Overview

The current release contains data from 1,570 participants (age: 21.5±2.9; female: 57.6%; right handed: 92.3%; years of education: 14.5±1.9; estimated IQ 110.7±6.7). Additional demographic characteristics of the participants are reported in Table 2. Participants were recruited from Boston area universities and colleges, and the surrounding communities. Consistent with this recruitment strategy, approximately 92% of the sample was under the age of 27 at the date of scan (Table 3). As detailed in Supplementary Appendix D, to protect participant identity, select demographic features and details of data collection have been removed or binned.

Table 2 Demographic characteristics and available phenotypes for the data release sample Full size table

Table 3 Participants by Age and Sex. Full size table

Reliability scans

Development of meaningful imaging-based measures and biomarkers requires estimates of phenotype reliability41. To support this need, a supplementary dataset (n=69) was acquired over the course of the primary collection effort. Data were collected on two independent days separated by less than 6 months (77.2±55.9 days). These data can be used to estimate test-retest reliability for existing morphometric and functional measures as well as the refinement and evaluation of novel methods and coupled with existing open science resources for the assessment of test-retest reliability (e.g., http://fcon_1000.projects.nitrc.org/indi/CoRR/html/)41. Many of the test pairs were acquired across two different scanners or across software console versions allowing reliability estimates that truly reflect the main sources of variance across the GSP sample.

As a demonstration of the utility of the reliability scan pairs, the structural images from each independent session were processed through the automated FreeSurfer pipeline separately. Pearson correlations were used to compare the morphometric estimates across the two visits (Table 4). Correlations range from 0.75 for the estimated cortical thickness of the right medial prefrontal cortex to 0.99 for the estimated intracranial volume. The observed patterns of regional variation in reliability could arise from instability in the morphometric pipeline, scan-rescan shifts in head positioning, hydration, or motion, which may disproportionately impact estimates of small structures and cortical thickness42–45. These reliability data are provided for analysis in isolation or to be combined with developing open repositories of reliability data41.

Table 4 Structural Phenotype Reliability Full size table

Construct validity of anatomic data

Having established the reliability of the morphometric estimates, anatomical features were analyzed to validate that commonly observed relations are present in the data and of typical magnitude. Estimated intracranial volume (ICV) and total brain volume are plotted in Fig. 1. As expected for a group of young adults where neurodegenerative processes have not begun46, ICV is highly correlated with brain volume (r=0.96). Head size differs between men and women40,47,48. Consistent with larger head size40,49,50, males displayed increased ICV, brain volume, and cortical surface area, relative to females, ranging from 12.4 to 13.8% [Fig. 1a–d; t(1568)=33.95, 32.88, 29.50, respectively; all ps<0.001]. Effective head size normalization should correct this difference. As highlighted in Supplementary Fig. 2, head size normalization accounts for sex differences in regional and whole-brain morphometric analyses40. No relations with sex emerged in the raw (uncorrected) data when considering average cortical thickness [t(1568)=0.80, P=0.43] consistent with models, and prior data, that suggest thickness increases minimally with head size49. Of interest, there was also no sex difference noted for cortical thickness as predicted by early neurodevelopmental models that hypothesize cortical surface area but not thickness differs across normal variability in head size. The dissociation between effects on surface area and thickness is quite dramatic in the contrasting plots of Fig. 1d and Fig. 1e.

