
==== Front
Hum Brain Mapp
Hum Brain Mapp
10.1002/(ISSN)1097-0193
HBM
Human Brain Mapping
1065-9471
1097-0193
John Wiley & Sons, Inc. Hoboken, USA

39254138
10.1002/hbm.26815
HBM26815
Research Article
Research Article
Dataset factors associated with age‐related changes in brain structure and function in neurodevelopmental conditions
Vandewouw et al.
Vandewouw Marlee M. 1 2
Ye Yifan (Julia) 1 3
Crosbie Jennifer 4 5
Schachar Russell J. 4 5
Iaboni Alana 1
Georgiades Stelios 6
Nicolson Robert 7
Kelley Elizabeth 8 9 10
Ayub Muhammad 10 11
Jones Jessica 8 9 10
Arnold Paul D. 12
Taylor Margot J. 13 14 15 16
Lerch Jason P. 14 17 18
Anagnostou Evdokia 1 14 19
Kushki Azadeh 1 2 akushki@hollandbloorview.ca

1 Autism Research Centre, Bloorview Research Institute Holland Bloorview Kids Rehabilitation Hospital Toronto Ontario Canada
2 Institute of Biomedical Engineering University of Toronto Toronto Canada
3 Division of Engineering Science University of Toronto Toronto Canada
4 Department of Psychiatry University of Toronto Toronto Canada
5 Department of Psychiatry The Hospital for Sick Children Toronto Ontario Canada
6 Department of Psychiatry and Behavioural Neurosciences McMaster University Hamilton Canada
7 Department of Psychiatry Western University London Canada
8 Department of Psychology Queen's University Kingston Canada
9 Centre for Neuroscience Studies Queen's University Kingston Canada
10 Department of Psychiatry Queen's University Kingston Canada
11 Division of Psychiatry University of College London London UK
12 The Mathison Centre for Mental Health Research & Education, Cumming School of Medicine University of Calgary Calgary Canada
13 Department of Diagnostic and Interventional Radiology The Hospital for Sick Children Toronto Canada
14 Program in Neurosciences and Mental Health The Hospital for Sick Children Toronto Canada
15 Department of Psychology University of Toronto Toronto Canada
16 Department of Medical Imaging University of Toronto Toronto Canada
17 Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences University of Oxford Oxford UK
18 Department of Medical Biophysics University of Toronto Toronto Canada
19 Institute of Medical Science University of Toronto Toronto Canada
* Correspondence
Azadeh Kushki, Autism Research Centre, Holland Bloorview Kids Rehabilitation Hospital, 150 Kilgour Road, Toronto, ON M4G 1R8, Canada.
Email: akushki@hollandbloorview.ca

10 9 2024
9 2024
45 13 10.1002/hbm.v45.13 e2681508 7 2024
07 9 2023
29 7 2024
© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Abstract

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium‐based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age‐related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age‐related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting‐state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age‐related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.

Age‐related changes in brain structure and function are not consistent between datasets of children and adolescents with neurodevelopmental conditions; these inconsistencies may be influenced by sociodemographic and acquisition variables.

brain function
brain structure
consistency
development
large‐scale datasets
New Frontiers in Research FundOntario Brain Institute 10.13039/100008914 Canadian Institutes of Health Research 10.13039/501100000024 source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:10.09.2024
Vandewouw, M. M. , Ye, Y. (J.) , Crosbie, J. , Schachar, R. J. , Iaboni, A. , Georgiades, S. , Nicolson, R. , Kelley, E. , Ayub, M. , Jones, J. , Arnold, P. D. , Taylor, M. J. , Lerch, J. P. , Anagnostou, E. , & Kushki, A. (2024). Dataset factors associated with age‐related changes in brain structure and function in neurodevelopmental conditions. Human Brain Mapping, 45 (13 ), e26815. 10.1002/hbm.26815 39254138

Russell J. Schachar is Emeritus.
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pmc1 INTRODUCTION

The brain's structure and function undergo complex and protracted changes throughout childhood and adolescence due to neurobiological processes such as synaptogenesis, myelination and synaptic pruning (Grayson & Fair, 2017; Mills & Tamnes, 2020; Norbom et al., 2021). Neurodevelopmental differences, such as those associated with autism spectrum disorder (autism), attention‐deficit/hyperactivity disorder (ADHD) and obsessive‐compulsive disorder (OCD), can further impact the presentation of age‐related changes, particularly given the heterogeneity in aetiology, biology and behaviour (Bruin et al., 2020; Lombardo et al., 2019; Wolfers et al., 2019). As such, age is a critical consideration in neuroimaging studies of neurotypical and neurodivergent populations.

While many studies directly examine age‐related effects, age is often treated as a nuisance covariate (Hyatt et al., 2020). With increasing awareness of the importance of replicability in neuroimaging studies (Poldrack et al., 2017), it is becoming more common to leverage multiple consortium‐based datasets to examine brain structure and function in neurotypical and neurodivergent populations (Abrol et al., 2023; Grotzinger et al., 2023; Marek et al., 2022; Nicolaisen‐Sobesky et al., 2022; Romer et al., 2019; Vandewouw et al., 2023). However, these datasets differ in composition across factors that may influence age‐related effects, such as participants' demographics (e.g., age, sex and gender, race and ethnicity and socioeconomic status), diagnosis, co‐occurring conditions and other phenotypic variables. As a result, it is possible that the patterns of age‐related changes in brain structure and function differ between datasets. Since it is common to apply the same assumptions about the development of neurobiology across datasets (e.g., linearity), the presence of dataset‐specific developmental effects would be problematic for downstream analyses, challenging replicability.

Studies have examined the consistency of age‐related changes in brain structure and function across neurotypical cohorts (Cao et al., 2017; Herting et al., 2018; Madan & Kensinger, 2017; Mills et al., 2016; Morandini et al., 2021; Tamnes et al., 2017; Wang et al., 2021). For example, yearly percentage changes in cortical thickness and surface area across childhood and adulthood have been shown to differ between four samples of typically developing individuals, with the precise patterns varying across lobes of the brain (Tamnes et al., 2017). White matter volume has also been shown to peak at different age ranges in different cohorts, with those from the United States showing decreased volume across age compared to European samples (Mills et al., 2016). These findings have important implications for those studies that are not only directly examining age‐related effects but also those which treat age as a covariate, as controlling for age in the same manner does not consider these differences. Yet, no studies have examined whether these changes are consistent between cohorts of neurodivergent children, and if not, what factors may be contributing to the inconsistencies. This is particularly critical to investigate given the large variability in demographic and phenotypic characteristics of samples across different cohorts, even when the same diagnostic labels are studied.

While there is a wealth of evidence showing that demographic and clinical factors can impact brain development (e.g., neurodivergence), most of this work has taken a case–control approach, where differences from neurotypical trajectories are established at the ‘average’ level. On the other hand, there is a considerable amount of developmental variation at the individual level (Foulkes & Blakemore, 2018), with emerging evidence that clinical features of autism and ADHD are associated with individual deviations from neurotypical maturational trajectories (Bethlehem et al., 2020; Floris et al., 2021; Kessler et al., 2016; Looden et al., 2022; Marquand et al., 2016; Oliveira‐Saraiva & Ferreira, 2023; Parkes et al., 2021; Tunç et al., 2019; Wolfers et al., 2019; Zabihi et al., 2019). Thus, to understand why datasets may exhibit distinct age‐related changes of neurobiology, it is important to consider how demographic and clinical factors impact individual variations.

To this end, we examined age‐related changes of brain structure (regional cortical thickness and surface area) and function (functional connectivity strength) in two transdiagnostic datasets of neurodiverse children and adolescents, namely the Province of Ontario Neurodevelopmental Network (POND) and the Healthy Brain Network (HBN). We first built dataset‐specific developmental models and evaluated whether deviations from these models differed between the POND and HBN individuals, separately for males and females. Next, we explored whether the patterns of between‐dataset differences were associated with demographic and phenotypic variables. We have previously reported that POND and HBN differ in their phenotypic profiles, specifically full‐scale intelligence quotient, social communication difficulties and ADHD symptoms (Vandewouw et al., 2023). Thus, we hypothesise that deviations from age‐related models of brain structure and function will differ between these datasets, and the deviations will be associated with these phenotypic measures.

2 METHODS

2.1 Participants

Neuroimaging and phenotype data were obtained from two independently collected datasets: the POND (exported April 2023), and the HBN (Release 9). For both datasets, participants who were between 5 and 19 years of age and who were either neurotypical or had diagnoses of autism, ADHD or OCD were selected. Details on the diagnostic assessments are provided elsewhere (Vandewouw et al., 2023). Structural magnetic resonance imaging (sMRI) data were available for 868 POND (255 ADHD, 377 autism, 75 OCD and 161 TD) and 1505 HBN (1100 ADHD, 162 autism, 36 OCD and 207 TD) participants and resting‐state functional magnetic imaging (rs‐fMRI) data were available for 864 POND (211 ADHD, 385 autism, 92 OCD and 185 TD) and 1459 HBN (1068 ADHD, 160 autism, 29 OCD and 202 TD) participants. Informed consent and/or assent were obtained from the participants and/or caregivers. The current study was approved by the institution's research ethics board, and both consortia studies were approved by the appropriate boards.

