Since their introduction more than a century ago, normative growth charts have become fundamental tools in pediatric medicine and also in many other areas of anthropometry (Cole, 2012). They provide the ability to quantify individual variation against centiles of variation in a reference population, which shifts focus away from group-level (e.g., case-control) inferences to the level of the individual. This idea has been adopted and generalized in clinical neuroimaging, and normative modeling is now established as an effective technique for providing inferences at the level of the individual in neuroimaging studies (Marquand et al., 2016; Marquand et al., 2019).

Although normative modeling can be used to estimate many different kinds of mappings—for example between behavioral scores and neurobiological readouts—normative models of brain development and aging are appealing considering that many brain disorders are grounded in atypical trajectories of brain development (Insel, 2014) and the association between cognitive decline and brain tissue in aging and neurodegenerative diseases (Jack et al., 2010; Karas et al., 2004). Indeed, normative modeling has been applied in many different clinical contexts, including charting the development of infants born pre-term (Dimitrova et al., 2020) and dissecting the biological heterogeneity across cohorts of individuals with different brain disorders, including schizophrenia, bipolar disorder, autism, and attention-deficit/hyperactivity disorder (Bethlehem et al., 2020; Wolfers et al., 2021; Zabihi et al., 2019).

A hurdle to the widespread application of normative modeling is a lack of well-defined reference models to quantify variability across the lifespan and to compare results from different studies. Such models should: (1) accurately model population variation across large samples; (2) be derived from widely accessible measures; (3) provide the ability to be updated as additional data come online, (4) be supported by easy-to-use software tools, and (5) should quantify brain development and aging at a high spatial resolution, so that different patterns of atypicality can be used to stratify cohorts and predict clinical outcomes with maximum spatial precision. Prior work on building normative modeling reference cohorts (Bethlehem et al., 2021) has achieved some of these aims (1–4), but has modeled only global features (i.e., total brain volume), which is useful for quantifying brain growth but has limited utility for the purpose of stratifying clinical cohorts (aim 5). The purpose of this paper is to introduce a set of reference models that satisfy all these criteria.

To this end, we assemble a large neuroimaging data set (Table 1) from 58,836 individuals across 82 scan sites covering the human lifespan (aged 2–100, Figure 1A) and fit normative models for cortical thickness and subcortical volumes derived from Freesurfer (version 6.0). We show the clinical utility of these models in a large transdiagnostic psychiatric sample (N=1985, Figure 2). To maximize the utility of this contribution, we distribute model coefficients freely along with a set of software tools to enable researchers to derive subject-level predictions for new data sets against a set of common reference models.

Figure 1

Download asset Open asset

Figure 2

Download asset Open asset

We split the available data into training and test sets, stratifying by site (Table 1, Supplementary files 1 and 2). After careful quality checking procedures, we fit normative models using a set of covariates (age, sex, and fixed effects for site) to predict cortical thickness and subcortical volume for each parcel in a high-resolution atlas (Destrieux et al., 2010). We employed a warped Bayesian linear regression model to accurately model non-linear and non-Gaussian effects (Fraza et al., 2021), whilst accounting for scanner effects (Bayer et al., 2021; Kia et al., 2021). These models are summarized in Figure 1 and Figure 3, Figure 3—figure supplements 1–3, and with an online interactive visualization tool for exploring the evaluation metrics across different test sets (overview of this tool shown in Video 1). The raw data used in these visualizations are available on GitHub (Rutherford, 2022a).

Figure 3 with 3 supplements see all

Download asset Open asset

Video 1

Download asset

We validate our models with several careful procedures: first, we report out of sample metrics; second, we perform a supplementary analysis on a subset of participants for whom input data had undergone manual quality checking by an expert rater (Table 1 – mQC). Third, each model fit was evaluated using metrics (Figure 3, Figure 3—figure supplements 1–3) that quantify central tendency and distributional accuracy (Dinga et al., 2021; Fraza et al., 2021). We also estimated separate models for males and females, which indicate that sex effects are adequately modeled using a global offset. Finally, to facilitate independent validation, we packaged pretrained models and code for transferring to new samples into an open resource for use by the community and demonstrated how to transfer the models to new samples (i.e., data not present in the initial training set).

Our models provide the opportunity for mapping the diverse trajectories of different brain areas. Several examples are shown in Figure 1C and D which align with known patterns of development and aging (Ducharme et al., 2016; Gogtay et al., 2004; Tamnes et al., 2010). Moreover, across the cortex and subcortex our model fits well, explaining up to 80% of the variance out of sample (Figure 3, Figure 3—figure supplements 1–3).

A goal of this work is to develop normative models that can be applied to many different clinical conditions. To showcase this, we apply the model to a transdiagnostic psychiatric cohort (Table 1 – Clinical; Figure 2) resulting in personalized, whole-brain deviation maps that can be used to understand inter-individual variability (e.g., for stratification) and to quantify group separation (e.g., case-control effects). To demonstrate this, for each clinical group, we summarized the individual deviations within that group by computing the proportion of subjects that have deviations in each region and comparing to matched (same sites) controls in the test set (Figure 2B–C). Additionally, we performed case-control comparisons on the raw cortical thickness and subcortical volumes, and on the deviation maps (Figure 2D), again against a matched sample from the test set. This demonstrates the advantages of using normative models for investigating individual differences in psychiatry, that is, quantifying clinically relevant information at the level of each individual. For most diagnostic groups, the z-statistics derived from the normative deviations also provided stronger case-control effects than the raw data. This shows the importance of accurate modeling of population variance across multiple clinically relevant dimensions. The individual-level deviations provide complimentary information to the group effects, which aligns with previous work (Wolfers et al., 2018; Wolfers et al., 2020; Zabihi et al., 2020). We note that a detailed description of the clinical significance of our findings is beyond the scope of this work and will be presented separately.

