Running Title: MODALITY-SELECTIVE ...

Supplementary Material for

Towards a human self-regulation system: Common and distinct neural signatures of emotional and behavioural control by Robert Langner, Susanne Leiberg, Felix Hoffstaedter & Simon B. Eickhoff Supplementary Methods Paradigms included The most commonly studied cognitive (also termed “explicit”) emotion regulation (CER) strategy is reappraisal, which refers to a re-interpretation of an emotion-eliciting stimulus or situation to change its selfrelevant meaning and, thereby, its impact on behaviour (Gross, 2002). Another form of CER consists of changing one’s perspective on the emotional situation by either distancing oneself from or empathizing with its protagonists (Leiberg et al., 2012). Distancing (or “detachment”) can be achieved, for instance, by taking a third- rather than first-person perspective, while the opposite is true for empathizing, where participants put themselves mentally into the protagonist’s shoes and feel more deeply into the emotional situation. Another CER strategy involves suppressing the emotional response (Gross, 1998). Here, the target of cognitive modulation is the final stage of the entire affective processing chain (i.e., the experiential, physiological, and/or behavioural responses). Several experimental paradigms have been devised to study cognitive action regulation (CAR). One way to induce inadequate action tendencies that need to be overridden by top-down control is building and exploiting expectations based on the (higher) frequency of a given action. This approach is used in go/no-go tasks (Donders, 1868/1969) and stop-signal tasks (Lappin and Eriksen, 1966), where frequent (i.e. predominant) responses need to be withheld or cancelled on certain trials. Predominant but inadequate response tendencies can also result from behavioural routines, such as responding to lateralized stimuli in a spatially congruent way or reading a visually presented word. The former (ipsilateral responding) is challenged in conditions requiring spatially incongruent responses, such as in the Simon task (Simon, 1969) or the stimulus–response compatibility task (Fitts and Deininger, 1954), which in case of ocular responses is also known as anti-saccade task (Hallett, 1978). The latter routine (automatic reading) is challenged in the Stroop task, in which some trials require naming the ink colour of a word instead of reading the colour denoted by the word (Stroop, 1935). Inadequate response tendencies can furthermore be induced by irrelevant distractor stimuli, such as in the flanker task, in which the target stimulus is “flanked” by incongruent distractors on a proportion of trials (Eriksen and Eriksen, 1974). Yet another way to elicit inappropriate action tendencies is priming through behavioural recency. This approach is used in tasks in which participants are required to shift from a recently performed task or task rule to an alternative one, such as in the task-switching paradigm (Jersild, 1927; Rogers and Monsell, 1995) or the Wisconsin card sorting task (Berg, 1948).

Study selection Studies on CER were identified through a standard search in the PubMed (http://www.pubmed.gov) and Web of Science® (http://apps.webofknowledge.com) databases using the terms ‘emotion’, ‘affect’, or ‘feeling’ together with combinations of ‘regulation’ or ‘control’ and ‘intentional’, ‘cognitive’, ‘explicit’, or ‘top-down’. Analogously, studies on CAR were searched for using the respective paradigm labels (e.g., ‘Stroop’, ‘flanker’, ‘stop signal’, or ‘card sorting’) as well as combinations of the terms ‘cognitive’ and ‘control’, ‘conflict’, or ‘interference’. In all cases, search strings were combined with the terms ‘fMRI’, ‘functional MRI’, ‘functional magnetic resonance’, ‘PET’, or ‘positron emission’. Further studies were obtained via the ‘related articles’ function of the PubMed database and by tracing the references in review articles and papers identified before. Next, the following selection criteria were applied: (1) Only studies reporting results of whole-brain group analyses as coordinates in a standard reference space (Talairach/Tournoux or Montreal Neurological Institute [MNI]) were retained. In particular, we excluded results based on region-of-interest analyses or partial brain coverage, as these entail a-priori biases for given

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brain regions, which is incompatible with the whole-brain statistical inference approach implemented in the ALE algorithm used for meta-analysis. (2) Only data from healthy adults were retained; experiments in other populations (e.g., patients, children) were discarded. Data from healthy adult control groups of clinical or developmental studies were included if separately reported or provided by authors upon request. (3) Only results from subtractions between target and “high-level” control conditions were included, aiming to cancel out stimulus- or response-related processing as much as possible. Specifically, eligible CER experiments were required to report activation in a target condition (taxing CER) contrasted against a control condition in which a comparable emotional stimulus was presented but no regulation was required. Analogously, eligible CAR experiments were required to report activation in a target condition (taxing CAR) contrasted against a control condition with comparable sensorimotor demands but no need to overcome a given action tendency by top-down control. (4) We did not consider deactivation data, correlations between brain activity and other predictors (e.g. performance), or results from connectivity analyses. Also, data from conditions with pharmacological or other interventions (e.g., sleep deprivation, transcranial stimulation, or training) were excluded. The final CER and CAR samples differed considerably in size (n = 70 CER experiments, n = 203 CAR experiments). To control for power differences that might have biased the results, we conducted a supplemental analysis with a random subsample of n = 67 CAR experiments. Some CAR paradigms were used more often than others in the literature. Thus, we implemented a stratified randomization procedure in Matlab v8.3.0.532 (The Mathworks Inc., Natick, USA) to avoid any bias towards any given task. From the entire sample of CAR experiments, eight experiments each using the task-switching paradigm or the flanker, Stroop, Simon, go/no-go, stop-signal, or stimulus–response compatibility task were randomly drawn. The stratified subsample was completed with six experiments using the Wisconsin card sorting task and five experiments using other types of conflict tasks (all the experiments there were for these two categories). In addition, to achieve a relatively balanced number of participants between the different task types, one or two experiments with unusually large sample sizes were manually exchanged in a few task categories.

