Significance Sex/gender differences in the brain are of high social interest because their presence is typically assumed to prove that humans belong to two distinct categories not only in terms of their genitalia, and thus justify differential treatment of males and females. Here we show that, although there are sex/gender differences in brain and behavior, humans and human brains are comprised of unique “mosaics” of features, some more common in females compared with males, some more common in males compared with females, and some common in both females and males. Our results demonstrate that regardless of the cause of observed sex/gender differences in brain and behavior (nature or nurture), human brains cannot be categorized into two distinct classes: male brain/female brain.

Abstract Whereas a categorical difference in the genitals has always been acknowledged, the question of how far these categories extend into human biology is still not resolved. Documented sex/gender differences in the brain are often taken as support of a sexually dimorphic view of human brains (“female brain” or “male brain”). However, such a distinction would be possible only if sex/gender differences in brain features were highly dimorphic (i.e., little overlap between the forms of these features in males and females) and internally consistent (i.e., a brain has only “male” or only “female” features). Here, analysis of MRIs of more than 1,400 human brains from four datasets reveals extensive overlap between the distributions of females and males for all gray matter, white matter, and connections assessed. Moreover, analyses of internal consistency reveal that brains with features that are consistently at one end of the “maleness-femaleness” continuum are rare. Rather, most brains are comprised of unique “mosaics” of features, some more common in females compared with males, some more common in males compared with females, and some common in both females and males. Our findings are robust across sample, age, type of MRI, and method of analysis. These findings are corroborated by a similar analysis of personality traits, attitudes, interests, and behaviors of more than 5,500 individuals, which reveals that internal consistency is extremely rare. Our study demonstrates that, although there are sex/gender differences in the brain, human brains do not belong to one of two distinct categories: male brain/female brain.

The question of whether males and females form two distinct categories has attracted thinkers from ancient times to this day. Whereas a categorical difference in the genitals has always been acknowledged, the question of how far these categories extend into human biology is still not resolved (for a historical overview, see refs. 1 and 2). Documented sex/gender* differences in the brain are often taken as support of a sexually dimorphic view of human brains (“female brain” vs. “male brain”), and consequently, of a sexually dimorphic view of human behavior, cognition, personality, attitudes, and other gender characteristics (3). Joel (4, 5) has recently argued that the existence of sex/gender differences in the brain is not sufficient to conclude that human brains belong to two distinct categories. Rather, such a distinction requires the fulfillment of two conditions: one, the form of the elements that show sex/gender differences should be dimorphic, that is, with little overlap between the forms of the elements in males and females. Two, there should be a high degree of internal consistency in the form of the different elements of a single brain (e.g., all elements have the “male” form).

Previous criticisms of the dichotomous view of human brain have focused on the fact that most sex/gender differences are nondimorphic population-level differences with extensive overlap of the distributions of females and males and have therefore claimed that human brains cannot be sorted into two distinct classes: “male brains” and “female brains” (6⇓–8). However, if brains are internally consistent in the degree of “maleness-femaleness” of each of their elements, it will still be possible to align brains on a “male-brain–female-brain” continuum (4, 5). Such an alignment may be predicted by the classic view of sexual differentiation of the brain, according to which masculinization and defeminization of the brain are under the sole influence of testosterone (9). In contrast, more recent evidence that masculinization and feminization are independent processes and that sexual differentiation progresses independently in different brain tissues (10), predicts poor internal consistency (4, 5). Poor internal consistency is further predicted by evidence that the effects of sex may be different and even opposite under different environmental conditions and that these sex-by-environment interactions may be different for different brain features (4, 5). There are indeed examples of lack of internal consistency within a single brain in the animal literature (4, 5), yet it is not clear whether this is a common phenomenon that involves most features that show sex differences and is seen in most individuals. Here we assess the degree of internal consistency in the human brain using data obtained from MRI, a method that allows the simultaneous assessment of multiple brain features in many individuals.

We used datasets obtained from several different imaging modalities and analyzed with different methods to ensure that our conclusion is not measure, analysis, or sample dependent. The number of subjects in these datasets ranged from 138 to 855. In each dataset, following an assessment of sex/gender differences in all regions, we focused on the regions showing the largest sex/gender differences (i.e., least overlap between females and males). Because also in these regions there was a considerable overlap between the distributions of females and males, which made a division into two distinct forms impossible, we tested whether individuals would be consistently at one end of the “femaleness-maleness” continuum across brain regions or show “substantial variability”, being at the one end of the “femaleness-maleness” continuum on some regions and at the other end on other regions. We found that regardless of sample, type of MRI, and method of analysis, substantial variability is much more prevalent than internal consistency.

