Distributed task coding throughout the multiple demand network of the human frontal–insular cortex

NeuroImage 52 (2010) 252–262

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NeuroImage j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g

Distributed task coding throughout the multiple demand network of the human frontal–insular cortex Peter Stiers a,b,⁎, Maarten Mennes c,b, Stefan Sunaert d a

Department of Neuropsychology and Psychopharmacology, Maastricht University, Universiteitssingel 40 (East), 6229 ER Maastricht, The Netherlands Department of Woman and Child, Section Paediatric Neurology, K.U.Leuven, Herestraat 49, B-3000 Leuven, Belgium Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York, NY 10016, USA d MR Research Center, Department of Radiology, University Hospitals K.U.Leuven, Herestraat 49, B-3000 Leuven, Belgium b c

a r t i c l e

i n f o

Article history: Received 30 December 2009 Revised 26 March 2010 Accepted 26 March 2010 Available online 1 April 2010 Keywords: Prefrontal cortex Insula Task-positive network Multi-voxel pattern analysis Functional connectivity

a b s t r a c t The large variety of tasks that humans can perform is governed by a small number of key frontal–insular regions that are commonly active during task performance. Little is known about how this network distinguishes different tasks. We report on fMRI data in twelve participants while they performed four cognitive tasks. Of 20 commonly active frontal–insular regions in each hemisphere, five showed a BOLD response increase with increased task demands, regardless of the task. Although active in all tasks, each task invoked a unique response pattern across the voxels in each area that proved reliable in split-half multivoxel correlation analysis. Consequently, voxels differed in their preference for one or more of the tasks. Voxel-based functional connectivity analyses revealed that same preference voxels distributed across all areas of the network constituted functional sub-networks that characterized the task being executed. © 2010 Elsevier Inc. All rights reserved.

In our daily lives we are capable of performing a seemingly endless number of tasks. Many of these tasks require specific actions in response to changing circumstances, when particular environmental conditions are satisfied. Brain structures are thought to be recursively engaged across such tasks, with the amount of overlap in the involved brain structures reflecting the overlap in cognitive demands made by the tasks (e.g., attention, response inhibition, working memory, planning, decision making, etc.). Meta-analyses of neuroimaging studies indicate that three larger regions in the anterior brain play a crucial role during performance of many tasks regardless of the specific task demands: 1) medially around the anterior cingulate sulcus, 2) on the lateral surface around the inferior frontal sulcus (IFS), and 3) ventrolateral in the anterior insula (Duncan, 2010; Duncan and Owen, 2000; Mennes et al., 2006; Owen et al., 2005). These regions are frequently reported to be jointly active in studies of cognitive functions (e.g., Bengtsson et al., 2009; Bor and Owen, 2007; Cole and Schneider, 2007; Dosenbach et al., 2006; Satterthwaite et al., 2007), but also in less structured situations such as listening to music (Sridharan et al., 2008), viewing natural scenes (Bartels and Zeki, 2004) and even during rest (Dosenbach et al., 2007; Fox et al., 2005; Seeley et al., 2007; Sridharan et al., 2008). Non-model driven analyses of fMRI signal fluctuations during task performance and rest support ⁎ Corresponding author. Maastricht University, Faculty of Psychology and Neuroscience, Department of Neuropsychology and Psychopharmacology, P.O. Box 616, 6200 MD Maastricht, The Netherlands. Fax: +31 43 388 45 60. E-mail address: [email protected] (P. Stiers). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.03.078

the idea that these regions work together as a network (Cole and Schneider, 2007; Sridharan et al., 2008), often referred to as the “multiple cognitive demands” (Duncan, 2006; Duncan and Owen, 2000) or “central executive” network (Sridharan et al., 2008). Despite the growing evidence for this shared network, it is not clear how the network is engaged during the execution of different tasks (Cabeza and Nyberg, 2002). Studies that directly investigate coactivation in the same participants confirm the existence of voxel clusters that are commonly activated by different tasks within regions of the central executive network (Blasi et al., 2006; Derrfuss et al., 2004; McNab et al., 2008; Nyberg et al., 2003). The question remains, however, whether these commonly active voxels in some way distinguish the different tasks being executed. Animal studies show that task conditions are distinguished in lateral prefrontal cortex by different, partially overlapping groups of neurons (Hoshi et al., 2000; Johnston and Everling, 2006; Wallis and Miller, 2003; White and Wise, 1999). In addition, different phases within the same task are distinguished in prefrontal cortex by successive independent activity patterns across the sampled neurons (Lapish et al., 2008; Sigala et al., 2008). These results suggest that performing different tasks engages different assemblies of neurons within shared prefrontal regions. Neuron populations cannot be studied directly with fMRI. Yet, stimuli known to activate different distributed neuron populations have been shown to induce subtle changes in the MR-signal of voxels monitoring these neural populations (Haynes and Rees, 2005; Kamitani and Tong, 2005). These signal changes may be too subtle to generate a significant effect in traditional univariate statistical

P. Stiers et al. / NeuroImage 52 (2010) 252–262

analysis of single voxel data. They are, however, sufficiently reliable to be detected by multivariate methods that compare the response pattern of a group of voxels in a series of data samples taken from different experimental conditions (De Martino et al., 2008; Haxby et al., 2001; Norman et al., 2006). Therefore, multi-voxel pattern analysis provides the opportunity to test whether different tasks are represented by different subpopulations of neurons within frontal– insular multiple demand areas (Peelen, and Downing, 2006). Moreover, an analysis of associations in response modulations over time of voxels allows to investigate whether neuronal subpopulations involved in different tasks are functional connected within and across the multiple demand areas. Before addressing these questions, we first identified the frontal– insular areas of the network in a rigorous way. In a first step we scrutinized the brain for shared activations across four cognitive tasks performed by 12 participants during functional MR imaging. Four typical response control tasks were included (e.g., Duncan and Owen, 2000; Dosenbach et al., 2006): Go–nogo, Response Scheme Switching, spatial Back-matching, and Gambling. They share the basic stimulus– response paradigm, but each focuses on a distinct cognitive function: response inhibition, response switching, working memory and decision making, respectively. In addition, within each task, we manipulated the demand on the specific cognitive function in two levels of “task difficulty”. Overlapping task activations were studied in individual data sets, instead of at the group level, to avoid pseudooverlap that may arise in a group analysis due to averaging over nearby activations that slightly vary in location across different individuals (Brett et al., 2002; Devlin and Poldrack, 2007; Seghier et al., 2004). In the second step, we determined which of the identified shared activation areas showed “multiple demand” properties. In particular, we looked for shared areas that showed a significant signal increase with increased task demand regardless of the specific task being performed. For the areas that met these criteria we subsequently investigated whether their voxels (and underlying neurons) were differently engaged by each task, given the fact that they were already significantly engaged by all four tasks. To this end we studied the differential response pattern of each area's voxels in each of the four tasks, using both multi-voxel pattern analysis and correlation methods. Materials and methods Behavioural tasks Twelve healthy right-handed volunteers (19–45 years, 7 males) participated in this study, which was approved by the local ethics committee. All participants gave their informed consent to participate in the study. All participants performed four runs with each of four tasks in the scanner, in two separate sessions. There was from 4 h to 7 days between the first and second scan session. The order of runs and tasks was randomized for each participant, with the restriction that two runs of each task occurred in each session. A grey background and black fixation cross were used in all tasks. Stimuli were presented synchronous with the start of a scan. Inter-stimulus intervals jittering and a 33% addition of dummy trials were included to optimize deconvolution of the hemodynamic response in a fast event-related experimental design. Dummy trials had the same timing and jittering as regular trials, but no stimulus was presented. Each task consisted of four conditions grouped in a 2 × 2 design, of which one dimension was a manipulation of task demand (easy versus difficult). Go–nogo Participants should press a button with the right index finger when a red square, but not when a red circle, was presented for 1.5 s with a varying inter-stimulus interval of 0.45–4.35 s. Nogo (red circle)