Activation likelihood estimation (ALE) Meta-analyses were performed using the current ALE algorithm for coordinate-based meta-analysis of neuroimaging results (Eickhoff et al., 2009; Eickhoff et al., 2012; Turkeltaub et al., 2012) implemented in Matlab v8.3.0.532 (The Mathworks Inc., Natick, USA). In this algorithm, reported coordinates are treated as centres of 3-D Gaussian probability distributions capturing the spatial uncertainty associated with each focus (Eickhoff et al., 2009). Thereby, the between-subject variance is weighted by the number of participants per experiment, since larger sample sizes are thought to provide more reliable approximations of the “true” activation effect and should therefore be modelled by narrower Gaussian distributions. Differences in coordinate spaces (Montreal Neurological Institute [MNI] vs. Talairach/Tournoux) between experiments were accounted for by using a linear Talairach-to-MNI coordinate transformation {Lancaster, 2007 #663}. For each voxel, the probabilities of all foci reported for a given experiment were combined to yield a modelled activation map (Turkeltaub et al., 2012). The convergence across experiments was then quantified by taking the union across all modelled activation maps, resulting in voxel-wise ALE scores for each location in the brain. To distinguish “true” from random convergence, ALE scores were compared to an analytical null distribution that reflects a random spatial association across all modelled activation maps, as described in Eickhoff et al. (2012). In brief, a distribution was computed that would be obtained when sampling a voxel at random from each of the modelled activation maps and taking the union of these values in the same manner as for the (spatially contingent) voxels in the original analysis. The p-value of a “true” ALE score was then given by the proportion of equal or higher values obtained under the null distribution. The resulting non-parametric pvalues were cut off at a threshold of p < .05 (family-wise error–corrected at cluster level; cluster inclusion threshold at voxel level: p < .001; cf. Eickhoff et al., 2016) and transformed into z-scores for display. The meta-analytic contrasts between CER and CAR were based on the voxel-wise differences between their respective ALE maps. Then, the experiments contributing to either ALE analysis were pooled and randomly divided into two groups of the same size as the two sets of contrasted experiments (Eickhoff et al., 2011). Voxel-wise ALE scores for these two randomly assembled groups were subtracted from each other. Repeating this process 5,000 times yielded an empirical null distribution of ALE-score differences between the two conditions. All results were anatomically labelled by reference to probabilistic cytoarchitectonic maps of the human brain using the Maximum Probability Maps included in the Anatomy Toolbox v2.2c (http://www.fz-

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juelich.de/SharedDocs/Downloads/INM/INM-7/DE/SPM_Toolbox/Toolbox_22c.html; Eickhoff et al., 2005) implemented in the SPM8 software package (www.fil.ion.ucl.ac.uk/spm/software/spm8). Convergence peaks were thus assigned to the most probable histologically defined area at the respective location. This histologybased anatomical labelling is reported in each result table; references to the respective cytoarchitectonic maps may be found in the table notes.

Resting-state fMRI data acquisition and preprocessing Resting-state fMRI data were obtained with a 3-T MR scanner using gradient-echo echo-planar imaging to record blood oxygen level–dependent (BOLD) activity in transversal slices covering the entire brain (404 volumes, repetition time: 1.4 s, echo time: 30 ms, flip angle: 65°, voxel size: 2 × 2 × 2 mm³). Participants lay supine in the scanner with their eyes open facing a fixation cross, instructed to let their mind wander without falling asleep. T1 images were acquired using an MPRAGE sequence (176 slices, repetition time: 1.9 s, echo time: 2.52 ms, flip angle: 9°, voxel size: 1 × 1 × 1 mm³). Resting-state data were cleaned from physiological and movement artifacts by applying FIX (FMRIB’s ICAbased Xnoiseifier v1.061; Salimi-Khorshidi et al., 2014) as implemented in FSL v5.0.9 (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL). FIX decomposes the data into independent components and automatically classifies noise components using a large number of distinct spatial and temporal features via pattern classification. We used the training datasets provided with FIX and the recommended settings (Griffanti et al., 2016; Griffanti et al., 2014). Variance uniquely related to the artefactual components was then removed from each participant’s raw data. Imaging data were further preprocessed using SPM8 and in-house Matlab tools. Images were corrected for head movement by affine registration using a two-pass procedure by which images were initially realigned to the first image and subsequently to the mean of the realigned images. Each participant’s data were then spatially normalized to the MNI 152-subject average template brain included in SPM8 using the “unified segmentation” approach (Ashburner and Friston, 2005), and the ensuing deformation was applied to all individual volumes. Finally, images were spatially smoothed by a 5-mm full-width at half-maximum Gaussian kernel and band-pass filtered preserving BOLD signal frequencies between 0.01 and 0.08 Hz (cf. Langner et al., 2015).