In this work, we create lifespan brain charts of cortical thickness and subcortical volume derived from structural MRI, to serve as reference models. Multiple data sets were joined to build a mega-site lifespan reference cohort to provide good coverage of the lifespan. We applied the reference cohort models to clinical data sets and demonstrated the benefits of normative modeling in addition to standard case-control comparisons. All models, including documentation and code, are made available to the research community. We also provide an example data set (that includes data from sites not in the training sample) along with the code to demonstrate how well our models can adapt to new sites, and how easy it is to transfer our pretrained models to users’ own data sets.

We identify three main strengths of our approach. First, our large lifespan data set provides high anatomical specificity, necessary for discriminating between conditions, predicting outcomes, and stratifying subtypes. Second, our models are flexible in that they can model non-Gaussian distributions, can easily be transferred to new sites, and are built on validated analytical techniques and software tools (Fraza et al., 2021; Kia et al., 2021; Marquand et al., 2019). Third, we show the general utility of this work in that it provides the ability to map individual variation whilst also improving case-control inferences across multiple disorders.

In recent work, a large consortium established lifespan brain charts that are complementary to our approach (Bethlehem et al., 2021). Benefits of their work include precisely quantifying brain growth using a large cohort, but they only provide estimates of four coarse global measures (e.g., total brain volume). While this can precisely quantify brain growth and aging this does not provide the ability to generate individualized fingerprints or to stratify clinical cohorts. In contrast, in this work, we focus on providing spatially specific estimates (188 different brain regions) across the post-natal lifespan which provides fine-grained anatomical estimates of deviation, offering an individualized perspective that can be used for clinical stratification. We demonstrate the transdiagnostic clinical value of our models (Figure 2) by showing how clinical variation is widespread in a fine-grain manner (e.g., not all individuals deviate in the same regions and not all disorders have the same characteristic patterns) and we facilitate clinical applications of our models by sharing tutorial code notebooks with sample data that can be run locally or online in a web browser.

We also identify the limitations of this work. We view the word ‘normative’ as problematic. This language implies that there are normal and abnormal brains, a potentially problematic assumption. As indicated in Figure 2, there is considerable individual variability and heterogeneity among trajectories. We encourage the use of the phrase ‘reference cohort’ over ‘normative model’. In order to provide coverage of the lifespan the curated data set is based on aggregating existing data, meaning there is unavoidable sampling bias. Race, education, and socioeconomic variables were not fully available for all included data sets, however, given that data were compiled from research studies, they are likely samples drawn predominantly from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies (Henrich et al., 2010) and future work should account for these factors. The sampling bias of UKBiobank (Fry et al., 2017) is especially important for users to consider as UKBiobank data contributes 59% of the full sample. By sampling both healthy population samples and case-control studies, we achieve a reasonable estimate of variation across individuals, however, downstream analyses should consider the nature of the reference cohort and whether it is appropriate for the target sample. Second, we have relied on semi-automated quality control (QC) for the full sample which—despite a conservative choice of inclusion threshold—does not guarantee either that low-quality data were excluded or that the data were excluded are definitively excluded because of artifacts. We addressed this by comparing our full test set to a manually quality check data set and observed similar model performance. Also, Freesurfer was not adjusted for the very young age ranges (2–7 yo) thus caution should be used when interpreting the model on new data in this age range. Finally, although the models presented in this study are comprehensive, they are only the first step, and we will augment our repository with more diverse data, different features, and modeling advances as these become available.

Data from 82 sites were combined to create the initial full sample. These sites are described in detail in Supplementary files 1-2, including the sample size, age (mean and standard deviation), and sex distribution of each site. Many sites were pulled from publicly available data sets including ABCD, ABIDE, ADHD200, CAMCAN, CMI-HBN, HCP-Aging, HCP-Development, HCP-Early Psychosis, HCP-Young Adult, IXI, NKI-RS, Oasis, OpenNeuro, PNC, SRPBS, and UKBiobank. For data sets that include repeated visits (i.e., ABCD and UKBiobank), only the first visit was included. Other included data come from studies conducted at the University of Michigan (Duval et al., 2018; Rutherford et al., 2020; Tomlinson et al., 2020; Tso et al., 2021; Weigard et al., 2021; Zucker et al., 2009), University of California Davis (Nordahl et al., 2020), University of Oslo (Nesvåg et al., 2017), King’s College London (Green et al., 2012; Lythe et al., 2015), and Amsterdam University Medical Center (Mocking et al., 2016). Full details regarding sample characteristics, diagnostic procedures, and acquisition protocols can be found in the publications associated with each of the studies. Equal sized training and testing data sets (split half) were created using scikit-learn’s train_test_split function, stratifying on the site variable. It is important to stratify based on site, not only study (Bethlehem et al., 2021), as many of the public studies (i.e., ABCD) include several sites, thus modeling study does not adequately address MRI scanner confounds. To test stability of the model performance, the full test set was randomly resampled 10 times and evaluation metrics were re-calculated on each split of the full test set (Figure 3—figure supplement 2). To show generalizability of the models to new data not included in training, we leveraged data from OpenNeuro.org (Markiewicz et al., 2021) to create a transfer data set (six sites, N=546, Supplementary file 3). This data are provided along with the code for transferring to walk users through how to apply these models to their own data.

The clinical validation sample consisted of a subset of the full data set (described in detail in Figure 1A, Figure 2A and Supplementary file 1). Studies (sites) contributing clinical data included: Autism Brain Imaging Database Exchange (ABIDE GU, KKI, NYU, USM), ADHD200 (KKI, NYU), CNP, SRPBS (CIN, COI, KTT, KUT, HKH, HRC, HUH, SWA, UTO), Delta (AmsterdamUMC), Human Connectome Project Early Psychosis (HCP-EP BWH, IU, McL, MGH), KCL, University of Michigan Children Who Stutter (UMich_CWS), University of Michigan Social Anxiety Disorder (UMich_SAD), University of Michigan Schizophrenia Gaze Processing (UMich_SZG), and TOP (University of Oslo).