Figure 1: Structural brain volume and morphometric measures. (a) A scatter plot of the derived structural MRI estimates from the 1,570 participants included in the present data release reveals expected relations between sex, intracranial volume (ICV), and brain volume. Histograms of both brain volume and ICV are represented on the x and y axes respectively. (b–e) Scatter plots display the correlations between age (2 year bins) and morphometric estimates of (b) ICV (Females r=−0.07; Males r=−0.01), (c) brain volume (Females r=−0.14; Males r=−0.11), (d) cortical surface area (Females r=−0.12; Males r=−0.05), and (e) mean cortical thickness (Females r=−0.28; Males r=−0.26). Note ICV differs by sex but minimally by age reflecting the sex difference in head size that is achieved by adolescence and remains stable. By contrast, cortical thickness is nearly identical between the sexes but decreases progressively with age. Full size image

ICV is stable across the adult lifespan. In the present data participant age did not associate with estimated ICV (r=−0.01; P=0.66). Brain volume (r=−0.08; P<0.005) and cortical surface area (r=−0.05; P<0.05) displayed modest relations, perhaps reflecting brain volume loss which is thought to be present, but small, in this age range50. Even with the compressed age range in the present sample, average cortical thickness was inversely associated with age (r=−0.27; P<0.001; Fig. 1e). Taken together, these results demonstrate that age-associated shifts in brain anatomy are evident early in life and that the extent of these effects varies based on the phenotype of interest.

Functional data quality

Data quality for the resting state scans was quantified through the Automated Functional MRI Quality Assessment Tool35. To facilitate quality assessment and data analyses a broad range of commonly used quantitative data quality metrics are included in the release dataset. Histograms of mean temporal sSNR values for the first and second rest runs are displayed in Fig. 2a. Histograms of number of relative movements in 3D space (>0.1 mm), and maximum absolute movement in 3D space (mm) for the first and second rest runs are presented in Supplementary Fig. 3a,b. Slice-based SNR was also used as exclusionary criteria. If the sSNR for the whole brain (mean sSNR over all slices within the brain mask weighted by the slice size) was less than 100 for the first BOLD run, all data from that participant were excluded from the release. If the temporal sSNR for the second BOLD run was less than 100, only that run was excluded. This means a participant could be included with a single BOLD run, when two runs were acquired, but the second run was lost due to data quality concerns.

Figure 2: Functional measures of brain networks. (a) Histograms of mean slice-based temporal signal-to-noise (sSNR) values for the first and second rest runs illustrate variance in data quality across subjects. (b) The mean voxel-based temporal SNR map of the first rest run from the full sample (n=1,570) illustrates spatial variance in data quality across the cortical surface. The map is displayed for multiple views of the left hemisphere in Caret PALS space. A, anterior; P, posterior; D, dorsal; V, ventral. Note the regions of reduced SNR near to the sinuses and inner ear space. (c) A correlation matrix shows the complete coupling architecture of the full cerebral cortex measured at rest. Regions determined based on the 17-network solution from Yeo et al.10. Values reflect z-transformed Pearson correlations between every region and every other region. Within-network correlations fall along the diagonal displayed in the center. Between-network correlations are plotted away from the diagonal and reveal both positive (red) and negative (blue) correlations. (d) The functional network organization of the human cerebral cortex revealed through intrinsic functional connectivity. Colors reflect regions estimated to be within the same network. The approach groups similar correlation profiles based on a winner-take-all solution, with every surface vertex assigned to its best-fitting network10. The present data fully cover the striatum, thalamus, and cerebellum allowing for analyses that extend beyond the cerebral cortex (see Buckner et al.11 and Choi et al.12). Full size image

Signal loss and susceptibility artifacts occur as a result of magnetic field inhomogeneities, potentially biasing or obscuring results from functional connectivity analyses. In T2*-dependent (BOLD) images, the decay in recoverable signal is exacerbated in regions where the brain is adjacent to air (e.g., sinus cavities)51. To estimate the topographic pattern of susceptibility artifacts in the present data we computed the voxel-level temporal SNR of the motion-corrected fMRI time series in each participant’s native volumetric space (the mean of the signal at each voxel over the BOLD run divided by the variance). The resulting voxel-level SNR was then projected to FreeSurfer surface space, averaged across the 1,570 subjects, and displayed in Caret PALS space (Fig. 2b)52. Clear spatial variation in voxel-level SNR was evident across the cortical mantle. As expected, decreased voxel-level SNR was pronounced in anterior aspects of inferior and medial temporal lobe, as well as in the orbital frontal cortex.