References (including only studies not cited in the main text) Ashburner, J., and Friston, K.J. (2005). Unified segmentation. NeuroImage 26, 839-851. Berg, E.A. (1948). A simple objective technique for measuring flexibility in thinking. J. Gen. Psychol. 39, 1522. Donders, F.C. (1868/1969). Over de snelheid van psychische processen. Acta Psychol. (Amst). 30, 412-431. Eriksen, B.A., and Eriksen, C.W. (1974). Effects of noise letters upon identification of a target letter in a nonsearch task. Percept. Psychophys. 16, 143-149. Fitts, P.M., and Deininger, R.L. (1954). S-R compatibility: Correspondence among paired elements within stimulus and response codes. J. Exp. Psychol. 48, 483-492. Griffanti, L., Rolinski, M., Szewczyk-Krolikowski, K., Menke, R.A., Filippini, N., Zamboni, G., Jenkinson, M., Hu, M.T., and Mackay, C.E. (2016). Challenges in the reproducibility of clinical studies with resting state fMRI: An example in early Parkinson's disease. NeuroImage 124, 704-713. Griffanti, L., Salimi-Khorshidi, G., Beckmann, C.F., Auerbach, E.J., Douaud, G., Sexton, C.E., Zsoldos, E., Ebmeier, K.P., Filippini, N., Mackay, C.E., et al. (2014). ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage 95, 232-247. Gross, J.J. (2002). Emotion regulation: Affective, cognitive, and social consequences. Psychophysiology 39, 281-291. Hallett, P.E. (1978). Primary and secondary saccades to goals defined by instructions. Vision Res. 18, 12791296. Jersild, A.T. (1927). Mental set and shift. Archives of Psychology 14 (whole no. 89). Lappin, J.S., and Eriksen, C.W. (1966). Use of a delayed signal to stop a visual reaction-time response. J. Exp. Psychol. 72, 805-811. Rogers, R.D., and Monsell, S. (1995). Costs of a predictable switch between simple cognitive tasks. J. Exp. Psychol. Gen. 124, 207-231. Salimi-Khorshidi, G., Douaud, G., Beckmann, C.F., Glasser, M.F., Griffanti, L., and Smith, S.M. (2014). Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449-468. Simon, J.R. (1969). Reactions toward the source of stimulation. J. Exp. Psychol. 81, 174-176. Stroop, J.R. (1935). Studies of interference in serial verbal reactions. J. Exp. Psychol. 18, 643-662.

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Supplementary Figure

Figure S1: Meta-analytic results using a stratified subsample of n = 67 experiments on cognitive action regulation (CAR). Related to Figures 2B and 3B. (A) Main effect of CAR, (B) differences in across-experiment convergence between cognitive emotion regulation (denoted in red) and CAR (denoted in green).

Supplementary Tables

Table S1. Regions of Significant Convergence of Activity Related to Cognitive Emotion Regulation. Related to Figure 2A. Cluster / Macroanatomical Structure Cluster 1 (k = 1354, 50 exp.) L preSMA R dorsal anterior midcingulate cortex L anterior midcingulate cortex

x, y, z

Histological Assignment

z score

-4 14 60 6 26 38 -6 28 28

-

8.3 5.3 4.2

Cluster 2 (k = 1203, 38 exp.) L inferior frontal gyrus (pars triangularis) L anterior insula / frontal operculum L inferior frontal gyrus (pars opercularis) L lateral orbitofrontal cortex

-54 20 4 -42 22 -4 -58 16 22 -46 44 -8

Area 44 Area 45 -

6.5 6.4 4.9 3.7

Cluster 3 (k = 612, 31 exp.) R inferior frontal gyrus (pars orbitalis) R anterior insula / frontal operculum R inferior frontal gyrus (pars triangularis)

50 30 -8 48 16 -2 58 24 4

Area 45

6.7 5.6 4.8

Cluster 4 (k = 604, 27 exp.) L middle temporal gyrus

-58 -38 -2

-

6.5

Cluster 5 (k = 443, 24 exp.) L posterior MFG

-42 12 48

-

5.6

Cluster 6 (k = 423, 24 exp.) R posterior MFG

46 10 48

-

6.0

Cluster 7 (k = 376, 27 exp.) R posterior temporo-parietal junction R temporo-parietal junction

58 -52 40 60 -46 30

Areas PFm, PGa, PGp Area PFm

5.0 4.3

Cluster 8 (k = 285, 21 exp.) L posterior temporo-parietal junction

-50 -60 24

Areas PFm, PGa, PGp

5.1

42 46 -8

-

4.9

Cluster 9 (k = 123, 11 exp.) R lateral orbitofrontal cortex

Note. Cluster-related values in brackets: k = number of voxels in cluster; exp. = number of experiments contributing to the cluster. Coordinates x, y, z of local maxima refer to MNI space. L = left; R = right; MFG = middle frontal gyrus; preSMA = pre-supplementary motor area. References for histological assignments: Areas 44, 45: Amunts et al. (1999); PFm, PGa, PGp: Caspers et al. (2006). Amunts, K., Schleicher, A., Bürgel, U., Mohlberg, H., Uylings, H.B., and Zilles, K. (1999). Broca's region revisited: cytoarchitecture and intersubject variability. J. Comp. Neurol. 412, 319-341. Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., and Zilles, K. (2006). The human inferior parietal cortex: cytoarchitectonic parcellation and interindividual variability. NeuroImage 33, 430-448.

Table S2. Regions of Significant Convergence of Activity Related to Cognitive Action Regulation. Related to Figure 2B. Cluster / Macroanatomical Structure

x, y, z

Histological Assignment

z score

Cluster 1 (k = 4287, 137 exp.) R anterior insula R inferior frontal junction R middle frontal gyrus R dorsal premotor cortex R thalamus R caudate nucleus R putamen R inferior frontal gyrus

36 22 -4 48 12 28 42 38 18 28 2 56 8 -14 0 14 0 12 20 4 8 56 20 6

Thal: Prefrontal Area 45

8.3 8.3 7.0 6.3 8.9 5.2 4.8 4.7

Cluster 2 (k = 2374, 123 exp.) L inferior frontal junction L anterior insula L middle frontal gyrus

-44 10 32 -34 20 -4 -46 36 20

-

8.3 8.3 4.7

0 18 48 8 24 34 0 36 16

-

8.4 6.8 3.7

Cluster 3 (k = 2342, 121 exp.) L/R preSMA R anterior midcingulate cortex L/R anterior midcingulate cortex Cluster 4 (k = 1478, 104 exp.) L intraparietal sulcus L superior parietal lobule