253

frequency alternated every 25 trials between 30% (frequent) and 15% (infrequent) in an “ABAB” order. Each run lasted 8.475 min and consisted of 100 go/nogo trials and 50 randomly mixed dummy trials (fixation cross). Response scheme switching A horizontal array of five empty square-shaped boxes filled the width of the screen. A coloured square randomly jumped either to the left or the right from one box to the next, with 0.0–3.9 s between jumps. If the square was green (compatible condition), participants should press the button according to the side the square had moved. If the square was red (incompatible condition), they had to press the button opposite to the movement direction. Additional interference was created in two runs by overlaying a black arrow randomly pointing left or right on the square. Each run lasted 6.435 min and contained 66 response trials and 33 dummy trials (grey square without jump). Back-matching A 3 × 3 array of empty square-shaped boxes was constantly presented, while an “X” shaped stimulus was successively presented in one of the 9 boxes for 1.5 s with 0.45–4.35 s inter-stimulus interval. Participants monitored successive stimulus positions and should press a button with the right index finger when its current position matched the Nth preceding position. N, or the number of successive positions to monitor was 1-back in two runs and 2-back in two runs. Each run lasted 6.615 min and contained 80 back-matching trials of which 24 (30%) were targets and an additional 23 randomly intermingled dummy trials (only 3 × 3 array shown). Gambling (Mennes et al., 2008) In each trial a horizontal bar was presented with a proportional division (range 0.5/0.5 to 0.05/0.95) of colour (blue-yellow) that varied from trial to trial. Participants had to guess which of the two coloured sides hid a fictitious token and indicate their choice by pressing the corresponding button. They gained (from 10 to 100) or lost (from 0 to 100) points, indicated respectively above and below the bar, if the guess was correct or not. Participants could also decide to pass and gain 20 points without any risk. Four trials types along two dimensions were defined: 1) go versus nogo trials, depending on whether a button was pressed or not; 2) exogenous versus endogenous trials, depending on whether the stimulus suggested the appropriate choice or not. Trials with 20 or less points to gain in which the participant passed were exogenous nogo trials; trials with 0.80 or greater odds and a gain of at least 30 were exogenous go trials; all other trials (odds 0.50 to 0.75 and gain of at least 30 points) were endogenous trials in which the participant (and not the stimulus) guided the decision to gamble or pass. The gambling stimulus was presented 3.5 s and was in 2/3 of the trials followed 0.4–2.35 s later by feedback for 1.1 s indicating the outcome of the trial and updated total score. Inter-trial interval varied between 2.8 and 6.7 s. Each run had 50 trials and lasted 8.789 min. Although each task consisted of four conditions grouped in a 2 × 2 design not all conditions were used in the different analyses performed on the data. For identifying shared activations sites and for characterizing their functional properties all four task conditions of each task were used (see “Region of interest identification” and “Functional properties analysis”). However, for studying task-related properties of voxels within the shared activation sites only about 1/4 of the trials were selected (see “Multi-voxel pattern analysis”, “Voxel preference profiles”, and “Functional connectivity analysis”). This was necessary due to the fast event-related design of the tasks. Because of the fast succession of trials the BOLD response invoked by a particular trial also partially reflected the cumulative signal changes invoked by immediately preceding trials. To avoid any form of carry-over between trials selected for analysis, only a subset of “representative

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trials” per task was used: the pass trials for Gambling, all nogo trials for Go–nogo, one-half of all incompatible trials (regardless of interference or not) for Switching, and one-half of all one- and twoback matching non-target trials for Back-matching. For Switching and Back-matching only every other trial was included to keep the number of trials comparable to the number of trials in the first two tasks. This yielded 22.5% of trials for Go–nogo, 25% for Switching and 33% for Back-matching. In Gambling, where categorization of trials is also dependent on the gambling behaviour of the participants, the number of trials included was around 25%. To further avoid any carryover of signal from one trial to the next, trials were discarded if they followed a previously selected trial by less than 6 s. Image acquisition and preprocessing Data were acquired on a 3.0-T MR system (Achieva, Philips, Best, the Netherlands) with an eight-channel phased-array head coil. Functional images were acquired using a T2*-weighted gradient echo (GE) echo planar imaging (EPI) sequence (TR 1950 ms, TE 33 ms; flip angle 90°; 28 axial slices; acquisition matrix 128 × 128; and voxel size 1.875 × 1.875 × 4.8 mm). The number of volumes per run was 280 for Go–nogo, 220 for Switching, 225 for Back-matching and 290 for Gambling. A high-resolution T1-weighted MPRAGE image was also obtained (TR = 9.735 ms; TE = 4.59 ms; flip angle = 8°; voxel size = 0.651 × 0.651 × 1 mm; and 230 coronal slices). Stimuli were presented using the Eloquence fMRI system (InvivoMDE, MRI Devices Corporation Inc., Orlando, FL, USA). Image processing was performed with the Statistical Parametric Mapping software (SPM5; Wellcome Department of Cognitive Neurology, University College, London, UK). Preprocessing included slice time correction, realignment, coregistration to the anatomical image, non-linear normalisation of the anatomical image using discrete cosine transform, and finally co-normalisation of the functional images, including a reslicing to a 2 mm isotropic voxel grid. Region of interest identification A mass univariate single subject GLM analysis including all four tasks was performed on the 6 mm FWHM Gaussian smoothed data. Each condition was modelled with eight delta functions with one TR spacing. Runs and error trials were modelled separately. Session specific mean regressors were added to neutralize baseline differences per run that may bias results in the tasks with conditions manipulated between runs (Back-matching and Switching). First, task-related effects were analysed per task. A global disjunctive Fcontrast involving the four experimental conditions of the task (excluding errors, and feedback in Gambling) was defined. This contrast identified for a particular task any condition effect at scans 3– 5 (4 to 10 s) after trial onset. For Switching left and right stimulus jumps were pooled. This yielded per task a quantification of the strength with which the signal in each voxel was modulated by any of the task's conditions. Second, a conjunction contrast involving these four task-specific contrasts was created to identify voxels with a significant response in all four tasks. An FDR-corrected alpha level (Genovese et al., 2002) of 0.05 was used as a single subject whole brain correction for multiple comparisons. Thresholded conjunction maps were compared between participants to identify frontal–insular clusters that showed anatomical consistency across participants. Clusters were included if they could be identified at least unilaterally in half of the participants. This yielded 20 clusters with shared activation across the four tasks. Several well-known cortical areas outside of frontal cortex were identified as control areas. Four visual processing areas were identified with additional fMRI data gathered for each participant with a fast visual mapping sequence (Stiers et al., 2006): the inferior lateral occipital area (LOi), the posterior lateral fusiform gyrus area (LFGp), the human MT/V5+ area, and the frontal