2.2 Data acquisition

For both datasets, T1‐weighted magnetization‐prepared rapid gradient echo sequences (sMRI data) and 5 min of resting‐state data with an echo planar imaging sequence (rs‐fMRI data) were acquired. In POND, the rs‐fMRI data were obtained as participants watched either a movie of their choosing or the naturalistic movie paradigm akin to a screensaver, Inscapes (Vanderwal et al., 2015), while HBN participants viewed a fixation cross. While HBN collected two 5‐min resting‐state acquisitions, only one was selected to be consistent with POND. Acquisition parameters for the sMRI and rs‐fMRI protocols for each POND and HBN site are presented in Table S1.

Biological sex was collected as part of both studies and used in the current analysis; insufficient data on gender was available at the time of export. Phenotypic measures of core and co‐occurring differences for the diagnostic categories (see Table S2), socioeconomic status and race and ethnicity data were also available for both datasets and were used to characterise each sample. Socioeconomic status was determined using the caregiver's highest level of education and annual household income. Race and ethnicity data were collected for both datasets according to each country's census guidelines and were categorised into five racial/ethnic categories: Hispanic/Latino, non‐Hispanic Asian, non‐Hispanic Black, non‐Hispanic White and Multi‐Racial/Other (Cardenas‐Iniguez & Gonzalez, 2024).

2.3 Image processing

The sMRI data were processed using the CIVET pipeline (version 2.1.0; Ad‐Dab'bagh et al., 2006) on CBRAIN (Sherif et al., 2014). CIVET first registers the T1‐weighted image to stereotaxic space (Collins et al., 1994) using the Montreal Neurological Institute template (MNI ICBM152; Fonov et al., 2009) and corrected for non‐uniformities (Sled et al., 1998). A brain mask was extracted (Smith, 2002), and tissues were classified into white matter, grey matter and cerebrospinal fluid using a discrete tag point classification and a correction for partial volumes (Tohka et al., 2004; Zijdenbos et al., 1998). The white matter and pial surfaces (40,962 vertices per hemisphere) were extracted using the marching‐cubes algorithm (Kabani et al., 2001; Kim et al., 2005; MacDonald et al., 2000). Vertex‐wise cortical thickness was calculated as the Euclidean distance between reference vertices of the white and pial surfaces (Ad‐Dab'bagh et al., 2005; Lerch & Evans, 2005). Vertex‐wise cortical surface area and volumes were calculated as local variations of contraction and expansion relative to the distribution of vertices (Lyttelton et al., 2009). Surface‐based diffusion smoothing kernels were applied (thickness: 30 mm full‐width at half‐maximum [FWHM]; area and volume: 40 mm FWHM) (Boucher et al., 2009). Regional measures of cortical thickness and surface area were extracted for 76 brain regions defined using the Automated Anatomical Labelling (AAL) atlas (Tzourio‐Mazoyer et al., 2002) by averaging across the vertices per region. The AAL atlas was chosen to be consistent with most studies using the CIVET pipeline, including work from our group (Kushki et al., 2019, 2021; Sadat‐Nejad et al., 2023; Sanjeevan et al., 2020; Sussman et al., 2015). The CIVET automatic quality control pipeline was used, details of which are described elsewhere (Hammill et al., 2021; Kushki et al., 2019, 2021). Of the remaining participants that passed quality control, propensity score matching (Rosenbaum & Rubin, 1985) was used to identify a smaller subset of participants to ensure the datasets showed no significant difference in median age or male‐to‐female ratio (see Vandewouw et al., 2023, for further details). Across both datasets, ComBat harmonisation (Fortin et al., 2018; Johnson et al., 2007) was used to adjust all measures for site effects, followed by the regression of mean cortical thickness and total surface area for the regional measures of thickness and area, respectively. For both steps, an iterative sampling procedure was performed to increase robustness (10,000 iterations, each selecting 63.2% of the sample; Da‐ano et al., 2020).

The rs‐fMRI data preprocessing was performed using fMRIPrep (Esteban et al., 2019, 2020) and is described in detail elsewhere (Vandewouw et al., 2023). As part of the pipeline, measures of framewise displacement (FD; Power et al., 2014) and the standardised derivative of root mean square variance over voxels (DVARS; Power et al., 2014) were obtained. Participants were required to have at least 2/3 of their data within the recommended motion thresholds (FD: 0.5 mm; DVARS: 1.5) to pass quality control. Of the remaining participants, propensity score matching was used to identify a smaller subset of individuals for which there were not statistically significant between‐dataset differences in median age, male‐to‐female ratio and median head motion (Vandewouw et al., 2023). The Schaefer cortical (Schaefer et al., 2018) and Melbourne subcortical (Tian et al., 2020) atlases were used to define 232 brain regions, which have been shown to produce functional network topology that is more similar across network construction pipelines compared to the AAL and other alternative functional parcellations (Luppi & Stamatakis, 2021). Pairwise Pearson correlations were computed between region‐averaged time series to construct rs‐fMRI connectomes. ComBat harmonisation (Da‐ano et al., 2020; Fortin et al., 2018; Johnson et al., 2007) was used to adjust the connectivity values for site effects across both datasets (10,000 iterations, each selecting 63.2% of the sample). The resulting connectomes were thresholded with orthogonal minimum spanning trees to remove spurious connections (Dimitriadis et al., 2017), and the functional connectivity strength of each brain region was extracted.

2.4 Modelling age‐related changes

Models of age‐related changes in the regional sMRI (area and thickness) and rs‐fMRI (connectivity strength) measures were built using Bayesian linear regression (BLR; Huertas et al., 2017). Unlike traditional regression, BLR estimates the posterior probability of the regression coefficient, capturing the expected variation or uncertainty around the predicted age‐related change; this enables the computation of a statistical z‐score, or deviation score, that is similar to a residual but also considers the uncertainty around the prediction (Huertas et al., 2017; Rutherford et al., 2022). BLR is comparable but less computationally expensive than similar techniques such as Gaussian process regression (Fraza et al., 2021; Huertas et al., 2017). We used the BLR implementation from the Predictive Clinical Neuroscience toolkit (version 0.25; https://pcntoolkit.readthedocs.io/en/latest) in Python (version 3.9), applying a B‐spline basis expansion of age with three evenly spaced knots to model non‐linearities, using warping to model normal and non‐normal distributions in the brain measures, and using the Powell method for optimization of the Bayesian hyper‐parameters.

Dataset (POND and HBN; N = 2) and sex (males and females; N = 2) specific models were built for each measure (regional cortical thickness, regional cortical surface area and functional connectivity strength; N = 3), resulting in 12 models. To promote generalizability, 80% of participants of a given dataset were used to build a model, with 20% withheld for testing. This procedure was repeated 10,000 times, each time with a different randomly selected subset of participants. To describe the performance of the models, the model fit was evaluated by examining the mean standardised log‐loss (MSLL; Rasmussen & Williams, 2006), which quantifies the error between the predicted and true mean of the data while also accounting for the predictive variances, and has an upper bound of 0, with negative values indicating a better model fit (Rutherford et al., 2022). Deviation scores (z‐scores) were calculated from a built model under two test scenarios: the model built on 80% of a given dataset was tested on (1) the remaining 20% of participants from the same dataset, and (2) the participants from the other dataset. Figure 1 depicts an example of this process where the model is built using 80% of the participants in POND and tested on the remaining 20% of participants from POND and 100% of the HBN participants. For robustness, the analysis was also performed with a 70%/30% train/test split. Median model fit metrics and deviation scores were computed across all iterations. This procedure was performed separately for males and females, with both the full samples and smaller, matched samples.

FIGURE 1 An example of the approach for constructing and comparing the dataset‐ and sex‐specific Bayesian linear regression models. HBN, Healthy Brain Network; POND, Province of Ontario Neurodevelopmental Network.

2.5 Statistics

First, differences between the two datasets in their demographic and clinical characteristics were examined, separately for males and females. Chi‐squared tests, Kruskal–Wallis tests (given non‐normality according to Shapiro–Wilks tests), and ordinal regressions were used for categorical, continuous and ordinal variables, respectively, reporting Cramer's v, eta‐squared and odds ratio effect sizes; in all cases, significance was held at p < 0.05.

For each of the brain measures, we compared the deviations between the datasets; this was performed with both POND and HBN used as the training dataset, and separately for males and females. One‐way analysis of variances or Kruskal–Wallis tests were used where appropriate, resulting p‐values were adjusted using the false discovery rate method, holding significance at q < 0.05 and reporting eta‐squared effect sizes.

Given differences in demographic (socioeconomic status and race/ethnicity) and clinical (diagnosis and phenotypic measures) variables between POND and HBN, we explored what factors may be contributing to any identified between‐dataset differences. To do so, for each brain measure, we extracted the deviations for each participant from their dataset‐ and sex‐specific model and used generalised linear mixed‐effects models to examine their associations with these variables; dataset and sex were modelled as random factors. Resulting p‐values were FDR‐corrected across the brain regions, and significance was held at q < 0.05.