-36 -46 46 -12 -60 60

hIP3 Area 7A

8.0 4.3

Cluster 5 (k = 1032, 84 exp.) R intraparietal sulcus R superior parietal lobule

38 -46 44 16 -60 60

Area hIP3 Area 7A

6.5 4.0

Cluster 6 (k = 462, 51 exp) L dorsal premotor cortex

-26 0 54

-

7.0

Cluster 7 (k = 253, 35 exp) R temporo-parietal junction

60 -44 24

Areas PFm, PF

5.1

Cluster 8 (k = 240, 37 exp) L fusiform gyrus

-40 -64 -12

Areas FG2, FG4

4.6

Note. Cluster-related values in brackets: k = number of voxels in cluster; exp. = number of experiments contributing to the cluster. Coordinates x, y, z of local maxima refer to MNI space. L = left; R = right; preSMA = pre-supplementary motor area. References for histological assignments: Thal: Prefrontal: Behrens et al. (2003); Area 45: Amunts et al. (1999); Areas hIP3, 7A: Scheperjans et al. (2008); Areas PFm, PF: Caspers et al. (2006); Area FG2: Caspers et al. (2013); Area FG4: Lorenz et al. (2015). Amunts, K., Schleicher, A., Bürgel, U., Mohlberg, H., Uylings, H.B., and Zilles, K. (1999). Broca's region revisited: cytoarchitecture and intersubject variability. J. Comp. Neurol. 412, 319-341. Behrens, T.E., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A., Boulby, P.A., Barker, G.J., Sillery, E.L., Sheehan, K., Ciccarelli, O., et al. (2003). Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6, 750-757. Caspers, J., Zilles, K., Eickhoff, S.B., Schleicher, A., Mohlberg, H., and Amunts, K. (2013). Cytoarchitectonical analysis and probabilistic mapping of two extrastriate areas of the human posterior fusiform gyrus. Brain Struct. Funct. 218, 511-526.

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Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., and Zilles, K. (2006). The human inferior parietal cortex: cytoarchitectonic parcellation and interindividual variability. NeuroImage 33, 430-448. Lorenz, S., Weiner, K.S., Caspers, J., Mohlberg, H., Schleicher, A., Bludau, S., Eickhoff, S.B., Grill-Spector, K., Zilles, K., and Amunts, K. (2015). Two new cytoarchitectonic areas on the human mid-fusiform gyrus. Cereb. Cortex [Advance online publication]. doi: 10.1093/cercor/bhv225. Scheperjans, F., Eickhoff, S.B., Hömke, L., Mohlberg, H., Hermann, K., Amunts, K., and Zilles, K. (2008). Probabilistic maps, morphometry, and variability of cytoarchitectonic areas in the human superior parietal cortex. Cereb. Cortex 18, 2141-2157.

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Table S3. Regions Showing Significant Convergence Differences Between Cognitive Emotion Regulation (CER) and Cognitive Action Regulation (CAR). Related to Figure 3B. Cluster / Macroanatomical Structure

x, y, z

Histological Assignment

z score

CER > CAR Cluster 1 (k = 995) L inferior frontal gyrus (pars triangularis)

-54 20 4

Areas 44, 45

6.5

L frontal operculum

-44 24 -6

-

6.0

-42 38 -12

-

3.4

-4 14 60

-

8.1

-58 -38 -2

-

6.5

-44 10 48

-

5.5

42 14 48

-

4.9

50 28 -10

-

4.1

R inferior frontal gyrus (pars triangularis)

58 26 0

Area 45

3.1

R anterior insula / frontal operculum

48 16 -2

-

2.4

58 -64 24

Areas PGa, PGp, PFm

3.9

-50 -60 24

Areas PGa, PGp, PFm

5.1

42 44 -10

-

4.2

-10 30 28

-

2.5

44 6 30

-

3.9

40 -46 42

Areas hIP1, hIP2, hIP3

5.4

-38 -44 46

Areas hIP1, hIP2, hIP3

5.8

-

3.9

L lateral orbitofrontal cortex Cluster 2 (k = 622) L preSMA Cluster 3 (k = 603) L middle temporal gyrus Cluster 4 (k = 399) L posterior MFG Cluster 5 (k = 369) R posterior MFG Cluster 6 (k = 310) R inferior frontal gyrus (pars orbitalis)

Cluster 7 (k = 280) R posterior temporo-parietal junction Cluster 8 (k = 278) L posterior temporo-parietal junction Cluster 9 (k = 123) R lateral orbitofrontal cortex Cluster 10 (k = 37) L anterior midcingulate cortex CAR > CER Cluster 1 (k = 273) R inferior frontal junction Cluster 2 (k = 270) R intraparietal sulcus Cluster 3 (k = 251) L intraparietal sulcus Cluster 4 (k = 244) R anterior insula / frontal operculum

38 28 2

1

Cluster 5 (k = 217) L anterior insula / frontal operculum

-28 -22 -4

-

3.4

L dorsal IFG (pars triangularis)

-46 32 12

Area 45

2.5

10 16 42

-

2.7

42 32 24

-

3.8

30 0 58

-

3.4

10 -18 0

Thal: Prefrontal

3.1

-28 -12 60

-

3.4

-48 8 34

Area 44

2.0

-42 22 26

-

2.2

6 28 28

-

2.4

Cluster 6 (k = 136) R preSMA / anterior midcingulate cortex Cluster 7 (k = 121) R dorsal IFG (pars triangularis) Cluster 8 (k = 112) R dorsal premotor cortex Cluster 9 (k = 87) R thalamus Cluster 10 (k = 56) L dorsal premotor cortex Cluster 11 (k = 22) L inferior frontal junction Cluster 12 (k = 14) L inferior frontal sulcus Cluster 13 (k = 9) R anterior midcingular cortex

