Pre-adolescent Brain Asymmetry: Developmental Trajectory, Cognitive and Psychiatric Effects, Neurobiological and Environmental Influences in ABCD Study

doi:10.21203/rs.3.rs-5253313/v1

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Pre-adolescent Brain Asymmetry: Developmental Trajectory, Cognitive and Psychiatric Effects, Neurobiological and Environmental Influences in ABCD Study

https://doi.org/10.21203/rs.3.rs-5253313/v1

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Cerebral asymmetry is fundamental to various cognitive functions but is often disrupted in neuropsychiatric disorders. While adolescent brain growth has been extensively studied, the maturation of brain asymmetry in children and its influencing factors remain poorly understood. Using longitudinal data from 11,000 children aged 10–14 in the ABCD Study, we mapped the developmental trajectory of structural brain asymmetry and revealed significant age-related, modality-specific development patterns, particularly linked to crystallized intelligence and general psychiatric risks. Genetically, structural asymmetry were related to synaptic processes and neurogenesis, likely through asymmetric synaptic pruning. At the macrostructural level, corpus callosum integrity emerged as a key factor in modulating longitudinal asymmetry. Environmentally, favorable perinatal conditions were associated with prolonged corpus callosum development, affecting future asymmetry patterns and cognitive outcomes. These findings underscore the dynamic yet predictable interactions between brain structural asymmetry, its determinants, and cognitive and psychiatric outcomes during this pivotal developmental stage. Our results provide empirical support for the adaptive plasticity theory in cerebral asymmetry and offer new insights into both cognitive maturation and potential risk for early-onset psychiatric disorder risks.

Biological sciences/Neuroscience/Development of the nervous system

Health sciences/Risk factors

Asymmetry of the cerebral hemispheric structure is one of the most prominent features of the human brain (Toga & Thompson, 2003). Both cortical (surface area and thickness) and subcortical (volume) morphological features exhibit significant asymmetrical patterns (Chiarello, Vazquez, Felton, & McDowell, 2016; Kong et al., 2018; Roe et al., 2023). Asymmetry is considered as an essential feature of neurocognitive fitness (Duboc, Dufourcq, Blader, & Roussigné, 2015) which promotes cognitive processing specialization and efficiency by limiting processing mainly in a single hemisphere (Corballis, 2019). The variations in structural asymmetry among youth and adults are linked to differences in cognitive abilities (Roe et al., 2023) and psychiatric symptoms (Dang et al., 2016; Kong et al., 2020; Postema et al., 2019; Sha et al., 2022).

The structural asymmetry of the human brain undergoes complex developmental processes: as early as prenatal, gene expression analyses of the embryonic central nervous system reveals notable asymmetry from left to right sides (De Kovel et al., 2017; Ocklenburg & Gunturkun, 2017), and then structural asymmetry becomes noticeable in fetal and neonatal periods (Gang, Weili, John, & Dinggang, 2015; Williams et al., 2023). From childhood to adulthood, asymmetry in cortical surface area exhibits relative stability, while cortical thickness asymmetry increases during adolescence and reaches its peak in early adulthood, followed by a nonlinear decline with age (Kurth et al., 2024; Roe et al., 2023; Roe et al., 2021).

While preliminary developmental trajectory of structural asymmetry across the lifespan have been constructed using cross-sectional data (Roe et al., 2023), there are still gaps in the field of asymmetry development in children. First, the neurobiological factors affecting the asymmetry development remain unclear. At genetic/molecular level, brain development involves a variety of biological processes, such as increased synaptic pruning, restructuring of the neurotransmitter system, and enhanced myelination (Brouwer et al., 2022; B. J. Casey, Getz, & Galvan, 2008). However, the pathways underlying the asymmetry development remain unexplored. At structal level, the relationship between brain asymmetry and the corpus callosum (CC), the largest white matter bundle connecting two hemispheres is also unknown. Current research focused on the effect of CC on functional lateralization (in language tasks (Bartha-Doering et al., 2021), and no consensus is reached as to whether CC has excitatory or inhibitory effects (Ocklenburg, Friedrich, Güntürkün, & Genç, 2016). The impact of CC on structural asymmetry remains unclear.