Discussion Consistent with previous findings (14, 15), our analysis of the structure of the human brain, which included most regions of gray and white matter, as well as measures of connectivity, revealed many nondimorphic group-level sex/gender differences in brain structure. There was extensive overlap of the distributions of females and males for all brain regions and connections assessed, irrespective of the type of sample, measure, or analysis (including analysis of absolute brain volumes). This extensive overlap undermines any attempt to distinguish between a “male” and a “female” form for specific brain features. Rather, the forms that are evident in most females are also the ones evident in most males (Fig. 1D). It is therefore more appropriate and informative to refer to measures of the brain in quantitative ways (Fig. 3) rather than in qualitative ways (e.g., “male”, “female” form). Another noteworthy observation is that the size of the sex/gender difference in some regions varied considerably between different datasets (Table S1). This finding is in line with previous reports that the existence and direction of sex/gender differences may depend on environmental events and developmental stage (4, 5). Fig. 3. The human brain mosaic. The gray matter volume of all 116 regions of gray matter in females (Left) and in males (Right) from the first sample is represented using a continuous high-low (green-white-yellow) scale. Each horizontal line represents the brain of a single subject and each column represents a single brain region. The continuous high-low scale represents the relative volume of a brain region in a given brain relative to the volume of this brain region in all other brains (i.e., within a column). The regions that showed the largest sex/gender differences and were included in the internal consistency analysis are marked with a black bar. The number above each bar corresponds to the region’s number in the AAL atlas and in Table S1. (Inset) Magnification of a small part of a horizontal line (i.e., a single brain). The number in each colored cell is the volume of this region for this brain. The novel aspect of the present study is the addition of another level of consideration to current thinking about the relation between sex and the brain. Specifically, this study is the first, to our knowledge, to move beyond the level of sex/gender differences in single brain elements (e.g., the volume of a brain region) to the level of the brain as a whole, by assessing internal consistency in the degree of “maleness-femaleness” of different elements within a single brain. Our results demonstrate that even when analyses are restricted to a small number of brain regions (or connections) showing the largest sex/gender differences, internal consistency is rare and is much less common than substantial variability (i.e., being at the one end of the “maleness-femaleness” continuum on some elements and at the other end on other elements). This finding was independent of sample, age, type of imaging, method of analysis of the imaging data, and the specific definition of the end of the continuum (i.e., the percent of individuals included in the “male-end” and “female-end” zones; Table S2). Our conclusion that substantial variability is much more common than internal consistency in the human brain may have implications for current theories of the sexual differentiation of the brain and, in particular, for the classic view that the female brain is the default pathway and the male brain is a differentiation away from that default (9). On this view, one could expect that there may be greater variability in males compared with females in the degree of differentiation, leading to a higher prevalence of substantial variability and of “nonconsistent” characteristics in males compared with females. Our data, however, do not support this view as the proportion of males and of females with substantial variability was not statistically different in any of the seven datasets, and the average proportion of “nonconsistent” characteristics was significantly higher in males compared with females in only one of the seven datasets (connectivity, P = 0.035). Thus, our findings that substantial variability is much more prevalent than internal consistency together with the lack of evidence for consistent sex/gender differences in the propensity to exhibit substantial variability do not support the classic view. Our findings are in line, however, with more recent thinking that masculinization and feminization are two independent processes and that sexual differentiation progresses independently in different brain tissues, “enabling genetically and environmentally induced variation in sexual differentiation of different tissues within a single brain” (4, p. 4; 10). Our study demonstrates that although there are sex/gender differences in brain structure, brains do not fall into two classes, one typical of males and the other typical of females, nor are they aligned along a “male brain–female brain” continuum. Rather, even when considering only the small group of brain features that show the largest sex/gender differences, each brain is a unique mosaic of features, some of which may be more common in females compared with males, others may be more common in males compared with females, and still others may be common in both females and males. The heterogeneity of the human brain and the huge overlap between the forms that brains of males and brains of females can take can be fully appreciated when looking at the entire brain (Fig. 3 and Figs. S3 and S4). Fig. S3. The human brain mosaic. The gray matter volume of all 116 regions of gray matter in females (Left) and in males (Right) from the subsample of the 18–26 y olds from the 1000 Functional Connectomes Project sample is represented using a continuous high-low (green-white-yellow) scale, created as in Fig. 3. Each horizontal line represents the brain of a single subject and each column represents a single brain region. The regions that showed the largest sex/gender differences and were included in the internal consistency analysis are marked with a black bar. The number above each bar corresponds to the region’s number in the AAL atlas and in Table S1. Fig. S4. The human brain mosaic. The absolute volume of 68 cortical regions, 23 subcortical regions, and 77 regions of white matter in females (Upper) and males (Lower) is represented using a continuous high-low (green-white-yellow) scale, created as in Fig. 3. The brains of both groups are separated into large (“male-end”), “intermediate”, and small (“female-end”), according to total brain volume, to foster the appreciation of size effects and of sex/gender effects. Each horizontal line represents the brain of a single subject and each column represents a single brain region. The regions that showed the largest sex/gender differences and were included in the internal consistency analysis are marked with a black bar. The number above each bar corresponds to the region’s number in Table S3. In accordance with the brain data, our analyses of gender-related data revealed extensive overlap between females and males in personality traits, attitudes, interests, and behaviors. Moreover, we found that substantial variability of gender characteristics is highly prevalent, whereas internal consistency is extremely rare, even for highly gender-stereotyped activities (Carothers and Reis’ data). These findings are in line with previous reports that sex/gender differences in abilities and qualities are mostly nonexistent or small, that there is extensive overlap between the distribution of males and females also in behaviors, interests, occupation preferences, and attitudes that show larger sex/gender differences (24, 25), and that there are no or only weak correlations between gender characteristics (18, 20, 21). Thus, most humans possess a mosaic of personality traits, attitudes, interests, and behaviors, some more common in males compared with females, others more common in females compared with males, and still others common in both females and males.