3 RESULTS

3.1 Participants

For the sMRI dataset, 754 HBN (214 female, 540 male) and 825 POND (270 female, 555 male) children and adolescents were included in the analysis. Given the difference in age between the two datasets (females: H(1) = 104.35, p < .001, η 2 = .04; males: H(1) = 203.75, p < .001, η 2 = .03), a smaller, matched subset of participants was identified for which no significant difference remained, with each dataset consisting of 609 (178 females, 431 males) individuals. Participant demographics for the matched sample are presented in Table 1, with phenotypic measures, socioeconomic status and race/ethnicity for each dataset are presented in Table 2. Co‐occurring conditions are summarised in Table S3.

TABLE 1 Participant demographics for the structural magnetic resonance imaging sample.

		HBN	POND	Test‐statistic a	p‐Value	Effect size	
Females	N	178	178	–	–	–	
Dx (N)	ADHD	124	51	76.33	<.001	0.46	
Autism	7	56	
OCD	9	24	
TD	38	47	
Age b (years)	10.1 [8.5, 13.3]	11.1 [9.0, 13.3]	2.61	.107	<0.01	
Males	N	431	431	–	–	–	
Dx (N)	ADHD	304	145	141.73	<.001	0.41	
Autism	55	190	
OCD	11	33	
TD	61	63	
Age b (years)	10.6 [8.6, 13.0]	10.9 [9.0, 13.0]	2.07	.150	<0.01	
Abbreviations: ADHD, attention‐deficit/hyperactivity disorder; HBN, Healthy Brain Network; OCD, obsessive‐compulsive disorder; POND, Province of Ontario Neurodevelopmental Network; TD, typically developing.

a Kruskal–Wallis H‐statistic for age with eta‐squared effect size, and chi‐squared statistic for diagnosis with Cramer's v effect size.

b Age is reported as median (interquartile range [IQR]).

TABLE 2 Socioeconomic status, race/ethnicity and phenotypic measures for the sMRI sample.

		HBN	POND	Test‐statistic a	p‐value	Effect size	
Females	Race/ethnicity b	
Hispanic/Latino	30	4	40.39	<.001	0.39	
Non‐Hispanic Asian	7	11	
Non‐Hispanic Black	15	1	
Non‐Hispanic White	67	90	
Other/multi‐racial	12	24	
Caregiver income c	
<$50 k/year	31	18	0.03	.974	0.99	
$50 k–$150 k	65	57	
>$150 k	51	34	
Caregiver education d	
<High school	6	1	3.88	<.001	2.44	
High school	19	15	
College	58	72	
Graduate	94	27	
SCQ	6.0 [4.0, 8.0]	5.0 [2.0, 14.2]	0.18	.675	0.02	
RBS‐R	11.0 [2.0, 28.0]	11.5 [2.0, 29.0]	0.22	.639	0.01	
SWAN‐I	2.0 [0.0, 5.0]	2.0 [0.0, 7.0]	0.04	.843	<0.01	
SWAN‐HI	0.0 [0.0, 2.0]	1.0 [0.0, 4.0]	7.16	.007	0.02	
TOCS	–	−6.0 [−40.0, 6.0]	–	–	–	
CBCL‐OCS	2.0 [1.0, 4.8]	–	–	–	–	
FSIQ	102.0 [87.0, 113.5]	106.0 [94.0, 115.0]	1.90	.168	0.01	
CBCL‐I	56.0 [48.0, 65.0]	60.0 [52.0, 69.0]	6.91	.009	0.02	
CBCL‐E	57.0 [47.0, 64.8]	55.0 [44.0, 64.0]	0.80	.372	<0.01	
Males	Race/ethnicity b	
Hispanic/Latino	91	25	88.90	<.001	0.38	
Non‐Hispanic Asian	8	25	
Non‐Hispanic Black	41	7	
Non‐Hispanic White	148	158	
Other/multi‐racial	30	75	
Caregiver income c	
<$50 k/year	67	47	3.09	.002	1.64	
$50 k–$150 k	133	123	
>$150 k	146	58	
Caregiver education d	
<High school	6	4	3.39	.001	1.66	
High school	79	30	
College	143	149	
Graduate	196	57	
SCQ	8.0 [5.0, 11.0]	10.0 [4.0, 20.0]	19.42	<.001	0.06	
RBS‐R	14.5 [4.0, 44.0]	16.0 [5.0, 31.0]	0.77	.381	<0.01	
SWAN‐I	3.0 [0.0, 6.0]	4.0 [1.0, 7.0]	11.37	<.001	0.01	
SWAN‐HI	1.0 [0.0, 4.0]	2.0 [0.0, 6.0]	22.60	<.001	0.03	
TOCS	–	−15.0 [−45.0, 2.0]	–	–	–	
CBCL‐OCS	2.0 [1.0, 5.0]	–	–	–	–	
FSIQ	100.5 [87.0, 111.0]	101.0 [88.0, 111.0]	0.07	.794	<0.01	
CBCL‐I	58.0 [50.0, 67.0]	62.0 [52.0, 68.0]	10.87	.001	0.01	
CBCL‐E	56.0 [48.0, 65.0]	56.0 [48.0, 66.0]	0.06	.814	<0.01	
Abbreviations: CBCL‐I, Child Behaviour Checklist internalising problems subscale; CBCL‐E: Child Behaviour Checklist externalising problems subscale; CBCL‐OCS, Child Behaviour Checklist obsessive‐compulsive subscale; FSIQ, full‐scale intelligence quotient; HBN, Healthy Brain Network; POND, Province of Ontario Neurodevelopmental Network; sMRI, structural magnetic resonance imaging; RBS‐R, Repetitive Behaviours Scale—Revised; SCQ, social communication questionnaire; SWAN‐I, Strengths and Weaknesses of ADHD symptoms and Normal Behaviour inattention subscale; SWAN‐HI, Strengths and Weaknesses of ADHD‐symptoms and Normal Behaviour hyperactivity/impulsivity subscale; TOCS, Toronto Obsessive‐Compulsive Scale.

a Kruskal–Wallis H‐statistic for the non‐normally distributed continuous variables with eta‐squared effect sizes, chi‐squared statistic for race/ethnicity with Cramer's v effect size, and ordinal regression with odds ratios for the ordinal variables.

b Collected according to the respective country's census guidelines.

c In the respective country's currency.

d College includes associate and bachelor's degrees.

For the fMRI dataset, 1072 HBN (349 females, 723 males) and 720 POND (207 females, 513 males) participants were included in the analysis. Significant between‐dataset differences in age (females: H(1) = 21.34, p < .001, η 2 = .04; males: H(1) = 40.82, p < .001, η 2 = .03) and motion (females: H(1) = 36.50, p < .001, η 2 = .03; males: H(1) = 29.50, p < .001, η 2 = .01) were observed. Thus, a smaller, matched subset of children and adolescents with no such differences was identified, with each dataset consisting of 627 (189 female, 438 male) individuals (Tables 3 and 4). Co‐occurring conditions are summarised in Table S4. In this matched sample, there were no significant between‐diagnosis differences in head motion in the POND (H(3) = 0.51, p = .676, η 2 = 0.01) nor HBN (H(3) = 1.07, p = .362, η 2 = 0.02) females. In the males, diagnosis was marginally significant in POND (H(3) = 2.68, p = .046, η 2 = 0.02) but not significant in HBN (H(3) = 1.79, p = .149, η 2 = 0.01).

TABLE 3 Participant demographics for the functional magnetic resonance imaging sample.

		HBN	POND	Test‐statistic a	p‐Value	Effect size	
Females	N	189	189	–	–	–	
Dx (N)	ADHD	130	38	107.87	<.001	0.53	
Autism	7	65	
OCD	12	31	
TD	40	55	
Age b (years)	10.7 [8.7, 13.4]	11.7 [9.4, 14.0]	2.77	.096	0.01	
Motion b (mm)	0.1 [0.1, 0.2]	0.1 [0.1, 0.2]	2.09	.148	<0.01	
Males	N	438	438	–	–	–	
Dx (N)	ADHD	321	121	202.91	<.001	0.48	
Autism	53	192	
OCD	7	48	
TD	57	77	
Age b (years)	10.9 [9.0, 13.1]	11.4 [9.4, 13.6]	2.75	.097	<0.01	
Motion b (mm)	0.2 [0.1, 0.2]	0.2 [0.1, 0.3]	0.53	.466	<0.01	
Abbreviations: ADHD, attention‐deficit/hyperactivity disorder; HBN, Healthy Brain Network; OCD, obsessive‐compulsive disorder; POND, Province of Ontario Neurodevelopmental Network; TD, typically developing.

a Kruskal–Wallis H‐statistic for age with eta‐squared effect size, and chi‐squared statistic for diagnosis with Cramer's v effect size.

b Age and motion is reported as median (IQR).

TABLE 4 Socioeconomic status, race/ethnicity and phenotypic measures for the functional magnetic resonance imaging sample.