Second, the environmental determinants, especially the early-life factors influencing asymmetry development remain poorly delineated. Harsh early environment have a notable impact on brain development and leads to a range of cognitive and psychiatric problems in adulthood (Bick & Nelson, 2016; Gao et al., 2019; Lenroot & Giedd, 2008; Romer, 2010). The adaptive developmental plasticity hypothesis (Nettle & Bateson, 2015) posit that adverse early environments may interact with risk genes, resulting in premature maturation of the nervous system (Nelson & Gabard-Durnam, 2020), to cope with adverse circumstances at the cost of causing cognitive decline or psychiatric symptoms. However, whether this theory applies to structural brain asymmetries remains unclear. Currently, how the early environmental factors such as maternal health and socioeconomic status shape asymmetry development is unknown.

This study investigated the developmental trajectory of brain structural asymmetry in childhood and pre-adolescence. We analyzed structural MRI data from over 11,000 children in ABCD Study, examining three time points at ages 10, 12, and 14 years. Asymmetry indices (AI) were calculated for cortical thickness (CT), cortical surface area (CSA), and subcortical volume (Vol). We assessed asymmetry developmental trends and their relationships with cognitive function and psychiatric symptoms, as well as the the interactions between early factors, corpus callosum (CC) integrity and asymmetry. Additionally, we combine genetic measurements, diffusion MRI, behavioral assessments to uncover the neurobiological (e.g., genetic and macrostructual) and environmental factors underlying asymmetry development. The purpose of this study is to: (1) characterize the longitudinal development of structure asymmetry during 10–14 years; (2) to understand the relationship between structural asymmetry and cognitive and mental health status in adolescents; (3) to investigate the neurobiological and early environmental determinants affecting asymmetry development. The main workflow is outlined in Fig. 1.

Developmental trajectory of cortical and subcortical structural asymmetry in youths

This study used ABCD datasets, comprising over 11,000 young participants aged from 10 to 14 years (SM Table 1). Asymmetry indices (AI), commonly defined as 2*(L-R)/(L + R), were utilized to evaluate the asymmetry in the cerebral structures (Kong et al., 2018), where “L” and “R” signifies the structural attributes of the left and right hemisphere, respectively. To ensure comparability of asymmetry indices across different age, we normalized the asymmetry measures at all three time points using the sum of left and right hemispheric attributes (L + R) at baseline (age 10).

Figure 2A displays the group-averaged asymmetry patterns in 3 ages. The asymmetry patterns of cortical surface area (CSA), cortical thickness (CT) and subcortical volume (Vol) are relatively stable, converge to those observed in the large adult sample (Kong et al., 2018). CSA mainly showed left-lateralization in sensorimotor, auditory area, anterior and posterior cingulate, and right lateralization in frontotemporal and inferior parietal cortex. CT mainly showed left-lateralization in anterior and dorsal prefrontal cortex and para-hippocampal gyrus, and right-lateralization in occipito-temporal areas. For subcortical structures, leftward lateralization in volume is observed in the lateral ventricle (LatVent), pallidum, ventral diencephalon (DC), and thalamus, while rightward lateralization occurs in the hippocampus, amygdala, caudate, pallidum, and inferior lateral ventricle (InfLatVent).

To explore the developmental trend of structural asymmetry, we utilized linear mixed models to delineate the effects of age on structural asymmetry (using R package lme4), where gender, pubertal stage, race, handedness and intracranial volume (ITV) were treated as fixed effects while family ID nested in image acquisition sites and subject IDs were separately accounted for as two random effects. Multiple comparisons were conducted using the False Discovery Rate (FDR) method (34 * 2 cortical asymmetry measures and 12 subcortical asymmetry measures, totaling 80 comparisons). We found that as age increases, motor area, medial prefrontal and temporo-occipital regions showed a left-lateralized trend in CSA, while the parietal lobule and frontal triangular gyrus showed a right-lateralized trend. For CT, the frontal and medial temporal lobes become more left-lateralized, while the parieto-occipital and lateral orbitofrontal lobes become more right-lateralized with age. For subcortex, thalamus, putamen and caudate exhibit increased left-lateralization with age, whereas pallidum tends to become more right-lateralized with age (Fig. 2B). Overall, the most pronounced developmental effect in asymmetry are observed in subcortical region volume (the smallest p = 1e-22, t = 9.77, thalamus), followed by CT (middle orbital frontal cortex, t = 5.93, p = 3e-9; inferior parietal lobe, t=-5.88, p = 4e-9) whereas CSA demonstrates relatively small age effects (inferior parietal lobe, t = 4.11, p = 3.9e-5) (Fig. 2B and SM Table 2).