eye fields (FEF). Two parietal clusters (IPL1–2) were also included. The hand representation in the primary motor (M1) cortex was identified as the local maximum closest to the lateral side of the hand knob in brain axial slices (Yousry et al., 1997). (See Table 1 for ROI details). Functional properties analysis Group-wise regions of interest (ROI) analyses were performed on percent signal change data from each task. Percent signal change data were computed voxel-wise from the betas estimated in the single subject single task GLM analysis described above. The areas identified as having shared activity according to the procedures described in the previous subsection were used as ROIs. Per participant and task, percent signal change data were averaged over the voxels belonging to one of the selected ROIs. This yielded per participant and task 32 percent signal change values, one for each of the four conditions by 8 time points of the design. These were entered into a 2-by-2 conditions by 8 time points repeated measures analysis of variance, with subjects as a random factor. A separate ANOVA was performed for each task and each ROI. Planned comparisons per time point were used to characterize task independent functional properties of ROIs. (1) Motor responsiveness was investigated with a contrast that looked for effects at scan 3 or at scan 4 after trial onset between trials with versus trials without a motor response, in the three tasks that included no-response trials: Go–nogo (go85% + go70% vs. nogo15% + nogo30%), Back-matching (1-back target + 2-back target vs. 1-back no-target + 2-back no-target), and Gambling (go-exogenous + go-endogenous vs. nogo-exogenous + nogo-endogenous). (2) A similar disjunctive contrast over scans 3–5 was defined between more and less cognitively demanding conditions in three tasks: Switching (incompatible arrow + incompatible no-arrow vs. compatible arrow + compatible no-arrow), Back-matching (2-back target + 2-back no-target vs. 1-back target + 1-back no-target), and Gambling (go-endogenous + nogo-endogenous vs. go-exogenous + nogo-exogenous). Go–nogo was not included because the 30% vs. 15% nogo frequency manipulation gave only modest BOLD response modulations that reached significance only in a few shared activation sites. A ROI was sensitive to motor responses and/or task difficulty if the respective contrasts were significant for each of the three tasks. A five out of six threshold was used if the ROI was considered bilaterally (three tasks × two hemispheres). The significance level was set to 0.05 without multiple comparisons correction, because the accumulation of chance was compensated by the unlikelihood of finding a positive result in three consecutive tests by chance. In five of the 20 shared activation clusters the task demand contrast was significant for all three tasks. These five clusters, which were consistently present in all participants (see Table 1), are said to have the multiple demand feature. They are the regions of interest for the remainder of the analyses. Multi-voxel pattern analysis A multi-voxel pattern analysis was performed on each of the 10 multiple demand clusters (5 in each hemisphere) identified as described above. The analysis was performed on each participant's data separately. The method was based on the procedure described in Haxby et al. (2001), and consisted of splitting each participant's dataset in two halves and establishing whether one-half of the data from one task correlated stronger with the other half of the same task compared to the other half of a different task. As outlined above, the identification of shared activation sites was based on co-activation across tasks and did not take into account differences in response strength between tasks, which are targeted in the multi-voxel pattern analysis. Therefore, the multi-voxel pattern analysis outcome is in no way determined by the voxel selection procedure. The correlation approach of Haxby et al. was chosen above more sophisticated

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Table 1 Activation sites commonly active during performance of cognitive tasks across participants, with overview of their functional characteristics. Activation sites N subj.b ID

Name

Side

Prefrontal areas 1 IFS1 Left Right 2 IFS2 Left Right 3 IFS3 Left Right 4 PCSi Left Right 5 INSa Left Right 6 SFSa Left Right 7 OFv Left Right 8 OFl Left Right 9 OFvl Left Right 10 CGi Left Right 11 MF7 Left Right 12 MF6 Left Right 13 MF5 Left Right 14 MF4 Left Right 15 MF3 Left Right 16 MF2 Left Right 17 MF1 Left Right 18 MFa Left Right 19 Thal Left Right 20 nCaud Left Right Control areas 21 IPL1 22

IPL2

23

MT/V5

24

FEF

25

LFGp

26

LOi

27

M1

Functional characteristicsa

MNI coordinates (mm)

Left Right Left Right Left Right Left Right Left Right Left Right Left Right

Bil.

Unil.

12 12 12 12 6 5 11 11 12 12 12 10 9 7 7 9 11 9 12 12 12 12 9 9 9 9 11 12 12 12 10 11 7 8 7 5 8 4 6 5

12

11 12 12 11 12 12 11 12 12 12 12 12 12 12

12 7 12 12 12 10 11 11 12 12 10 11 12 12 12 9 8 9 6

12 12 12 12 12 12 12

X

Y

Z

M

SD

M

SD

− 48.0 50.4 − 45.5 47.8 − 42.5 41.1 − 54.9 55.3 − 33.0 33.8 − 23.7 24.3 − 32.6 33.6 − 26.2 25.7 − 48.0 49.2 − 55.7 56.7 − 6.9 7.6 − 8.8 9.1 − 7.4 6.0 − 6.0 5.7 − 6.2 5.7 − 5.7 6.8 − 7.0 5.8 − 9.0 10.7 − 10.9 10.9 − 21.8 21.1

5.3 4.6 8.1 6.3 4.3 4.9 7.7 3.7 2.7 2.3 3.6 3.9 4.6 6.5 9.3 6.5 4.3 3.8 5.0 4.5 3.1 2.9 3.1 2.1 2.7 3.7 2.4 3.8 2.1 3.2 2.0 2.5 3.0 3.3 2.4 8.9 3.1 2.2 2.7 4.1

5.6 4.9 20.6 18.4 35.5 32.3 11.1 8.7 25.8 24.5 35.9 37.1 39.6 39.4 54.6 55.0 33.8 33.3 − 14.1 − 16.0 0.0 5.3 − 16.5 − 18.7 − 8.4 − 8.2 1.3 4.6 11.0 12.0 14.8 17.0 24.9 25.4 54.7 52.1 − 16.3 − 13.1 − 13.2 − 11.4

3.4 3.5 5.2 4.0 1.5 8.3 3.5 6.4 4.6 4.6 5.2 6.7 4.7 4.9 5.4 4.8 5.4 4.4 4.9 5.7 3.5 3.6 3.6 4.7 5.0 5.9 4.9 3.5 3.5 3.8 4.8 3.1 3.3 5.8 5.5 6.2 2.3 1.0 4.1 5.9