To provide an additional data quality metric, fractional Amplitude of Low Frequency Fluctuations (fALFF)53,54 were computed for each participant. fALFF reflects the total power in the low frequency range (0.01–0.08 Hz) of an fMRI image, normalized by the total power across all frequencies. fALFF has been theorized to suppress non-specific signal components in the resting-state fMRI, providing improved sensitivity and specificity to detect regional spontaneous brain activity. Histograms of mean fALFF for the first and second rest runs are displayed in Supplementary Fig. 4a. Voxel-level fALFF estimates were averaged across the 1,570 subjects, and displayed in Caret PALS space (Supplementary Fig. 4b)52.

The present data sample is of generally high quality because of the exclusion criteria. However, scan quality is not uniformly distributed across the sample. Factors such as head motion can systematically influence resting-state network measures15,32–34. To facilitate informed analyses of the available data, the sSNR, number of micro-movements, and maximum movements across several key group divisions are depicted in Supplementary Fig. 5. Particular care should be taken when selecting sub-populations that could bias results (for example splitting groups by sex or number of available BOLD runs).

IQ, personality, and behavioral measures

Selected analyses of the available behavioral phenotypes are reported to highlight data quality, scale/measure validity, and potential analysis applications. The first analyses establish the validity of our online estimates and the sample characteristics for IQ. The analyses that follow explore personality assessments and then cognitive task performance.

To estimate validity of the online IQ estimates, online estimates of full scale IQ were examined in relation to Wechsler Abbreviated Scale of Intelligence (WASI) derived estimates of full-scale IQ collected in person55. Thirty-three participants completed the WASI on the day of scan in addition to the full GSP online battery. A strong relation was found between the average estimated IQ from the WASI with that derived from the online estimates (r=0.80; Fig. 3a). As expected, the derived estimates of full scale IQ were normally distributed across the sample (Fig. 3b). Consistent with the sample recruitment from Boston area universities and colleges, MGH, and the surrounding communities, the mean estimated full scale IQ for the sample was elevated (110.7±6.7) relative to the expected distribution for the general population. Histograms reflecting the respective distributions of matrix reasoning and derived estimates of full scale IQ are presented in Supplementary Fig. 6.

Figure 3: IQ, behavioral, and personality measures. (a) Online estimates of full scale IQ are consistent with standard Wechsler Abbreviated Scale of Intelligence (WASI) full-scale IQ estimates. Scatter plot reflects relation between average online and WASI estimates of full scale IQ (n=33; r=0.80). (b) Histogram reflects the distribution of the mean derived estimates of full scale IQ. Consistent with the sample recruitment from Boston area universities and colleges, MGH, and the surrounding communities, the mean estimated full scale IQ for the sample is 110.7±6.7. (c) Participants exhibit expected personality and temperamental characteristics. Scatter plot of available data reflects expected relations between STAI trait anxiety and NEO neuroticism. Histograms of anxiety and neuroticism are represented on the x and y axes respectively. (d) Graphs reflect mental rotation task performance for females and males. White boxes indicate standard error, colored boxes reflect standard deviation, and the black lines denote the sample mean for each condition. Performance decreases with more difficult rotations. Full size image

Regarding personality estimates, participants exhibited the anticipated relations linking conceptually overlapping personality and temperamental characteristics. Consistent with a substantial literature on negative affect56–58, a strong association linked trait anxiety and neuroticism (r=0.80, P<0.001; Fig. 3c). Substantial co-variation exists across exploratory and disinhibitory behaviors, such as novelty seeking and impulsivity59,60. Analyses of the present data highlight the predicted relation between self-reported novelty seeking and impulsivity (r=0.62, P<0.001; Supplementary Fig. 7).