We further investigated the correlation between regional CSA/CT/subcortical volume with age to understand how structural changes with age may affect asymmetry development (Fig. 2C). We noted a general decreasing trend in regional CSA/CT from 10 to 14 years, which suggest the presence of neural pruning (Juraska & Drzewiecki, 2020; Spear, 2013). This result indicates that the development of cortical asymmetry in pre-adolescence may be primarily driven by more pruning on one hemisphere compared to the other.

Relationship between asymmetry, cognition/psychiatric phenotypes and early factors

Linear mixed models were utilized to assess the relationships between baseline structural asymmetry and cognitive functions cognition [fluid, crystal and total intelligence from NIH Toolbox Cognitive Battery, Average reaction time of Little Man Task (LMT, measuring visual-spatial processing), and WISC-V Matrix Reasoning Total Raw Score (WISCV TRS)] and psychiatric symptoms [internalization, externalization, and total problem scores from Child Behavior Checklist (CBCL), and common mental disorder diagnoses from parent-reported KSADS] (B. Casey et al., 2018), with FDR correction. Baseline data were selected to maximize sample size (N = 11,362). Alongside common covariates described above, the models include additional fixed-effect covariates of age, while grouping variables (nested image acquisition sites and family) were treated as random effects in ABCD association analyses.

We observed a series of associations between asymmetry and behavioral phenotypes (Fig. 3A-B, SM Table 3). Specifically, for cortical structures, cognitive abilities (particularly crystallized intelligence) was negatively associated with asymmetry of lateral orbital frontal cortex (LOrbF) (CSA, NIH fluid intelligence: t=-4.68, p = 3e-6; NIH crystallized intelligence, t=-5.27, p = 1e-7; NIH total intelligence: t=-6.03, p = 2e-9) and superior frontal cortex (SF) (CT, NIH fluid: t=-3.67, p = 2e-4; NIH crystal: t=-5.1, p = 3e-7; NIH total: t=-5.13, p = 3e-7), and was positively related with para-hippocampus (PaH) (CT, NIH fluid: t = 3.17, p = 2e-3; NIH crystal: t = 5.31, p = 1e-7; NIH total: t = 4.51, p = 7e-6; WISCV TRS, t = 3.61, p = 3e-4, see Fig. 4B). The CT asymmetry of insula, fusiform gyrus and isthmic cingulate also positively correlated with intelligence. For subcortical structures, asymmetry of thalamus correlated positively with cognitive abilities including NIH total intelligence (volume, t = 2.57, p = 0.01) and fluid intelligence (t = 3.41, p = 0.0007). Additionally, CBCL total problems were positively associated with asymmetry of LOrbF (CSA, t = 3.57, p = 0.0004) and middle cingulate (CT, t = 3.02, p = 0.0026), and was negatively associated with asymmetry in the inferior lateral ventricles (volume, t=-3.25, p = 0.0012) and thalamus (volume, t=-2.84, p = 0.0047). For psychiatric diagnosis, only decreased ventral diencephalon volume was observed in post-traumatic stress disorder (PTSD, t=-3.47, p = 5e-4).

To understand the function of regions whose asymmetry was associated with behaviroal phenotypes, we calculated the correlation between t-maps of crystallized intelligence (with CSA/CT asymmetry, Fig. 3B) and meta-analysis maps from Neurosynth (https://www.neurosynth.org/) corresponding to 127 mental terms (Hansen et al., 2022). The t-map of CSA asymmetry-cognition association was positively correlated with meta-maps of memory and language (speech perception) while negatively correlated with decision-making (uncertainty, reward expectation). The t-map of CT asymmetry-cognition association was positively correlated with visual processing, imagination, and consciousness, and negatively with memory (autobiographical, retrieval), language, and sociability (social cognition, semantic memory, context), see Fig. 3C.

