials; average SOA = 3.5 s; approx. 30 min recording time). The sequence was divided into three blocks of 180 trials and each block was followed by a brief rest. Data analysis The first two trials of every block, trials associated with an incorrect response, trials immediately following an incorrect response, and trials with a response occurring outside a 200–2000 ms window after stimulus onset were excluded from behavioral, ERP and fMRI analyses. This resulted in a 7% reduction in ERP data and 12% reduction in fMRI data. The EEG data of three participants contained high levels of noise and were excluded from further analysis. The fMRI data of four participants had to be removed, one due to data corruption, another due to ghosting artifact in the functional images, and a further two due to high levels of motion artifact. Fifteen participants from the final sample had both EEG and fMRI data. For both behavioral and ERP analyses, degrees of freedom for factors with more than two levels were adjusted using Greenhouse–Geisser correction for the violation of the assumption of sphericity (Vasey and Thayer, 1987). Behavioral data analysis RT data are presented for informatively cued and non-informatively cued trials. RT data were analyzed with a 2 session (ERP, fMRI) × 2 cue type (informative, non-informative) × 2 trial type (repeat, switch) × 2 task (letter, digit) within subjects ANOVA. There was no effect of task. There was a marginally significant effect of session (F(1,40) = 5.38, p = .026) reflecting a generalized response slowing in the fMRI session (Koch et al., 2003). Analysis for proportion error data were run on arc sine transformed scores to normalize the distribution and analyzed with a 2 session (ERP, fMRI) × 2 cue type (informative, non-informative) × 2 trial type (repeat, switch) within subjects ANOVA. There was no main effect of session (p N .10). As the ERP results have been reported previously (Jamadar et al., 2009) and session did not interact with any other factor, behavioral data are presented for the fMRI session only. EEG recording and data analysis EEG was recorded using a Quik-cap from 62 scalp electrodes referenced to the nose electrode. Vertical and horizontal EOG were recorded via electrodes positioned above and below the left eye, and on the outer canthi of each eye, respectively. EEG and EOG were continuously sampled at 500 Hz/channel on a Synamps 1 system (Neuroscan) with a bandpass of 0.01–30 Hz using a 50 Hz notch filter. Vertical eyeblink artifact was corrected in the continuous EEG files using the algorithm developed by Semlitsch et al. (1986) as implemented by Neuroscan software. These files were visually inspected and sections of EEG contaminated with channel saturation or noise were excluded from further analysis. Continuous EEG files were re-referenced to the average of left and right mastoids to be consistent with previous methods in our laboratory (e.g. Karayanidis et al. 2003). Cue- and stimulus-locked averages were created by extracting 1400 ms epochs around the onset of the cue/stimulus (−200 to 1200 ms) for switch and repeat trials for each sequence. A −50 to 50 ms baseline was employed to account for the shifting precue and pre-stimulus baseline apparent in these waveforms (see also Karayanidis et al., 2003). Mean amplitude windows were extracted using EEGDisplay software (Fulham, 2005). Although ERPs are shown at single electrode sites only, recording from a large montage allows us to make inferences regarding differences in scalp topographies between experimental conditions. An ERP component that shows a similar amplitude and duration but different scalp topography

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between conditions is likely to have been generated by distinct neuronal populations (Otten and Rugg, 2005). Mean amplitudes were extracted for the early cue-locked differential positivity (300–400 ms post-cue, Pz); late cue-locked differential positivity (450–550 ms post-cue, Pz); the stimulus-locked differential negativity (450–550 ms post-stimulus, Pz); and the no-go P3 (350–450 ms post-stimulus, Fz; see Jamadar et al., 2009 for details on epoch selection). MR image acquisition and data analysis Magnetic resonance images were acquired using a Siemens Vision 1.5 T whole-body MR scanner equipped with a Siemens quadrature head coil. Anatomical images were collected using a T1-weighted MPRAGE protocol (TR = 9.7 ms, flip angle = 12°, 224 × 256 matrix, FOV = 250 mm, voxel size = 1 × 1 × 1 mm, 164 slices). Functional images were acquired using a T2⁎-weighted echo planar imaging (EPI) sequence (4 mm slice thickness, 32 slices, TR = 3.839, TE = 70 ms, flip angle = 90°, FOV = 256 mm, 64 × 64 matrix, voxel size = 4 × 4 × 4 mm, 170 scans per run). EPIs were acquired as interleaved slices (no gap) beginning at the top of the head and positioned according to the anterior–posterior commissural line, maximizing brain volume imaged. Image preprocessing and subsequent statistical analyses were performed using SPM2 (Wellcome Department of Neurology, London). The first five images from each imaging run were discarded to allow for T1 saturation effects. Differences in EPI slice acquisition timing were corrected using the central slice as reference. Imaging time series were then realigned to the first EPI image and a mean realigned EPI image was created. Motion was corrected using a rigid-body rotation and translation correction and unwarping (Andersson et al., 2001). Each participant's T1 image was coregistered to the mean image normalized to the T1 template provided with SPM2. The parameters from this transformation were then applied to all EPI images. Accuracy of registration between functional and structural data was assessed by visual inspection of the overlay of each individual subjects’ mean EPI and T1 image. Normalized EPIs were then smoothed with a 10 mm FWHM Gaussian kernel. Slice and volume artifact correction occurred prior to slice-time correction, and spatial smoothing, respectively (Mazaika et al., 2005). Experimental blocks with more than 5% corrected slices were removed from further analysis; resulting in an 8% reduction in data. Slices and volumes deviating in intensity by ±3 SD from the individual mean were removed from the time-series by creating a weighted vector that was included in the model as a covariate of no interest. fMRI data were analyzed at three levels. For first level analyses, each subject was modeled independently. Prior to model estimation, all images were globally scaled and time series were filtered to remove low frequency signals (b60 s). fMRI time series were analyzed by fitting a convolved canonical hemodynamic response function (HRF) and its temporal derivative (Josephs et al., 1997) to the onset of the fixation cross for informatively cued, non-informatively cued and no-go switch and repeat trials, separately.2 Each block was modeled separately. Switch and repeat trials were modeled separately for each condition and trials associated with errors were modeled as a separate factor resulting in seven experimental regressors for each block (informatively cued switch, informatively cued repeat, noninformatively cued switch, non-informatively cued repeat, no-go 2 We also ran a second model that was time-locked to stimulus onset and produced results highly compatible with the cue-locked model. Thus, within the current paradigm, the fMRI data represent the entire trial (over cue- and stimulus/responseperiods), whereas the ERP data can dissociate between cue- and stimulus-locked activity. A further model was also calculated in which switch and repeat trials for each condition were modeled separately for each task. Consistent with the behavioral results, no significant differences between the tasks were observed in either the letterNnumber or numberNletter contrasts and therefore this model is not discussed further.