Conclusions The lack of internal consistency in human brain and gender characteristics undermines the dimorphic view of human brain and behavior and calls for a shift in our conceptualization of the relations between sex and the brain. Specifically, we should shift from thinking of brains as falling into two classes, one typical of males and the other typical of females, to appreciating the variability of the human brain mosaic. Scientifically, this paradigm shift entails replacing the currently dominant practice of looking for and listing sex/gender differences with analysis methods that take into account the huge variability in the human brain (rather than treat it as noise), as well as individual differences in the specific composition of the brain mosaic. At the social level, adopting a view that acknowledges human variability and diversity has important implications for social debates on long-standing issues such as the desirability of single-sex education and the meaning of sex/gender as a social category.

Methods Data Collection and Preparation for Analysis. Brain-related data. Data were obtained from four sources: Tel-Aviv University (the first brain-related dataset), University of Zurich (26) (DTI data), the 1000 Functional Connectomes Project (12), and the NKI enhanced sample (FreeSurfer analysis). For details of the imaging protocols, the datasets included from the 1000 Functional Connectomes Project, and the analysis of the images, see SI Methods. Gender-related data. Data were obtained from the MADICS (22), ADD Health (27), and from Harry Reis (the Carothers and Reis’s sample). We used data from the sixth wave of MADICS and the third wave of ADD Health because these waves included data of young adults (between 20 and 23 y old in MADICS and between 18 and 28 y old in ADD Health) on many variables that are known to show sex/gender differences, such as personality traits, relationships, activities, and attitudes. For further details, see SI Methods. Data Analysis. For each dataset, we calculated the significance [using the false discovery rate (FDR) method to correct for multiple comparisons] (28) and the effect size [Cohen’s d = (Mfemales − Mmales)/√([(SDfemales2 + SDmales2)]/2)] of the sex/gender difference for every variable. In calculating Cohen’s d, we weighted the variances according to the proportion of males and females in the population (∼50%) and not according to the actual proportion in each dataset so as not to bias the estimate of the size of the difference due to the nonequal number of males and females in most of our datasets. In each dataset, of the variables showing significant sex/gender differences, subsequent analyses used only the variables showing the largest sex/gender differences, because in large datasets, as were some of the datasets we used, even very small differences with a great overlap between females and males are significant. For each of the variables chosen for further analysis, we defined “male-end” and “female-end” zones as the scores of the 33% most extreme males and females, respectively, and an “intermediate” zone in between these two (Fig. 1D). For gender-related variables with discrete scoring, we chose as the “male-end”/“female-end” zone the zone that was nearest to 33%. Note that for such variables, the proportion of males at the “male-end” may not equal the proportion of females at the “female-end”. Once the three zones were defined for each variable, we defined for each subject his/her form in each of the variables and then defined for each subject whether s/he was internally consistent at the “male-end,” “female-end,” or “intermediate” zone or whether s/he had substantial variability (having at least one characteristic at the “female-end” and one characteristic at the “male-end”). In addition, we calculated each subject’s proportion of “female-end” and “male-end” characteristics. The Student t test was used to compare the mean proportion of “nonconsistent” characteristics in females and males, and the two-proportion z-test was used to compare the proportion of males and females showing substantial variability. Creating a Continuous Color Code. Pink-white-blue. The scale was created separately for each brain region (and for each gender characteristic) on the basis of the definitions of its “male-end”, “female-end”, and “intermediate” zones. Values in the “female-end” (“male-end”) zone were colored using the three-color scale conditional formatting function in Excel (Version 14.5.2), with the most extreme score defined as pink (blue), and the score bordering the “intermediate” zone defined as white (Fig. 1D). Green-white-yellow. The scale was created separately for each brain region using the three-color scale conditional formatting function in Excel. The highest score was defined as green, the lowest score was defined as yellow, and the middle score was defined as white. In samples with equal numbers of females and males, the middle score was the median. In the other samples, the middle score was chosen so that the proportion of males on one side of this score equals the proportion of females on the other side to not bias the estimate of the middle of the distribution due to the nonequal number of males and females.