− 47.1 48.2 − 46.9 46.3 − 47.5 46.3 − 28.9 26.5 − 37.4 37.8 − 40.7 38.8 − 37.3 41.2

5.1 7.7 6.0 3.4 5.1 3.5 5.5 3.4 5.7 4.6 5.3 4.8 5.3 5.1

− 31.1 − 33.4 − 63.3 − 67.8 − 64.8 − 64.7 − 1.4 − 3.0 − 71.5 − 69.0 − 80.8 − 81.2 − 17.5 − 18.2

7.0 3.8 7.1 5.9 6.9 5.3 6.2 7.7 3.6 4.9 3.9 2.8 4.0 8.2

N voxels

M

Response

Motor response gng

SD

M

SD

direction

37.9 38.7 28.7 30.5 22.2 21.2 16.3 14.7 5.9 5.3 45.1 44.4 − 8.5 − 9.2 17.9 22.0 5.0 3.1 19.7 19.7 69.1 69.1 50.2 51.5 54.4 54.3 57.5 56.4 51.4 54.2 45.6 45.7 36.4 37.6 11.8 4.1 6.9 9.4 27.3 26.4

6.1 5.7 4.4 2.9 4.7 4.5 7.3 7.2 3.8 3.4 5.9 7.9 4.9 4.8 8.3 9.5 4.7 5.4 4.5 4.8 3.5 3.8 6.0 3.6 3.2 5.3 5.5 5.2 6.7 5.0 4.5 4.0 4.3 7.0 4.5 5.9 2.5 1.9 3.4 3.5

112.3 111.5 53.8 37.8 34.7 34.8 41.2 39.7 46.9 50.7 45.7 55.5 7.8 10.7 41.6 33.8 58.4 29.1 136.1 62.5 21.7 22.5 25.0 24.7 49.0 58.4 117.5 56.9 89.8 84.1 75.9 58.5 13.4 43.8 4.9 11.2 30.9 14.0 12.0 18.8

59.6 43.1 46.5 17.3 26.1 35.9 31.5 27.9 30.8 58.2 29.6 40.8 6.7 7.1 42.2 30.1 40.5 33.0 54.9 43.2 9.3 9.0 7.9 10.4 44.2 34.0 51.9 36.0 65.3 53.9 46.8 35.0 10.6 39.0 2.9 7.4 33.3 14.3 11.0 13.0

+ + + + + + + + + + neg neg neg neg + + neg neg + + + + neg neg + + + + + + + + + + neg neg + + neg neg

21.6 20.7 35.3 33.9 6.7 5.4 58.7 59.7 − 14.7 − 16.5 4.6 2.1 59.6 57.3

3.5 5.3 7.4 6.6 4.9 4.6 5.7 5.7 3.3 2.2 4.6 7.5 4.9 5.5

44.5 43.3 88.2 57.8 99.1 134.4 72.0 51.3 144.2 150.0 145.6 114.1 173.3 128.9

31.3 31.4 59.0 65.7 40.2 48.7 37.5 29.4 65.6 69.7 63.5 45.5 67.2 64.6

+ + neg neg + + + + + + + + + +

bm

gbl

Task difficulty MS

gng

x

x

x x x x

x x

swt

bm

gbl

TS

x x x x

x x x x x

x x x x

X X X X

x x

x x

x x x x

X X

x x

x x

X X

x

x x x

X

X X

x x x x x x x x x

x x x x

x x x x x x x x

x x x x

x x x x x x

x x x x x x x

X X X X

x x x

x

X X X X

x

x x x x

x

x x

x x x

x

x x x

x x x

x x

x

x x

x x

X x

x x x

x x x

x x x

X X

Note. IFS inferior frontal sulcus; PCSi precentral sulcus, inferior segment; INSa anterior insula; SFSa anterior part of superior frontal sulcus; OF orbitofrontal cortex, ‘v’ ventral, ‘l’ lateral, ‘vl’ ventrolateral; PCGi inferior part of precentral gyrus; MF medial frontal cortex, ‘a’ anterior; Thal thalamus; nCaud caudate nucleus; IPL inferior parietal lobule; MTV5 medial temporal/V5 areas; FEF frontal eye fields; LFGp posterior visual area in lateral fusiform gyrus (1); LOi inferior visual area in lateral occipital cortex (1); M1 primary motor cortex. a Task transcending functional characteristics. Response direction = stimulus related MR-signal increase (“+”) or decrease (“neg”) consistent across tasks. Motor response sensitivity (MS): significant main effect of motor trials over no motor trials in Go–nogo (“gng”), Back-matching (“bm”) and Gambling (“gbl”). Task difficulty sensitivity (TS): significant main effect of more over less difficult conditions for three of the four tasks, i.e., infrequent (15%) versus more frequent (30%) nogo trials for Go–nogo, incompatible versus compatible trials for Switching (“swt”), two versus one-back trials for Back-matching, endogenous versus exogenous trials in Gambling. “x” = significant main effect; “X” = sensitivity criterion met. b Number of individual data sets in which the shared activation side could be identified, either bilateral (Bil.) or unilateral (Unil.).

machine learning approaches to pattern analysis, because our fast event-related task design generated BOLD response overlap in subsequent trials. Cumulation of BOLD responses over time points prevents accurate estimation of the signal change invoked by a

particular trial. By averaging signal over trials, as in the correlation method, the “noise” invoked by a trial's prior history is randomized. For each voxel, epochs were created from the unsmoothed time-series data after removing low frequency fluctuations and linear trends.

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Epochs spanned the 3rd to 5th scan (i.e., 4–10 s) after the onset of a representative trial for each task as defined in the section “Behavioural tasks” above. A peak response value for the first and the second half of the trials was determined per task. To this end, the raw signal values were averaged over the trials to yield an average epoch and the maximum or peak value in the average epoch was established. Within-task correlation was computed between first and second half voxel peak response values obtained from the first and second half of the data of one task. Note that this coincides with splitting the data according to the first and second data acquisition session. Betweentask correlations were calculated between the peak values from the first half of one task and the peak values in the same voxels from the second half of each of the other three tasks. The frequency of the within-task correlation being higher than each of the between-task correlations, divided by the number of comparisons (12 participants × 4 tasks × 3 comparisons per task), yielded the identification accuracy for a particular activation site (Table 2). Voxel preference profiles Voxel preference profiles were established from the peak response values in each task, averaged over all epochs. Fourteen possible preference profiles can be distinguished. These prototypical preference profiles can be represented in a binary way as vectors with the positions of ones in the vector indicating which task(s) being preferred by a voxel: [1 0 0 0], [0 1 0 0], …, [1 0 1 0], …, [1 1 1 0], …, and [1 1 1 1]. The digit order corresponded to Go–nogo, Switching, Back-matching, and Gambling. Each voxel's observed profile of peak task response values was correlated with each of the 14 binary profile types and the voxel was assigned to the type that yielded the highest correlation. The 15th profile type [0 0 0 0], signifying no preference, was assigned when there was no significant univariate response strength difference over tasks, as assessed with a global F-contrast defined on the single subject GLM analysis that integrated data from the four tasks (see also above, section “Functional properties analysis”). Functional connectivity analysis For the functional connectivity analysis voxel-wise time-series vectors were created that represented the response strength in the