437

switch, no-go repeat, errors). At cue onset, informatively cued and nogo trials are identical. Therefore comparison of these two trials reveals activity associated with post-stimulus processes related to go and no-go performance. Three experimental contrasts of interest were created: informatively cued switch N informatively cued repeat (effects of taskswitching), informatively cued N non-informatively cued (performance with vs. without advance preparation) and no-go N informatively cued go (withholding a response following advance preparation to switch or repeat). The non-informative switch b/N non-informative repeat contrasts produced no suprathreshold voxels (see Discussion). Second level analysis consisted of a random effects analysis across subjects. Contrast images from all participants were submitted to one sample t-tests. Contrasts were thresholded at p b .001 (uncorrected), followed by a cluster-wise threshold of p b .05 (corrected for multiple comparisons, K = 11 contiguous voxels). Anatomical loci were determined by converting cluster maxima to Talairach space (Talairach and Tournox, 1988; http://imaging.mrc-cbu.cam.ac.uk/ imaging/MniTalairach) and entering these coordinates into the Talairach Daemon (Lancaster et al., 2000). This output was crosschecked using the atlases of Talairach and Tournox (1988) and Damasio (2005). The third level analysis examined the relationship between RTfMRI and ERP-fMRI outcomes and consisted of three multiple regressions in which the respective contrast values were entered into the regressions with the aforementioned behavioral or ERP measure as covariates. The first analysis examined the relationship between the informatively cued N non-informatively cued contrast and both the informatively cued minus non-informatively cued RT difference (RT informative-non-informative difference) and the early cue-locked differential positivity ERP amplitude. The second analysis examined the relationship between the informatively cued switch N repeat contrast and both the RT switch cost and the late cue-locked differential positivity ERP amplitude. The final regression analysis examined the relationship between the no-go N go (informatively cued) contrast and the no-go minus go (informatively cued) P3 difference waveform. Pearson correlation values were calculated for the peak of activity using the VBM toolbox (http://dbm.neuro.unijena.de/vbm/) and are reported for each of the multiple regressions in Table 4. Similar to the fMRI contrast analyses, these analyses were thresholded at p b .001 (uncorrected), followed by a cluster-wise threshold of p b .05 (corrected for multiple comparisons, K = 11 contiguous voxels). ROIs were defined by the functional clusters activated in these multiple regression analyses. For each ROI, scatterplots were generated to illustrate the correlation between the mean value of the contrast-of-interest and the RT or ERP measures. Contrast values were extracted using MarsBaR (Brett et al., 2002). Finally, we employed dipole modeling (as implemented in BESA software) to determine if the locations of fMRI activation correlated with specific ERP activity are plausible sources of that ERP component. Dipole modeling was performed using a 4-shell ellipsoidal head model. Dipole seed locations were derived from the peak of activity in ERP-fMRI correlations. Seeded dipoles were chosen on the basis of apriori hypotheses of the expected generators of the ERP effect. Dipoles were not seeded in subcortical and cerebellar areas as activity generated in regions contributes little to scalp-recorded ERPs (e.g., Luck, 2005). Regional dipoles were fitted to the group-averaged ERP components in the latency window of interest (early cue-locked differential positivity: 300–400; late cue-locked differential positivity: 450–550; no-go P3: 350–450) by adjusting the dipole's strength and orientation, but not its location. The solutions were then applied to each individual's data set to create source waveforms for each dipole for each subject. A grand average source waveform was calculated for the strongest orientation for each dipole. Point-by-point t-tests (Guthrie and Buchwald, 1991) with α = .05 and an autocorrelation coefficient of 0.9 were used to determine significant deviation of each source waveform from baseline. Significant deviance from baseline

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around the interval of interest for each ERP component was interpreted as indicating that dipole as a likely generator of the ERP effect. Results Behavioral data RT for switch and repeat trials following informative and noninformative cues are shown in Fig. 1A (left). RT was longer for noninformatively than informatively cued trials (F(1,14) = 46.85, p b .001) and for switch than repeat trials (F(1,14) = 7.67, p = .015). RT switch cost was larger for non-informatively as compared to informatively cued trials (F(1,14) = 7.65, p = .015). A significant switch cost (Fig. 1A, right) was observed for non-informatively cued, but not informatively cued trials (t(14) = 3.85, p = .002; t b 1.0; respectively). Error rate (Fig. 1B) was larger for non-informatively cued trials (F(1,14) = 10.48, p = .006) and for switch than repeat trials (F(1,14) = 15.45, p = .002) but there was no cue × trial type interaction (F b 1). These results are consistent with previous studies showing greater switch cost for unprepared (non-informatively cued) than prepared (informatively cued) trials. Interestingly, we found no residual switch cost in the informative condition in these highly practiced participants.3

Fig. 1. (A) RT and (B) Proportion errors for each trial type (left) and switch cost (right) for informatively cued and non-informatively cued trials.

ERP data Cue-locked waveforms In cue-locked waveforms (Fig. 2A), mean amplitude over 300–400 and 450–550 ms targeted the early differential positivity and the late differential positivity, respectively. A three trial type (informatively cued-switch, informatively cued-repeat, non-informatively cued) repeated measures ANOVA showed a main effect of trial for both time windows (F(2,28) = 9.73, p = .001; F(2,28) = 8.27, p = .002). In the early window, the cue-locked positivity was larger for informatively cued as compared to non-informatively cued trials (F(1,14) = 15.94; p = .001), but did not differ between informatively cued switch and repeat trials (F b 1.0). In the late window, the cue-locked positivity was larger for informatively cued switch as compared to informatively cued repeat trials (F(1,14) = 17.38, p = .001), but did not differ between informatively cued and non-informatively cued trials (F b 1.0). The late differential positivity for informatively cued switch relative to repeat trials is consistent with the switch-positivity reported previously (e.g. Karayanidis et al., 2003, 2006; Nicholson et al., 2005, 2006a,b; Swainson et al., 2006). Stimulus-locked waveforms In stimulus-locked (Fig. 2B), mean amplitude over 450–550 ms was analyzed using a 2 cue type (informative, non-informative) × 2 trial type (switch, repeat) repeated-measures ANOVA. Informatively cued trials were significantly more positive than non-informatively cued trials (F(1,14) = 12.81, p = .003). The significant interaction between cue type and trial type (F(1,14) = 9.21, p = .009) indicated that informatively cued repeat trials were significantly more positive than informatively cued switch trials (F(1,14) = 6.27, p = .025), but switch and repeat trials did not differ on non-informatively cued trials. No-go trials showed a large frontocentral P3 over 350–450 ms (Fig. 2C) that was not evident on informatively cued trials (F(1,14) = 32.48, p b .001). The ERP results for this subset of participants (n = 15) are highly similar to those reported by Jamadar et al. (in press) in the larger group (n = 24). 3 Note that this was at least partly due to reduction of RT switch cost on informatively cued trials preceded by a no-go trial (e.g., Schuch & Koch, 2003). These sequence effects are discussed in detail in Jamadar et al. (in press).

fMRI data Fig. 3 shows regions activated for each of the three contrasts: informatively cued N non-informatively cued, informatively cued switch N repeat, and no-go N go (informatively cued). Activity associated with the forward contrast is shown in warm colors (red– yellow) and activity associated with the reverse contrast is shown in cool colors (blue–green). Informatively cued trials (Fig. 3A, red– yellow activity; Table 1) were associated with greater activity than non-informatively cued trials in mostly temporal and posterior cingulate regions. The reverse contrast (non-informatively cued N informatively cued) is associated with performance on unprepared trials and showed extensive activation in a distributed PFC/PPC (left MedFG, precentral gyrus, bilateral MFG, IFG, IPL, right SPL; BA 44, 47, 8/32, 46, 7, 40) and cerebellar network (Fig. 3A, blue–green activity). Informatively cued switch trials showed significantly greater activity than informatively cued repeat trials mostly in the PPC, particularly bilateral SPL/precuneus (Fig. 3B, red–yellow activity; Table 2). Informatively cued repeat trials showed greater activity than informatively cued switch trials in a single cluster in the right IPL (Fig. 3B, blue–green activity; Table 2). No-go trials were associated with activity in a distributed frontoparietal and cerebellar network (Fig. 3C, red–yellow activity; Table 3) encompassing superior, middle, inferior and medial aspects of the PFC (BA 6, 9, 46), superior and inferior PPC (BA 7, 40) and preand post-central gyri (BA 5, 6, 3). The reverse contrast (go N no-go) represents activity associated with response execution in a distributed frontoparietal (BA 8, 10, 47, 7, 40, 6) and temporal network (BA 20, 21). Relationships between behavioral, ERP and fMRI measures Table 4 shows Talairach coordinates, t-values and r-values for correlations between fMRI contrasts, RT and ERP component amplitudes. Informatively cued N non-informatively cued activity in left SPL (BA 7) was correlated with the RT difference between informatively and non-informatively cued trials, whereas activity in right superior/middle frontal gyri (BA 8) and bilateral posterior cingulate (BA 30) was correlated with early cue-locked differential