SI Methods Brain Imaging. Data from Tel-Aviv were obtained with a 3-T (GE) MRI system. T1-weighted images were acquired with a 3D spoiled gradient-recalled echo sequence with the following parameters: up to 160 axial slices (whole brain coverage), TR/TE = 9/3 ms, resolution, 1 × 1 × 1 mm3, and scan time 4 min. Data from Zurich were obtained with a 3-T Philips Achieva MRI scanner (Philips Medical Systems). One diffusion-weighted spin-echo echo-planar imaging sequence was applied with the following parameters: 75 axial slices (whole brain coverage), TR/TE = 13.5 s/55 ms, spatial resolution 2 × 2 × 2 mm3, b-value = 1,000 s/mm2, 32 noncollinear diffusion directions, one non–diffusion-weighted image, and scan time 10 min. Datasets from the 1000 Functional Connectomes Project were included from the following sites: Atlanta; Baltimore; Beijing; Berlin; Cambridge, MA; the International Consortium for Brain Mapping (ICBM), Montreal; Leiden, The Netherlands; Milwaukee; Munich; Newark, NJ; New York City; Orangeburg, NY; Oulu, Finland; Palo Alto, CA; Queensland, Australia; St. Louis). Other datasets were excluded from analysis due to preprocessing difficulties or because they included skull-stripped images, as we wanted to avoid systematic differences between datasets. Analysis of T1-weighted images. Volume-based analysis. Images were analyzed using MATLAB (MathWorks) and SPM8 (Wellcome Department of Cognitive Neurology; www.fil.ion.ucl.ac.uk/spm). Gray matter volume was assessed with the optimized voxel-based morphometry (VBM) protocol (29), using the standard segmentation and registration tools available in the software. Images were normalized, segmented, modulated, and smoothed with a 12-mm Gaussian kernel. Voxels were mapped into 116 regions according to the AAL atlas (11), and mean gray matter volume was calculated for each region for each participant. For the full list of regions assessed see neuro.imm.dtu.dk/wiki/Automated_Anatomical_Labeling. Surface-based analysis. The FreeSurfer software package (Athinoula A. Martinos Center for Biomedical Imaging, Harvard University; surfer.nmr.mgh.harvard.edu/fswiki) was used to generate the surface representations of the cortex and to delineate 68 regions (regions 1001–1003, 1005–1035, 2001–2003, and 2005–2035 in the list of all regions that can be delineated using FreeSurfer, which appears in www.slicer.org/slicerWiki/index.php/Documentation/4.1/SlicerApplication/LookupTables/Freesurfer_labels). For each participant, we calculated the average cortical thickness and total cortical volume for each of these regions, as well as the volumes of 77 white matter regions (regions 7, 46, 251–255, 3001–3003, 3005–3035, 4001–4003, 4005–4035, and 5001–5002) and of 23 subcortical structures (4–5, 8, 10–18, 26, 28, 47, 49–54, 58, and 60). Diffusion tensor imaging analysis. Data analysis was performed with FSL (30) (www.fmrib.ox.ac.uk/fsl) using FMRIB’s diffusion toolbox (32) and following the standard preprocessing steps implemented in tract-based spatial statistics (31) with default parameters, but excluding the skeletonization step. Voxels were mapped into 116 regions according to the AAL atlas (11), and average fractional anisotropy and average mean diffusivity were calculated for each region and for each participant. For the analysis of brain connectivity, we preprocessed the diffusion-weighted connectivity data with FDT (32). For deterministic fiber tractography, we used the Diffusion Toolkit (DTK; trackvis.org/) and TrackVis software (trackvis.org/). The connectivity matrix, based on the number of reconstructed streamlines between 90 AAL regions of interest (the 26 cerebellar AAL regions were excluded because the cerebellum was not covered completely in some subjects), was computed using MATLAB scripts written by Andrew Zalesky (33). A more detailed description of the connectivity methods