In addition to the sample-specific inclusion criteria, inclusion criteria for the full sample were based on participants having basic demographic information (age and sex), a T1-weighted MRI volume, and Freesurfer output directories that include summary files that represent left and right hemisphere cortical thickness values of the Destrieux parcellation and subcortical volumetric values (aseg.stats, lh.aparc.a2009s.stats, and rh.aparc.a2009s.stats). Freesurfer image analysis suite (version 6.0) was used for cortical reconstruction and volumetric segmentation for all studies. The technical details of these procedures are described in prior publications (Dale et al., 1999; Fischl et al., 2002; Fischl and Dale, 2000). UK Biobank was the only study for which Freesurfer was not run by the authors. Freesurfer functions aparcstats2table and asegstats2table were run to extract cortical thickness from the Destrieux parcellation (Destrieux et al., 2010) and subcortical volume for all participants into CSV files. These files were inner merged with the demographic files, using Pandas, and NaN rows were dropped.

QC is an important consideration for large samples and is an active research area (Alfaro-Almagro et al., 2018; Klapwijk et al., 2019; Rosen et al., 2018). We consider manual quality checking of images both prior to and after preprocessing to be the gold standard. However, this is labor intensive and prohibitive for very large samples. Therefore, in this work, we adopt a pragmatic and multi-pronged approach to QC. First, a subset of the full data set underwent manual quality checking (mQC) by author S.R. Papaya, a JavaScript-based image viewer. Manual quality checking was performed during December 2020 when the Netherlands was in full lockdown due to COVID-19 and S.R. was living alone in a new country with a lot of free time. Data included in this manual QC step was based on what was available at the time (Supplementary file 2). Later data sets that were included were not manually QC’d due to resource and time constraints. Scripts were used to initialize a manual QC session and track progress and organize ratings. All images (T1w volume and Freesurfer brain.finalsurfs) were put into JSON files that the mQC script would call when loading Papaya. Images were rated using a ‘pass/fail/flag’ scale and the rating was tracked in an automated manner using keyboard inputs (up arrow=pass, down arrow=fail, F key=flag, and left/right arrows were used to move through subjects). Each subject’s T1w volume was viewed in 3D volumetric space, with the Freesurfer brain.finalsurfs file as an overlay, to check for obvious quality issues such as excessive motion, ghosting or ringing artifacts. Example scripts used for quality checking and further instructions for using the manual QC environment can be found on GitHub(Rutherford, 2022c copy archived at swh:1:rev:70894691c74febe2a4d40ab0c84c50094b9e99ce). We relied on ABCD consortium QC procedures for the QC for this sample. The ABCD study data distributes a variable (freesqc01.txt; fsqc_qc = = 1/0) that represents manual quality checking (pass/fail) of the T1w volume and Freesurfer data, thus this data set was added into our manual quality checked data set bringing the sample size to 24,354 individuals passing manual quality checks. Note that QC was performed on the data prior to splitting of the data to assess generalizability. Although this has a reduced sample, we consider this to be a gold-standard sample in that every single scan has been checked manually. All inferences reported in this manuscript were validated against this sample. Second, for the full sample, we adopted an automated QC procedure that quantifies image quality based on the Freesurfer Euler Characteristic (EC), which has been shown to be an excellent proxy for manual labeling of scan quality (Monereo-Sánchez et al., 2021; Rosen et al., 2018) and is the most important feature in automated scan quality classifiers (Klapwijk et al., 2019). Since the distribution of the EC varies across sites, we adopt a simple approach that involves scaling and centering the distribution over the EC across sites and removing samples in the tail of the distribution (see Kia et al., 2021 for details). While any automated QC heuristic is by definition imperfect, we note that this is based on a conservative inclusion threshold such that only samples well into the tail of the EC distribution are excluded, which are likely to be caused by true topological defects rather than abnormalities due to any underlying pathology. We separated the evaluation metrics into full test set (relying on automated QC) and mQC test set in order to compare model performance between the two QC approaches and were pleased to notice that the evaluation metrics were nearly identical across the two methods.

Normative modeling was run using python 3.8 and the PCNtoolkit package (version 0.20). Bayesian Linear Regression (BLR) with likelihood warping was used to predict cortical thickness and subcortical volume from a vector of covariates (age, sex, and site). For a complete mathematical description and explanation of this implementation, see Fraza et al., 2021. Briefly, for each brain region of interest (cortical thickness or subcortical volume), $y$ is predicted as:

where $w^T$ is the estimated weight vector, $\varphi \left(x\right)$ is a basis expansion of the of covariate vector x, consisting of a B-spline basis expansion (cubic spline with five evenly spaced knots) to model non-linear effects of age, and $ϵ=\eta \left(0,\beta \right)$ a Gaussian noise distribution with mean zero and noise precision term β (the inverse variance). A likelihood warping approach (Rios and Tobar, 2019; Snelson et al., 2003) was used to model non-Gaussian effects. This involves applying a bijective non-linear warping function to the non-Gaussian response variables to map them to a Gaussian latent space where inference can be performed in closed form. We employed a ‘sinarcsinsh’ warping function, which is equivalent to the SHASH distribution commonly used in the generalized additive modeling literature (Jones and Pewsey, 2009) and which we have found to perform well in prior work (Dinga et al., 2021; Fraza et al., 2021). Site variation was modeled using fixed effects, which we have shown in prior work provides relatively good performance (Kia et al., 2021), although random effects for site may provide additional flexibility at higher computational cost. A fast numerical optimization algorithm was used to optimize hyperparameters (L-BFGS). Computational complexity of hyperparameter optimization was controlled by minimizing the negative log-likelihood. Deviation scores (Z-scores) are calculated for the n-th subject, and d-th brain area, in the test set as:

Where $y_{nd}$ is the true response, ${\displaystyle {\hat{y}}_{nd}}$ is the predicted mean, ${\sigma }_d^2$ is the estimated noise variance (reflecting uncertainty in the data), and ${\left({\sigma }_{}^2\right)}_d$ is the variance attributed to modeling uncertainty. Model fit for each brain region was evaluated by calculating the explained variance (which measures central tendency), the mean squared log-loss (MSLL, central tendency, and variance) plus skew and kurtosis of the deviation scores (2) which measures how well the shape of the regression function matches the data (Dinga et al., 2021). Note that for all models, we report out of sample metrics.