Cognitive task performance also suggests measurement validity. In the mental rotation task included in the initial release, participants were asked to compare two 3D objects and indicate if they were identical or mirror images of each other61,62. Since Shepard and Metzler61 first elaborated the concept, mental rotation has been a commonly used measure of spatial ability. In the mental rotation task, participants were presented with pairs of 3D, asymmetrical groupings of cubes. The relative rotation of each object pair in 3D space varied over the course of the experiment (0°, 80°, 120°, or 180°). Participants completed 9 trials for each rotation condition, 36 in total. In half of the available trials the shapes were identical or mirror images of each other. Participants’ performance was estimated based on their speed and accuracy to distinguish between the mirrored and non-mirrored pairs. As the extent of object rotation increased, participants displayed the expected decrease in performance (Fig. 3d)61,62. As predicted by prior evidence of sex differences in spatial processing63,64, the males in our sample exhibited increased mental rotation accuracy, relative to the females, across each non-0° rotation condition (ts>4.10; ps<0.001).

Cognitive control over information processing can be dynamically adjusted in response to environmental demands65,66. To establish an index of behavioral responses to shifting task demands participants completed a modified version of the Eriksen flanker task65. The flanker task requires the participant to focus on a given stimulus while inhibiting attention to flanking stimuli, providing estimates of both attentional and inhibitory control. In the included flanker task, participants were presented with groups of 5 arrows pointing left or right. They were instructed to respond to the center arrow. When the arrows were printed in green font participants responded in the same direction as the middle arrow. When the arrows were printed in red font participants responded in the opposite direction of the middle arrow. Participants completed 192 flanker trials, with 12 trials in each block. Over the course of the task participants completed 8 switch and 8 non-switch blocks. In switch blocks the color of the presentation alternated between red and green font throughout the block. As expected, the increased demand on selective visual attention and inhibition in the switch blocks resulted in decreased accuracy and increased response times, relative to non-switch blocks (ts>25.51, ps<0.001; Supplementary Fig. 8).

Analysis applications

Selected analyses of the anatomical data are reported to illustrate (1) the potential of the available data through a typical use case that partials out nuisance variables and (2) a brain-behavior relation that requires a large sample size to detect.

A well-defined amygdala-medial prefrontal cortex (mPFC) circuit contributes to emotional processes67–70. Subtle shifts within the anatomy of this circuit, present in the general population, have been reported to track with the expression of negative affect in a subset of the present data13. To examine the presence of these relations in the formal GSP release sample, analyses were conducted mirroring those in the recent Holmes et al.13 publication (n=897). Due to partially overlapping data, these analyses should not be interpreted as a true replication of the observed effect. Briefly, trait negative affect was computed as the average of the Z-scores for five self-report measures associated with the experience of negative affect56–58. These scales included the trait form of the Spielberger State/Trait Anxiety Inventory71, the neuroticism scale from the NEO five-factor inventory72, the behavioral inhibition component of the Behavioral Inhibition/Behavioral Activation Scale73, the total mood disturbance score from the Profile of Mood States30, and the harm avoidance scale from the Temperament and Character Inventory74. Block linear regressions were conducted separately for both the left and right amygdala. Analyses partialed out the variance associated with site, console software version, estimated IQ75, age, sex, and ICV and then examined the relation between amygdala volume and negative affect. Given prior evidence suggesting opposing relations in the amygdala and the mPFC with negative affect, surface-based cortical thickness analyses were conducted on the FreeSurfer parcelation of the region labeled by Desikan et al.76 as the rostral anterior cingulate. Block linear regression partialed out the variance associated with site, console software version, estimated IQ75, age, and sex and then examined the relation between mPFC cortical thickness and negative affect.