applied in the present study can be found elsewhere (26). Gender-Related Data. Datasets and questionnaires that had specific instructions for the creation of variables were analyzed as instructed. Additional variables were created by grouping together variables intended to measure a single construct (e.g., depression) or that content-wise seemed to assess related constructs (e.g., different measures of the amount of house work) and showed a high correlation (in case there were only two such variables) or Cronbach’s α (in case there were multiple items). This approach was taken to reduce intervariable correlations that reflect measuring the same construct in different ways, rather than the consistent effects of sex/gender on different variables within an individual. Because there were many missing data, we first chose the variables showing the largest sex/gender differences over the entire sample and then performed the analysis only on subjects who had no more than one (MADICS) or two (ADD Health) missing data for these variables. The data reported in Table 1 and Table S5 were calculated over the subjects included in the final analysis. Hypothetical Distributions of “Male-End” and “Female-End” Scores Under Different Degrees of Internal Consistency. These simulations were created on the basis of the data of the first sample. For all simulations, we created 10,000 “male brains” and 10,000 “female brains”, each with 10 “regions” (one for each brain region in the actual data). The score for each “brain” on a “region” was a number between 1 and 10,000 (because in our method of determining the “male-end” and “female-end” only the order of scores within a region matters). Next, for the “female brains”, for each “region”, we determined the “female-end” zone as the lowest 33% scores and the “male-end” zone as the highest X% scores, with X for each “region” taken from the actual percent of females with a “male-end” region for the simulated brain region in the actual data (because the degree of overlap between females and males in the 10 brain regions in the actual data were not identical, the percent of females in the “male-end” differed between the 10 regions ranging between 0.14 and 0.19). The same was done for the “male brains” (the percent of males in the “female-end” in the 10 regions ranged between 0.12 and 0.20). Last, we determined for each “brain” the number of “male-end”, “intermediate”, and “female-end” “regions”, and plotted a bivariate scattergram of the number of “regions” at the “female-end” (x axis) and at the “male-end” (y axis) in “females” (red) and “males” (green, as was done in Fig. 1F). Perfect internal consistency was created by giving the same score (i.e., the same location along the “femaleness-maleness” continuum) for all “regions” of a single “brain” (e.g., if a “brain” had a score of 150 in the first “region”, it also had a score of 150 on the other nine “regions”). Thus, the correlations between all possible pairs of two “brain regions” were 1. Internal consistency with noise was simulated by adding noise to the perfectly consistent “brains”. Noise was added by randomly adding to each score a number between −2,500 and +2,500 (average absolute noise was 1,250, average correlation between all possible pairs of two “brain regions” was 0.80), or between −3,333 and +3,333 (average absolute noise was 1,666.5, average correlation between all possible pairs of two “brain regions” was 0.70), or between −5,000 and +5,000 (average absolute noise was 2,500, average correlation between all possible pairs of two “brain regions” was 0.50). No internal consistency was simulated by randomly assigning a number between 1 and 10,000 for each of the 10 “regions” in each of the 10,000 “female brains” and each of the 10,000 “male brains”. The average correlation between all possible pairs of two “brain regions” was 0.0009.