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positivity amplitude (Fig. 4A). Informatively cued switch N repeat activity in the left superior/middle frontal gyri (BA 8) was positively correlated with RT switch cost (Fig. 4B) whereas activity in a posterior network including superior portions of the PPC (BA 7) and in the cerebellum was positively correlated with late cue-locked differential

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positivity amplitude (Table 4). No-go N go (informatively cued) activity in bilateral superior/middle frontal gyri (BA 8/9), right precuneus (BA 7) and posterior cingulate, hippocampus and middle temporal gyrus was positively correlated with no-go P3 amplitude. In contrast, no-go N go activity in a mostly left-lateralized frontoparietal

Fig. 2. (A) Cue-locked ERP waveforms for informatively cued switch, informatively cued repeat and non-informatively cued trials (left). Difference waveforms showing early cuelocked differential positivity for informatively cued minus non-informatively cued waveforms and late cue-locked differential positivity for informatively cued switch minus informatively cued repeat waveforms (right). Scalp topographies are shown of the early differential positivity at 300–400 ms and late differential positivity at 450–550 ms. (B) Stimulus-locked ERP waveforms. Top: Informatively cued and non-informatively cued switch and repeat trials (left) and switch-repeat difference waveforms for informatively cued and non-informatively cued trials (right). Scalp topography for the informatively cued stimulus-locked differential negativity at 450–550 ms. Bottom: Informatively cued (go) and no-go trials (left) and no-go minus go difference waveform (right). Scalp topography of the no-go P3 at 350–450 ms.

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Fig. 3. (A) Informatively cued vs. non-informatively cued trials contrast. Red activity depicts informatively cued N non-informatively cued; blue activity depicts non-informatively cued N informatively cued. (B) Informatively cued switch vs. informatively cued repeat trials contrast. Red activity depicts switch N repeat; blue activity depicts repeat N switch. (C) No-go vs. informatively cued (go) trials contrast. Red activity depicts no-go N informatively cued (go); blue activity depicts informatively cued (go) N no-go.

network including the VLPFC (BA 44, 9), basal ganglia (thalamus, subthalamic nucleus, globus pallidus, putamen), superior and inferior PPC (BA 7, 40) and cerebellum (Fig. 4C) was negatively correlated with no-go P3 amplitude. It is important to note that significant correlations between ERP and fMRI measures do not necessarily indicate that the location of the fMRI activity is the source of that ERP component. Rather, an ERP-fMRI correlation indicates that fMRI activity in that location covaries with the

amplitude of the ERP component. Dipole modeling was used to assess whether the region of fMRI activity correlated with the amplitude of an ERP component is a plausible generator of that ERP activity. A two-source model (right superior frontal gyrus: 24, 33, 39; right posterior cingulate: 8, −61, 14) for the early cue-locked differential positivity explained 86% of the variance and additional sources did not significantly add to the explained variance of the model (b1% for both left posterior cingulate and right lingual gyrus). Source waveforms

S. Jamadar et al. / NeuroImage 51 (2010) 432–449 Table 1 Talairach coordinates and T-values of the peak of activity in informatively cued N noninformatively cued trials. Region (BA)

Left hemisphere Tal

Informatively cued N non-informatively cued Parietal/occipital Posterior cingulate (30) – Cingulate (31) – Cuneus (18) − 16, − 92, 19 Temporal Superior temporal gyrus (21/22) − 36, − 8, − 10 Middle temporal gyrus (21) − 48, 3, − 17 Parahippocampal gyrus − 28, − 12, − 13 Non-informatively cued N informatively cued Frontal Medial frontal gyrus (8/32) − 4, 29, 39 Middle frontal gyrus (46/47) − 44, 25, 28 Inferior frontal gyrus (9/44) − 48, 8, 14 Precentral gyrus (44) − 48, 4, 11 Parietal Superior parietal lobule (7) – Inferior parietal lobule (40) − 36, − 45, 39 Precuneus (7/19) − 32, − 64, 40 Occipital Fusiform gyrus (37) − 32, − 47, − 14 Inferior occipital gyrus (19) − 44, − 70, − 7 Cerebellar Cerebellar culmen − 36, − 52, − 21 Cerebellar declive – Cerebellar tonsil − 32, − 48, − 35

Right hemisphere T

Tal

T

– 16, − 50, 14 – 12, − 45, 20 5.93 20, − 92, 19

5.08 4.38 6.03

5.93 – 4.88 – 4.24 –

– – –

– 44, 21, 25 40, 5, 26 –

– 28, − 63, 51 6.52 36, − 48, 43 5.70 32, − 64, 40

– 4.80 4.57 – 7.66 6.64 5.15

4.44 40, − 55, − 11 6.44 3.88 40, − 82, − 6 4.22 7.07 32, − 52, − 21 7.58 – 36, − 71, − 17 5.55 4.64 – –

Contrast was thresholded at p b .001, with cluster-wise threshold p b .05.

from these two dipoles are shown in Fig. 5 (top). Significant activity within the latency range of the early cue-locked differential positivity emerged first at the posterior dipole (280–430 ms) and was followed by activity in the frontal dipole (BA 8; 320–530 ms) suggesting that these areas are plausible sources of the early cue-locked differential positivity. A two-source model (left superior parietal lobule: −28, −51, 62; right precuneus: 20, − 80, 51) of the late cue-locked differential positivity explained 68% of the variance. In order to examine the residual variance, we added an additional pair of symmetrical dipoles which were free to vary in location. The model fitted these to sources in the temporal lobe (BA 37) increasing explained variance to 92%. However, as these were not part of the fMRI model, they are not discussed further. While clearly the two source model is not sufficient to fully account for the cue-locked late differential positivity, Fig. 5 (middle) shows that both dipoles in the posterior parietal cortex dipoles (BA 7) showed significant activity within the timeframe of this late differential positivity. This activity emerged around 400 ms at Table 2 Talairach coordinates and T-values of the peak of activity in informative switch N repeat. Region (BA)

Left hemisphere Tal

both dipoles but reached statistical significance first for the right precuneus (450–470 ms) and extended beyond 700 ms at the left SPL. For the no-goP3, we fitted the following locations based on the areas of significant ERP-fMRI correlations (left superior frontal gyrus: −8, 48, 31; right superior frontal gyrus: 16, 63, 15; left middle frontal gyrus: − 20, 25, 36; right middle frontal gyrus: 28, 17, 29; right precuneus: 20, −76, 26; right posterior cingulate: 12, −49, 28; left medial frontal gyrus: − 4, − 1, 55; left inferior frontal gyrus: −56, −8, 14; left postcentral gyrus: − 39, −44, 61; left superior parietal

Table 3 Talairach coordinates and T-values of the peak of activity in no-go N informatively cued (go). Region (BA)

5.07 5.75 6.78 4.29

Right hemisphere T

Tal

T

Switch N repeat Parietal Superior parietal lobule (7) Inferior parietal lobule (40) Precuneus (7) Postcentral gyrus (5) Temporal/cerebellar Inferior temporal gyrus (37) Temporo-occipital gyrus (19) Cerebellar declive

− 28, − 51, 65 − 40, − 44, 57 − 4, − 59, 62 –

5.91 4.46 4.21 –

36, − 51, 62 – 12, − 59, 66 36, − 43, 65

4.56 – 4.86 4.93

− 51, − 70, 0 − 24, − 67, − 10 − 32, − 59, − 14

5.18 4.83 4.50

– –

– –

Repeat N switch Inferior parietal lobule (40)

–

–

48, − 56, 36

Contrast was thresholded at p b .001, with cluster-wise p b .05.