Analyses revealed slight, yet opposing structural differences in the amygdala and medial prefrontal cortex in the present sample of young adults. Consistent with its hypothesized role in anxiety and affective illnesses, amygdala volumes co-varied with negative affect (left: F 1,889 =11.36; P<0.001; r=0.11; Supplementary Fig. 9a; right: F 1,889 =4.34; P<0.05; r=0.07). In line with the suggested role of the mPFC in the downregulation of amygdala activity, reduced left hemisphere rostral anterior cingulate cortical thickness associated with subtle increases in negative affect (F 1,890 =4.73; P<0.05; r=−0.07; Supplementary Fig. 9b).

Impairments in affective experience are hypothesized to result from a breakdown in the interactions between subcortical and cortical structures77,78. To further examine how the correlation between amygdala volume and mPFC thickness associates with negative affect, the sample was split into groups with low-medium (n=760), and high (n=137) negative affect. High and low-medium groups were defined as one standard deviation above or below the mean negative affect score (0.00±0.83). No detectable relation was observed between amygdala volume and mPFC thickness in the low-medium negative affect participants (F 1,753 =0.886; P=0.347; r=0.03; Supplementary Fig. 9c). A negative correlation between left amygdala volume and mPFC thickness was evident among individuals reporting the most extreme negative affect (F 1,130 =3.84; P=0.05; r=−0.17; Supplementary Fig. 9d). The amygdala-mPFC correlation in the high negative affect participants was significantly different from the relation observed in the remaining participants (Z=2.19, P<0.05).

To mitigate spurious effects resulting from population admixture and cultural biases in self-reported affect79, the original Holmes et al.13 analyses were restricted to white non-Hispanic participants of European ancestry. When considering these participants (n=566) in the current sample, negative affect co-varied with amygdala volumes (left: F 1,558 =9.57; P<0.005; r=0.13; right: F 1,558 =4.04; P<0.05; r=0.09) and was associated with decreases in mPFC thickness (F 1,559 =3.94; P<0.05; r=−0.08). When dividing the participants into low-medium (n=473) and high (n=93) negative affect, no detectable relation was observed between amygdala volume and mPFC thickness in the low-medium negative affect participants (F 1,466 =0.086; P=0.769; r=0.01). An inverse correlation between left amygdala volume and mPFC thickness was evident among the individuals with the most extreme negative affect (F 1,86 =3.96; P<0.05; r=−0.21).

Analysis of functional network properties

Estimates of intrinsic functional coupling can be used to explore brain organization80 as well as the basis for graph theoretical analyses of network properties81. To illustrate the current data’s utility for such analyses, we estimated a cortical functional coupling matrix across all available region pairs based on the functional atlas of Yeo et al.10; (see also Power et al.82). This matrix is a comprehensive description of the correlation strength of all region pairs across the cortex for the complete dataset of 1,570 participants (Fig. 2c). This matrix or similar matrices derived from subsets of participants can provide a powerful means to explore relations between network properties and function.

One caveat in interpreting the magnitude of functional correlations is that the correlation structure of resting-state data is inherently biased by a nonuniform distribution of SNR51. This point should be carefully considered when using the present data. To illustrate this caveat, we assessed the reliability of the correlation estimates using the test-retest data. Consistent with the observed spatial variation in SNR across the cortical mantle (Fig. 2b), estimates of intrinsic functional coupling are not uniformly reliable across the cortex (Supplementary Fig. 10). Decreased test-retest reliability was particularly evident in the ‘Limbic network,’ encompassing aspects of orbital frontal and inferior medial prefrontal cortex as well as portions of temporal pole. This analysis is a reminder that spatial variation in signal quality across the brain should be considered in all analyses of functional coupling derived from BOLD data.

As a final illustration of how the functional coupling can be used to derive network properties, the topographic organization of the human cerebral cortex across both rough and fine-grained resolutions was estimated from the coupling of each vertex across the entire cortical mantle mimicking Yeo et al.10 (Fig. 2d; Supplementary Fig. 11; Supplementary Fig. 12). Other approaches can be productively applied to these data83,84.