We furthermore investigated whether regions in left or right hemisphere are responsible for the observed asymmetry pattern that was related to cognition. We focused on 7 regions whose asymmetry was most strongly correlated with crystallized intelligence, using a linear mixed model (FDR q < 0.001) to assess group (top 30% vs. bottom 30% in crystallized intelligence), hemisphere (left vs. right), and interaction effects (Supplemental Method). In high-intelligence group, changed asymmetry occurred alongside increased CSA, CT, and volume, compared to the low-intelligence group. For instance, bilateral thalamic volume in the high-intelligence group was larger than that in the low-intelligence group (t = 22.2, p = 1.2e-105), with the right thalamus being larger than the left (t = 105, p < 0.0001). The interaction effect was also significant (t = 5.51, p = 3.72e-8), indicating that the left > right asymmetry in the high-intelligence group was increased compared to the low-intelligence group (Fig. 4 and SM Table 9).

To explore the impact of environmental factors on brain asymmetry, three latent factors defined by Group Factor Analysis (GFA) based on 22 baseline variables from ABCD 2.0.1 (Gonzalez et al., 2019) were used for association analysis: general socioeconomic status (SES) factor (parent income/education, housing security and neighborhood safety), social factor (adolescents' perceived social support from family and school) and perinatal health factor (positively associated with birth weight/gestational age, negatively with maternal age/prenatal medical conditions). We found that perinatal factors were negatively associated with laterality of SF (CT, t=-6.33, p = 3e-10), rostral middle frontal gyrus and anterior/isthmus cingulate, and with asymmetry of LOrbF (CSA, t=-6.59, p = 5e-11) and transverse/inferior temporal cortex. Conversely, perinatal factors were positively associated with asymmetry of inferior parietal cortex (CT) and thalamus, amygdala and ventral diencephalon (volume). SES factors show a positive correlation with asymmetry of thalamus (volume, t = 3.83, p = 0.0001), while social factors did not exhibit significant correlation (Fig. 5A).

Perinatal factors shape cognitive abilities via corpus callosum-mediated structural asymmetry

To further investigate the neurobiological (macrostructural) and environmental factors shaping asymmetry and whether such factors impact cognitive phenotypes, we conducted a series of pathway analyses using longitudinal data. All models were build using the lavaan package of R, and all covariates in association analysis using linear mixed models were regressed out from CC and AIs before modeling.

Considering the influence of the corpus callosum (CC) on brain asymmetry (Hinkley et al., 2016; Karolis, Corbetta, & Thiebaut de Schotten, 2019; Tzourio-Mazoyer, 2016), we first examined the relationship between CC integrity and brain asymmetry. We used the first principal component (CC PC1) from 4 diffusion imaging indices which explains 50.17% variance, correlates positively with microstructural integrity, and increases with age (t = 38.2, p = 1.66e-300) (Supplemental Method, SM Table 2&4). Cross-lagged panel models (CLPMs) revealed that CC integrity at age 10 significantly influenced structural asymmetry at age 12. Specifically, CC PC1 correlated positively with asymmetry of orbitofrontal cortex (CSA, z = 7.61, p = 2.7e-14), frontal cortex (CT, especially superior frontal gyrus, z = 8.45, p < 0.0001), anterior cingulate (CT), superior temporal cortex (CT), pallidum (volume, z = 10.9, p < 0.0001), and amygdala (volume, z = 11.4, p < 0.0001), and correlated negatively with asymmetry of insula (CT, z=-9.62, p < 0.0001), parietal and temporal cortex (CT), and thalamus (volume), see Fig. 5A. Interestingly, this pattern was inversely related to that between asymmetry and crystallized intelligence (Pearson r=-0.47, p = 1e-5). Asymmetry at age 10 did not significantly affect CC at age 12, indicating a unidirectional effect of CC on asymmetry during pre-adolescence (SM Tables 5–6).

We then examined the cascading relationship between four factors by longitudinal structural equation models (SEMs): (1) perinatal factors, which correlated most with asymmetry among early factors, (2) CC PC1 at age 10, (3) CT asymmetry at age 12, and (4) crystallized intelligence at age 12. Our findings show that better perinatal health is associated with lower CC PC1 levels at age 10 (β = 0.02, p = 0.04), which correlates positively with asymmetry in superior frontal gyrus (CT, β = 3e-3, p < 0.0001) and pallidum (volume, β = 0.01, p < 0.0001) and negatively correlates with asymmetry of para-hippocampus (CT, β = 3e-4, p = 1e-6) at age 12, ultimately leading to enhanced crystallized intelligence at age 12 (Fig. 5B and SM Table 7–8).