To provide a summary of individual variation within each clinical group, deviation scores were summarized for each clinical group (Figure 2B–C) by first separating them into positive and negative deviations, counting how many subjects had an extreme deviation (positive extreme deviation defined as Z>2, negative extreme deviation as Z<−2) at a given ROI, and then dividing by the group size to show the percentage of individuals with extreme deviations at that brain area. Controls from the same sites as the patient groups were summarized in the same manner for comparison. We also performed classical case versus control group difference testing on the true data and on the deviation scores (Figure 2D) and thresholded results at a Benjamini-Hochberg false discovery rate of p<0.05. Note that in both cases, we directly contrast each patient group to their matched controls to avoid nuisance variation confounding any reported effects (e.g., sampling characteristics and demographic differences).

All pretrained models and code are shared online with straightforward directions for transferring to new sites and including an example transfer data set derived from several OpenNeuro.org data sets. Given a new set of data (e.g., sites not present in the training set), this is done by first applying the warp parameters estimating on the training data to the new data set, adjusting the mean and variance in the latent Gaussian space, then (if necessary) warping the adjusted data back to the original space, which is similar to the approach outlined in Dinga et al., 2021. Note that to remain unbiased, this should be done on a held-out calibration data set. To illustrate this procedure, we apply this approach to predicting a subset of data that was not used during the model estimation step. We leveraged data from OpenNeuro.org (Markiewicz et al., 2021) to create a transfer data set (six sites, N=546, Supplementary file 3). This data are provided along with the code for transferring to walk users through how to apply these models to their own data. These results are reported in Figure 3 (transfer) and Supplementary file 3. We also distribute scripts for this purpose in the GitHub Repository associated with this manuscript. Furthermore, to promote the use of these models and remove barriers to using them, we have set up access to the pretrained models and code for transferring to users’ own data, using Google Colab, a free, cloud-based platform for running python notebooks. This eliminates the need to install python/manage package versions and only requires users to have a personal computer with stable internet connection.

All pre-trained models and code for transferring to new sites are shared online via GitHub (https://github.com/predictive-clinical-neuroscience/braincharts, copy archived at swh:1:rev:ee2b7ebcb46bab0f302f73f8d6fc913f63fccda5). We have also shared the models on Zenodo (https://zenodo.org/record/5535467#.YVRECmYzZhF).

1. 1. Rutherford S
   2. Andre M

   (2021) Zenodo

   Braincharts.

   https://doi.org/10.5281/zenodo.5535467

1. 1. National Institute of Mental Health

   (2020) nih

   ABCD.

   https://doi.org/10.15154/1503209
2. 1. Cambridge Centre for Ageing and Neuroscience

   (2010) NCBI

   ID PMC4219118. CAMCAN.

   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219118/

1. 1. Alfaro-Almagro F
   2. Jenkinson M
   3. Bangerter NK
   4. Andersson JLR
   5. Griffanti L
   6. Douaud G
   7. Sotiropoulos SN
   8. Jbabdi S
   9. Hernandez-Fernandez M
   10. Vallee E
   11. Vidaurre D
   12. Webster M
   13. McCarthy P
   14. Rorden C
   15. Daducci A
   16. Alexander DC
   17. Zhang H
   18. Dragonu I
   19. Matthews PM
   20. Miller KL
   21. Smith SM

   (2018) Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank

   NeuroImage 166:400–424.

   https://doi.org/10.1016/j.neuroimage.2017.10.034

   • PubMed
   • Google Scholar
2. Preprint

   1. Bayer JMM
   2. Dinga R
   3. Kia SM
   4. Kottaram AR
   5. Wolfers T
   6. Lv J
   7. Zalesky A
   8. Schmaal L
   9. Marquand A

   (2021) Accommodating Site Variation in Neuroimaging Data Using Normative and Hierarchical Bayesian Models

   bioRxiv.

   https://doi.org/10.1101/2021.02.09.430363

   • Google Scholar
3. 1. Bethlehem RAI
   2. Seidlitz J
   3. Romero-Garcia R
   4. Trakoshis S
   5. Dumas G
   6. Lombardo MV

   (2020) A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder

   Communications Biology 3:486.

   https://doi.org/10.1038/s42003-020-01212-9

   • PubMed
   • Google Scholar
4. Preprint

   1. Bethlehem R
   2. Seidlitz J
   3. White SR
   4. Vogel JW
   5. Anderson KM
   6. Adamson C
   7. Adler S
   8. Alexopoulos GS
   9. Anagnostou E
   10. Areces-Gonzalez A
   11. Astle DE
   12. Auyeung B
   13. Ayub M
   14. Ball G
   15. Baron-Cohen S
   16. Beare R
   17. Bedford SA
   18. Benegal V
   19. Beyer F
   20. Alexander-Bloch AF

   (2021) Brain Charts for the Human Lifespan

   bioRxiv.

   https://doi.org/10.1101/2021.06.08.447489

   • Google Scholar
5. 1. Cole TJ

   (2012) The development of growth references and growth charts

   Annals of Human Biology 39:382–394.

   https://doi.org/10.3109/03014460.2012.694475

   • PubMed
   • Google Scholar
6. 1. Dale AM
   2. Fischl B
   3. Sereno MI

   (1999) Cortical Surface-Based Analysis

   NeuroImage 9:179–194.