		HBN	POND	Test‐statistic a	p‐value	Effect size	
Females	Race/ethnicity b	
Hispanic/Latino	35	7	37.68	<.001	0.35	
Non‐Hispanic Asian	7	13	
Non‐Hispanic Black	18	2	
Non‐Hispanic White	88	100	
Other/multi‐racial	14	27	
Caregiver income c	
<$50 k/year	32	20	0.00	.999	1.00	
$50 k–$150 k	68	62	
>$150 k	71	52	
Caregiver education d	
<High school	2	1	4.04	<.001	2.45	
High school	16	16	
College	69	77	
Graduate	102	38	
SCQ	5.0 [3.0, 8.5]	5.0 [2.0, 14.2]	0.01	.917	.03	
RBS‐R	10.0 [2.0, 27.2]	11.0 [2.0, 29.0]	0.11	.741	.01	
SWAN‐I	2.0 [0.0, 5.8]	2.0 [0.0, 6.0]	0.00	.987	<.01	
SWAN‐HI	0.0 [0.0, 2.0]	1.0 [0.0, 3.0]	6.81	.009	.03	
TOCS	–	−5.0 [−39.8, 7.8]	–	–	–	
CBCL‐OCS	2.0 [1.0, 5.0]	–	–	–	–	
FSIQ	104.0 [91.0, 116.0]	106.0 [94.0, 115.0]	0.37	.544	<.01	
CBCL‐I	56.0 [48.0, 65.0]	62.0 [52.0, 70.0]	10.73	.001	.03	
CBCL‐E	54.0 [47.0, 64.2]	54.0 [44.0, 64.0]	0.94	.333	<.01	
Males	Race/ethnicity b	
Hispanic/Latino	100	28	99.32	<.001	0.38	
Non‐Hispanic Asian	11	29	
Non‐Hispanic Black	48	5	
Non‐Hispanic White	189	183	
Other/multi‐racial	33	80	
Caregiver income c	
<$50 k/year	66	46	1.37	.171	1.23	
$50 k–$150 k

>$150 k

	158	124	
169	97	
Caregiver education d	
<High school	5	4	3.24	.001	1.60	
High school	77	33	
College	144	155	
Graduate	205	73	
SCQ	7.0 [4.0, 10.0]	9.0 [3.0, 18.5]	14.08	<.001	.06	
RBS‐R	12.0 [2.0, 40.8]	15.0 [5.0, 27.0]	0.51	.474	<.01	
SWAN‐I	3.0 [0.0, 6.0]	4.0 [1.0, 7.0]	5.85	.016	.01	
SWAN‐HI	0.0 [0.0, 3.0]	2.0 [0.0, 6.0]	38.64	<.001	.05	
TOCS	–	−15.0 [−44.0, 2.0]	–	–	–	
CBCL‐OCS	2.0 [1.0, 4.0]	–	–	–	–	
FSIQ	99.0 [86.0, 110.0]	101.0 [89.5, 111.5]	1.32	.250	<.01	
CBCL‐I	58.0 [49.0, 67.0]	61.0 [52.0, 68.0]	8.84	.003	.01	
CBCL‐E	54.0 [46.0, 63.0]	56.0 [46.0, 66.2]	1.97	.161	<.01	
Abbreviations: CBCL‐I, Child Behaviour Checklist internalising problems subscale; CBCL‐E, Child Behaviour Checklist externalising problems subscale; CBCL‐OCS, Child Behaviour Checklist obsessive‐compulsive subscale; FSIQ, full‐scale intelligence quotient; HBN, Healthy Brain Network; POND, Province of Ontario Neurodevelopmental Network; SCQ, social communication questionnaire; RBS‐R, Repetitive Behaviours Scale—Revised; SWAN‐I, Strengths and Weaknesses of ADHD‐symptoms and Normal Behaviour inattention subscale; SWAN‐HI, Strengths and Weaknesses of ADHD‐symptoms and Normal Behaviour hyperactivity/impulsivity subscale; TOCS, Toronto Obsessive‐Compulsive Scale.

a Kruskal–Wallis H‐statistic for the non‐normally distributed continuous variables with eta‐squared effect sizes, chi‐squared statistic for race/ethnicity with Cramer's v effect size, and ordinal regression with odds ratios for the ordinal variables.

b Collected according to the respective country's census guidelines.

c In the respective country's currency.

d College includes associate and bachelor's degrees.

3.2 Dataset differences in deviations

Models of age‐related changes in cortical thickness, surface area and functional connectivity strength were fit separately for each dataset and sex. To evaluate the model performance, the distributions of MSLL across all brain regions are presented in Figure S1, and distributions of deviation scores are presented in Figure S2. The deviation scores for the age models were compared between datasets, separately for the males and females. Significant differences were observed for cortical surface area and functional connectivity, but not cortical thickness, with significant differences presented in Figure 2. Findings were robust to the size of the training and test sets (Figure S3). Significant between‐dataset differences within each diagnostic label are presented in Figure S4. To explore what factors may be contributing to the observed differences, associations between deviations and the demographic variables (age, sex, race/ethnicity, caregiver highest level of education and annual household income) and phenotypic variables (social communication difficulties, repetitive behaviours, inattention, hyperactivity/impulsivity, obsessive‐compulsive traits, intelligence, internalising problems and externalising problems) were investigated.

FIGURE 2 Effect sizes (eta‐squared) for significant between‐dataset differences in deviations from age‐related models of cortical surface area (a) and functional connectivity strength (b) for the females (left) and males (right) when the models were built using each dataset (HBN, Healthy Brain Network; POND, Province of Ontario Neurodevelopmental Network). No differences in cortical thickness were observed.

For regional measures of cortical surface area (Figure 2a), deviation scores differed between the datasets throughout the brain. When examining the median deviation scores for the significant regions, deviations for the POND participants were negative when the models were built using the HBN participants, indicating that the HBN models overestimated surface area for the POND individuals; on the other hand, POND models underestimated surface area in the HBN participants (positive deviation scores). Significant associations between deviations and race/ethnicity were observed in the left orbital part of the middle frontal gyrus, left medial superior frontal gyrus, right supplementary motor area, right angular gyrus, right middle and inferior occipital gyri, right fusiform gyrus, right parahippocampal gyrus and right median cingulate and paracingulate gyrus (Figure S5A). Post hoc tests indicated that while deviations in White participants were closer to zero, deviations in minoritized participants were increased in magnitude. All other associations were insignificant. For regional measures of cortical thickness, no regions showed significant between‐dataset differences in the deviations.

For functional connectivity strength (Figure 2b), significant differences in the deviation scores between POND and HBN were observed throughout the brain, with more widespread effects observed in the males (HBN: 202 regions, POND: 199 regions), albeit with reduced overall effect sizes, compared to females (HBN: 135 regions, POND: 122 regions). When models were built using the HBN individuals, median deviation scores indicated that they underestimated functional connectivity strength in POND; conversely, POND models overestimated connectivity strength in the HBN participants. Deviation scores were significantly positively correlated with head motion (Figure S5B).

4 DISCUSSION

In this study, using data from two consortia, namely POND and HBN, we built dataset‐specific models that describe age‐related changes in measures of brain structure and function. We determined that deviations from the models were significantly different between the two datasets for measures of regional cortical surface area and functional connectivity strength throughout the brain, but not regional cortical thickness; these differences were observed within both males and females. For cortical surface area, the patterns of differences were associated with race/ethnicity, suggesting that it may be contributing to the between‐dataset findings. For functional connectivity strength, positive associations were observed with head motion.

Overall, our findings highlight that patterns of age‐related changes in the brain may not be consistent between cohorts of neurodiverse individuals, and these inconsistencies may be related to differences in the demographic and phenotypic composition of the sample. While previous work has identified factors such as methodology and medication (Bednarz & Kana, 2018) as contributors to the lack of convergence in the literature on the nature and directionality of brain differences in neurodevelopmental conditions, we hypothesise that differences in age‐related changes between cohorts, potentially driven by differences in measures like race/ethnicity and head motion, are also contributing to the lack of consistent findings. Even though age may not be the focus of a particular study, adjusting for age in the same manner in multiple cohorts despite the presence of cohort‐specific age‐related effects can produce downstream effects in subsequent analyses.

For the regional measures of cortical surface area, differences in deviations from age models differed between POND and HBN in every brain region, irrespective of which group was used to construct the model. The magnitude of this effect differed throughout the brain, for example, larger effects were observed in parietal regions. The development of cortical surface area shows the largest changes across childhood, adolescents and early adulthood in the parietal lobe (Tamnes et al., 2017). Thus, we hypothesise that while dataset‐specific age‐related changes of cortical surface area are pervasive, brain regions, which show more drastic developmental differences may show more exaggerated effects.

Deviations from age models of cortical surface area were also larger in magnitude for minoritized compared to White individuals, including frontal, occipital and cingulate regions. Given that deviations from the models were higher in magnitude in the minoritized compared to white participants, we hypothesise that this may reflect the effect of underrepresentation of minoritized individuals in the two datasets. Interestingly, despite race/ethnicity being significantly associated with measures of socioeconomic status (household income, highest education level), we did not observe an association between socioeconomic status and the deviations alongside race/ethnicity. Potential mechanisms contributing to racial/ethnic differences in brain structure have been suggested to include, for example, childhood adversity (Dumornay et al., 2023), although we did not explicitly test this in our study.