Biological processes that modulate asymmetry development

To elucidate the genetic underpinnings of structural brain asymmetry both at baseline and throughout its longitudinal development in youth, we conducted Genome-Wide Association Study (GWAS) using whole-genome sequencing data from the ABCD study. We focus on baseline (bl) structural asymmetry, denoted as AI_bl (10 years old), and the rate of asymmetry change with age, denoted as ΔAI [(12-year follow-up − 10-year baseline)/age gap]. Our analysis revealed that ΔAI exhibited a more extensive correlation with single nucleotide polymorphisms (SNPs) (p < 1e-8) across the brain compared to AI_bl (Fig. 6A-B). Subsequently, all significant SNPs were mapped to gene loci based on the Genome Reference Consortium Human Build 37 (GRCh37) database, enabling the identification of gene sets significantly associated with AI_bl and ΔAI. Enrichment analysis based on Functional Mapping and Annotation (FUMA) platform (https://fuma.ctglab.nl/) (Watanabe, Taskesen, Van Bochoven, & Posthuma, 2017) show that both baseline and longitudinal structural asymmetry are related to synapse-related processes, neuron projection, neurogenesis, and membrane transport (Fig. 6C).

This study portrayed the longitudinal developmental trajectories of brain structural asymmetry of pre-adolescence, and comprehensively elucidated its cognitive/psychiatric associations, and neurobiological and environmental determinants influencing it. We found significant correlation between structural asymmetry and cognitive ability (especially crystallized intelligence) and psychiatric symptoms. Genetic analyses unveiled a gene set associated with asymmetry and its longitudinal changes, primarily enriched in biological pathways related to neuronal functions and synaptic processes. Notebly, we found that these asymmetries were modulated by perinatal health factors affecting the development of corpus callosum integrity, which in turn impacted intelligence. Overall, our findings underscore the significant role of genetic, white matter, and early enviromantal influences in driving the maturation of brain asymmetry.

Our results revealed that asymmetries in specific brain regions are associated with better cognitive function and lower psychiatric symptoms at baseline. These regions, including the right-lateralized orbitofrontal CSA, superior frontal CT, pallidum volume, and left-lateralized para-hippocampal CT and thalamic volume, are involved in higher-order cognitive functions such as cognitive control, memory, and attention. For instance, the orbitofrontal lobe aids in decision-making (Jonker, Jonker, Scheltens, & Scherder, 2015); superior frontal cortex plays a crucial role in cognitive control (Lu et al., 2022); the para-hippocampal gyrus supports episodic memory (Aminoff, Kveraga, & Bar, 2013); pallidum is associated with behavioral flexibility (Aristieta & Gittis, 2021), and its volume asymmetry has been found to be associated with allocation of visuospatial attention (Mazzetti et al., 2019).

Our study sheds light on the neurobiological (genetic and macrostructural) mechanisms underlying asymmetry development. At the genetic level, gene enrichment results suggest that asymmetry development is related to synaptic processes and neurogenesis. Combined with our observation that age-related reductions in CT/CSA lead to asymmetric development (Fig. 2C), we propose that asymmetric development may result from asymmetric synaptic pruning, a known mechanism during adolescence that drives cortical thinning and promotes processing specialization (Sakai, 2020; Vijayakumar et al., 2016). Specifically, stronger synaptic pruning in one hemisphere compared to the other during pre-adolescence may drive asymmetry and affect cognitive function.

At the macrostructural level, we found a unidirectional relationship from corpus callosum integrity to structural asymmetry. Children with greater corpus callosum fiber integrity at baseline showed increased structural asymmetry after 2 years, including left-lateralized frontotemporal CT, lateral orbitofrontal CSA, and thalamic and amygdala volumes (Fig. 5A). These results support the hypothesis that the corpus callosum exerts interhemispheric inhibitory effects (van der Knaap & van der Ham, 2011), i.e., the corpus callosum can progressively reinforce functional lateralization by enhancing the inhibitory influence of highly lateralized regions on their contralateral counterparts (Ocklenburg et al., 2016). Importantly, our findings suggest that this effect extends beyond short-term enhancements in functional lateralization (e.g., activation during language or dichotic listening tasks) (Bartha-Doering et al., 2021; Westerhausen, 2024) to persist throughout yearly developmental changes. We speculate, the corpus callosum may promote synaptic pruning of non-dominant hemisphere through inhibition from the dominant hemisphere (Ocklenburg et al., 2016), thus promotes the development of brain asymmetry.