   https://doi.org/10.1006/nimg.1998.0395

   • PubMed
   • Google Scholar
7. 1. Destrieux C
   2. Fischl B
   3. Dale A
   4. Halgren E

   (2010) Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature

   NeuroImage 53:1–15.

   https://doi.org/10.1016/j.neuroimage.2010.06.010

   • PubMed
   • Google Scholar
8. 1. Dimitrova R
   2. Pietsch M
   3. Christiaens D
   4. Ciarrusta J
   5. Wolfers T
   6. Batalle D
   7. Hughes E
   8. Hutter J
   9. Cordero-Grande L
   10. Price AN
   11. Chew A
   12. Falconer S
   13. Vecchiato K
   14. Steinweg JK
   15. Carney O
   16. Rutherford MA
   17. Tournier J-D
   18. Counsell SJ
   19. Marquand AF
   20. Rueckert D
   21. Hajnal JV
   22. McAlonan G
   23. Edwards AD
   24. O’Muircheartaigh J

   (2020) Heterogeneity in Brain Microstructural Development Following Preterm Birth

   Cerebral Cortex (New York, N.Y 30:4800–4810.

   https://doi.org/10.1093/cercor/bhaa069

   • PubMed
   • Google Scholar
9. Preprint

   1. Dinga R
   2. Fraza CJ
   3. Bayer JMM
   4. Kia SM
   5. Beckmann CF
   6. Marquand AF

   (2021) Normative Modeling of Neuroimaging Data Using Generalized Additive Models of Location Scale and Shape

   bioRxiv.

   https://doi.org/10.1101/2021.06.14.448106

   • Google Scholar
10. 1. Ducharme S
    2. Albaugh MD
    3. Nguyen TV
    4. Hudziak JJ
    5. Mateos-Pérez JM
    6. Labbe A
    7. Evans AC
    8. Karama S
    9. Brain Development Cooperative Group

    (2016) Trajectories of cortical thickness maturation in normal brain development--The importance of quality control procedures

    NeuroImage 125:267–279.

    https://doi.org/10.1016/j.neuroimage.2015.10.010

    • PubMed
    • Google Scholar
11. 1. Duval ER
    2. Joshi SA
    3. Russman Block S
    4. Abelson JL
    5. Liberzon I

    (2018) Insula activation is modulated by attention shifting in social anxiety disorder

    Journal of Anxiety Disorders 56:56–62.

    https://doi.org/10.1016/j.janxdis.2018.04.004

    • PubMed
    • Google Scholar
12. 1. Fischl B
    2. Dale AM

    (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images

    PNAS 97:11050–11055.

    https://doi.org/10.1073/pnas.200033797

    • PubMed
    • Google Scholar
13. 1. Fischl B
    2. Salat DH
    3. Busa E
    4. Albert M
    5. Dieterich M
    6. Haselgrove C
    7. van der Kouwe A
    8. Killiany R
    9. Kennedy D
    10. Klaveness S
    11. Montillo A
    12. Makris N
    13. Rosen B
    14. Dale AM

    (2002) Whole Brain Segmentation

    Neuron 33:341–355.

    https://doi.org/10.1016/S0896-6273(02)00569-X

    • PubMed
    • Google Scholar
14. 1. Fraza CJ
    2. Dinga R
    3. Beckmann CF
    4. Marquand AF

    (2021) Warped Bayesian linear regression for normative modelling of big data

    NeuroImage 245:118715.

    https://doi.org/10.1016/j.neuroimage.2021.118715

    • PubMed
    • Google Scholar
15. 1. Fry A
    2. Littlejohns TJ
    3. Sudlow C
    4. Doherty N
    5. Adamska L
    6. Sprosen T
    7. Collins R
    8. Allen NE

    (2017) Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population

    American Journal of Epidemiology 186:1026–1034.

    https://doi.org/10.1093/aje/kwx246

    • PubMed
    • Google Scholar
16. 1. Gogtay N
    2. Giedd JN
    3. Lusk L
    4. Hayashi KM
    5. Greenstein D
    6. Vaituzis AC
    7. Nugent TF
    8. Herman DH
    9. Clasen LS
    10. Toga AW
    11. Rapoport JL
    12. Thompson PM

    (2004) Dynamic mapping of human cortical development during childhood through early adulthood

    PNAS 101:8174–8179.

    https://doi.org/10.1073/pnas.0402680101

    • PubMed
    • Google Scholar
17. 1. Green S
    2. Lambon Ralph MA
    3. Moll J
    4. Deakin JFW
    5. Zahn R

    (2012) Guilt-selective functional disconnection of anterior temporal and subgenual cortices in major depressive disorder

    Archives of General Psychiatry 69:1014–1021.

    https://doi.org/10.1001/archgenpsychiatry.2012.135

    • PubMed
    • Google Scholar
18. 1. Henrich J
    2. Heine SJ
    3. Norenzayan A

    (2010) The weirdest people in the world?