5.91

441

No-go N informatively cued (go) Frontal Superior frontal gyrus (8) Medial frontal gyrus (6/10) Middle frontal gyrus (8) Inferior frontal gyrus (47) Precentral gyrus (6) Insula Parietal Inferior parietal lobule (40) Precuneus (7) Cingulate gyrus (31) Paracentral lobule (5/6) Postcentral gyrus (3) Temporal Hippocampus Parahippocampal gyrus Superior temporal gyrus (21) Middle temporal gyrus (21) Inferior temporal gyrus (21/20) Lingual gyrus (19) Occipital Posterior cingulate (29) Cuneus (19) Middle occipital gyrus (18) Inferior occipital gyrus (18) Fusiform gyrus (37) Cerebellar Cerebellar culmen Informatively cued (go) N no-go Frontal Superior frontal gyrus (6) Medial frontal gyrus (6) Middle frontal gyrus (6/46) Inferior frontal gyrus (9) Precentral gyrus (6) Insula Parietal Superior parietal lobule (7) Inferior parietal lobule (40) Precuneus (7) Postcentral gyrus (5/3) Subcortical Putamen Globus pallidus Thalamus Occipital/temporal Middle occipital gyrus (37) Lingual gyrus (17) Cerebellar Cerebellar declive Cerebellar culmen Cerebellar tuber Cerebellar tonsil Cerebellar pyramis Uvula

Left hemisphere

Right hemisphere

Tal

T

Tal

− 24, 45, 38 − 4, 55, 19 − 28, 37, 39 − 51, 31, − 8 – − 36, − 18, 19

10.60 6.18 9.26 5.64 – 4.68

24, 41, 38 12, − 25, 53 28, 29, 39 51, 27, − 1 − 40, − 14, 30 44, − 38, 20

− 48, − 49, 25 − 8, − 41, 43 − 12, − 44, 40 − 16, − 36, 50 − 20, − 32, 61

7.37 9.97 9.47 8.88 4.31

− 32, − 28, − 9 − 28, − 35, − 8 − 64, − 23, − 2

T

44, − 45, 24 4, − 49, 36 12, − 44, 40 8, − 32, 53 –

9.07 32, − 20, − 12 11.18 28, − 36, − 12 5.60 44, − 49, 21

6.87 8.99 8.65 5.46 5.29 7.17 8.04 10.89 7.71 12.13 – 5.62 9.16 8.24

− 51, − 8, − 13 − 55, − 9, − 20

6.88 60, − 4, − 16 4.67 − 55, − 9, − 20

7.76 4.67

− 12, − 47, 2

3.92 − 20, − 47, − 1

4.97

− 12, – − 32, − 32, − 32,

9.66 – 9.05 8.16 8.51

12, − 50, 16, − 80, 32, − 89, 32, − 88, 32, − 47,

14 37 1 − 12 −8

6.88 4.93 6.33 6.39 11.14

6.55 28, − 47, − 11

7.49

− 50, 14 − 93, 1 − 89, − 2 − 51, − 8

− 28, − 47, − 11

− 24, 7, 55 − 4, − 1 52 − 28, − 5, 56 − 55, 5, 29 − 40, − 9, 52 − 40, − 3, 11

4.39 11.87 7.60 7.80 7.77 5.30

32, − 5, 63 4, − 1, 52 36, − 5, 56 – 36, − 9, 59 –

6.93 8.86 7.53 – 8.71 –

− 32, − 36, − 24, − 36,

9.36 11.30 8.19 13.42

24, − 55, 58 40, − 40, 50 – 44, − 24, 6

5.44 6.69 – 6.49

− 51, − 44, − 56, − 44,

61 57 43 61

− 24, 0, 4 − 20, 0, 0 − 12, − 15, 12

5.26 – 4.60 – 8.88 2, − 15, 8

– – 6.17

− 48, − 63, − 7 –

5.07 – – 12, − 85, 1

– 6.45

− 32, – − 36, − 28, − 20, –

8.11 – 8.27 4.03 3.67 –

− 63, − 24 − 63, − 24 − 52, − 31 − 63, − 27

28, − 59, − 21 40, − 56, − 24 44, − 56, − 24 32, − 48, − 31 16, − 60, − 27 4, − 67, − 27

Contrast was thresholded at p b .001, with cluster-wise threshold p b .05.

10.71 9.77 8.77 7.69 7.17 4.30

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Table 4 Talairach coordinates, T- and R-values of the peak of activity in fMRI-RT-ERP correlations. Asterisks denote regions that also show significant fMRI activity in corresponding contrast analyses. Region (BA)

Left hemisphere

Informatively cued N non-informatively cued RT (informative–non-informative) Superior parietal lobule (7) Early cue-locked differential positivity Superior/middle frontal gyrus (8/9) Posterior cingulate (30)⁎ Lingual gyrus (18) Informatively cued switch N repeat RT switch cost Superior frontal gyrus (6) Medial frontal gyrus (6) Middle frontal gyrus (6) Late cue-locked differential positivity Superior parietal lobule (7)⁎ Precuneus (7)⁎ Postcentral gyrus (3/40) Paracentral lobule (5) Fusiform gyrus (19) Cerebellar culmen Stimulus-locked differential negativity Cingulate gyrus (24) No-go N go (informatively cued) w. no-go P3 Positive correlations Frontal/parietal Superior frontal gyrus (10/9)⁎ Middle frontal gyrus (8/9)⁎ Precuneus (31)⁎ Posterior cingulate (31)⁎ Paracentral lobule (5/6)⁎ Temporal/occipital/cerebellar Hippocampus⁎ Middle temporal gyrus (21)⁎ Cuneus (19)⁎ Middle occipital gyrus (19)⁎ Inferior occipital gyrus (19)⁎ Fusiform gyrus (37)⁎ Cerebellar culmen⁎

Right hemisphere

Tal

T

R

Tal

T

R

− 20, − 71, 48

5.78

.85

– − 4, − 61, 14 –

– 5.18 –

– .81 –

24, 33, 39 8, − 61, 14 12, − 62, 3

6.07 5.17 4.59

.86 .82 .79

− 16, − 9, 59 − 16, 3, 59 –

6.47 3.16 –

.87 .74 –

– – 28, − 13, 41

– – 4.44

.78

− 28, − 51, 62 – − 28, − 32, 57 0, − 40, 68 − 20, − 79, − 16 –

4.92 – 4.68 5.17 4.91 –

.81 – .79 .82 .81 –

– 20, − 75, 52 – – – 32, − 44, − 28

– 5.25 – – – 6.64

– .82 – – – .88

− 4, − 14, 34

5.27

.83

–

–

–

− 8, 48, 31 − 20, 25, 36 – – − 12, − 28, 57

6.31 7.16 – – 7.32

.86 .89 – – .89

16, 63, 15 28, 17, 29 20, − 76, 26 12, − 49, 28 − 16, − 32, 50

5.61 6.63 5.55 6.70 6.49

.83 .87 .83 .87 .87

− 32, − 28, − 9 − 51, − 9, − 16 – − 32, − 93, 1 − 44, − 57, 28 –

5.44 4.65 – 5.38 5.25 –

.82 .78 – .82 .81 –

28, 55, 12, – – 36,

5.00 5.82 3.98 – – 5.07

.80 .84 .73 – – .80

− 4, − 1, 55 − 56, 8, 14 – − 40, − 3, 11 − 28, − 63, 55 40, − 37, 42 − 39, − 44, 61