Note. Coordinates x, y, z of local maxima refer to MNI space; k = number of voxels in cluster. L = left; R = right; MFG = middle frontal gyrus. References for histological assignments: Thal: Prefrontal: Behrens et al. (2003); Areas 44, 45: Amunts et al. (1999); PGa, PGp, PFm: Caspers et al. (2006); hIP1, hIP2: Choi et al. (2006); hIP3: Scheperjans et al. (2008). Amunts, K., Schleicher, A., Bürgel, U., Mohlberg, H., Uylings, H.B., and Zilles, K. (1999). Broca's region revisited: cytoarchitecture and intersubject variability. J. Comp. Neurol. 412, 319-341. Behrens, T.E., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A., Boulby, P.A., Barker, G.J., Sillery, E.L., Sheehan, K., Ciccarelli, O., et al. (2003). Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6, 750-757. Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., and Zilles, K. (2006). The human inferior parietal cortex: cytoarchitectonic parcellation and interindividual variability. NeuroImage 33, 430-448. Choi, H.J., Zilles, K., Mohlberg, H., Schleicher, A., Fink, G.R., Armstrong, E., and Amunts, K. (2006). Cytoarchitectonic identification and probabilistic mapping of two distinct areas within the anterior ventral bank of the human intraparietal sulcus. J. Comp. Neurol. 495, 53-69. Scheperjans, F., Eickhoff, S.B., Hömke, L., Mohlberg, H., Hermann, K., Amunts, K., and Zilles, K. (2008). Probabilistic maps, morphometry, and variability of cytoarchitectonic areas in the human superior parietal cortex. Cereb. Cortex 18, 2141-2157.

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Table S4. Overview of all contrasts from 47 studies on cognitive emotion regulation included in the metaanalysis. Study

n

Strategy

Direction

Valence

Foci

*Albein-Urios et al. 2014a Delgado et al. 2008 Domes et al. 2010

18 12 33

Eippert et al. 2007

24

Erk et al. 2010 Felder et al. 2012

17 12

Goldin et al. 2008

17

Golkar et al. 2012 Grecucci et al. 2013a

58 21

Grecucci et al. 2013b Harenski and Hamann 2006 Hayes et al. 2010

21 10 25

*Holland and Kensinger 2013b

22

Holland and Kensinger 2013a Kalisch et al. 2006 *Kanske et al. 2011

18 15 30

Kim and Hamann 2007

10

Kim et al. 2009 Kober et al. 2010 Koenigsberg et al. 2010 Krendl et al. 2012 Lang et al. 2012a

21 21 16 20 15

Leiberg et al. 2012

24

Mak et al. 2009

12

McRae et al. 2008 *McRae et al. 2010 McRae et al. 2012a *McRae et al. 2012b Modinos et al. 2010b Modinos et al. 2010a New et al. 2009a

23 18 38 26 18 34 14

Ochsner et al. 2002 Ochsner et al. 2004

15 24

Ohira et al. 2006 Opitz et al. 2012 Phan et al. 2005 Schardt et al. 2010 Schulze et al. 2011a

10 31 14 37 15

Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Other Reappraisal Suppression Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Suppression Reappraisal Reappraisal Reappraisal Suppression Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Other Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Suppression Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal

Down Down Down Up Down Up Down Down Down Down Down Down Down Up Up/Down Down Down Down Down Up Up/Down Down Down Down Down Down Up Up Up Down Down Down Down Up Down Up Down Down Down Down Down Down Down Down Down Up Down Down Up Down Up/Down Down Down Down Up

Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Positive Negative Negative Positive Negative Positive Negative Positive Negative Negative Negative Negative Negative Negative Negative Positive Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative/Positive Negative Negative Negative/Neutral Negative Negative

15 5 17 20 10 26 4 10 6 18 17 11 5 8 6 7 7 8 59 4 8 3 14 16 35 18 15 43 16 11 6 16 13 5 2 9 5 2 16 11 43 9 10 10 14 1 12 41 37 5 2 9 11 11 17

1

Study

n

Strategy

Direction

Valence

Foci

Smoski et al. 2013a Staudinger et al. 2009 Staudinger et al. 2011 Urry et al. 2006

19 16 24 19

Urry et al. 2009

26

Vanderhasselt et al. 2013

42

van Reekum et al. 2007

29

Walter et al. 2009 Winecoff et al. 2011

20 42

Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Suppression Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal Reappraisal

Down Down Down Down Up Down Up Down Down Down Up Down Down Down

Negative Negative/Positive Negative/Positive Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Positive