    The Behavioral and Brain Sciences 33:61–83.

    https://doi.org/10.1017/S0140525X0999152X

    • PubMed
    • Google Scholar
19. 1. Insel TR

    (2014) Mental disorders in childhood: shifting the focus from behavioral symptoms to neurodevelopmental trajectories

    JAMA 311:1727–1728.

    https://doi.org/10.1001/jama.2014.1193

    • PubMed
    • Google Scholar
20. 1. Jack CR
    2. Knopman DS
    3. Jagust WJ
    4. Shaw LM
    5. Aisen PS
    6. Weiner MW
    7. Petersen RC
    8. Trojanowski JQ

    (2010) Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade

    The Lancet. Neurology 9:119–128.

    https://doi.org/10.1016/S1474-4422(09)70299-6

    • PubMed
    • Google Scholar
21. 1. Jones MC
    2. Pewsey A

    (2009) Sinh-arcsinh distributions

    Biometrika 96:761–780.

    https://doi.org/10.1093/biomet/asp053

    • Google Scholar
22. 1. Karas GB
    2. Scheltens P
    3. Rombouts SARB
    4. Visser PJ
    5. van Schijndel RA
    6. Fox NC
    7. Barkhof F

    (2004) Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease

    NeuroImage 23:708–716.

    https://doi.org/10.1016/j.neuroimage.2004.07.006

    • PubMed
    • Google Scholar
23. Preprint

    1. Kia SM
    2. Huijsdens H
    3. Rutherford S
    4. Dinga R
    5. Wolfers T
    6. Mennes M
    7. Andreassen OA
    8. Westlye LT
    9. Beckmann CF
    10. Marquand AF

    (2021) Federated Multi-Site Normative Modeling Using Hierarchical Bayesian Regression

    bioRxiv.

    https://doi.org/10.1101/2021.05.28.446120

    • Google Scholar
24. 1. Klapwijk ET
    2. van de Kamp F
    3. van der Meulen M
    4. Peters S
    5. Wierenga LM

    (2019) Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data

    NeuroImage 189:116–129.

    https://doi.org/10.1016/j.neuroimage.2019.01.014

    • PubMed
    • Google Scholar
25. 1. Lythe KE
    2. Moll J
    3. Gethin JA
    4. Workman CI
    5. Green S
    6. Lambon Ralph MA
    7. Deakin JFW
    8. Zahn R

    (2015) Self-blame-Selective Hyperconnectivity Between Anterior Temporal and Subgenual Cortices and Prediction of Recurrent Depressive Episodes

    JAMA Psychiatry 72:1119–1126.

    https://doi.org/10.1001/jamapsychiatry.2015.1813

    • PubMed
    • Google Scholar
26. 1. Markiewicz CJ
    2. Gorgolewski KJ
    3. Feingold F
    4. Blair R
    5. Halchenko YO
    6. Miller E
    7. Hardcastle N
    8. Wexler J
    9. Esteban O
    10. Goncavles M
    11. Jwa A
    12. Poldrack R

    (2021) The OpenNeuro resource for sharing of neuroscience data

    eLife 10:e71774.

    https://doi.org/10.7554/eLife.71774

    • PubMed
    • Google Scholar
27. 1. Marquand AF
    2. Rezek I
    3. Buitelaar J
    4. Beckmann CF

    (2016) Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies

    Biological Psychiatry 80:552–561.

    https://doi.org/10.1016/j.biopsych.2015.12.023

    • PubMed
    • Google Scholar
28. 1. Marquand AF
    2. Kia SM
    3. Zabihi M
    4. Wolfers T
    5. Buitelaar JK
    6. Beckmann CF

    (2019) Conceptualizing mental disorders as deviations from normative functioning

    Molecular Psychiatry 24:1415–1424.

    https://doi.org/10.1038/s41380-019-0441-1

    • PubMed
    • Google Scholar
29. 1. Mocking RJT
    2. Figueroa CA
    3. Rive MM
    4. Geugies H
    5. Servaas MN
    6. Assies J
    7. Koeter MWJ
    8. Vaz FM
    9. Wichers M
    10. van Straalen JP
    11. de Raedt R
    12. Bockting CLH
    13. Harmer CJ
    14. Schene AH
    15. Ruhé HG

    (2016) Vulnerability for new episodes in recurrent major depressive disorder: protocol for the longitudinal DELTA-neuroimaging cohort study

    BMJ Open 6:e009510.

    https://doi.org/10.1136/bmjopen-2015-009510

    • PubMed
    • Google Scholar
30. 1. Monereo-Sánchez J
    2. de Jong JJA
    3. Drenthen GS
    4. Beran M
    5. Backes WH
    6. Stehouwer CDA
    7. Schram MT
    8. Linden DEJ
    9. Jansen JFA

    (2021) Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study

    NeuroImage 237:118174.

    https://doi.org/10.1016/j.neuroimage.2021.118174

    • PubMed
    • Google Scholar
31. 1. Nesvåg R
    2. Jönsson EG
    3. Bakken IJ
    4. Knudsen GP
    5. Bjella TD
    6. Reichborn-Kjennerud T
    7. Melle I
    8. Andreassen OA

    (2017) The quality of severe mental disorder diagnoses in a national health registry as compared to research diagnoses based on structured interview

    BMC Psychiatry 17:93.

    https://doi.org/10.1186/s12888-017-1256-8

    • PubMed
    • Google Scholar
32. 1. Nordahl CW
    2. Iosif AM
    3. Young GS
    4. Hechtman A
    5. Heath B
    6. Lee JK
    7. Libero L
    8. Reinhardt VP
    9. Winder-Patel B
    10. Amaral DG
    11. Rogers S
    12. Solomon M
    13. Ozonoff S

    (2020) High Psychopathology Subgroup in Young Children With Autism: Associations With Biological Sex and Amygdala Volume

    Journal of the American Academy of Child and Adolescent Psychiatry 59:1353–1363.

    https://doi.org/10.1016/j.jaac.2019.11.022

    • PubMed
    • Google Scholar
33. 1. Rios G
    2. Tobar F

    (2019) Compositionally-warped Gaussian processes

    Neural Networks 118:235–246.

    https://doi.org/10.1016/j.neunet.2019.06.012

    • PubMed
    • Google Scholar
34. 1. Rosen AFG
    2. Roalf DR
    3. Ruparel K
    4. Blake J
    5. Seelaus K
    6. Villa LP
    7. Ciric R
    8. Cook PA
    9. Davatzikos C
    10. Elliott MA
    11. Garcia de La Garza A
    12. Gennatas ED
    13. Quarmley M
    14. Schmitt JE
    15. Shinohara RT
    16. Tisdall MD
    17. Craddock RC
    18. Gur RE
    19. Gur RC
    20. Satterthwaite TD