While between‐dataset differences in deviations from age models were observed for regional cortical surface area, this finding did not extend to regional cortical thickness. The development of cortical thickness and surface area are driven by different genetic and biological processes and show distinct maturational trajectories (Geschwind & Rakic, 2013; Panizzon et al., 2009; Tamnes et al., 2017; Vijayakumar et al., 2016; Wierenga et al., 2014). Surface area has also been shown to uniquely relate to general psychopathology, while this association is not observed with cortical thickness in several well‐powered studies (Jalbrzikowski et al., 2019; Mewton et al., 2022; Romer et al., 2023; Schmaal et al., 2016), although we acknowledge that smaller studies have reported a link between psychopathology and cortical thickness (e.g., Luking et al., 2022). Thus, given the differences between POND and HBN in the core and co‐occurring domains of neurodevelopmental conditions, we hypothesise that the unique relationship between psychopathology and surface area is why differences in deviations are restricted to this structural measure rather than cortical thickness.

We also observed differences between POND and HBN in deviations from models describing changes in functional connectivity strength with age, with the largest effects observed in limbic, default mode and the subcortical regions. Deviations from the models of age‐related effects were also shown to be positively associated with head motion, with, again, the largest effect sizes in limbic, default, mode and subcortical regions. Head motion is known to increase functional connectivity (Satterthwaite et al., 2013), and head motion is higher in HBN compared to POND, possibly due to the use of a fixation‐cross resting‐state (HBN) compared to movie paradigms (POND) (Vanderwal et al., 2015). Thus, together with the noticeable overlap in the spatial pattern of effects, we suggest that head motion may be driving deviations in age‐related changes of functional connectivity. This conclusion is supported by the largest effect sizes being localised to the subcortical structures, which are located distally from other brain regions, and thus may be more impacted by head motion due to its effect on long‐range connectivity (van Dijk et al., 2012). Note that while differences in head motion are often reported between children with and without neurodevelopmental conditions (Vandewouw et al., 2023), in our matched samples, this was not the case—only marginal differences were observed in the POND males, with no other groups showing this pattern. Coupled with the lack of associations between‐diagnosis and model deviations, it does not appear that the correlations between deviations and head motion were being driven by diagnosis.

A strength of this study is the stratification of the analyses by biological sex. Sex and gender differences in the prevalence rates of neurodevelopmental conditions have long contributed to biases in existing research samples and neuroimaging literature (Mo et al., 2021). Specifically, the male preponderance of many of these conditions has resulted in a poor understanding of females with these conditions (Mo et al., 2021). This has resulted in calls for stratifying by sex and gender in neurodevelopmental research (Bölte et al., 2023), which motivated the decision to fit sex‐specific age‐related models. However, we recognise that the binarized measure of biological sex used in this study is an oversimplification of sex and gender; future work should account for more nuanced identities.

The results of this study should be interpreted considering several limitations. This study is cross‐sectional, and future studies should leverage longitudinal data to validate our developmental findings. The resting‐state acquisitions were also different between the two datasets: while POND participants viewed a movie, HBN participants were instructed to fixate on a cross. Thus, the harmonisation that was applied across both datasets may be inadvertently removing effects specific to each type of acquisition. We also only considered regional measures of cortical thickness and surface area, which greatly reduced the computational expenses of our analysis; future work could take a less limited spatial resolution and examine between‐dataset differences in vertex‐wise measures of brain structure. Finally, given the sample size required to establish reproducible brain‐behaviour associations (Marek et al., 2022), the contribution of the identified factors to deviations from age models should be validated in larger samples.

The replicability crisis has posed a significant challenge to the neuroscience field and to the broader scientific community. In this context, we have questioned the extent to which age‐related changes of brain structure and function are consistent between two independent cohorts of individuals with neurodevelopmental conditions. While deriving the neurobiological changes that occur during development is often a specific research goal, the presence or absence of replicable developmental effects is also highly relevant for all investigations, which include age at any step in the methodology. Given our findings that age‐related differences are not consistent across samples, this urges future consortium‐based studies to carefully examine age‐related changes within each dataset prior to applying the same assumptions (e.g., linearity) across datasets. This is particularly pertinent in the context of between‐dataset differences in race/ethnicity and head motion, which may be important covariates given their observed associations with deviations from age‐related models. While previous work has established cohort‐specific effects in neurotypical samples, the heterogeneity associated with neurodivergence makes this finding specifically relevant to neurodivergent populations, where it is less likely that two samples have comparable characteristics. While inconsistencies were observed in both males and females, effect sizes were, on average, larger in the females compared to the males, suggesting that stratification approaches may be more suitable for addressing dataset‐specific age‐related changes in neurobiology.

FUNDING INFORMATION

Funding was provided by the Ontario Brain Institute, Canadian Institutes of Health Research, and New Frontiers in Research Fund.

CONFLICT OF INTEREST STATEMENT

R. Nicolson has received grants from Brain Canada, Hoffman La Roche, Otsuka Pharmaceuticals, and Maplight Therapeutics. E. Anagnostou has received grants from Roche and Anavex, served as a consultant to Roche, Quadrant Therapeutics, Ono and Impel Pharmaceuticals, has received in‐kind support from AMO Pharma and CRA‐Simons Foundation, holds a patent for the device, ‘Anxiety Meter,’ has received royalties from APPI and Springer, and has received an editorial honorarium from Wiley. A. Kushki received grants from the National Science and Engineering Research Council and holds a patent for Anxiety Meter with royalties paid from Awake Labs. R. Schachar has served as a consultant to Ehave, Lilly Corporation and Highland Therapeutics. The remaining authors have reported no biomedical financial interests or potential conflicts of interest.

Supporting information

Data S1.Supporting Information.

ACKNOWLEDGEMENTS

We would like to thank all participants and their families for their involvement in this study. We would also like to thank all individuals who work for POND for their invaluable assistance in data collection. This manuscript was prepared using a limited access dataset obtained from the Child Mind Institute Biobank, the HBN. This manuscript reflects the views of the authors and does not necessarily reflect the opinions or views of the Child Mind Institute. This research was conducted with the support of the Ontario Brain Institute (POND, PIs: Anagnostou/Lerch), an independent non‐profit corporation, funded partially by the Ontario government. The opinions, results and conclusions are those of the authors, and no endorsement by the Ontario Brain Institute is intended or should be inferred.

DATA AVAILABILITY STATEMENT

Data is available from the Province of Ontario Neurodevelopmental Disorders (POND) network (exported April 2023; now available via a controlled data release through Ontario Brain Institute's Brain‐CODE: https://www.braincode.ca/) and the Healthy Brain Network (Release 9; http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/) datasets.
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REFERENCES