9 4 3 1 19 4 8 4 7 1 13 4 18 18

Note. n = number of participants; Direction = direction in which induced emotions needed to be regulated; Foci = number of foci reported for the given contrast * Contrasts that have not been explicitly reported in the publication but were provided by the authors upon request. a Only data from the healthy control sample were included in the meta-analysis. References: Albein-Urios, N., Verdejo-Roman, J., Asensio, S., Soriano-Mas, C., Martinez-Gonzalez, J.M., and Verdejo-Garcia, A. (2014). Re-appraisal of negative emotions in cocaine dependence: dysfunctional corticolimbic activation and connectivity. Addict Biol 19, 415-426. Delgado, M.R., Nearing, K.I., Ledoux, J.E., and Phelps, E.A. (2008). Neural circuitry underlying the regulation of conditioned fear and its relation to extinction. Neuron 59, 829-838. Domes, G., Schulze, L., Bottger, M., Grossmann, A., Hauenstein, K., Wirtz, P.H., Heinrichs, M., and Herpertz, S.C. (2010). The neural correlates of sex differences in emotional reactivity and emotion regulation. Hum. Brain Mapp. 31, 758-769. Eippert, F., Veit, R., Weiskopf, N., Erb, M., Birbaumer, N., and Anders, S. (2007). Regulation of emotional responses elicited by threat-related stimuli. Hum. Brain Mapp. 28, 409-423. Erk, S., Mikschl, A., Stier, S., Ciaramidaro, A., Gapp, V., Weber, B., and Walter, H. (2010). Acute and sustained effects of cognitive emotion regulation in major depression. J. Neurosci. 30, 15726-15734. Felder, J.N., Smoski, M.J., Kozink, R.V., Froeliger, B., McClernon, J., Bizzell, J., Petty, C., and Dichter, G.S. (2012). Neural mechanisms of subclinical depressive symptoms in women: a pilot functional brain imaging study. BMC Psychiatry 12, 152. Goldin, P.R., McRae, K., Ramel, W., and Gross, J.J. (2008). The neural bases of emotion regulation: reappraisal and suppression of negative emotion. Biol. Psychiatry 63, 577-586. Golkar, A., Lonsdorf, T.B., Olsson, A., Lindstrom, K.M., Berrebi, J., Fransson, P., Schalling, M., Ingvar, M., and Öhman, A. (2012). Distinct contributions of the dorsolateral prefrontal and orbitofrontal cortex during emotion regulation. PLoS One 7, e48107. Grecucci, A., Giorgetta, C., Bonini, N., and Sanfey, A.G. (2013a). Reappraising social emotions: the role of inferior frontal gyrus, temporo-parietal junction and insula in interpersonal emotion regulation. Front. Hum. Neurosci. 7, 523. Grecucci, A., Giorgetta, C., Van't Wout, M., Bonini, N., and Sanfey, A.G. (2013b). Reappraising the ultimatum: An fMRI study of emotion regulation and decision making. Cereb. Cortex 23, 399-410. Harenski, C.L., and Hamann, S. (2006). Neural correlates of regulating negative emotions related to moral violations. NeuroImage 30, 313-324. Hayes, J.P., Morey, R.A., Petty, C.M., Seth, S., Smoski, M.J., McCarthy, G., and Labar, K.S. (2010). Staying cool when things get hot: emotion regulation modulates neural mechanisms of memory encoding. Front. Hum. Neurosci. 4, 230. Holland, A.C., and Kensinger, E.A. (2013a). An fMRI investigation of the cognitive reappraisal of negative memories. Neuropsychologia 51, 2389-2400. Holland, A.C., and Kensinger, E.A. (2013b). The neural correlates of cognitive reappraisal during emotional autobiographical memory recall. J. Cogn. Neurosci. 25, 87-108. Kalisch, R., Wiech, K., Herrmann, K., and Dolan, R.J. (2006). Neural correlates of self-distraction from anxiety and a process model of cognitive emotion regulation. J. Cogn. Neurosci. 18, 1266-1276.

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Kanske, P., Heissler, J., Schonfelder, S., Bongers, A., and Wessa, M. (2011). How to regulate emotion? Neural networks for reappraisal and distraction. Cereb. Cortex 21, 1379-1388. Kim, J.W., Kim, S.E., Kim, J.J., Jeong, B., Park, C.H., Son, A.R., Song, J.E., and Ki, S.W. (2009). Compassionate attitude towards others' suffering activates the mesolimbic neural system. Neuropsychologia 47, 2073-2081. Kim, S.H., and Hamann, S. (2007). Neural correlates of positive and negative emotion regulation. J. Cogn. Neurosci. 19, 776-798. Kober, H., Mende-Siedlecki, P., Kross, E.F., Weber, J., Mischel, W., Hart, C.L., and Ochsner, K.N. (2010). Prefrontal-striatal pathway underlies cognitive regulation of craving. Proc. Natl. Acad. Sci. U.S.A. 107, 14811-14816. Koenigsberg, H.W., Fan, J., Ochsner, K.N., Liu, X., Guise, K., Pizzarello, S., Dorantes, C., Tecuta, L., Guerreri, S., Goodman, M., et al. (2010). Neural correlates of using distancing to regulate emotional responses to social situations. Neuropsychologia 48, 1813-1822. Krendl, A.C., Kensinger, E.A., and Ambady, N. (2012). How does the brain regulate negative bias to stigma? Soc. Cogn. Affect. Neurosci. 7, 715-726. Lang, S., Kotchoubey, B., Frick, C., Spitzer, C., Grabe, H.J., and Barnow, S. (2012). Cognitive reappraisal in trauma-exposed women with borderline personality disorder. NeuroImage 59, 17271734. Leiberg, S., Eippert, F., Veit, R., and Anders, S. (2012). Intentional social distance regulation alters affective responses towards victims of violence: an FMRI study. Hum. Brain Mapp. 33, 2464-2476. Mak, A.K., Hu, Z.G., Zhang, J.X., Xiao, Z.W., and Lee, T.M. (2009). Neural correlates of regulation of positive and negative emotions: an fmri study. Neurosci. Lett. 457, 101-106. McRae, K., Gross, J.J., Weber, J., Robertson, E.R., Sokol-Hessner, P., Ray, R.D., Gabrieli, J.D., and Ochsner, K.N. (2012a). The development of emotion regulation: an fMRI study of cognitive reappraisal in children, adolescents and young adults. Soc. Cogn. Affect. Neurosci. 7, 11-22. McRae, K., Hughes, B., Chopra, S., Gabrieli, J.D.E., Gross, J.J., and Ochsner, K.N. (2010). The neural bases of distraction and reappraisal. J. Cogn. Neurosci. 22, 248-262. McRae, K., Misra, S., Prasad, A.K., Pereira, S.C., and Gross, J.J. (2012b). Bottom-up and top-down emotion generation: implications for emotion regulation. Soc. Cogn. Affect. Neurosci. 7, 253-262. McRae, K., Ochsner, K.N., Mauss, I.B., Gabrieli, J.J.D., and Gross, J.J. (2008). Gender Differences in Emotion Regulation: An fMRI Study of Cognitive Reappraisal. Group Processes & Intergroup Relations 11, 143-162. Modinos, G., Ormel, J., and Aleman, A. (2010a). Altered activation and functional connectivity of neural systems supporting cognitive control of emotion in psychosis proneness. Schizophr. Res. 118, 88-97. Modinos, G., Ormel, J., and Aleman, A. (2010b). Individual differences in dispositional mindfulness and brain activity involved in reappraisal of emotion. Soc. Cogn. Affect. Neurosci. 5, 369-377. New, A.S., Fan, J., Murrough, J.W., Liu, X., Liebman, R.E., Guise, K.G., Tang, C.Y., and Charney, D.S. (2009). A functional magnetic resonance imaging study of deliberate emotion regulation in resilience and posttraumatic stress disorder. Biol. Psychiatry 66, 656-664. Ochsner, K.N., Bunge, S.A., Gross0, and Gabrieli, J.D. (2002). Rethinking feelings an FMRI study of the cognitive regulation of emotion. J. Cogn. Neurosci. 14, 1215-1229. Ochsner, K.N., Ray, R.D., Cooper, J.C., Robertson, E.R., Chopra, S., Gabrieli, J.D., and Gross, J.J. (2004). For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion. NeuroImage 23, 483-499. Ohira, H., Nomura, M., Ichikawa, N., Isowa, T., Iidaka, T., Sato, A., Fukuyama, S., Nakajima, T., and Yamada, J. (2006). Association of neural and physiological responses during voluntary emotion suppression. NeuroImage 29, 721-733. Opitz, P.C., Rauch, L.C., Terry, D.P., and Urry, H.L. (2012). Prefrontal mediation of age differences in cognitive reappraisal. Neurobiol. Aging 33, 645-655. Phan, K.L., Fitzgerald, D.A., Nathan, P.J., Moore, G.J., Uhde, T.W., and Tancer, M.E. (2005). Neural substrates for voluntary suppression of negative affect: a functional magnetic resonance imaging study. Biol. Psychiatry 57, 210-219. Schardt, D.M., Erk, S., Nusser, C., Nothen, M.M., Cichon, S., Rietschel, M., Treutlein, J., Goschke, T., and Walter, H. (2010). Volition diminishes genetically mediated amygdala hyperreactivity. NeuroImage 53, 943-951. Schulze, L., Domes, G., Kruger, A., Berger, C., Fleischer, M., Prehn, K., Schmahl, C., Grossmann, A., Hauenstein, K., and Herpertz, S.C. (2011). Neuronal correlates of cognitive reappraisal in borderline patients with affective instability. Biol. Psychiatry 69, 564-573.