An interesting result is that the pattern of asymmetry changes in the high-intelligence group (compared to low-intelligence group) is very similar to those found in large samples of autism (Postema et al., 2019) and obsessive-compulsive disorder (OCD) (Kong et al., 2020). However, the underlying mechanisms were different: our results (Fig. 4) showed that high-intelligence group has increased bilateral CSA, CT and volume than low-intelligence group, while both ASD and OCD patients showed decreased CSA, CT and subcortical volume than healthy group in Postema and Kong's results. Thus, asymmetry changes of autism patients may be due to asymmetric damage rather than normal synapse pruning in our study. This provides us with an interesting perspective: similar asymmetry alterations may result from different biological mechanisms and health consequences, and the alterations of raw structural measures on both hemispheres need to be considered in addition to the asymmetry measure.

Interestingly, we also discovered that favorable perinatal conditions can slow the maturation of the corpus callosum, leading to structural asymmetry patterns that was correlated with crystallized intelligence. Our findings reveal a key principle of asymmetry development that aligns with the adaptive plasticity hypothesis (Nettle & Bateson, 2015), but in an inverse direction. The hypothesis posit that adverse early conditions may interact with risk genes, resulting in prematurity, or accelerated development the brain. This prematurity was linked to elevated risk of psychiatric disorders, and was reflected by an increase in “brain age” of psychiatric population (Ballester et al., 2023). Our findings suggest that favorable early environmental conditions like perinatal health may enhance cognitive performance by protracting CC development, which in turn affects the trajectory of structural asymmetry. These results provide empirical evidence for adaptive plasticity in brain asymmetry, with significant implications for anatomically staging or grading many disorders where such asymmetry is disrupted.

Limitations

The age span of this longitudinal study is relatively narrow (10–14 years), and future studies should be expanded to cover the entire childhood through early adulthood to fully characterize the influence of neurobiological and environmental factors on asymmetry development. Second, while our study has examined the role of the corpus callosum (CC) in shaping long-term structural asymmetry development, our results only indicate a statistical association and do not clarify how CC leads to cortical asymmetry reduction at the molecular level; thus, further investigation through animal studies is needed.

In this study, a large sample of longitudinal data was used to study the asymmetric development of brain structure and its neurobiological and environmental influencing factors in children aged 10 to 14 years. We found developmental tendency of asymmetry of cortical surface area, thickness, and subcortical area volume, and demonstrated association between brain asymmetry and cognitive / psychiatric phenotypes. From the neurobiological prospective, we identified that the genes associated with synapse and neurogenesis processes, as well as the white matter integrity of CC are crucial in shaping structural asymmetry development. Environmentally, our study underscores the impact of prenatal health factors on brain asymmetry and cognitive development by protracting CC growth. These findings inform both animal and human studies on the influencing factors in shaping brain structure and function, with implications for the etiology of neurodevelopmental and severe mental disorders, most of which have an onset in the adolescent age group in focus.

The Participants

A sample of 11,878 children recruited from 22 research sites across the United States in the Adolescent Brain Cognitive Development (ABCD) study were included in this study (B. Casey et al., 2018). The present study used the third public ABCD data release (version 5.0, released October 2023) which included the clinical, behavioral, cognitive, and neuroimaging data of baseline (9–10 years old), 2-year and 4-year follow-up assessments.

MRI Data Acquisition and Preprocessing

High-resolution T1- structural Magnetic Resonance Images (MRI) and diffusion tensor imaging (DTI) of all children were collected, and processed through ABCD pipelines (B. Casey et al., 2018). Cortical area, thickness and subcortical volume was obtained from T1-weighted structural images, segmented and reconstructed by Freesurfer 5.3 (Fischl, 2012). Labels for cortical regions were assigned based on Desikan parcellation, which divides cortex into 68 regions of interest (ROIs) (Desikan et al., 2006), and subcortical structures are labelled using the automatic subcortical segmentation (ASEG) procedure of Freesurfer and contained 14 ROIs. Major white matter tracts were automatically segmented using AtlasTrack (Hagler Jr et al., 2009), and common DTI parameters such as average factional anisotropy (FA), mean diffusivity (MD), longitudinal diffusivity (LD), transverse diffusivity (TD) of 37 main tracts were acquired.