6.54 4.78 – 4.67 7.21 4.15 7.42

.87 .79 – .78 .89 .74 .89

– – 36, − 8, 63 – 28, − 55, 62 – –

– – 5.10 – 5.20 – –

– – .81 – .81 – –

− 8, − 19, 1

5.90

.84

16, − 8, 4

5.64

.83

− 32, − 63, − 20 − 48, − 52, − 24 –

7.12 4.62 –

.89 .78 –

32, − 59, − 21 – 44, − 48, − 25

4.92 – 5.10

.80 – .80

− 31, − 5 − 1, − 23 − 76, 37

− 51, − 8

a

Negative correlations Frontal/parietal Medial frontal gyrus (6/8)⁎ Inferior frontal gyrus (44)⁎ Precentral gyrus (6)⁎ Insula⁎ Superior parietal lobule (7)⁎ Inferior parietal lobule (40)⁎ Postcentral gyrus (5)⁎ Subcortical Thal/subthal nucl/ globus pallidusb,⁎ Cerebellar Cerebellar declive⁎ Cerebellar tuber⁎ Cerebellar culmen⁎

Contrasts were thresholded at p b .001, with cluster-wise threshold p b .05. a Regions just under threshold: R Inferior Frontal Gyrus (BA 47; 36, 19, − 1; t = 4.57); L Putamen (− 24, 4, 8; t = 4.43). b Cluster extended throughout all of these basal ganglia/thalamic regions (Ahsan et al., 2007; Damasio, 2005). ⁎ Regions also active in the general contrast in Tables 1, 2, and 3.

lobule: − 28, −63, 55; right superior parietal lobule: 25, − 55, 62; left inferior parietal lobule: − 40, 37, 42) which explained 98.7% of the variance. Given the large number of possible sources, we used a multiple source probe scan (MSPS) as implemented in BESA software to examine the dipoles necessary to account for the variance in a stepwise manner. This analysis suggested that the left middle frontal and medial frontal gyri were sufficient to account for the electrical activity. Fig. 5 (bottom) shows that these regions also showed significant activation within the time-frame of the no-go P3. This two-source model accounted for 96.5% of the variance.

Finally, informatively cued switch N repeat activity in left cingulate gyrus (BA 24; Table 4) was positively correlated with stimulus-locked differential negativity amplitude over 450–550 ms (Fig. 4D). Dipole modeling confirmed this region as a plausible source for the differential negativity and explained 92% of the variance. Discussion The aim of the present study was to use a multimodal approach to elucidate the neuroanatomical bases and dynamics of task-switching

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Fig. 4. Multiple regression analyses of fMRI contrast activations with ERP and behavioral contrasts. (A) Informatively cued N non-informatively cued contrast with RT preparation benefit (informatively cued–non-informatively cued RT difference; blue) and mean amplitude of early cue-locked differential positivity over 300–400 ms. (B) Informatively cued switch N repeat trials contrast with informatively cued RT switch cost (blue) and mean amplitude of late cue-locked differential positivity over 450–550 ms (red). (C) No-go N informatively cued (go) contrast with mean amplitude of no-go P3 over 350–450 ms. Red activity depicts positive correlation, blue activity depicts negative correlation. (D) Informatively cued switch N repeat contrast with mean amplitude of stimulus-locked differential negativity measured 450–500 ms. The correlation remained significant after removal of the outlier with contrast value = − 4 (r = .643, p = .013). Scatterplots show the relationship between the RT or ERP measure and the mean of the BOLD contrast within the ROI. ROIs were defined by the clusters in the multiple regression analysis. Abbreviations: L, left; R, right, B, bilateral; SFG, superior frontal gyrus, MFG, middle frontal gyrus, IFG, inferior frontal gyrus, MedFG, medial frontal gyrus; SPL, superior parietal lobule, Post Cent, postcentral gyrus; STN, subthalamic nucleus.

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and no-go performance by utilizing analytical methods that combine behavioral, ERP and fMRI data. Before discussing the details of our findings, we will firstly clarify how fMRI contrasts and RT-fMRI and ERP-fMRI correlations offer different, yet complementary information on the neural basis of task-switching. Relationship between fMRI contrasts and RT-fMRI and ERP-fMRI correlations It is important to note that the regions of fMRI activity that showed significant correlations with RT and ERP effects (Fig. 4 and Table 4) show only partial overlap with regions of significant differential activation in the fMRI contrasts (Tables 1, 2, and 3 and Fig. 3). Differences between these analyses can be attributed to the fact that simple fMRI contrasts identify cortical regions with significantly elevated BOLD activity across subjects (relative to the error term) in a comparison of two conditions. Any individual differences in the BOLD signal are thus included in the error variance component of simple fMRI contrasts. The correlation analysis, on the other hand, can reveal significant covariation between individual differences in BOLD signal and in RT and ERP indices, respectively. Given that these individual differences in RT and ERP measures reflect inter-individual variation in processes that contribute to task-switching performance such as anticipatory preparation, stimulus-related processing and response preparedness, the analysis of the correlation between BOLD signal and these measures can potentially reveal the brain regions involved in these sub-component processes. It is possible, therefore, that brain regions found to be significantly activated in these correlational analyses are not found to be significantly activated in simple fMRI contrast analyses (see Forstmann et al., 2008 for another example of different outcomes from fMRI contrasts and correlation analyses). Conversely, it is also possible that behavioral measures that do not differ significantly on a particular comparison nonetheless show individual differences that are correlated with regional measures of BOLD signal. So, this analysis represents an opportunity to examine whether individual variability in BOLD signal for a particular fMRI contrast is associated with variability in the corresponding contrast for behavioral and ERP measures of task switching and response inhibition. When combined with the excellent temporal resolution of ERP data, this provides important information about the temporal dynamics of regional activation. Informatively cued N non-informatively cued contrast Informatively cued trials that afforded anticipatory preparation for an impending switch or repeat trial were associated with increased activity in right posterior cingulate and left superior/middle temporal gyri, relative to non-informatively cued trials that provided only stimulus timing information. The amplitude of the early cue-locked differential positivity was positively correlated with activity in bilateral posterior cingulate and right DLPFC. Dipole modeling suggested that the right posterior cingulate and right DLPFC are plausible generator sources of this differential positivity. The DLPFC has been implicated in top-down control during goal-directed behavior (Hester et al., 2004; Fassbender et al., 2006; Egner and Hirsch, 2005; Wylie et al., 2006). Thus, the relationship between the early cue-locked positivity and DLPFC activity supports the contention that this positivity is associated with goal activation on informatively cued trials. The posterior cingulate has been associated with cueinduced expectancy and initiating an expectant attentional bias (Small et al., 2003). Here, given that at the onset of an informative cue, participants have a valid expectation regarding the nature of the upcoming trial, the generic posterior cingulate activity in the informatively cued N non-informatively cued contrast is likely to reflect, at least in part, increased expectancy load for the upcoming task in the former trial type. The timing of the early differential

Fig. 5. Source waveforms for early and late cue-locked positivities (top) and no-go P3 (bottom). Original ERP average waveform is shown in gray above each plot. Bars represent areas of significant deviation from baseline. See Fig. 4 legend for abbreviations.

positivity is consistent with the contention that goal activation occurs in anticipation of the stimulus on informatively cued trials. This fits nicely with the associated superior temporal gyrus activation, as this area is important for temporal action planning (Kirscher et al., 2004; Paulus et al., 2005), as well as for the generation of verbal task labels (Price, 2000) a process shown to be critical in task-switching literature (Goschke, 2000).