SI Results Hypothetical Distributions of “Male-End” and “Female-End” Scores Under Different Degrees of Internal Consistency. Under absolutely no internal consistency, ∼80% of “brains” showed substantial variability compared with 0.1% that showed internal consistency (Fig. S1E). Under perfect internal consistency, ∼93% of “brains” showed internal consistency compared with 0% that showed substantial variability (Fig. S1A; the percent of internally consistent “brains” is not 100% because the percent of subjects in the “nonconsistent”-end differs between the 10 regions, so that even under perfect consistency there are some individuals who have a combination of “nonconsistent”-end and “intermediate” regions). Adding noise disrupted internal consistency more than it increased substantial variability. Thus, moving from perfect internal consistency to higher degrees of noise decreased internal consistency from ∼93% (Fig. S1A) to ∼24% (±2,500; Fig. S1B), ∼9% (±3,333; Fig. S1C), and ∼1.4% (±5,000; Fig. S1D) of “brains”, while increasing substantial variability from 0% (Fig. S1A) to 0.005% (Fig. S1B), ∼3.5% (Fig. S1C), and ∼20.5% (Fig. S1D) of “brains”. A comparison of the ±5,000 simulated condition to the actual data, in which 6% of brains showed internal consistency and 35% showed substantial variability, suggests that noise cannot explain the pattern of results we obtained, because less noise is expected to account for the percent of internal consistency but more noise to account for the percent of substantial variability. Future studies will explore the type of model that may account for the pattern of results obtained in the first sample, as well as in the other datasets analyzed in the present study.

Acknowledgments We thank Dr. Bobbi Carothers (Washington University in St. Louis) and Prof. Harry Reis (University of Rochester) for allowing us to use their data and Prof. Reis for stimulating discussions of the mosaic hypothesis. This research used the Maryland Adolescent Development In Context Study of Adolescent Development in Multiple Contexts, 1991–1998 (Log 1066) dataset (made accessible in 2000, numeric data files). These data were collected by Jacqueline S. Eccles (Producer) and are available through the Henry A. Murray Research Archive of the Institute for Quantitative Social Science at Harvard University (Distributor). Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. This research used data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill and funded by Eunice Kennedy Shriver National Institute of Child Health and Human Development Grant P01-HD31921, with cooperative funding from 23 other federal agencies and foundations. Information on how to obtain the Add Health data files is available on the National Longitudinal study of Adolescent Health (Add Health) website (www.cpc.unc.edu/addhealth). No direct support was received from Grant P01-HD31921 for this analysis. This work was supported by Swiss National Science Foundation Grants 320030-120661, 320030B-138668, 20030B-138668, and 4-62341-05 and European Union Future and Emerging Technologies Integrated Project Presence: Research Encompassing Sensory Enhancement, Neuroscience, Cerebral-Computer Interfaces and Application (PRESENCCIA) Grant 27731 (to L.J.).

Footnotes Author contributions: D.J. designed research; D.J., Z.B., S.U., F.L., J.H., and L.J. performed research; D.J. and Y.A. contributed new analytic tools; D.J., Z.B., I.T., N.W., O.G., Y.S., N.S., J.P., S.U., F.L., and J.H. analyzed data; and D.J. and D.S.M. wrote the paper.

The authors declare no conflict of interest.

↵*We use the term sex/gender to indicate that studies typically assess subjects’ sex (i.e., whether one is male or female) but observed differences may reflect the effects of both sex and gender (that is, the social construction of sex). We ignore here the important issue of the probable effects of gender on observed differences between females and males in brain and behavior, because we want to emphasize that regardless of the cause of these differences (sex, gender, or their interactions), they do not add up to create two distinct categories, one typical of males and the other typical of females.

This article is a PNAS Direct Submission.

Data deposition: Our anonymized raw neuroimaging data and accompanying metadata have been deposited at psy-neuro-nassy.uzh.ch and are accessible with a username and password that can be obtained from the authors by email (djoel{at}post.tau.ac.il or j.haenggi{at}psychologie.uzh.ch).

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1509654112/-/DCSupplemental.