Asymmetry Index (AI)

This study employed the widely used asymmetry index to assess structural brain asymmetry in young individuals, calculated as follows: 2 * (L-R) / (L + R), where L represents the left hemispheric region and R represents the homologous right brain region. This method normalizes asymmetry based on the sum of indicators from the left and right brain regions.

Behavioral Phenotype Measurement

The five intelligence phenotypes were derived from three tasks: fluid intelligence, crystallized intelligence and total intelligence scores from the NIH Toolbox Cognitive Battery (B. Casey et al., 2018; Weintraub et al., 2013); Total Raw Score (WISCV TRS) from WISC-V Matrix Reasoning Scale, to view a child's general intellectual functioning (Wechsler & Kodama, 1949); the reaction time from Little Man Task, which engages visual-spatial processing, specifically mental rotation with varying degrees of difficulty (Luciana et al., 2018).

Three symptom phenotypes were derived from Child Behavior Checklist (CBCL), which provides dimensional ratings of children’s internalization, externalization and total symptoms (Achenbach, 1999).

Ten mental disorder diagnoses were derived from parented computerized Kiddie-Schedule for Affective Disorders (K-SADS COMP) (Kaufman et al., 1997; Townsend et al., 2020) to assess children’s lifetime (past, present, or partial remission) DSM-5 diagnoses (Barch et al., 2018). All disorders with more than 1,000 cases were selected.

Association Analysis

Using linear mixed models with age as an independent variable, gender, handedness, race, and total intracranial volume (ITV) as fixed effects, and data collection site as a random effect, the study examined the association patterns between brain CT, CSA, and subcortical volume asymmetry and age. The linear mixed model utilized the "lmerTest" function of R package lme4 providing single-sample t-tests for each predictor’s regression coefficients and their corresponding p-values. P-values underwent false discovery rate (FDR) correction across all structural asymmetry features (34 CT AI + 34 CSA AI + 12 Subcortical Vol AI = 80). The Storey et al.’s FDR method of R package qvalue was used, as this method is considered more suitable for analyses with sample sizes greater than 5000 and high covariance among variables (Storey, 2002). Apart from age-associated analysis, we separately investigated the associations between gender, handedness, ITV, and asymmetry of brain structure in young individuals.

Cognitive Term Meta-Analysis

A meta-analysis was conducted on the effects map of age-related asymmetry in CSA and CT based on a publicly available database (Neurosynth) comprising multiple brain atlases related to various cognitive terms. We employed cortical meta-analysis atlas data segmented using the DK template by Hensen et al. (Hansen et al., 2022), containing 124 cognitive terms, each encompassing 68 columns corresponding to associations between specific cognitive activities and various brain regions. We calculated Spearman correlations for our age effects map (with 34 values repeated once to match the DK template’s 68 brain regions) and performed a permutation test based on spherical rotation (spin test) to assess the significance of term-age effects map associations (Alexander-Bloch et al., 2018).

Longitudinal Pathway Analysis

Cross-Lagged Panel Model (CLPM) is a discrete-time structural equation modeling technique applied to panel data, widely utilized in psychological research to infer potential Granger causality or reciprocal associations between variables. Employing R package lavvan, we conducted CLPM analyses on CC PC1 and 80 AIs across two time points (ages 10 and 12), adjusting for aforementioned covariates. Similar FDR method (N = 80) was applied to address multiple comparisons.

Structural Equation Modeling (SEM) was employed to examine the complex interrelationships among prenatal factor, CC PC1, AIs significantly associated with crystallized intelligence, and crystallized intelligence. This multivariate statistical technique allows for the simultaneous estimation of multiple regression equations, making it ideal for testing hypothesized causal pathways. Using the lavaan package, we constructed models with CC PC1 and AIs hypothesized to mediate the relationship between prenatal factor and crystallized intelligence.

Model fits of both CLPM and SEM were assessed using multiple indices: Chi-square test, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). The model was considered to have good fit if CFI and TLI exceeded 0.95, RMSEA was below 0.06, and SRMR was less than 0.08. Maximum Likelihood estimation was used and bootstrapping with 5000 samples was performed to obtain confidence intervals.

Genetic Association Analysis

Leveraging genetic data from the ABCD dataset, this study identified genetic markers associated with asymmetry and its developmental rate. The asymmetry developmental rate (ΔAI) was calculated as follows: (AI_2y – AI_bl) / (Age_2y – Age_bl).