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The size of the RT benefit for informative relative to noninformatively cued trials was correlated with activity in the left superior PPC (Fig. 4A), an area involved in maintenance and manipulation of C-R rules (Bunge et al., 2002; Andersen, 1987; Andersen and Buneo, 2002). Greater activation in the PPC was associated with less RT benefit for informative compared to non-informative cues. This could indicate that participants who used the informative cue efficiently to complete anticipatory preparation also implemented C-R rules more efficiently than participants who did not consistently or efficiently complete anticipatory preparation. However, RT (informative–non-informative) difference did not correlate with the amplitude of the early cue-locked differential positivity (p N .50), indicating that variability in RT benefit for informatively cued compared to non-informatively cued trials cannot be explained by individual differences in preparatory processing. It seems likely therefore that all participants engaged in anticipatory preparation, but those showing less performance benefit on informatively cued vs. non-informatively cued trials may have encountered more interference during C-R rule implementation. Trials that did not afford the opportunity for anticipatory preparation (non-informatively cued N informatively cued contrast) were associated with increased activity mostly in ventrolateral and medial PFC regions and the PPC. This activity is likely to reflect increased demand on cortical regions associated with goal-directed behavior as well as C-R rule activation and implementation under time pressure. Increased VLPFC and medial PFC activity on noninformatively cued trials could also reflect greater conflict monitoring and suppression on trials where across-task interference is greater due to the absence of anticipatory preparation. Informatively cued switch N repeat contrast Task-switching effects were examined on informatively cued trials that allowed for anticipatory task-set reconfiguration. Informatively cued switch trials were associated with greater activation than repeat trials in bilateral superior PPC, left inferior PPC and left inferior temporal gyri. Bilateral superior PPC was also positively correlated with the amplitude of the late cue-locked differential positivity (i.e., informative switch vs. repeat trials) and dipole modeling confirmed this region as a plausible source for this differential positivity (although the model accounted for less than 70% of the variance, suggesting that other generators also contribute to the late cue-locked differential positivity). In contrast, informatively cued repeat trials were associated with greater activation than switch trials in the right inferior PPC. The inferior temporal gyrus activity in the informatively cued switch N repeat contrast may reflect updating of the representations of task-relevant stimuli on a switch trial (Schumacher et al., 2003). The remaining activations occurred in the PPC and are likely to reflect the activation and/or maintenance of S-R associations or C-R rules (Andersen, 1987; Andersen and Buneo, 2002; Braver et al., 2003; Brass and von Cramon, 2004; Bunge et al., 2002; Fassbender et al., 2006; Hester et al., 2007; Ruge et al., 2005). Given that more superior PPC regions were associated with switching and more inferior regions with repetition, it is possible that superior PPC is involved in activating and manipulating task-relevant C-R rules that were not recently implemented (or that were recently suppressed), whereas more inferior PPC is involved in maintaining recently active representations that will be used again shortly. These two suggestions are consistent with the literature. The superior PPC has been shown to be involved in reconfiguring and updating task-set information immediately following a switch in task (Braver et al., 2003) as well as in shifting attention (Andersen and Buneo, 2002; Behrmann et al., 2004; Cavanna and Trimble, 2006; Corbetta and Shulman, 2002), whereas a recent meta-analysis showed consistent right inferior parietal lobule involvement in tasks of working memory and sustained attention (Nickel and Seitz, 2005). The positive correlation between late cue-locked differential positivity and PPC activation is

445

therefore consistent with our argument that, given an informative cue and sufficient time to prepare, C-R rules can be activated prior to stimulus onset. As the identity of the stimulus is not known during the preparation interval, it is impossible to activate any specific S-R association (e.g. “letter is vowel, press left”). However, the information provided by the cue is sufficient to activate the task relevant C-R rules (i.e., if cue indicates letter task is relevant, it is possible to activate the rules that link each category with responses for that task e.g., “vowel = left, consonant = right”). The precise neural circuits involved in C-R rule activation may depend on specific sensory/ perceptual task demands. We could not expect any task-related difference in the present study, as the stimulus-sets and associated tasks are not expected to involve processing in distinct neural circuits. However, Wylie et al. (2006) found that preparatory activity in neurally distinct task-relevant cortical regions when shifting to color vs. motion classification tasks. The relationship between the late cuelocked differential positivity and localized preparatory activation in task-specific cortical regions remains to be determined. Consistent with a number of previous studies (e.g., Brass et al., 2003; Forstmann et al., 2005), we found no overall differential fMRI activity for informative switch as compared to informative repeat trials in the frontal cortex (but see for example Braver et al., 2003; Ruge et al., 2005 for significant prefrontal switch effects). However, the behavioral RT switch cost (switch–repeat) was associated with activity in the left superior frontal and right middle frontal gyri (Fig. 4B). Interestingly, RT switch cost was not significant overall but showed marked individual differences across participants with approximately half showing a positive switch cost and the other half showing a negative switch cost (i.e. slower RT for repeat trials). Activity in the left superior frontal gyrus predicted whether participants would show a positive or negative switch cost (see Fig. 4B, part 3). This region corresponds to the dorsal premotor cortex (PMd; Mayka et al., 2006), an area involved in sensorimotor processing related to motor generation, collecting multiple sets of information on actions and integrating them to perform an intended action (Hoshi and Tanji, 2007; Serrien et al., 2007; O'Shea et al., 2007). The PMd receives its main PFC inputs from the DLPFC and its main parietal inputs from the SPL (Hoshi and Tanji, 2007), making it particularly well-suited for preparing motor plans associated with goal-directed behavior. Within the context of S-R priming account of RT switch cost of (Allport and Wylie, 2000; Waszak et al., 2003; Wylie and Allport 2000), this activity could reflect priming effects during response preparation. So, activity in PMd may vary systematically as a function of whether there is increased interference on switch trials or reduced repetition benefit on repeat trials, resulting in a positive or negative switch cost, respectively. This conclusion is consistent with the argument of Jamadar et al. (2009) that the magnitude of the RT switch cost is proportional to the ease of response implementation, as reflected in response-locked ERPs. Both correlational analyses and dipole modeling indicated an association between the stimulus-locked differential negativity and activity in the cingulate gyrus, a region known to be involved in interference monitoring and suppression (Blasi et al., 2006; Botvinick et al., 2004). Although our data did not show a relationship between RT switch costs and cingulate gyrus activation, other behavioral and ERP evidence supports an interference-related account of residual switch cost (Allport and Wylie, 2000; Waszak et al., 2003; Wylie and Allport, 2000) as well as the stimulus-locked differential negativity (Astle et al., 2006; Jamadar et al., 2009; Lavric et al., 2008). Manipulations that eliminate the RT repetition benefit also reduce the amplitude of the stimulus-locked differential negativity, an effect driven by reduced stimulus-locked positivity for repeat trials (Jamadar et al., 2009; Swainson et al., 2006). Therefore, the link between the stimuluslocked differential negativity and cingulate gyrus activity is consistent with the interpretation of this negativity as an index of stimulusrelated interference/facilitation S-R priming effects.