Abrol, A. , Fu, Z. , Du, Y. , Wilson, T. W. , Wang, Y. P. , Stephen, J. M. , & Calhoun, V. D. (2023). Developmental and aging resting functional magnetic resonance imaging brain state adaptations in adolescents and adults: A large N (>47K) study. Human Brain Mapping, 1 , 2158–2175. 10.1002/HBM.26200
Ad‐Dab'bagh, Y. , Einarson, D. , Lyttelton, O. , Muehlboeck, J.‐S. , Mok, K. , Ivanov, O. , Vincent, R. D. , Lepage, C. , Lerch, J. , Fombonne, E. , & Evans, A. C. (2006). The CIVET image‐processing environment: A fully automated comprehensive pipeline for anatomical neuroimaging research. Proceedings of the 12th Annual Meeting of the Organization for Human Brain Mapping, Firenze, Italy, 11–15 June 2006.
Ad‐Dab'bagh, Y. , Singh, V. K. , Robbins, S. , Lerch, J. A. , Lyttelton, O. C. , Fombonne, E. J. , & Evans, A. (2005). Native space cortical thickness measurement and the absence of correlation to cerebral volume. Proceedings of the 11th Annual Meeting of the Organization for Human Brain Mapping, Toronto, Canada, 2005.
Bednarz, H. M. , & Kana, R. K. (2018). Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders. Neuroscience & Biobehavioral Reviews, 90 , 50–69. 10.1016/J.NEUBIOREV.2018.03.025 29608989
Bethlehem, R. A. I. , Seidlitz, J. , Romero‐Garcia, R. , Trakoshis, S. , Dumas, G. , & Lombardo, M. V. (2020). A normative modelling approach reveals age‐atypical cortical thickness in a subgroup of males with autism spectrum disorder. Communications Biology, 3 (1 ), 486. 10.1038/s42003-020-01212-9 32887930
Bölte, S. , Neufeld, J. , Marschik, P. B. , Williams, Z. J. , Gallagher, L. , & Lai, M. C. (2023). Sex and gender in neurodevelopmental conditions. Nature Reviews Neurology, 19 (3 ), 136–159. 10.1038/s41582-023-00774-6 36747038
Boucher, M. , Whitesides, S. , & Evans, A. (2009). Depth potential function for folding pattern representation, registration and analysis. Medical Image Analysis, 13 (2 ), 203–214. 10.1016/j.media.2008.09.001 18996043
Bruin, W. B. , Taylor, L. , Thomas, R. M. , Shock, J. P. , Zhutovsky, P. , Abe, Y. , Alonso, P. , Ameis, S. H. , Anticevic, A. , Arnold, P. D. , Assogna, F. , Benedetti, F. , Beucke, J. C. , Boedhoe, P. S. W. , Bollettini, I. , Bose, A. , Brem, S. , Brennan, B. P. , Buitelaar, J. K. , … van Wingen, G. A. (2020). Structural neuroimaging biomarkers for obsessive‐compulsive disorder in the ENIGMA‐OCD consortium: Medication matters. Translational Psychiatry, 10 (1 ), 1–12. 10.1038/s41398-020-01013-y 32066695
Cao, B. , Mwangi, B. , Passos, I. C. , Wu, M. J. , Keser, Z. , Zunta‐Soares, G. B. , Xu, D. , Hasan, K. M. , & Soares, J. C. (2017). Lifespan gyrification trajectories of human brain in healthy individuals and patients with major psychiatric disorders. Scientific Reports, 7 (1 ), 1–8. 10.1038/s41598-017-00582-1 28127051
Cardenas‐Iniguez, C. , & Gonzalez, M. R. (2024). Recommendations for the responsible use and communication of race and ethnicity in neuroimaging research. Nature Neuroscience, 27 (4 ), 615–628. 10.1038/s41593-024-01608-4 38519749
Collins, D. L. , Neelin, P. , Peters, T. M. , & Evans, A. C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18 (2 ), 192–205. 10.1097/00004728-199403000-00005 8126267
Da‐ano, R. , Masson, I. , Lucia, F. , Doré, M. , Robin, P. , Alfieri, J. , Rousseau, C. , Mervoyer, A. , Reinhold, C. , Castelli, J. , De Crevoisier, R. , Rameé, J. F. , Pradier, O. , Schick, U. , Visvikis, D. , & Hatt, M. (2020). Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Scientific Reports, 10 (1 ), 1–12. 10.1038/s41598-020-66110-w 31913322
Dimitriadis, S. I. , Antonakakis, M. , Simos, P. , Fletcher, J. M. , & Papanicolaou, A. C. (2017). Data‐driven topological filtering based on orthogonal minimal spanning trees: Application to multigroup magnetoencephalography resting‐state connectivity. Brain Connectivity, 7 (10 ), 661–670. 10.1089/brain.2017.0512 28891322
Dumornay, N. M. , Lebois, L. A. M. , Ressler, K. J. , & Harnett, N. G. (2023). Racial disparities in adversity during childhood and the false appearance of race‐related differences in brain structure. American Journal of Psychiatry, 180 (2 ), 127–138. 10.1176/APPI.AJP.21090961/ASSET/IMAGES/LARGE/APPI.AJP.21090961F3.JPEG 36722118
Esteban, O. , Markiewicz, C. J. , Blair, R. W. , Moodie, C. A. , Isik, A. I. , Erramuzpe, A. , Kent, J. D. , Goncalves, M. , DuPre, E. , Snyder, M. , Oya, H. , Ghosh, S. S. , Wright, J. , Durnez, J. , Poldrack, R. A. , & Gorgolewski, K. J. (2019). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16 (1 ), 111–116. 10.1038/s41592-018-0235-4 30532080
Esteban, O. , Markiewicz, C. J. , Goncalves, M. , DuPre, E. , Kent, J. D. , Salo, T. , Ciric, R. , Pinsard, B. , Blair, R. W. , Poldrack, R. A. , & Gorgolewski, K. J. (2020). fMRIPrep: A robust preprocessing pipeline for functional MRI (20.2.0). Zenodo . 10.5281/zenodo.4252786
Floris, D. L. , Wolfers, T. , Zabihi, M. , Holz, N. E. , Zwiers, M. P. , Charman, T. , Tillmann, J. , Ecker, C. , Dell'Acqua, F. , Banaschewski, T. , Moessnang, C. , Baron‐Cohen, S. , Holt, R. , Durston, S. , Loth, E. , Murphy, D. G. M. , Marquand, A. , Buitelaar, J. K. , Beckmann, C. F. , & EU‐AIMS Longitudinal European Autism Project Group . (2021). Atypical brain asymmetry in autism – a candidate for clinically meaningful stratification. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6 (8 ), 802–812. 10.1016/J.BPSC.2020.08.008 33097470
Fonov, V. S. , Evans, A. C. , McKinstry, R. C. , Almli, C. R. , & Collins, D. L. (2009). Unbiased nonlinear average age‐appropriate brain templates from birth to adulthood. NeuroImage, 47 , S102. 10.1016/s1053-8119(09)70884-5
Fortin, J. P. , Cullen, N. , Sheline, Y. I. , Taylor, W. D. , Aselcioglu, I. , Cook, P. A. , Adams, P. , Cooper, C. , Fava, M. , McGrath, P. J. , McInnis, M. , Phillips, M. L. , Trivedi, M. H. , Weissman, M. M. , & Shinohara, R. T. (2018). Harmonization of cortical thickness measurements across scanners and sites. NeuroImage, 167 , 104–120. 10.1016/J.NEUROIMAGE.2017.11.024 29155184
Foulkes, L. , & Blakemore, S. J. (2018). Studying individual differences in human adolescent brain development. Nature Neuroscience, 21 (3 ), 315–323. 10.1038/s41593-018-0078-4 29403031
Fraza, C. J. , Dinga, R. , Beckmann, C. F. , & Marquand, A. F. (2021). Warped Bayesian linear regression for normative modelling of big data. NeuroImage, 245 , 118715. 10.1016/J.NEUROIMAGE.2021.118715 34798518
Geschwind, D. H. , & Rakic, P. (2013). Cortical evolution: Judge the brain by its cover. Neuron, 80 (3 ), 633–647. 10.1016/j.neuron.2013.10.045 24183016
Grayson, D. S. , & Fair, D. A. (2017). Development of large‐scale functional networks from birth to adulthood: A guide to the neuroimaging literature. NeuroImage, 160 , 15–31. 10.1016/j.neuroimage.2017.01.079 28161313
Grotzinger, A. D. , Mallard, T. T. , Liu, Z. , Seidlitz, J. , Ge, T. , & Smoller, J. W. (2023). Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis. Nature Communications, 14 (1 ), 1–13. 10.1038/s41467-023-36605-x
Hammill, C. , Lerch, J. P. , Taylor, M. J. , Ameis, S. H. , Chakravarty, M. M. , Szatmari, P. , Anagnostou, E. , & Lai, M. C. (2021). Quantitative and qualitative sex modulations in the brain anatomy of autism. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6 (9 ), 898–909. 10.1016/J.BPSC.2021.03.001 33713843
Herting, M. M. , Johnson, C. , Mills, K. L. , Vijayakumar, N. , Dennison, M. , Liu, C. , Goddings, A. L. , Dahl, R. E. , Sowell, E. R. , Whittle, S. , Allen, N. B. , & Tamnes, C. K. (2018). Development of subcortical volumes across adolescence in males and females: A multisample study of longitudinal changes. NeuroImage, 172 , 194–205. 10.1016/J.NEUROIMAGE.2018.01.020 29353072
Huertas, I. , Oldehinkel, M. , van Oort, E. S. B. , Garcia‐Solis, D. , Mir, P. , Beckmann, C. F. , & Marquand, A. F. (2017). A Bayesian spatial model for neuroimaging data based on biologically informed basis functions. NeuroImage, 161 , 134–148. 10.1016/J.NEUROIMAGE.2017.08.009 28782681
Hyatt, C. S. , Owens, M. M. , Crowe, M. L. , Carter, N. T. , Lynam, D. R. , & Miller, J. D. (2020). The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables. NeuroImage, 205 , 116225. 10.1016/J.NEUROIMAGE.2019.116225 31568872