    (2018) Quantitative assessment of structural image quality

    NeuroImage 169:407–418.

    https://doi.org/10.1016/j.neuroimage.2017.12.059

    • PubMed
    • Google Scholar
35. Preprint

    1. Rutherford S
    2. Angstadt M
    3. Sripada C
    4. Chang SE

    (2020) Leveraging Big Data for Classification of Children Who Stutter from Fluent Peers

    bioRxiv.

    https://doi.org/10.1101/2020.10.28.359711

    • Google Scholar
36. Software

    1. Rutherford S

    (2022a) braincharts, version swh:1:rev:ee2b7ebcb46bab0f302f73f8d6fc913f63fccda5

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:cd28f07707409fed9c4e3bcfefaa08b9638dbccb;origin=https://github.com/predictive-clinical-neuroscience/braincharts;visit=swh:1:snp:ad9fb4794977fbb77f867431c767d66d31781b3f;anchor=swh:1:rev:ee2b7ebcb46bab0f302f73f8d6fc913f63fccda5
37. Software

    1. Rutherford S

    (2022b) brainviz-app, version swh:1:rev:021fff9a48b26f2d07bbb4b3fb92cd5202418905

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:5172634bcf0ed341052462df7780d000c5bd4f9e;origin=https://github.com/saigerutherford/brainviz-app;visit=swh:1:snp:70e2db995b8f63e9bdcb233217089d22000d2147;anchor=swh:1:rev:021fff9a48b26f2d07bbb4b3fb92cd5202418905
38. Software

    1. Rutherford S

    (2022c) lifespanqcscripts, version swh:1:rev:70894691c74febe2a4d40ab0c84c50094b9e99ce

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:9c98ca93b3fb3b463607286eec7dfc9c4c3e97db;origin=https://github.com/saigerutherford/lifespan_qc_scripts;visit=swh:1:snp:84918033541e80549e91c96e85a29d191321d0a3;anchor=swh:1:rev:70894691c74febe2a4d40ab0c84c50094b9e99ce
39. Conference

    1. Snelson E
    2. Rasmussen CE
    3. Ghahramani Z

    (2003)

    Proceedings of the 16th International Conference on Neural Information Processing Systems

    Warped Gaussian processes. pp. 337–344.

    • Google Scholar
40. 1. Tamnes CK
    2. Ostby Y
    3. Fjell AM
    4. Westlye LT
    5. Due-Tønnessen P
    6. Walhovd KB

    (2010) Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure

    Cerebral Cortex (New York, N.Y 20:534–548.

    https://doi.org/10.1093/cercor/bhp118

    • PubMed
    • Google Scholar
41. 1. Tomlinson RC
    2. Burt SA
    3. Waller R
    4. Jonides J
    5. Miller AL
    6. Gearhardt AN
    7. Peltier SJ
    8. Klump KL
    9. Lumeng JC
    10. Hyde LW

    (2020) Neighborhood poverty predicts altered neural and behavioral response inhibition

    NeuroImage 209:116536.

    https://doi.org/10.1016/j.neuroimage.2020.116536

    • PubMed
    • Google Scholar
42. 1. Tso IF
    2. Angstadt M
    3. Rutherford S
    4. Peltier S
    5. Diwadkar VA
    6. Taylor SF

    (2021) Dynamic causal modeling of eye gaze processing in schizophrenia

    Schizophrenia Research 229:112–121.

    https://doi.org/10.1016/j.schres.2020.11.012

    • PubMed
    • Google Scholar
43. 1. Weigard AS
    2. Brislin SJ
    3. Cope LM
    4. Hardee JE
    5. Martz ME
    6. Ly A
    7. Zucker RA
    8. Sripada C
    9. Heitzeg MM

    (2021) Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood

    Psychopharmacology 238:2629–2644.

    https://doi.org/10.1007/s00213-021-05885-w

    • PubMed
    • Google Scholar
44. 1. Wolfers T
    2. Doan NT
    3. Kaufmann T
    4. Alnæs D
    5. Moberget T
    6. Agartz I
    7. Buitelaar JK
    8. Ueland T
    9. Melle I
    10. Franke B
    11. Andreassen OA
    12. Beckmann CF
    13. Westlye LT
    14. Marquand AF

    (2018) Mapping the Heterogeneous Phenotype of Schizophrenia and Bipolar Disorder Using Normative Models

    JAMA Psychiatry 75:1146–1155.

    https://doi.org/10.1001/jamapsychiatry.2018.2467

    • PubMed
    • Google Scholar
45. 1. Wolfers T
    2. Beckmann CF
    3. Hoogman M
    4. Buitelaar JK
    5. Franke B
    6. Marquand AF

    (2020) Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models

    Psychological Medicine 50:314–323.

    https://doi.org/10.1017/S0033291719000084

    • PubMed
    • Google Scholar
46. 1. Wolfers T
    2. Rokicki J
    3. Alnaes D
    4. Berthet P
    5. Agartz I
    6. Kia SM
    7. Kaufmann T
    8. Zabihi M
    9. Moberget T
    10. Melle I
    11. Beckmann CF
    12. Andreassen OA
    13. Marquand AF
    14. Westlye LT

    (2021) Replicating extensive brain structural heterogeneity in individuals with schizophrenia and bipolar disorder

    Human Brain Mapping 42:2546–2555.

    https://doi.org/10.1002/hbm.25386

    • PubMed
    • Google Scholar
47. 1. Zabihi M.
    2. Oldehinkel M
    3. Wolfers T
    4. Frouin V
    5. Goyard D
    6. Loth E
    7. Charman T
    8. Tillmann J
    9. Banaschewski T
    10. Dumas G
    11. Holt R
    12. Baron-Cohen S
    13. Durston S
    14. Bölte S
    15. Murphy D
    16. Ecker C
    17. Buitelaar JK
    18. Beckmann CF
    19. Marquand AF

