White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

A systematic review and meta-analysis of neuroimaging studies examining synaptic density in individuals with psychotic spectrum disorders.

BACKGROUND: Psychotic disorders have long been considered neurodevelopmental disorders where excessive synaptic pruning and cortical volume loss are central to disease pathology. We conducted a systematic review of the literature to identify neuroimaging studies specifically examining synaptic density across the psychosis spectrum. METHODS: PRISMA guidelines on reporting were followed. We systematically searched MEDLINE, Embase, APA PsycINFO, Web of Science and The Cochrane Library from inception to December 8, 2023, and included all original peer-reviewed articles or completed clinical neuroimaging studies of any modality measuring synaptic density in participants with a diagnosis of psychosis spectrum disorder as well as individuals with psychosis-risk states. The NIH quality assessment tool for observational cohort and cross-sectional studies was used for the risk of bias assessment. RESULTS: Five studies (k = 5) met inclusion criteria, comprising n = 128 adults (psychotic disorder; n = 61 and healthy volunteers; n = 67 and specifically measuring synaptic density via positron emission tomography (PET) imaging of the synaptic vesicle glycoprotein 2 A (SV2A). Three studies were included in our primary meta-analysis sharing the same outcome measure of SV2A binding, volume of distribution (VT). Regional SV2A VT was reduced in psychotic disorder participants in comparison to healthy volunteers, including the occipital lobe (Mean Difference (MD)= -2.17; 95% CI: -3.36 to -0.98; P < 0.001 ), temporal lobe (MD: -2.03; 95% CI: -3.19 to -0.88; P < 0.001 ), parietal lobe (MD:-1.61; 95% CI: -2.85 to -0.37; P = 0.01), anterior cingulate cortex (MD= -1.47; 95% CI: -2.45 to -0.49; P = 0.003), frontal cortex (MD: -1.16; 95% CI: -2.18 to -0.15; P = 0.02), amygdala (MD: -1.36; 95% CI: -2.20 to -0.52, p = 0.002), thalamus (MD:-1.46; 95% CI:-2.46 to -0.46, p = 0.004) and hippocampus (MD= -0.96; 95% CI: -1.59 to -0.33; P = 0.003). CONCLUSIONS: Preliminary studies provide in vivo evidence for reduced synaptic density in psychotic disorders. However, replication of findings in larger samples is required prior to definitive conclusions being drawn. PROSPERO: CRD42022359018.

An Integrated Care Pathway for depression in adolescents: protocol for a Type 1 Hybrid Effectiveness-implementation, Non-randomized, Cluster Controlled Trial.

INTRODUCTION: Our group developed an Integrated Care Pathway to facilitate the delivery of evidence-based care for adolescents experiencing depression called CARIBOU-2 (Care for Adolescents who Receive Information 'Bout OUtcomes, 2nd iteration). The core pathway components are assessment, psychoeducation, psychotherapy options, medication options, caregiver support, measurement-based care team reviews and graduation. We aim to test the clinical and implementation effectiveness of the CARIBOU-2 pathway relative to treatment-as-usual (TAU) in community mental health settings. METHODS AND ANALYSIS: We will use a Type 1 Hybrid Effectiveness-Implementation, Non-randomized Cluster Controlled Trial Design. Primary participants will be adolescents (planned n = 300, aged 13-18 years) with depressive symptoms, presenting to one of six community mental health agencies. All sites will begin in the TAU condition and transition to the CARIBOU-2 intervention after enrolling 25 adolescents. The primary clinical outcome is the rate of change of depressive symptoms from baseline to the 24-week endpoint using the Childhood Depression Rating Scale-Revised (CDRS-R). Generalized mixed effects modelling will be conducted to compare this outcome between intervention types. Our primary hypothesis is that there will be a greater rate of reduction in depressive symptoms in the group receiving the CARIBOU-2 intervention relative to TAU over 24 weeks as per the CDRS-R. Implementation outcomes will also be examined, including clinician fidelity to the pathway and its components, and cost-effectiveness. ETHICS AND DISSEMINATION: Research ethics board approvals have been obtained. Should our results support our hypotheses, systematic implementation of the CARIBOU-2 intervention in other community mental health agencies would be indicated.

Utility of a virtual small group cognitive behaviour program for autistic children during the pandemic: evidence from a community-based implementation study.