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Table S5. Overview of all contrasts from 158 studies on cognitive action regulation included in the metaanalysis. Study

n

Task Type

Foci

Aarts et al. 2008 Adleman et al. 2002 Aichert et al. 2012 Altshuler et al. 2005 Ansari et al. 2006 Armbruster et al. 2012 Aron and Poldrack 2006 Asahi et al. 2004 Banich et al. 2001

12 11 54 13 21 20 13 17 14

Barros-Loscertales et al. 2011 Basten et al. 2011 Becker et al. 2008 Bellgrove et al. 2004 Bench et al. 1993

16 46 17 42 6

Boehler et al. 2010 Bohland and Guenther 2006 Booth et al. 2003 Brass et al. 2005

15 13 12 20

Brown et al. 2006 Brown et al. 2007 Bush et al. 1998 Cai and Leung 2009

10 11 9 12

Cai and Leung 2011 Carter et al. 1995

23 15

Chevrier et al. 2007 Chikazoe et al. 2007 Chikazoe et al. 2009a Chikazoe et al. 2009b Christakou et al. 2009

14 24 25 22 63

Christensen et al. 2011 Cieslik et al. 2010 Coderre et al. 2008

26 24 9

de Zubicaray et al. 2001 DiGirolamo et al. 2001 Doricchi et al. 1997 Dove et al. 2000 Dreher and Berman 2002 Eckert et al. 2009 Ettinger et al. 2009 Fan et al. 2003

8 8 10 16 14 11 24 12

Fan et al. 2005 Fan et al. 2008 *Fauth-Bühler et al. 2012 Ford et al. 2005

16 16 18 10

Stimulus-response compatibility Stroop Stimulus-response compatibility Go/no-go Numerical size congruity Task-switching Stop signal Go/no-go Stroop Stroop Stroop Stroop Stroop Go/no-go Stroop Stroop Stroop Stop signal Go/no-go Go/no-go Stroop Imitation-inhibition Stimulus-response compatibility Stimulus-response compatibility Stroop Stop signal Stop signal Stop signal Stroop Stroop Stop signal Stimulus-response compatibility Go/no-go Stop signal Task-switching Simon Stroop Stimulus-response compatibility Stroop Stroop Stroop Stroop Stroop Task-switching Stimulus-response compatibility Task-switching Task-switching Flanker Stimulus-response compatibility Flanker Stroop Simon Flanker Flanker Stop signal Stimulus-response compatibility

33 4 12 4 211 15 361 11 9 4 14 11 9 19 6 2 5 30 40 13 7 81 15 111 7 81 141 21 9 9 3 37 52 571 4 5 5 10 5 7 5 6 9 351 161 131 4 141 61 14 14 11 9 71 53 81

1

Study

n

Task Type

Foci

Frühholz et al. 2011

24

Geng et al. 2009 Georgiou-Karistianis et al. 2012 Grandjean et al. 2012 Grandjean et al. 2013 Haupt et al. 2009

16 14 25 25 29

Hazeltine et al. 2000 Hazeltine et al. 2003 Hendrick et al. 2010 *Hirose et al. 2012 Horn et al. 2003 Iacoboni et al. 1996 Jahfari et al. 2011 Jasinska et al. 2012 Jeong et al. 2005 Kaladjian et al. 2007 Kaladjian et al. 2009a

8 10 60 59 21 6 20 15 10 21 10

Kaladjian et al. 2009b Kanske and Kotz 2011 Kawashima et al. 1996 Kerns et al. 2005 Kiehl et al. 2000 Kim et al. 2011 Kim et al. 2012