Table 2 Multi-voxel pattern identification and preference profile analysis in 10 activation sites that showed task difficulty sensitivity. Activation sites

Identification proportiona

% voxels with sign profileb

ID name

Side

% corr

M

SD

IFS1

Left Right Left Right Left Right Left Right Left Right

63.2** 61.8* 57.6* 53.0 ns 58.3* 59.2* 64.6** 66.0** 64.2** 67.4** 87.5**

65.0 65.0 41.5 56.1 64.0 51.0 75.8 78.4 71.5 73.7

27.1 33.1 37.3 44.2 41.4 40.5 32.2 30.8 34.0 35.2

IFS2 INSa 1MF3 1MF2 All sites combined a

Percentage of pair-wise within-task versus between-task comparisons, summed over tasks and participants, in which the former yielded the highest correlation. Statistics are cumulative binomial coefficients: **significant at alpha 0.005 (corresponding to the Bonferroni corrected level 0.05); *significant at alpha 0.05, uncorrected. b Average percentage of voxels within the cluster that showed a significant difference in response amplitude in an overall voxel-wise F-contrast between the representative condition of each of the four tasks.

fourth scan after trial onset in the representative set of trials for a particular task (see section “Multi-voxel pattern analysis”). For each voxel in the 10 shared activation sites (i.e. 5 bilateral sites) four such vectors were created, one for each task. Voxels were included in this analysis based on their preference profile. In fact, we ran two separate analyses, one for voxels with a single task preference (i.e., profiles of the type [1 0 0 0] or [0 1 0 0], etc.), and one for voxels preferring two of the four tasks (i.e., profiles of the type [1 0 1 0] or [0 1 1 0], etc.). Because both analyses yielded very similar results, only the results of the first analysis are presented with some detail in the Results section. Within each analysis the vectors of all selected voxels derived from the same task were cross-correlated, yielding a voxel-by-voxel correlation matrix for a particular task. This matrix was computed for each task. To investigate specific hypotheses, pairs of voxels within these matrices were selected to create specific voxel pair groups (e.g., same preference voxels in the same activation site, or voxel pairs with different preference types during a particular task regardless of their location, etc.), and the correlation strengths of the pairs were used as dependent measures in statistical tests. These tests were run separately on individual participant data at the uncorrected 0.05 significance level. Consistency of correlation patterns across participants was investigated in two ways. First, the frequency of significant outcome of a particular test across participants was obtained and its likelihood tested given the expected proportion 0.05 with the cumulative binomial coefficient. Bonferroni correction was applied in evaluating the significance of these frequencies. Second, the average correlation strengths in particular voxel pair groups for each participant were used as dependent variables in group-wise analyses of variance. To evaluate the significance of an association between two activation sites, voxel pairs with the same preference located each in one of the activation sites were selected, and their average correlation was computed. To evaluate the significance of functional connectivity, a sample of voxel pair correlations similar to the original data was created by computing for each voxel pair initially selected for the analysis the correlation between time courses during different task (as opposed to time courses from the same task). For each evaluation of functional association the same number of correlations was randomly selected from this null correlation sample as used in the original correlation, and the average correlation was computed. This was repeated 10 000 times and the frequency of finding an average correlation in the null correlation sample, that was equal to or higher than the one observed in the real data was noted. If this frequency was less than 0.05, the functional connectivity between same preferences voxels in both sites, and hence between the sites, was said to be significant. Data from one participant were excluded from connectivity analysis because the number of voxels with a significantly different response across tasks was too low to yield sufficient numbers of voxel pairs in the various comparisons made. Results Identification of frontal–insular multiple demand areas The first of two steps to identify multiple demand areas consisted of finding clusters of voxel that were activated in all four tasks. For each participant such voxels with shared activity across all four tasks were identified by a conjunction of four task-specific contrasts each depicting a significant effect in any condition of one task. Clusters in frontal–insular cortex or related subcortical structures were included if they could be identified at least unilaterally in half of the participants. Twenty activation sites met this criterion (see Fig. 1 and Table 1). Seven additional shared activation sites outside prefrontal cortex were included to serve as control sites. In the second step towards identifying multiple demand areas we asked whether the BOLD response in the shared activation sites

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sensitivity (subtype 2) and the negative responsive activation sites (subtype 3)). Of the 20 shared activation sites, five (i.e., 10 when both hemispheres are considered) showed a positive BOLD response that increased with task demand. These were located in the anterior insula (INSa), two in the inferior frontal sulcus (IFS1 and IFS2) and two in the cingulate sulcus (MF3 and MF2) (Fig. 2). The location of these sites corresponds well with previous reports of common activation in the medial, lateral and ventrolateral regions of frontal cortex (Derrfuss et al., 2004; Dosenbach et al., 2006; Duncan and Owen, 2000; Mennes et al., 2006; Nyberg et al., 2003) (see SM, Table S1 for a comparison with other studies). In addition, two lateral frontal sites showed a positive response unaffected by motor execution (IFS3 and OFl, see Fig. 2). Because task difficulty modulation was inconsistent in these areas, they were not included as key areas. On the medial side, bilaterally, MF1 showed a somewhat unique pattern, since a significant task difficulty effect was found only in Gambling. Because in all other tasks the response decision is determined exogenously by the stimulus, this finding suggests that MF1 is involved in endogenous decision making. This is in line with previous reports of a rostral cingulate area (see SM, Table S1) that is more active when participants make voluntary choices compared to being instructed which task to perform (Bengtsson et al., 2009; Forstmann et al., 2006). Multi-voxel response patterns in multiple demand sites are task selective Fig. 1. Location of brain areas commonly active during performance of four cognitive tasks. Areas were identified in each of 12 participants. Colour coding, superimposed on anatomical template brain image (Colin brain; http://brainmap.wustl.edu:8081/ sums/directory.do?id=636032; displayed using CARET software; http://brainmap. wustl.edu/caret; Van Essen et al., 2001; Van Essen, 2002) indicates for each voxel the number of participants in which that voxel was part of a shared activation site (light green to red: shared voxel in one to six or more participants). Dots represent the centre of mass of the site averaged across participants. Red dots depict frontal–insular areas; yellow dots depict control areas (see Table 1). Each area is identified by a number attributed in Table 1. (A) Flat map of the right hemisphere. (B) Partial inflated reconstruction of the left hemisphere, showing a medial and a lateral view. Black arrows indicate major sulci: CS central sulcus; PrCS precentral sulcus; CiS cingulate sulcus; SFS superior frontal sulcus; IFS inferior frontal sulcus; ITS inferior temporal sulcus; OTS occipital–temporal sulcus; CoS collateral sulcus; and IPS intraparietal sulcus.