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Non-informatively cued switch N repeat Surprisingly, despite significantly longer RT and larger RT switch cost for non-informatively cued as compared to informatively cued trials, stimulus-locked ERPs did not show the expected early differential switch positivity previously reported on trials that do not afford anticipatory preparation (Karayanidis et al., 2003; Nicholson et al., 2005) or the stimulus-locked differential switch negativity. These have been consistently reported previously regardless of preparation and were clearly evident on informatively cued trials in the current study. fMRI measures also showed no suprathreshold activity in the non-informatively cued switch vs. repeat contrasts, although with a lower threshold (p b .005 with clusterwise threshold p b .05), the switch N repeat contrast for the noninformatively cued trials resulted in activations largely consistent with those seen for informatively cued trials, i.e., right cerebellar declive (40, −59, −17; t = 4.36; 47 voxels), right superior parietal lobule (16, − 67, 59; t = 3.64, 9 voxels) and left inferior parietal lobule (−40, − 44, 57; t = 3.54, 8 voxels). The absence of the expected stimulus-locked differences between switch and repeat trials is puzzling. However, the fact that we have found this same result using a similar paradigm (identical tasks, timing and trial types but excluding no-go trials) in another three groups of participants (Expt. 2 in Jamadar et al., 2009 and data from control and schizophrenia patients in Jamadar et al., in press) suggests that it is reliable. No-go N go contrast Informatively cued and no-go trials were indistinguishable within the cue-stimulus interval but differed at stimulus onset. While on informatively cued trials the activated C-R rules could be implemented leading to stimulus classification and response selection, on no-go trials the stimulus was not mapped to any response and therefore the activated C-R rules could not be implemented and the readiness to respond had to be inhibited. Therefore, the informatively cued N no-go contrast reveals activity associated with stimulus classification, response selection and execution, whereas the no-go N informatively cued comparison reveals activity related to withholding the prepared C-R rules and the tendency to respond. The former contrast resulted in activation in left DLPFC, PPC, premotor cortex, pre-supplementary motor area (pre-SMA) and basal ganglia, areas associated with goaldirected selection, as well as rule-based activation and execution of a response (Alexander and Crutcher, 1990; Andersen and Buneo, 2002; Behrmann et al., 2004; Bunge et al., 2002; Corbetta and Shulman, 2002; Egner and Hirsch, 2005; Hester et al., 2007; Mink, 1996; Thobois et al., 2007). The latter (no-go N informatively cued) contrast resulted in activity in bilateral DLPFC, frontopolar cortex, VLPFC, hippocampus, parahippocampal gyrus, PPC, posterior cingulate and right premotor cortex. This activity in the DLPFC, frontopolar cortex, hippocampus, PPC, posterior cingulate and premotor cortex, among others, was positively correlated with the amplitude of the stimuluslocked no-go P3, with dipole modeling suggesting the DLPFC and dorsomedial PFC as plausible generators. Activity in the VLPFC, on the other hand, showed a negative correlation with the amplitude of the no-go P3. Although activation in both DLPFC and VLPFC has been consistently associated with response inhibition (Aron et al., 2003; Aron and Poldrack, 2006; Aron et al., 2007; Aron, 2007; Blasi et al., 2006; Ridderinkhof et al., 2004), there is evidence that right VLPFC activation is critical for this inhibition to occur (Aron, 2007; Chambers et al., 2007). The current finding that DLPFC areas were activated for both response execution (i.e., informatively cued N no-go) and response inhibition (i.e., no-go N informatively cued) contrasts whereas the VLPFC was activated uniquely for the response inhibition contrast (although in the left, not the right hemisphere), is consistent with the proposal that a functional dissociation exists in the

dorsolateral–ventrolateral plane of the PFC, with the DLPFC involved in top-down control of goal-directed behavior and the VLPFC in inhibitory control. The parahippocampal/hippocampal activity in the response inhibition contrast may reflect the need to rapidly encode the stimulus and search for memory representations (Bast, 2007) before initiating inhibition of response-related processes. Alternatively, this activity may reflect inhibition of a state of readiness to respond to a stimulus as neurons in the parahippocampal gyrus have been found to represent the readiness to respond to the second stimulus in an S1-S2 paradigm (Salzmann et al., 1993). This region has been known to be involved in response inhibition for some time (Douglas, 1967; Winocur and Mills, 1969; McNaughton, 2006; Salzmann et al., 1993). This alternate interpretation fits nicely our conclusion from analyses of sequence effects in this paradigm (Jamadar et al., 2009) that no-go/go sequence effects are due to inhibition of the readiness to respond in a no-go trial, making it more difficult to activate a response on the following go trial. Given that the no-go P3 is believed to be an index of inhibitory control, it is surprising that no-go P3 amplitude did not correlate positively with activity in the right VLPFC, an area believed to be crucial for response inhibition (Aron et al., 2004). However, our finding is consistent with Ford et al. (2004) who also failed to find a positive correlation between no-go P3 and BOLD-fMRI activity in the right VLPFC, despite significant correlations with activity in the DLPFC, anterior cingulate, inferior parietal lobule and caudate nucleus. Source localization studies have identified no-go P3 sources predominantly in the ACC (Kiefer et al., 1998; Verleger et al., 2006), an area that detects conflict/interference and signals to the PFC the need for cognitive control (Botvinick et al., 2004). Together, these findings of an association between no-go P3 and a frontoparietal network are consistent with the argument that this positivity reflects the activation of control processes initiated by the signal from the ACC. An even more surprising finding was the negative correlation between the amplitude of the no-go P3 and activity in a largely leftlateralized network encompassing the pre-SMA, premotor cortex, DLPFC, VLPFC, thalamus, subthalamic nucleus, globus pallidus and bilateral PPC, as well as marginal activation in the putamen. Dipole modeling confirmed the DLPFC, pre-SMA and VLPFC as plausible sources for this ERP. This network corresponds closely to the ‘motor’ basal ganglia network involved in preparation, initiation and execution of voluntary movements (Alexander and Crutcher, 1990; Parent and Hazrati, 1995a,b; Mink, 1996; Temel et al., 2005; Nambu et al., 2002). That is, larger no-go P3 is associated with greater activity in the anterior cingulate and frontoparietal control network but reduced activity in the cortico-subcortical motor pathway. While these findings implicate an association with processes related to conflict detection, increased cognitive control and motor inhibition, the current results do not provide information about the temporal relationship between these effects. Alternatively, it could be argued that since, within the current paradigm, no-go trials occur after an informative cue has built up preparation for a particular task, these trials did not involve response inhibition, but instead were associated with violation of expectancy of a valid task stimulus. In this scenario, no-go trials could be considered as invalidly cued events that are infrequent, unexpected and/or surprising. Hence, the frontocentral P3 would represent a ‘novelty P3’ elicited by alphanumeric stimuli that are not mapped to any response and the differential fMRI activations between informatively cued and ‘no-go’ trials would represent violation of expectancy. However, recall that 50% of informative cues were followed by ‘no-go’ stimuli, and in fact these ‘informative cue / no-go stimulus’ sequences constituted 1/3 of the total stimuli. Therefore the occurrence of a nogo stimulus could hardly be considered a violation of expectancy or a rare, novel event, especially as participants were highly practiced on the task.