Jalbrzikowski, M. , Freedman, D. , Hegarty, C. E. , Mennigen, E. , Karlsgodt, K. H. , Olde Loohuis, L. M. , Ophoff, R. A. , Gur, R. E. , & Bearden, C. E. (2019). Structural brain alterations in youth with psychosis and bipolar spectrum symptoms. Journal of the American Academy of Child & Adolescent Psychiatry, 58 (11 ), 1079–1091. 10.1016/J.JAAC.2018.11.012 30768396
Johnson, W. E. , Li, C. , & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8 (1 ), 118–127. 10.1093/biostatistics/kxj037 16632515
Kabani, N. , Le Goualher, G. , MacDonald, D. , & Evans, A. C. (2001). Measurement of cortical thickness using an automated 3‐D algorithm: A validation study. NeuroImage, 13 (2 ), 375–380. 10.1006/NIMG.2000.0652 11162277
Kessler, D. , Angstadt, M. , & Sripada, C. (2016). Growth charting of brain connectivity networks and the identification of attention impairment in youth. JAMA Psychiatry, 73 (5 ), 481–489. 10.1001/JAMAPSYCHIATRY.2016.0088 27076193
Kim, S. J. , Singh, V. , Lee, J. K. , Lerch, J. , Ad‐Dab'bagh, Y. , MacDonald, D. , Jong, M. L. , Kim, S. I. , & Evans, A. C. (2005). Automated 3‐D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage, 27 , 210–221. 10.1016/j.neuroimage.2005.03.036 15896981
Kushki, A. , Anagnostou, E. , Hammill, C. , Duez, P. , Brian, J. , Iaboni, A. , Schachar, R. , Crosbie, J. , Arnold, P. , & Lerch, J. P. (2019). Examining overlap and homogeneity in ASD, ADHD, and OCD: A data‐driven, diagnosis‐agnostic approach. Translational Psychiatry, 9 (1 ), 318. 10.1038/s41398-019-0631-2 31772171
Kushki, A. , Cardy, R. E. , Panahandeh, S. , Malihi, M. , Hammill, C. , Brian, J. , Iaboni, A. , Taylor, M. J. , Schachar, R. , Crosbie, J. , Arnold, P. , Kelley, E. , Ayub, M. , Nicolson, R. , Georgiades, S. , Lerch, J. P. , & Anagnostou, E. (2021). Cross‐diagnosis structural correlates of autistic‐like social communication differences. Cerebral Cortex, 31 (11 ), 5067–5076. 10.1093/cercor/bhab142 34080611
Lerch, J. P. , & Evans, A. C. (2005). Cortical thickness analysis examined through power analysis and a population simulation. NeuroImage, 24 , 163–173. 10.1016/j.neuroimage.2004.07.045 15588607
Lombardo, M. V. , Lai, M. C. , & Baron‐Cohen, S. (2019). Big data approaches to decomposing heterogeneity across the autism spectrum. Molecular Psychiatry, 24 (10 ), 1435–1450. 10.1038/s41380-018-0321-0 30617272
Looden, T. , Floris, D. L. , Llera, A. , Chauvin, R. J. , Charman, T. , Banaschewski, T. , Murphy, D. , Marquand, A. F. , Buitelaar, J. K. , Beckmann, C. F. , & AIMS‐2‐TRIALS group . (2022). Patterns of connectome variability in autism across five functional activation tasks: Findings from the LEAP project. Molecular Autism, 13 (1 ), 1–11. 10.1186/S13229-022-00529-Y/FIGURES/5 34980259
Luking, K. R. , Jirsaraie, R. J. , Tillman, R. , Luby, J. L. , Barch, D. M. , & Sotiras, A. (2022). Timing and type of early psychopathology symptoms predict longitudinal change in cortical thickness from middle childhood into early adolescence. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 7 (4 ), 397–405. 10.1016/J.BPSC.2021.06.013 34273555
Luppi, A. I. , & Stamatakis, E. A. (2021). Combining network topology and information theory to construct representative brain networks. Network Neuroscience, 5 (1 ), 96–124. 10.1162/netn_a_00170 33688608
Lyttelton, O. C. , Karama, S. , Ad‐Dab'bagh, Y. , Zatorre, R. J. , Carbonell, F. , Worsley, K. , & Evans, A. C. (2009). Positional and surface area asymmetry of the human cerebral cortex. NeuroImage, 46 (4 ), 895–903. 10.1016/J.NEUROIMAGE.2009.03.063 19345735
MacDonald, D. , Kabani, N. , Avis, D. , & Evans, A. C. (2000). Automated 3‐D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage, 12 , 340–356. 10.1006/nimg.1999.0534 10944416
Madan, C. R. , & Kensinger, E. A. (2017). Age‐related differences in the structural complexity of subcortical and ventricular structures. Neurobiology of Aging, 50 , 87–95. 10.1016/J.NEUROBIOLAGING.2016.10.023 27939959
Marek, S. , Tervo‐Clemmens, B. , Calabro, F. J. , Montez, D. F. , Kay, B. P. , Hatoum, A. S. , Donohue, M. R. , Foran, W. , Miller, R. L. , Hendrickson, T. J. , Malone, S. M. , Kandala, S. , Feczko, E. , Miranda‐Dominguez, O. , Graham, A. M. , Earl, E. A. , Perrone, A. J. , Cordova, M. , Doyle, O. , … Dosenbach, N. U. F. (2022). Reproducible brain‐wide association studies require thousands of individuals. Nature, 603 (7902 ), 654–660. 10.1038/S41586-022-04492-9 35296861
Marquand, A. F. , Rezek, I. , Buitelaar, J. , & Beckmann, C. F. (2016). Understanding heterogeneity in clinical cohorts using normative models: Beyond case‐control studies. Biological Psychiatry, 80 (7 ), 552–561. 10.1016/J.BIOPSYCH.2015.12.023 26927419
Mewton, L. , Lees, B. , Squeglia, L. M. , Forbes, M. K. , Sunderland, M. , Krueger, R. , Koch, F. C. , Baillie, A. , Slade, T. , Hoy, N. , & Teesson, M. (2022). The relationship between brain structure and general psychopathology in preadolescents. Journal of Child Psychology and Psychiatry, 63 (7 ), 734–744. 10.1111/JCPP.13513 34468031
Mills, K. L. , Goddings, A. L. , Herting, M. M. , Meuwese, R. , Blakemore, S. J. , Crone, E. A. , Dahl, R. E. , Güroğlu, B. , Raznahan, A. , Sowell, E. R. , & Tamnes, C. K. (2016). Structural brain development between childhood and adulthood: Convergence across four longitudinal samples. NeuroImage, 141 , 273–281. 10.1016/J.NEUROIMAGE.2016.07.044 27453157
Mills, K. L. , & Tamnes, C. K. (2020). Longitudinal structural and functional brain development in childhood and adolescence. In The Oxford handbook of developmental cognitive neuroscience. Oxford University Press.
Mo, K. , Sadoway, T. , Bonato, S. , Ameis, S. H. , Anagnostou, E. , Lerch, J. P. , Taylor, M. J. , & Lai, M. C. (2021). Sex/gender differences in the human autistic brains: A systematic review of 20 years of neuroimaging research. NeuroImage: Clinical, 32 , 102811. 10.1016/J.NICL.2021.102811 34509922
Morandini, H. A. E. , Rao, P. , Hood, S. D. , Zepf, F. D. , Silk, T. J. , & Griffiths, K. R. (2021). Age‐related resting‐state functional connectivity of the vigilant attention network in children and adolescents. Brain and Cognition, 154 , 105791. 10.1016/J.BANDC.2021.105791 34509772
Nicolaisen‐Sobesky, E. , Mihalik, A. , Kharabian‐Masouleh, S. , Ferreira, F. S. , Hoffstaedter, F. , Schwender, H. , Maleki Balajoo, S. , Valk, S. L. , Eickhoff, S. B. , Yeo, B. T. T. , Mourao‐Miranda, J. , & Genon, S. (2022). A cross‐cohort replicable and heritable latent dimension linking behaviour to multi‐featured brain structure. Communications Biology, 5 (1 ), 1–11. 10.1038/s42003-022-04244-5 34987157
Norbom, L. B. , Ferschmann, L. , Parker, N. , Agartz, I. , Andreassen, O. A. , Paus, T. , Westlye, L. T. , & Tamnes, C. K. (2021). New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro‐ and microstructural MRI findings. Progress in Neurobiology, 204 , 102109. 10.1016/J.PNEUROBIO.2021.102109 34147583
Oliveira‐Saraiva, D. , & Ferreira, H. A. (2023). Normative model detects abnormal functional connectivity in psychiatric disorders. Frontiers in Psychiatry, 14 , 1068397. 10.3389/FPSYT.2023.1068397/BIBTEX 36873218
Panizzon, M. S. , Fennema‐Notestine, C. , Eyler, L. T. , Jernigan, T. L. , Prom‐Wormley, E. , Neale, M. , Jacobson, K. , Lyons, M. J. , Grant, M. D. , Franz, C. E. , Xian, H. , Tsuang, M. , Fischl, B. , Seidman, L. , Dale, A. , & Kremen, W. S. (2009). Distinct genetic influences on cortical surface area and cortical thickness. Cerebral Cortex, 19 (11 ), 2728–2735. 10.1093/CERCOR/BHP026 19299253
Parkes, L. , Moore, T. M. , Calkins, M. E. , Cook, P. A. , Cieslak, M. , Roalf, D. R. , Wolf, D. H. , Gur, R. C. , Gur, R. E. , Satterthwaite, T. D. , & Bassett, D. S. (2021). Transdiagnostic dimensions of psychopathology explain individuals' unique deviations from normative neurodevelopment in brain structure. Translational Psychiatry, 11 (1 ), 1–13. 10.1038/s41398-021-01342-6 33414379
Poldrack, R. A. , Baker, C. I. , Durnez, J. , Gorgolewski, K. J. , Matthews, P. M. , Munafò, M. R. , Nichols, T. E. , Poline, J. B. , Vul, E. , & Yarkoni, T. (2017). Scanning the horizon: Towards transparent and reproducible neuroimaging research. Nature Reviews Neuroscience, 18 (2 ), 115–126. 10.1038/nrn.2016.167 28053326