identified in the previous step was modulated by the task difficulty level. For each of the shared sites average percent signal change data were extracted per participant for each condition in each task, at eight time points (scans) after trial onset. These percent signal change data were entered into an ANOVA for each activation site and task separately, with 2 × 2 task conditions by 8 time points as between subject variables. This group-wise region of interest analysis revealed that most sites exhibited consistent, task and participant transcending, functional characteristics. Four functional subtypes could be distinguished (see Table 1, and Fig. S1 in SM): (1) positive signal change modulated by the difficulty of the task condition (i.e., main effect of difficult over easy conditions in Switching, Backmatching, and Gambling); (2) positive signal change modulated by the execution of a motor response (i.e., main effect of response over no response in Go–nogo, Back-matching, and Gambling); (3) consistent negative signal change contingent upon stimulus presentation; and (4) positive signal change without consistent modulations across tasks (see Materials and methods, Functional properties analysis for a characterization of these subtypes). The spatial distribution of these functional types is shown in Fig. 2. Our current interest is in the core multiple demand network. Therefore, we present here only results on activation sites that showed a positive response not dominated by the motor aspects of the tasks (i.e., functional subtypes 1 and 4; see SM, Functional characteristics of frontal–insular shared activation sites for details on motor response

To address the question whether the neurons underlying shared activation sites exhibit a distinctive response pattern during the execution of each task, we did a split-half correlation analysis (Haxby et al., 2001) of multi-voxel responses in individual participant data sets. Unsmoothed data from 4 to 10 s after the onset of the trails of a representative condition of each task were extracted in multiple demand voxels. The trials were split in half (i.e., data from the first and second session separated), and for each half and task the peak value in the average signal in the time window 4–10 s after trial start was computed. These peak response values from different halves of the same task were used to calculate the within-task correlation — i.e., the correlation between the peak values across the voxels of a site during the first and second half (or session) of the same task. Next, we compared the within-task correlation for each task with the betweentask correlations of each of the three other tasks — i.e., the correlation of the voxel peak values during the first session of task A and the second session of task B. If a task drives a selective subgroup of neurons in the activation site, the distributed pattern of voxel peak values should be characteristic for that task. Consequently, the responses across voxels should be more similar when performing the same task twice than when performing different tasks. This expectation was clearly confirmed by our correlation analysis. In all but one of the shared activation sites the incidence of higher withinthan between-task correlation was significantly above chance (see Table 2, middle section). Furthermore, because there was from 4 h to 7 days between the first and second scan sessions, this result implies that the task characteristic voxel response patterns are reliable over time. Multiple demand voxels have reliable task preference profiles The task-specific voxel response patterns are made directly visible in Fig. 3. For a selection of shared activation sites and individual participants Fig. 3 shows the relative peak response values associated with each of the four tasks in successive voxels at the grey/white matter boundary in cross sections through the clusters. The fact that each task invoked a significant increase in BOLD signal in all voxels of the commonly active areas, even in voxels that did not “prefer” the particular task at hand, indicates that neurons involved in representations relevant for each separate task were scattered throughout the

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Fig. 2. Distribution of functional response types in shared frontal–insular activation sites on the lateral and medial side of the brain. Bar graphs represent the percent signal difference between conditions selected in each task to define a particular functional property (see Materials and methods, Functional properties analysis). Differences between conditions with vs. without a motor response are represented by blue bars. Differences between more vs. less demanding task conditions are represented by brown bars. In both cases background bars represent the difference value for the left and foreground bars for the right hemisphere homologue of an activation site. Activation sites (mean centre of mass across participants) are indicated by coloured dots on the same cortical surface reconstruction as in Fig. 1. Dot colour represents functional category of activation site: orange = sites that meet the task difficulty criterion; blue = sites that meet the motor response sensitivity criterion; pink = endogenous decisions only site; green = sites with consistent negative signal change across tasks and conditions; yellow = control sites with none of these functional characteristics. INSa = anterior insula; IFS = inferior frontal sulcus; MF = medial frontal; Ofl = orbital frontal lateral; PCSi = precentral sulcus inferior; CGi = central gyrus inferior; M1 = primary motor cortex (central gyrus); and THAL = thalamus.

entire activated area. Fluctuations in the density of neurons involved in each particular task are probably responsible for the stronger or weaker signal measured in different voxels in different tasks, and hence, for the observed task preference profiles of each voxel. In fact, the response strength difference of voxels to different tasks was so strong that more than half of the voxels showed a univariate significant difference in response strength between tasks (see Table 2, last two columns). The differential responsiveness of voxels during different tasks justified the grouping of voxels into task preference subcategories based on their peak task response values as illustrated in Fig. 3. The classification of voxels in discreet preference classes provides the opportunity to study functional connectivity between voxels in the same or in different preference classes (see below). Thus, a voxel preferring Go–nogo was placed in the category [1 0 0 0], a voxel preferring Gambling was categorized as [0 0 0 1], and a voxel preferring both was placed in [1 0 0 1], etc. Categorization was based on correlating a voxel's actual profile of peak task responses with each of these category prototype vectors, and assigning the voxel to the category whose prototype vector yielded the highest correlation, as detailed in the Materials and methods subsection Voxel preference profiles. We used statistical cross-validation to demonstrate the reliability of these task preference categories (Baker et al., 2007). The first half of the trials of each task was used to classify voxels into task preference profiles, while the second half was used to obtain an independent confirmation of the statistical significance of the response strength difference within each profile. For the latter step

same preference voxels from all 10 shared activation sites were pooled and entered into a one-factor analysis of variance with each task comprising one level of the factor (separate for each participant, p b 0.05 uncorrected). For all preference types the incidence of participants having a significant second half response profile was much higher than the 5% expected by chance, given the uncorrected significance level of 0.05 (see Table 3). Fig. 4 illustrates some task preference profiles for some participants.

Same preference voxels show stronger functional connectivity The fact that subregions of shared activation sites exhibit different task preference profiles raises the question whether same preference voxels are functionally connected between, as well as within areas. The strength of correlations in response amplitudes over time between same preference voxels provides an indication of the strength of such functional connections. Response amplitude measures in time were obtained for each voxel and task by taking the signal strength in unsmoothed data in the 4th scan after onset of each representative trial. Only voxels with a preference for one task were included (e.g., [1 0 0 0], or [0 1 0 0], etc.). For each of the selected voxels the response strength over time was correlated with that of all other selected voxels, yielding a complete voxel-by-voxel correlation matrix. Next, subgroups of voxel pairs were selected from this matrix based on task preference and site location, and their mean correlations were statistically compared within as well as across participants.

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Table 3 Reliability of preference profiles. Task preference types

Number of voxels

Significant preference profile

gng

swt

bm

gbl

M

SD

%a

Sign.b

1 0 0 0 1 1 1 0 0 0 1 1 1 0

0 1 0 0 1 0 0 1 1 0 1 1 0 1

0 0 1 0 0 1 0 1 0 1 1 0 1 1

0 0 0 1 0 0 1 0 1 1 0 1 1 1

131.8 309.8 70.7 293.2 166.4 63.8 110.3 186.5 213.8 154.8 127.6 99.0 102.3 364.1

77.7 156.9 80.9 267.8 169.6 67.6 77.7 142.7 177.1 149.5 109.7 94.9 111.9 208.0

83.3 100.0 75.0 91.7 100.0 66.7 91.7 100.0 100.0 91.7 91.7 100.0 91.7 100.0

** ** ** ** ** ** ** ** ** ** ** ** ** **

Note. gng Go–nogo; swt Switching; bm Back-matching; gbl Gambling. a Percentage of participants that showed a reliable preference profile over sessions for voxels of the task preference type indicated in the first column. The profile was reliable if the response strength of the voxels with this particular preference type in the second session was significantly different for the four tasks. The response profile type of a voxel was determined independently on similar data from the first session. b Cumulative binomial coefficient: **significant at Bonferroni corrected alpha 0.05/ 14; *significant at alpha 0.05 uncorrected.