BACKGROUND: Autistic children often experience socioemotional difficulties relating to emotion regulation and mental health problems. Supports for autistic children involve the use of adapted interventions that target emotion regulation and social skills, alongside mental health symptoms. The Secret Agent Society Small Group (SAS: SG), an adapted cognitive behavioural program, has demonstrated efficacy through lab-delivered randomized control trials. However, research is still needed on its effectiveness when delivered by publicly funded, community-based autism providers under real-world ecologically valid conditions, especially within the context of a pandemic. The COVID-19 pandemic has disrupted access to community-based supports and services for autistic children, and programs have adapted their services to online platforms. However, questions remain about the feasibility and clinical utility of evidence-based interventions and services delivered virtually in community-based settings. METHODS: The 9-week SAS: SG program was delivered virtually by seven community-based autism service providers during 2020-2021. The program included the use of computer-based games, role-playing tasks, and home missions. Caregivers completed surveys at three timepoints: pre-, post-intervention, and after a 3-month follow-up session. Surveys assessed caregivers' perception of the program's acceptability and level of satisfaction, as well as their child's social and emotional regulation skills and related mental health challenges. RESULTS: A total of 77 caregivers (94% gender identity females; Mean = 42.1 years, SD = 6.5 years) and their children (79% gender identity males; Mean = 9.9 years, SD = 1.3 years) completed the SAS: SG program. Caregivers agreed that the program was acceptable (95%) and were highly satisfied (90%). Caregivers reported significant reduction in their child's emotion reactivity from pre- to post-intervention (-1.78 (95% CI, -3.20 to -0.29), p = 0.01, d = 0.36), that continued to decrease after the 3-month booster session (-1.75 (95% CI, -3.34 to -0.16), p = 0.02, d = 0.33). Similarly, improvements in anxiety symptoms were observed (3.05 (95% CI, 0.72 to 5.36), p = 0.006, d = 0.39). CONCLUSIONS: As online delivery of interventions for autistic children remains popular past the pandemic, our findings shed light on future considerations for community-based services, including therapists and agency leaders, on how best to tailor and optimally deliver virtually based programming. TRIAL REGISTRATION: This study has been registered with ISRCTN Registry (ISRCTN98068608) on 15/09/2023. The study was retroactively registered.

Systematic comparisons of different quality control approaches applied to three large pediatric neuroimaging datasets.

INTRODUCTION: Poor quality T1-weighted brain scans systematically affect the calculation of brain measures. Removing the influence of such scans requires identifying and excluding scans with noise and artefacts through a quality control (QC) procedure. While QC is critical for brain imaging analyses, it is not yet clear whether different QC approaches lead to the exclusion of the same participants. Further, the removal of poor-quality scans may unintentionally introduce a sampling bias by excluding the subset of participants who are younger and/or feature greater clinical impairment. This study had two aims: (1) examine whether different QC approaches applied to T1-weighted scans would exclude the same participants, and (2) examine how exclusion of poor-quality scans impacts specific demographic, clinical and brain measure characteristics between excluded and included participants in three large pediatric neuroimaging samples. METHODS: We used T1-weighted, resting-state fMRI, demographic and clinical data from the Province of Ontario Neurodevelopmental Disorders network (Aim 1: n = 553, Aim 2: n = 465), the Healthy Brain Network (Aim 1: n = 1051, Aim 2: n = 558), and the Philadelphia Neurodevelopmental Cohort (Aim 1: n = 1087; Aim 2: n = 619). Four different QC approaches were applied to T1-weighted MRI (visual QC, metric QC, automated QC, fMRI-derived QC). We used tetrachoric correlation and inter-rater reliability analyses to examine whether different QC approaches excluded the same participants. We examined differences in age, mental health symptoms, everyday/adaptive functioning, IQ and structural MRI-derived brain indices between participants that were included versus excluded following each QC approach. RESULTS: Dataset-specific findings revealed mixed results with respect to overlap of QC exclusion. However, in POND and HBN, we found a moderate level of overlap between visual and automated QC approaches (rtet=0.52-0.59). Implementation of QC excluded younger participants, and tended to exclude those with lower IQ, and lower everyday/adaptive functioning scores across several approaches in a dataset-specific manner. Across nearly all datasets and QC approaches examined, excluded participants had lower estimates of cortical thickness and subcortical volume, but this effect did not differ by QC approach. CONCLUSION: The results of this study provide insight into the influence of QC decisions on structural pediatric imaging analyses. While different QC approaches exclude different subsets of participants, the variation of influence of different QC approaches on clinical and brain metrics is minimal in large datasets. Overall, implementation of QC tends to exclude participants who are younger, and those who have more cognitive and functional impairment. Given that automated QC is standardized and can reduce between-study differences, the results of this study support the potential to use automated QC for large pediatric neuroimaging datasets.