Power, J. D. , Mitra, A. , Laumann, T. O. , Snyder, A. Z. , Schlaggar, B. L. , & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84 , 320–341.23994314
Rasmussen, C. E. , & Williams, C. K. I. (2006). Gaussian processes for machine learning. The MIT Press.
Romer, A. L. , Knodt, A. R. , Sison, M. L. , Ireland, D. , Houts, R. , Ramrakha, S. , Poulton, R. , Keenan, R. , Melzer, T. R. , Moffitt, T. E. , Caspi, A. , & Hariri, A. R. (2019). Replicability of structural brain alterations associated with general psychopathology: Evidence from a population‐representative birth cohort. Molecular Psychiatry, 26 (8 ), 3839–3846. 10.1038/s41380-019-0621-z 31796893
Romer, A. L. , Ren, B. , & Pizzagalli, D. A. (2023). Brain structure relations with psychopathology trajectories in the adolescent brain cognitive development study. Journal of the American Academy of Child & Adolescent Psychiatry, 62 , 895–907. 10.1016/J.JAAC.2023.02.002 36773698
Rosenbaum, P. R. , & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician, 39 (1 ), 33–38. 10.1080/00031305.1985.10479383
Rutherford, S. , Mostafa Kia, S. , Wolfers, T. , Fraza, C. , Zabihi, M. , Dinga, R. , Berthet, P. , Worker, A. , Verdi, S. , Ruhe, H. G. , Beckmann, C. F. , & Marquand, A. F. (2022). The normative modeling framework for computational psychiatry. Nature Methods, 17 , 1711–1734. 10.1038/s41596-022-00696-5
Sadat‐Nejad, Y. , Vandewouw, M. M. , Cardy, R. , Lerch, J. , Taylor, M. J. , Iaboni, A. , Hammill, C. , Syed, B. , Brian, J. A. , Kelley, E. , Ayub, M. , Crosbie, J. , Schachar, R. , Georgiades, S. , Nicolson, R. , Anagnostou, E. , & Kushki, A. (2023). Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance. Frontiers in Child and Adolescent Psychiatry, 2 , 1171337. 10.3389/FRCHA.2023.1171337
Sanjeevan, T. , Hammill, C. , Brian, J. , Crosbie, J. , Schachar, R. , Kelley, E. , Liu, X. , Nicolson, R. , Iaboni, A. , Day Fragiadakis, S. , Ristic, L. , Lerch, J. P. , & Anagnostou, E. (2020). Exploring the neural structures underlying the procedural memory network as predictors of language ability in children and adolescents with autism Spectrum disorder and attention deficit hyperactivity disorder. Frontiers in Human Neuroscience, 14 , 587019. 10.3389/FNHUM.2020.587019/BIBTEX 33362492
Satterthwaite, T. D. , Elliott, M. A. , Gerraty, R. T. , Ruparel, K. , Loughead, J. , Calkins, M. E. , Eickhoff, S. B. , Hakonarson, H. , Gur, R. E. R. C. , Gur, R. E. R. C. , & Wolf, D. H. (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting‐state functional connectivity data. NeuroImage, 64 , 240–256.22926292
Schaefer, A. , Kong, R. , Gordon, E. M. , Laumann, T. O. , Zuo, X. N. , Holmes, A. J. , Eickhoff, S. B. , & Yeo, B. T. T. (2018). Local‐global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 28 (9 ), 3095–3114. 10.1093/cercor/bhx17 28981612
Schmaal, L. , Hibar, D. P. , Sämann, P. G. , Hall, G. B. , Baune, B. T. , Jahanshad, N. , Cheung, J. W. , Van Erp, T. G. M. , Bos, D. , Ikram, M. A. , Vernooij, M. W. , Niessen, W. J. , Tiemeier, H. , Hofman, A. , Wittfeld, K. , Grabe, H. J. , Janowitz, D. , Bülow, R. , Selonke, M. , … Veltman, D. J. (2016). Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA major depressive disorder working group. Molecular Psychiatry, 22 (6 ), 900–909. 10.1038/mp.2016.60 27137745
Sherif, T. , Rioux, P. , Rousseau, M.‐E. , Kassis, N. , Beck, N. , Adalat, R. , Das, S. , Glatard, T. , & Evans, A. C. (2014). CBRAIN: A web‐based, distributed computing platform for collaborative neuroimaging research. Frontiers in Neuroinformatics, 8 , 54. 10.3389/fninf.2014.00054 24904400
Sled, J. G. , Zijdenbos, A. P. , & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17 (1 ), 87–97. 10.1109/42.668698 9617910
Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17 (3 ), 143–155. 10.1002/hbm.10062 12391568
Sussman, D. , Leung, R. C. , Vogan, V. M. , Lee, W. , Trelle, S. , Lin, S. , Cassel, D. B. , Chakravarty, M. M. , Lerch, J. P. , Anagnostou, E. , & Taylor, M. J. (2015). The autism puzzle: Diffuse but not pervasive neuroanatomical abnormalities in children with ASD. NeuroImage: Clinical, 8 , 170–179. 10.1016/J.NICL.2015.04.008 26106541
Tamnes, C. K. , Herting, M. M. , Goddings, A. L. , Meuwese, R. , Blakemore, S. J. , Dahl, R. E. , Güroğlu, B. , Raznahan, A. , Sowell, E. R. , Crone, E. A. , & Mills, K. L. (2017). Development of the cerebral cortex across adolescence: A multisample study of inter‐related longitudinal changes in cortical volume, surface area, and thickness. Journal of Neuroscience, 37 (12 ), 3402–3412. 10.1523/JNEUROSCI.3302-16.2017 28242797
Tian, Y. , Margulies, D. S. , Breakspear, M. , & Zalesky, A. (2020). Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nature Neuroscience, 23 (11 ), 1421–1432. 10.1038/s41593-020-00711-6 32989295
Tohka, J. , Zijdenbos, A. , & Evans, A. (2004). Fast and robust parameter estimation for statistical partial volume models in brain MRI. NeuroImage, 23 (1 ), 84–97. 10.1016/j.neuroimage.2004.05.007 15325355
Tunç, B. , Yankowitz, L. D. , Parker, D. , Alappatt, J. A. , Pandey, J. , Schultz, R. T. , & Verma, R. (2019). Deviation from normative brain development is associated with symptom severity in autism spectrum disorder. Molecular Autism, 10 (1 ), 1–14. 10.1186/S13229-019-0301-5 30647876
Tzourio‐Mazoyer, N. , Landeau, B. , Papathanassiou, D. , Crivello, F. , Etard, O. , Delcroix, N. , Mazoyer, B. , & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single‐subject brain. NeuroImage, 15 , 273–289. 10.1006/nimg.2001.0978 11771995
van Dijk, K. R. A. , Sabuncu, M. R. , & Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. NeuroImage, 59 (1 ), 431–438. 10.1016/j.neuroimage.2011.07.044 21810475
Vanderwal, T. , Kelly, C. , Eilbott, J. , Mayes, L. C. , & Castellanos, F. X. (2015). Inscapes: A movie paradigm to improve compliance in functional magnetic resonance imaging. NeuroImage, 122 , 222–232. 10.1016/j.neuroimage.2015.07.069 26241683
Vandewouw, M. M. , Brian, J. , Crosbie, J. , Schachar, R. J. , Iaboni, A. , Georgiades, S. , Nicolson, R. , Kelley, E. , Ayub, M. , Jones, J. , Taylor, M. J. , Lerch, J. P. , Anagnostou, E. , & Kushki, A. (2023). Identifying replicable subgroups in neurodevelopmental conditions using resting‐state functional magnetic resonance imaging data. JAMA Network Open, 6 (3 ), e232066. 10.1001/JAMANETWORKOPEN.2023.2066 36912839
Vijayakumar, N. , Allen, N. B. , Youssef, G. , Dennison, M. , Yücel, M. , Simmons, J. G. , & Whittle, S. (2016). Brain development during adolescence: A mixed‐longitudinal investigation of cortical thickness, surface area, and volume. Human Brain Mapping, 37 (6 ), 2027–2038. 10.1002/HBM.23154 26946457
Wang, Q. , Hu, K. , Wang, M. , Zhao, Y. , Liu, Y. , Fan, L. , & Liu, B. (2021). Predicting brain age during typical and atypical development based on structural and functional neuroimaging. Human Brain Mapping, 42 (18 ), 5943–5955. 10.1002/HBM.25660 34520078
Wierenga, L. M. , Langen, M. , Oranje, B. , & Durston, S. (2014). Unique developmental trajectories of cortical thickness and surface area. NeuroImage, 87 , 120–126. 10.1016/J.NEUROIMAGE.2013.11.010 24246495
Wolfers, T. , Beckmann, C. F. , Hoogman, M. , Buitelaar, J. K. , Franke, B. , & Marquand, A. F. (2019). Individual differences v. the average patient: Mapping the heterogeneity in ADHD using normative models. Psychological Medicine, 50 (2 ), 314–323. 10.1017/S0033291719000084 30782224
Zabihi, M. , Oldehinkel, M. , Wolfers, T. , Frouin, V. , Goyard, D. , Loth, E. , Charman, T. , Tillmann, J. , Banaschewski, T. , Dumas, G. , Holt, R. , Baron‐Cohen, S. , Durston, S. , Bölte, S. , Murphy, D. , Ecker, C. , Buitelaar, J. K. , Beckmann, C. F. , & Marquand, A. F. (2019). Dissecting the heterogeneous cortical anatomy of autism Spectrum disorder using normative models. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 4 (6 ), 567–578. 10.1016/J.BPSC.2018.11.013 30799285
Zijdenbos, A. , Forghani, R. , & Evans, A. (1998). Automatic quantification of MS lesions in 3D MRI brain data sets: Validation of INSECT. In Medical Image Computing and Computer‐Assisted Intervention (pp. 439–448). Springer.