20 22 9 13 14 13 16

King et al. 2012 Konishi et al. 2005 Konishi et al. 2003 Kronhaus et al. 2006 Lamar et al. 2009 Lee et al. 2008 Leung et al. 2000

25 31 36 11 13 14 19

Liddle et al. 2001 Lie et al. 2006 Liu et al. 2004

16 12 11

Liu et al. 2006 Luks et al. 2007 Maclin et al. 2001 Maguire et al. 2003 Maltby et al. 2005 Manoach et al. 2001 Marco-Pallares et al. 2008 Matthews et al. 2004 Matsuda et al. 2004 Matsumoto et al. 2004

14 11 8 6 11 21 10 18 21 20

Mazzola-Pomietto et al. 2009 McNab et al. 2008

16 14

Mead et al. 2002 Melcher and Gruber 2006 Menon et al. 2001 Milham et al. 2001

18 12 14 16

Flanker Simon Go/no-go Simon Stroop Stroop Stroop Stroop Flanker Flanker Stop signal Go/no-go Go/no-go Stimulus-response compatibility Stop signal Multi-source interference Stroop Go/no-go Go/no-go Go/no-go Go/no-go Simon Go/no-go Stroop Go/no-go Stroop Task-switching Stroop Stop signal Wisconsin card sorting Wisconsin card sorting Stroop Simon Simon Stroop Stroop Go/no-go Wisconsin card sorting Simon Stroop Stroop Flanker Simon Go/no-go Go/no-go Stimulus-response compatibility Stop signal Stroop Stimulus-response compatibility Stimulus-response compatibility Stimulus-response compatibility Go/no-go Flanker Go/no-go Stop signal Stroop Stroop Go/no-go Stroop

221 11 151 271 19 7 3 11 41 6 181 52 141 2 7 181 9 11 12 8 161 41 39 13 81 16 20 22 91 101 161 6 41 101 16 16 23 101 34 15 7 10 51 61 51 19 101 12 12 91 11 7 5 6 16 1 14 13 7

2

Study

n

Task Type

Foci

Milham et al. 2002 Milham et al. 2003 Milham and Banich 2005 Mitchell 2005

Monchi et al. 2001 Monchi et al. 2004 Nakao et al. 2005 Norris et al. 2002 Ochsner et al. 2009 O'Driscoll et al. 1995 Pardo et al. 1990 Parris et al. 2007 Paus et al. 1993

12 16 18 15 14 13 11 9 14 4 16 10 8 21 9

Paus et al. 1993

8

Peterson et al. 2002

10

Philipp et al. 2012

23

Polk et al. 2008 Potenza et al. 2003 Prakash et al. 2009 Rahm et al. 2013 Ravnkilde et al. 2002 Roelofs et al. 2006 Rogers et al. 2000 Roth et al. 2006 Roth et al. 2007 Rothmayr et al. 2011 Rubia et al. 2006

14 11 25 11 46 12 12 11 14 12 23

Ruff et al. 2001 Rushworth et al. 2002

12 10

Sharp et al. 2010 Schulte et al. 2009 Schumacher and D'Esposito 2002 Schumacher et al. 2007 Sebastian et al. 2012

26 24 10 11 24

Silton et al. 2010 Sommer et al. 2008 Steel et al. 2001 Swainson et al. 2003

30 12 7 12

Sweeney et al. 1996 Sylvester et al. 2003

11 14

Tabu et al. 2011 Tabu et al. 2012

13 13

Tang et al. 2006

18

Stroop Stroop Stroop Stroop Stroop Stroop Wisconsin card sorting Wisconsin card sorting Stroop Stroop Flanker Stimulus-response compatibility Stroop Task-switching Stimulus-response compatibility Stimulus-response compatibility Stimulus-response compatibility Task-switching Task-switching Task-switching Simon Stroop Task-switching Task-switching Stroop Stroop Stroop Stroop Stroop Stroop Wisconsin card sorting Stroop Go/no-go Go/no-go Go/no-go Simon Task-switching Stroop Task-switching Task-switching Stop signal Stroop Stimulus-response compatibility Stimulus-response compatibility Simon Go/no-go Stop signal Stroop Simon Stroop Task-switching Task-switching Stimulus-response compatibility Stimulus-response compatibility Stimulus-response compatibility Stop signal Stop signal Stop signal Stroop

7 16 231 8 2 13 91 51 271 11 16 81 13 19 7 51 31 9 51 31 13 13 2 1 10 11 12 15 6 9 131 4 13 10 111 9 7 10 41 2 10 8 151 20 10 19 28 14 81 261 21 31 5 111 12 61 15 14 111

3

Study

n

Taylor et al. 1994 Taylor et al. 1997

8 12

Ullsperger and von Cramon 2001 van Veen et al. 2001 van Veen and Carter 2005 Videbech et al. 2004 Vink et al. 2005 Wager et al. 2005

12 12 14 46 20 14

Walther et al. 2010 Watanabe et al. 2002 Witt and Stevens 2012 Wittfoth et al. 2006

17 11 134 20

Wittfoth et al. 2008 Ye and Zhou 2009

15 19

Yücel et al. 2007 Zheng et al. 2008

19 18

Zhu et al. 2010 Zhu et al. 2013 Zoccatelli et al. 2010

22 26 10

Zurawska et al. 2011 Zysset et al. 2001

18 9

Task Type

Foci

Stroop Stimulus-response compatibility Stroop Stroop Flanker Flanker Stroop Stroop Stop signal Flanker Simon Go/no-go Go/no-go Go/no-go Task-switching Simon Simon Simon Stroop Flanker Multi-source interference Go/no-go Stop signal Flanker Stroop Stroop Stroop Flanker Stroop

11 3 11 11 34 8 71 13 41 9 12 13 151 4 231 10 101 14 14 61 61 8 10 8 7 131 13 61 4

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