We performed a Genome-Wide Association Study (GWAS) on AI and ΔAI using plink v2.0 (Chang et al., 2015). On the premise of additive genetic effects, general linear regression models were fitted to determine the association between AI and ΔAI and allele dosages of single nucleotide polymorphisms (SNPs) in genetically unrelated European-ancestry preadolescents who passed structural image quality control (n = 4,716). In order to reduce false positives caused by population stratification, principal component analysis (PCA) for genome was performed before GWAS. Sex, age, ITV, 10 principal components (PCs) and study sites were included as covariates. The R package snpsettest (https://github.com/HimesGroup/snpsettest) was used for mapping significant SNPs (p < 5e-8) to gene loci, and the reference gene set was GRCh37.

Data availability

ABCD data are available from a dedicated database at https://abcdstudy.org/.

Code availability

The code that supports the findings of this study is available on GitHub at https://github.com/XinRanWu/ABCD_YoungAsym/.

Acknowledgements

JZ was supported by Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project (Grant No. 2021ZD0200204), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZJLab, NSFC 61973086, and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China. KZ was supported by National Natural Science Foundation 62276099, and Shanghai Pujiang Program. LP acknowledges research support from the Canada First Research Excellence Fund, awarded to the Healthy Brains, Healthy Lives initiative at McGill University (through New Investigator Supplement to LP); Monique H. Bourgeois Chair in Developmental Disorders and Graham Boeckh Foundation (Douglas Research Centre, McGill University) and salary award from the Fonds de recherche du Quebec-Sante ́ (FRQS). JF was supported by the 111 Project (No. B18015), the key project of Shanghai Science and Technology (No. 16JC1420402), National Key R&D Program of China (No. 2018YFC1312900), National Natural Science Foundation of China (NSFC 91630314). Xiang-Zhen Kong is supported by the National Natural Science Foundation of China (32171031), the Fundamental Research Funds for the Central Universities (2021XZZX006), and Information Technology Center of Zhejiang University. XW was supported by State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, and Institutes of Brain Science, Fudan University.

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA0401048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Disclosures

LP reports personal fees for serving as chief editor from the Canadian Medical Association Journals, speaker/consultant fee from Janssen Canada and Otsuka Canada, SPMM Course Limited, UK, Canadian Psychiatric Association; book royalties from Oxford University Press; investigator-initiated educational grants from Janssen Canada, Sunovion and Otsuka Canada outside the submitted work.

Funding

LP acknowledges research support from the Monique H. Bourgeois Chair in Developmental Disorders and Graham Boeckh Foundation (Douglas Research Centre, McGill University) and salary award from the Fonds de recherche du Quebec-Sante ́ (FRQS).

Author information

XW and JZ contributed to the conception of the study; XW, NY and KZ performed the data analysis; XW, JZ and LP wrote the manuscript; LP, XK, MC helped perform the analysis with constructive discussions; YZ, GY, ZL, WC, TJ, BS, TR, JF, GS helped with the revision of the article.

Ethics declarations

The ABCD study conforms to each site’s institutional review board’s rules and procedures, and all participants provided informed consent (parents) or informed assent (children). Informed consent was sought from all participants and a parent/guardian of each participant.

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Yes there is potential Competing Interest. LP reports personal fees for serving as chief editor from the Canadian Medical Association Journals, speaker/consultant fee from Janssen Canada and Otsuka Canada, SPMM Course Limited, UK, Canadian Psychiatric Association; book royalties from Oxford University Press; investigator-initiated educational grants from Janssen Canada, Sunovion and Otsuka Canada outside the submitted work.

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Pre-adolescent Brain Asymmetry: Developmental Trajectory, Cognitive and Psychiatric Effects, Neurobiological and Environmental Influences in ABCD Study

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Abstract

Figures

Main

Results

Developmental trajectory of cortical and subcortical structural asymmetry in youths

Relationship between asymmetry, cognition/psychiatric phenotypes and early factors

Perinatal factors shape cognitive abilities via corpus callosum-mediated structural asymmetry

Biological processes that modulate asymmetry development

Discussion

Limitations

Conclusion

Methods

The Participants

MRI Data Acquisition and Preprocessing

Asymmetry Index (AI)

Behavioral Phenotype Measurement

Association Analysis

Cognitive Term Meta-Analysis

Longitudinal Pathway Analysis

Genetic Association Analysis

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