Fig. 3. Spatial distribution of task preference within three multiple demand areas (columns) in three representative participants (rows). Each sub-figure depicts an anatomical slice through the site mentioned at the MNI-position indicated in the left upper corner. The double-pointed arrow in the right upper corner depicts anatomical dimension (‘a’ = anterior; ‘p’ = posterior; ‘s’ = superior; and ‘i’ = inferior). Voxels within the segmented grey matter of the site are represented by coloured squares. Each task is coded by a different colour (see legend), and square colours are the added task colours weighted by the tasks relative response strength in the voxel. Voxels with no univariate difference between tasks are represented as white. Line graphs at the bottom of each slice represent the peak response values for each task from one voxel to the next. For representation the peak values were normalised to the range of peak value in that set of voxels. Only successive voxels at the grey/white matter boundary are depicted, starting at the point indicated by “A”, and following the boundary up to the point “B” (see the dotted blue arrow in the upper left sub-figure, and the grey arrow at the bottom of each graph).

The correlation analysis fully confirmed the hypothesis that same preference voxels are strongly connected (see details in Table 4). Voxel correlations were significantly higher between pairs preferring the same task than between pairs preferring different tasks (t (20) = 5.6, p b 0.001). This pattern was independent of whether only voxels were considered that lay in the same cluster, or whether only voxels lying in a different cluster were included, even though voxel correlations were overall higher for within cluster pairs (Table 4, first section). The result was also the same for each of the tasks separately and for each preference type separately (Table 4, second and third section, respectively). The relative strengths of

same preference functional connections between different activation sites are graphically presented in Fig. S2 (see SM, Table S2 for details). Similar analyses for voxels preferring two of the four tasks (e.g., [1 1 0 0], [1 0 1 0], etc.) yielded similar results (data not shown). These connectivity results are consistent with the idea that voxels across the multiple demand sites that exhibit similar preference profiles constitute sub-networks within the larger network of shared activated sites. Lapish et al. (2008) observed similar inter-correlated activity between neurons in the rat anterior cingulate cortex that coded for a particular task phase. They suggested that these functional connections might be temporary, because the output of the same neurons was not significantly correlated during other phases of the same task. This temporary nature of functional connections is not supported by the present data, as we did not consistently found that same preference voxels have higher response inter-correlations during performance of the preferred compared to non-preferred tasks (t (20) = 1.01, ns; see Table 4, last section, for more details). Therefore, based on the current results we have to conclude that voxels with the same task preference show more permanent functional associations that are also evident during performance of less preferred tasks. Discussion The variety of tasks that humans can perform in a wide range of contexts makes it highly likely that the same cortical areas are recursively used when performing these tasks. Our survey of the extent of within-subject co-activation across tasks in frontal–insular cortex provides direct support for this hypothesis, as 20 bilateral shared activation sites could be identified consistently across participants. In some of these sites the response was larger for trials involving a motor response, indicating that these sites play a role in motor response execution. Other shared activation sites showed a negative BOLD response, the signature of default mode areas, which are thought to be inhibited during active stimulus processing (Buckner et al., 2008; Singh and Fawcett, 2008; Sridharan et al., 2008). Only five shared activation sites showed a consistent response strength increase with increasing task demands, indicating that their activity level increases whenever a task becomes more difficult, regardless of the specific task requirements. This warrants the claim

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Fig. 4. Reliability of preference profiles for three participants (participants 3, 8 and 10) and seven profile types. Voxels were categorized into preference categories from their response strength in each of the four tasks during the first half of the trials of each task (see Materials and methods, Voxel preference profiles). Preference categories are indicated by a four digit vector. Each digit indicates one task: Go–nogo (GN), Switching (SW), Back-matching (BM), Gambling (GA). “1” indicated preferred and “0” indicates not preferred. The graphs present the average response strength recorded during the second half of the trials of the same four tasks. For visualization the response strengths were normalised to the maximum and minimum average response strength observed over response types within one participant. The graphs illustrate that the average response strength observed during the second half of the trials in most cases confirms the profile type attributed to the same voxels based on their response profile during the first half of the trials. Table 4 Functional connectivity. Analysis of correlations between trial-wise response strengths in pairs of voxels in relation to task preference profiles and the tasks being executed. Conditions

Group analysisa

Descriptive statistics n

M

SD

t

df

p 1−t

11 11 11 11 11 11

0.283 0.173 0.462 0.249 0.224 0.161

0.072 0.048 0.072 0.051 0.073 0.050

5.6

20

0.0000

7.9

20

0.0000

2.6

20

0.0160

Same preference voxels Different preference voxels Same preference voxels Different preference voxels Same preference voxels Different preference voxels Same preference voxels Different preference voxels

11 11 11 11 11 11 11 11

0.228 0.131 0.304 0.192 0.327 0.195 0.271 0.175

0.068 0.063 0.100 0.091 0.110 0.082 0.078 0.067

4.3

20

0.0004

4.7

20

0.0001

5.5

20

0.0000

4.2

20

0.0004

Preference similarity per preference category Preference type Preference similarity [1,0,0,0] Same preference voxels Different preference voxels [0,1,0,0] Same preference voxels Different preference voxels [0,0,1,0] Same preference voxels Different preference voxels [0,0,0,1] Same preference voxels Different preference voxels

11 11 7 8 11 11 8 11

0.270 0.172 0.187 0.139 0.290 0.176 0.237 0.159

0.102 0.048 0.065 0.051 0.102 0.047 0.118 0.071

4.2

20

0.0004

2.4

13

0.0338

4.3

20

0.0003

2.2

16

0.0394

0.244 0.279 0.240 0.169 0.316 0.281 0.253 0.232 0.296 0.278

0.123 0.096 0.137 0.050 0.146 0.096 0.086 0.140 0.114 0.063

1.2

20

0.2304

1.7

12

0.1163

1.2

20

0.2587

0.9

14

0.3602

1.0

20

0.3228

Preference similarity across tasks/profiles All voxel pairs Same preference voxels Different preference voxels Within siteb voxel pairs Same preference voxels Different preference voxels b Between sites voxel pairs Same preference voxels Different preference voxels Preference similarity per task Gambling Go–nogo Switching Back-matching

Correlation strength of same preference voxel pairs during preferred or non-preferred task Preference type Tasks [1,0,0,0] Preferred task 11 Non-preferred tasks 11 [0,1,0,0] Preferred task 7 Non-preferred tasks 7 [0,0,1,0] Preferred task 11 Non-preferred tasks 11 [0,0,0,1] Preferred task 8 Non-preferred tasks 8 All profiles Preferred task: 11 Non-preferred task: 11 a