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Implications for models of cognitive control In an earlier paper, Jamadar et al. (2009) argued that preparatory processing in task-switching involves at least two distinguishable processes: goal activation and C-R rule activation which are indexed electrophysiologically by the early differential positivity and late differential positivity, respectively. In the current paper, we examined the relationship between brain hemodynamic activity and these electrophysiological measures of preparation to repeat or switch task. The early differential positivity was associated with activity in the DLPFC, a locus of a top-down biasing signal in goal-directed behavior, whereas the late differential positivity was associated with activity in the PPC, a locus of activation and maintenance of C-R mappings. These results are compatible with a multi-component model of anticipatory preparation in task-switching that includes both general preparation processes, such as activation of a goal to repeat or switch tasks (i.e., Rubinstein et al., 2001), as well as switch-specific preparation or taskset reconfiguration processes (Rogers and Monsell, 1995), that may include activation of the task-relevant C-R rules in anticipation of the stimulus. The results are also consistent with a growing body of fMRI literature showing that task-switching involves a distributed frontoparietal network encompassing lateral and medial PFC, premotor areas and superior and inferior PPC (Barber and Carter, 2005; Brass and von Cramon, 2004; Braver et al., 2003; Liston et al., 2006; Loose et al., 2006; Ruge et al., 2005; Sohn et al., 2000). The relative temporal dynamics of frontal and parietal activity is compatible with a frontal decision process that prepares the system to switch by biasing task-relevant activity in parietal areas. A similar frontal–parietal temporal dynamic has been previously reported. In an MEG study, Perianez et al. (2004) reported that differential switch– repeat activity in PFC (inferior frontal gyrus, anterior cingulate) preceded PPC activity (supramarginal gyrus) during anticipatory preparation. Brass, Ullsperger, Knoesche, von Cramon and Phillips (2005) seeded dipoles in frontal and parietal areas that had previously been shown to be related to switch-specific anticipatory preparation with the same paradigm and found that activity in the inferior frontal gyrus preceded activity in the intraparietal sulcus. While this frontoparietal network appears to result in task readiness in anticipation of a stimulus requiring a change in taskset, the current findings strongly suggest that anticipatory preparation does not eliminate switch vs. repeat differences in terms of poststimulus processing. A stimulus-locked differential negativity was found on informatively cued switch trials as compared to repeat trials even when there was optimal anticipatory preparation (long CSI, taskspecific cues) replicating many previous studies (e.g., Karayanidis et al., 2003; Nicholson et al., 2005; Rushworth et al., 2002, 2005; Swainson et al., 2006). In the current study, RT switch cost was related to differential switch-repeat activity in the PMd, suggesting that switch cost may index differential priming at the level of response preparation/execution. In addition, the association between the amplitude of the stimulus-locked differential negativity and differential switch N repeat activity in cingulate gyrus suggests greater stimulus-elicited interference and hence need for greater conflict monitoring on switch as compared to repeat trials. These findings provide direct support for a contribution of both anticipatory preparation and stimulus-triggered interference processes to the behavioral costs of switching tasks. Therefore, at least with bivalent incongruent stimuli, anticipatory preparation is not sufficient to override greater stimulus-triggered interference on switch as compared to repeat trials. These findings are consistent with the conclusion by Karayanidis et al. (2009) that anticipatory and stimulus-related processes may independently contribute to behavioral differences between switch and repeat trials. Finally, while DLPFC activity was obtained on both informatively cued and no-go trials, VLPFC activity was only evident on no-go trials, consistent with a role for the DLPFC in initiating a top-down biasing

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signal in goal-directed behavior and the VLPFC in response inhibition (Corbetta and Shulman, 2002). While no-go P3 amplitude was surprisingly not related to activity in the right VLPFC, no-go P3 was positively related to activity in the DLPFC and negatively related to activity in the fronto-basal ganglia motor network, consistent with a role in increased cognitive control and inhibition of the response network (Verleger et al., 2006; Kiefer et al., 1998). Given that the informative cues permitted goal activation and activation of the taskrelevant C-R rules, but not of the specific response, the no-go trials are likely to involve inhibition of the activated C-R rule or the readiness to respond (see Jamadar et al., 2009 for examination of these two alternatives). Overall, these results show that combining multiple imaging modalities can inform models of task-switching and cognitive control beyond what each modality can achieve alone. Acknowledgments This research was supported by an Australian Postgraduate Award and Schizophrenia Research Institute Postgraduate Scholarship to Sharna Jamadar as well as University of Newcastle Research Grants Committee and Hunter Medical Research Institute project grants to Frini Karayanidis and Pat Michie. The project was approved by the University of Newcastle and Hunter New England Area Health Human Research Ethics Committees. We wish to thank Dr Janette Smith for assistance with dipole modeling, Gavin Cooper for software development, Gary O'Connor and Steven Hudson for assistance with fMRI recordings. References Ahsan, R.L., Allom, R., Gousias, I.S., Habib, H., Turkheimer, F.E., Free, S., Lemieux, L., Myers, R., Duncan, J.S., Brooks, D.J., Koepp, M.J., Hammers, A., 2007. Volumes, spatial extents and a probabilistic atlas of the human basal ganglia and thalamus. NeuroImage 38, 261–270. Alexander, G.E., Crutcher, M.D., 1990. Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends Neurosci. 13, 266–271. Allport, D.A., Wylie, G., 2000. Task-switching, stimulus response bindings, and negative priming. In: Monsell, S., Driver, J. (Eds.), Attention and Performance XVIII. MIT Press, Cambridge, MA, pp. 35–70. Allport, A., Styles, E.A., Hsieh, S., 1994. Shifting intentional set: exploring the dynamic control of tasks. In: Umilta, C., Moscovitvh, M. (Eds.), Attention and Performance XV. MIT Press, Cambridge, MA, pp. 421–452. Andersen, R.A., 1987. The role of the inferior parietal lobule in spatial perception and visual-motor integration. In: Plum, F., Mountcastle;, V.B., Geiger, S.R. (Eds.), The Handbook of Physiology. Section I: The Nervous System Volume V. Higher Functions of the Brain Part, 2. American Physiological Society, Bethesda, MD, pp. 483–518. Andersen, R.A., Buneo, C.A., 2002. Intentional maps in posterior parietal cortex. Annu. Rev. Neurosci. 25, 189–220. Andersson, J.L.R., Hutton, C., Ashburner, J., Turner, R., Friston, K., 2001. Modeling geometric deformations in EPI time series. NeuroImage 13, 903–919. Aron, A.R., 2007. Neural basis of inhibition in cognitive control. Neuroscientist 13, 214–228. Aron, A.R., Poldrack, R.A., 2006. Cortical and subcortical contributions to stop signal response inhibition: role of the subthalamic nucleus. J. Neurosci. 26, 2424–2433. Aron, A.R., Robbins, T.W., Poldrack, R.A., 2004. Inhibition and the right inferior frontal cortex. Trends Cogn. Sci. 8, 170–177. Aron, A., Fletcher, P., Bullmore, E., Sahakian, B., Robbins, T., 2003. Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nat. Neurosci. 6, 115–116. Aron, A.R., Behrens, T.E., Smith, S., Frank, M.J., Poldrack, R.A., 2007. Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (MRI) and functional MRI. J. Neurosci. 27, 3743–3752. Astle, D.E., Jackson, G.M., Swainson, R., 2006. Dissociating neural indices of dynamic cognitive control in advance task-set preparation: an ERP study of task switching. Brain Res. 1125, 94–103. Astle, D.E., Jackson, G.M., Swainson, R., 2008. Fractionating the cognitive control required to bring about a change in task: a dense-sensor event-related potential study. J. Cogn. Neurosci. 20, 1–13. Barber, A.D., Carter, C.S., 2005. Cognitive control involved in overcoming prepotent response tendencies and switching between tasks. Cereb. Cortex 157, 899–912. Bast, T., 2007. Toward an integrative perspective on hippocampal function: from the rapid encoding of experience to adaptive behavior. Rev. Neurosci. 18, 253–281. Behrmann, M., Geng, J.J., Shomstein, S., 2004. Parietal cortex and attention. Curr. Opin. Neurobiol. 14, 212–217.

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