    (2019) Dissecting the Heterogeneous Cortical Anatomy of Autism Spectrum Disorder Using Normative Models

    Biological Psychiatry. Cognitive Neuroscience and Neuroimaging 4:567–578.

    https://doi.org/10.1016/j.bpsc.2018.11.013

    • PubMed
    • Google Scholar
48. 1. Zabihi M
    2. Floris DL
    3. Kia SM
    4. Wolfers T
    5. Tillmann J
    6. Arenas AL
    7. Moessnang C
    8. Banaschewski T
    9. Holt R
    10. Baron-Cohen S
    11. Loth E
    12. Charman T
    13. Bourgeron T
    14. Murphy D
    15. Ecker C
    16. Buitelaar JK
    17. Beckmann CF
    18. Marquand A
    19. EU-AIMS LEAP Group

    (2020) Fractionating autism based on neuroanatomical normative modeling

    Translational Psychiatry 10:1–10.

    https://doi.org/10.1038/s41398-020-01057-0

    • PubMed
    • Google Scholar
49. 1. Zucker RA
    2. Ellis DA
    3. Fitzgerald HE
    4. Bingham CR
    5. Sanford K

    (2009) Other evidence for at least two alcoholisms II: Life course variation in antisociality and heterogeneity of alcoholic outcome

    Development and Psychopathology 8:831–848.

    https://doi.org/10.1017/S0954579400007458

    • Google Scholar

Author details

1. Saige Rutherford

   1. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
   2. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
   3. Department of Psychiatry, University of Michigan, Ann Arbor, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   saige.rutherford@donders.ru.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3006-9044

2. Charlotte Fraza

   1. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
   2. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Methodology, Software, Writing – review and editing

   Competing interests

   No competing interests declared

3. Richard Dinga

   1. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
   2. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Methodology

   Competing interests

   No competing interests declared

4. Seyed Mostafa Kia

   1. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
   2. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
   3. Department of Psychiatry, Utrecht University Medical Center, Utrecht, Netherlands

   Contribution

   Methodology, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7128-814X

5. Thomas Wolfers

   1. Department of Psychology, University of Oslo, Oslo, Norway
   2. Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway

   Contribution

   Methodology, Software, Writing – review and editing

   Competing interests

   No competing interests declared

6. Mariam Zabihi

   1. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
   2. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Methodology, Software, Writing – review and editing

   Competing interests

   No competing interests declared

7. Pierre Berthet

   1. Department of Psychology, University of Oslo, Oslo, Norway
   2. Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway

   Contribution

   Methodology, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6878-6842

8. Amanda Worker

   Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Serena Verdi

   1. Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
   2. Dementia Research Centre, UCL Queen Square Institute of Neurology, London, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

10. Derek Andrews

    The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California, Davis, Sacramento, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

11. Laura KM Han

    1. Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
    2. GGZ inGeest, Amsterdam Neuroscience, Amsterdam, Netherlands

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

12. Johanna MM Bayer

    1. Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
    2. Orygen Youth Health, Melbourne, Australia

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4891-6256

13. Paola Dazzan

    1. Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
    2. National Institute for Health Research Mental Health Biomedical Research Centre, South London and Maudsley National Health Service Foundation Trust and King’s College London, London, United Kingdom

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

14. Phillip McGuire

    Department of Psychosis Studies, Institute of Psychiatry, King’s College London, London, United Kingdom

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

15. Roel T Mocking

    Department of Psychiatry, Amsterdam UMC, Location AMC, Amsterdam, Netherlands

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

16. Aart Schene

    1. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
    2. Department of Psychiatry, Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

17. Chandra Sripada

    Department of Psychiatry, University of Michigan, Ann Arbor, United States

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9025-6453

18. Ivy F Tso

    Department of Psychiatry, University of Michigan, Ann Arbor, United States

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

19. Elizabeth R Duval

    Department of Psychiatry, University of Michigan, Ann Arbor, United States

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

20. Soo-Eun Chang

    Department of Psychiatry, University of Michigan, Ann Arbor, United States

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

21. Brenda WJH Penninx

    1. Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
    2. GGZ inGeest, Amsterdam Neuroscience, Amsterdam, Netherlands

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

22. Mary M Heitzeg

    Department of Psychiatry, University of Michigan, Ann Arbor, United States

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

23. S Alexandra Burt

    Department of Psychology, Michigan State University, East Lansing, United States

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

24. Luke W Hyde

    Department of Psychology, University of Michigan, Ann Arbor, United States

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

25. David Amaral

    The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California, Davis, Sacramento, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

26. Christine Wu Nordahl

    The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California, Davis, Sacramento, United States

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

27. Ole A Andreasssen

    1. Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
    2. KG Jebsen Centre for Neurodevelopmental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    is a consultant for HealthLytix and received speaker's honorarium from Lundbeck and Sunovion

28. Lars T Westlye

    1. Department of Psychology, University of Oslo, Oslo, Norway
    2. Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
    3. KG Jebsen Centre for Neurodevelopmental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

29. Roland Zahn

    Centre for Affective Disorders at the Institute of Psychiatry, King's College London, London, United Kingdom

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

30. Henricus G Ruhe

    1. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
    2. Department of Psychiatry, Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Conceptualization, Funding acquisition, Resources, Supervision, Writing – original draft, Writing – review and editing

    Competing interests

    received speaker's honorarium from Lundbeck and Janssen

31. Christian Beckmann

    1. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
    2. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
    3. Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review and editing

    Contributed equally with

    Andre F Marquand

    Competing interests

    is director and shareholder of SBGNeuro Ltd

32. Andre F Marquand

    1. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
    2. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Visualization, Writing – original draft, Writing – review and editing

    Contributed equally with

    Christian Beckmann

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5903-203X

• 7,321

  views

• 1,109

  downloads

• 83

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.