For each participant the average correlation in each condition was entered into a t-test to assess the significance of connectivity at group level. Probabilities are one-tailed. Same comparison as the first one (i.e., pooling over tasks and preference profiles), but with inclusion only of correlations between voxels located in a the same cluster (“within site voxel pairs”) or of correlations between voxels located in different clusters (“between site voxel pairs”). b

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that they are multiple demand areas (Duncan, 2006; Duncan and Owen, 2000). The location of these five activation sites corresponds well with the three regions of a “central executive” network (Duncan and Owen, 2000; Mennes et al., 2006; Cole and Schneider, 2007; Sridharan et al., 2008; Derrfuss et al., 2004; Dosenbach et al., 2006): one in the anterior insula, two in the lateral prefrontal cortex, and two in the dorsal anterior cingulate cortex (dACC) (see SM, Table S1 for a comparison with other studies). Given that the four tasks each focus on a different cognitive process – i.e., inhibition of prepotent response, response scheme switching, working memory, and endogenous decision making – the multiple demand property implies that the five areas show no specificity for the distinctive cognitive processes targeted by each of the four tasks. Rather, they must be involved in the processing required by the basic stimulus–response control paradigm common to the four tasks (e.g., response control functions, attention mechanisms, interference handling, error monitoring, memory retrieval, etc.). Since these processes are central to any form of attentive performance it seems justified coining these activation sites “central executive” areas (Sridharan et al., 2008). What the distinct contribution of each multiple demand area to these central executive processes may be is a current topic of research (e.g. Cole and Schneider, 2007; Dosenbach et al., 2008). Although the voxels in the five sites show a significant signal increase in each of the four tasks, most of the co-active voxels show a higher (or lower) signal change in one or more of the tasks (see Fig. 3). This indicates that during execution, each task is associated with a unique distributed pattern of responses across the voxels in the shared activation site. This distributed pattern was sufficiently reliable over time to allow identifying above chance which task was performed based on each voxel's response amplitudes. Previous studies showed that voxel response patterns can reliably reflect the stimulus that is currently viewed (Haynes and Rees, 2005; Kamitani and Tong, 2005), and even what stimulus of a previously trained set is imagined (Kamitani and Tong, 2005). Recently, Haynes et al. (2007) found that voxel samples in prefrontal cortex distinguish which of two task options a person chooses to perform, even before s/he starts to perform. Our results show that similar encoding is also evident in each of the multiple demand areas during actual task execution. This finding is mirrored by recent results obtained with multiple single-unit recording methods in animals. These studies show that different phases of a task are represented by the distributed pattern of responses in the group of neurons sampled. Sigala et al. (2008) reported independent activity patterns distributed over several hundreds of neurons recorded in the macaque monkey lateral prefrontal cortex for the cue, delay and target phase of a cue–target association task. Similarly, Lapish et al. (2008) found reliably distinct population activity patterns in the rat anterior cingulate cortex during different phases of a radial arm-maze task. If we depict the four tasks of the present study as different phases in an extended fMRI task, the cell recording data suggest that different, possibly overlapping subpopulations of neurons within the multiple demand areas sustain the distributed patterns observed for each of the four tasks. The present data extend the results by Lapish et al. and Sigala et al. in two important ways. First, while these authors show distributed coding of task phases independently for two cortical areas, the whole brain approach of fMRI allowed us to establish these functional characteristics at once for five multiple demand areas. Second, by correlating voxel time-series data across areas we showed that finescale functional associations exist between these areas that are strongest for voxels with similar task preference profiles. To the extent that voxel preference profiles reflect distributions of neuron assemblies, this finding indicates that the subpopulations of neurons that are specifically engaged during a particular task are functionally connected across areas. In other words, functional neuron assemblies are not restricted to an area but extend across areas as functional sub-

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networks within the large-scale network (see SM, Fig. S3 for a graphical depiction of a model that would fit our data). The most intriguing question about the distributed encoding patterns concerns what they actually represent. Since the patterns are task selective, it is likely that their content is related to some of the features that distinguish different tasks. This is consistent with the biased competition model of frontal functioning (Bunge et al., 2003; Duncan, 2006; Miller, 2000; Sakai, and Passingham, 2006). According to this view, frontal cortex is responsible for asserting the cognitive control necessary to produce well-organized goal-directed behaviour by representing the various pieces of information (stimulus features, environmental factors, motivational states, motor responses and stimulus–response schemes) necessary to perform a particular task, and biasing signals in other brain structures in favour of these elements. Competition between such representations initiates the difficulty of doing several things at once. In line with this, frontal cortex has often been depicted as a working memory storage for the complex sets of information that bind the distributed representations and states that make up the task. A more cognitive version of this biased competition model may be put forward if we assume that the unique combination of cognitive functions needed to perform a task are also part of what makes the task. While specific cognitive functions are implemented by specialized structures elsewhere in the brain, sub-networks in the multiple demand areas might be responsible for the binding and coordination of these other brain structures to successfully perform a particular task. For instance, the need for working memory processes in a particular task would be represented by a different neural assembly than the need for decision processes, response preparation processes, or multiple response schemes, etc. This could explain why we found that functional connections are not restricted to execution of the preferred task. In this view, cognitive processes are not (solely) associated with specialized areas within frontal cortex, but with different distributed sub-networks of neurons within the multiple demand areas, combining the cognitive resources of the brain in a task-specific manner. It will be the challenge of future research to establish whether this is the case by studying the stability of the distributed sub-networks representing a particular task under varying stimulus, response, task rule and task context conditions. Acknowledgments We thank F. De Martino and R. Goebel for their helpful comments during data analysis and M. Milham for the helpful comments on the manuscript. Funding. This work was supported by the K.U.Leuven Research Fund (K.U.Leuven Onderzoeksfonds) (grant numbers OT/01/43, PDM/03/251, IMPH/06/GHW, and IDO 05/010 EEG-fMRI) and the Research Foundation Flanders, Belgium (grant number G.0211.03). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2010.03.078. References Baker, C.I., Hutchison, T.L., Kanwisher, N., 2007. Does the fusiform face area contain subregions highly selective for nonfaces? Nat. Neurosci. 10, 3–4. Bartels, A., Zeki, S., 2004. The chronoarchitecture of the human brain natural viewing conditions reveal a time-based anatomy of the brain. NeuroImage 22, 419–433. Bengtsson, S.L., Haynes, J.-D., Sakai, K., Buckley, M.J., Passingham, R.E., 2009. The representation of abstract task rules in the human prefrontal cortex. Cereb. Cortex 19, 1926–1936. Blasi, G., Goldberg, T.E., Weickert, T., Das, S., Kohn, P., Zoltick, B., Bertolino, A., Callicott, J.H., Weinberger, D.R., Mattay, V.S., 2006. Brain regions underlying response inhibition and interference monitoring and suppression. Eur. J. NeuroSci. 23, 1658–1664.

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