Cortical Gyrification Morphology in ASD and ADHD: Implication for Further Similarities or Disorder-Specific Features?

Shared etiological pathways are suggested in ASD and ADHD given high rates of comorbidity, phenotypic overlap and shared genetic susceptibility. Given the peak of cortical gyrification expansion and emergence of ASD and ADHD symptomology in early development, we investigated gyrification morphology in 539 children and adolescents (6-17 years of age) with ASD (n=197) and ADHD (n=96) compared to typically developing controls (n=246) using the local Gyrification Index (lGI) to provide insight into contributing etiopathological factors in these two disorders. We also examined IQ effects and functional implications of gyrification by exploring the relation between lGI and ASD and ADHD symptomatology beyond diagnosis. General Linear Models yielded no group differences in lGI, and across groups, we identified an age-related decrease of lGI and greater lGI in females compared to males. No diagnosis-by-age interactions were found. Accounting for IQ variability in the model (n=484) yielded similar results. No significant associations were found between lGI and social communication deficits, repetitive and restricted behaviours, inattention or adaptive functioning. By examining both disorders and controls using shared methodology, we found no evidence of atypicality in gyrification as measured by the lGI in these conditions.

Middle-childhood executive functioning mediates associations between early-childhood autism symptoms and adolescent mental health, academic and functional outcomes in autistic children.

BACKGROUND: Executive functioning (EF) varies in children with autism spectrum disorder (ASD) and is associated with clinical symptoms, academic, and adaptive functioning. Here, we examined whether middle-childhood EF mediates associations between early-childhood autism symptoms and adolescent outcomes in children with ASD. METHODS: The Pathways in ASD Cohort comprising children recruited at the time of ASD diagnosis (at 2-4 years-of-age) and followed prospectively across eight subsequent timepoints over ~10 years was used. A subset of Pathways participants (n = 250) with Behavior Rating Inventory of Executive Function (BRIEF)-Parent Form data from at least one timepoint when participants were school-aged was analyzed. A mediation framework was used to examine whether BRIEF-measured EF across age 7-10 years (middle-childhood) mediated associations between early-childhood autism symptoms (measured using the parent-report Social Responsiveness Scale across age 2-6 years) and clinical, academic, and functional outcomes, indexed at age >10-11.8 years (early-adolescence) using the Child Behavior Checklist (CBCL)-Internalizing and Externalizing Scales, Academic Performance from the Teacher's Report Form, and Vineland Adaptive Behavior Scales. Models were rerun substituting clinician-rated and teacher-rated measures, where possible. RESULTS: Mediation models indicated a significant indirect effect of middle-childhood EF on associations between early-childhood autism symptoms and externalizing behavior, academic performance, or adaptive functioning in early adolescence; kappa squared (κ2 ) effect sizes ranged from large to small. Model findings were stable across raters. Middle-childhood EF did not mediate associations between early-childhood autism symptoms and adolescent internalizing behavior. CONCLUSIONS: Among children with an ASD diagnosis, middle-childhood EF may be one pathway through which early-childhood autism symptoms influence a variety of outcomes in early-adolescence. An experimental study targeting middle-childhood EF to improve adolescent academic, emotional/behavioral, and adaptive functioning is needed to evaluate the clinical meaningfulness of these findings.

Cortical Gyrification Morphology in Individuals with ASD and ADHD across the Lifespan: A Systematic Review and Meta-Analysis.

Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are common neurodevelopmental disorders (NDDs) that may impact brain maturation. A number of studies have examined cortical gyrification morphology in both NDDs. Here we review and when possible pool their results to better understand the shared and potentially disorder-specific gyrification features. We searched MEDLINE, PsycINFO, and EMBASE databases, and 24 and 10 studies met the criteria to be included in the systematic review and meta-analysis portions, respectively. Meta-analysis of local Gyrification Index (lGI) findings across ASD studies was conducted with SDM software adapted for surface-based morphometry studies. Meta-regressions were used to explore effects of age, sex, and sample size on gyrification differences. There were no significant differences in gyrification across groups. Qualitative synthesis of remaining ASD studies highlighted heterogeneity in findings. Large-scale ADHD studies reported no differences in gyrification between cases and controls suggesting that, similar to ASD, there is currently no evidence of differences in gyrification morphology compared with controls. Larger, longitudinal studies are needed to further clarify the effects of age, sex, and IQ on cortical gyrification in these NDDs.

Practitioner Review: Pharmacological treatment of attention-deficit/hyperactivity disorder symptoms in children and youth with autism spectrum disorder: a systematic review and meta-analysis.

BACKGROUND: Clinically significant attention-deficit/hyperactivity disorder (ADHD) symptoms are common and impairing in children and youth with autism spectrum disorder(ASD). The aim of this systematic review and meta-analysis was to (a) evaluate the efficacy and safety of pharmacotherapy for the treatment of ADHD symptoms in ASD and (b) distil findings for clinical translation. METHODS: We searched electronic databases and clinical trial registries (1992 onwards). We selected randomized controlled trials conducted in participants <25 years of age, diagnosed with ASD that evaluated ADHD outcomes (hyperactivity/impulsivity and inattention) following treatment with stimulants (methylphenidate or amphetamines), atomoxetine, alpha-2 adrenergic receptor agonists, antipsychotics, tricyclic antidepressants, bupropion, modafinil, venlafaxine, or a combination, in comparison with placebo, any of the listed medications, or behavioral therapies. Data were pooled using a random-effects model. RESULTS: Twenty-five studies (4 methylphenidate, 4 atomoxetine, 1 guanfacine, 14 antipsychotic, 1 venlafaxine, and 1 tianeptine) were included. Methylphenidate reduced hyperactivity (parent-rated: standardized mean difference [SMD] = -.63, 95%CI = -.95,-.30; teacher-rated: SMD = -.81, 95%CI = -1.43,-.19) and inattention (parent-rated: SMD = -.36, 95%CI = -.64,-.07; teacher-rated: SMD = -.30, 95%CI = -.49,-.11). Atomoxetine reduced inattention (parent-rated: SMD = -.54, 95%CI = -.98,-.09; teacher/investigator-rated: SMD = -0.38, 95%CI = -0.75, -0.01) and parent-rated hyperactivity (parent-rated: SMD = -.49, 95%CI = -.76,-.23; teacher-rated: SMD = -.43, 95%CI = -.92, .06). Indirect evidence for significant reductions in hyperactivity with second-generation antipsychotics was also found. Quality of evidence for all interventions was low/very low. Methylphenidate was associated with a nonsignificant elevated risk of dropout due to adverse events. CONCLUSIONS: Direct pooled evidence supports the efficacy and tolerability of methylphenidate or atomoxetine for treatment of ADHD symptoms in children and youth with ASD. The current review highlights the efficacy of standard ADHD pharmacotherapy for treatment of ADHD symptoms in children and youth with ASD. Consideration of the benefits weighed against the limitations of safety/efficacy data and lack of data evaluating long-term continuation is undertaken to help guide clinical decision-making regarding treatment of co-occurring ADHD symptoms in children and youth with ASD.

Commentary: 'Camouflaging' in autistic people - reflection on Fombonne (2020).

Fombonne's (2020) editorial is a thought-provoking appraisal of the literature on 'camouflaging', whereby some autistic people mask or compensate for their autistic characteristics as an attempt to fit in and to cope with disabilities under neurotypical social norms. Fombonne (2020) highlights three issues of contention: (a) construct validity and measurement of camouflaging; (b) camouflaging as a reason for late autism diagnosis in adolescence/adulthood; and (c) camouflaging as a feature of the 'female autism phenotype'. Here, we argue that (a) establishing construct validity and measurement of different aspects of camouflaging is warranted; (b) subjective experiences are important for the differential diagnosis of autism in adolescence/adulthood; and (c) camouflaging is not necessarily a feature of autism in female individuals - nevertheless, taking into account sex and gender influences in development is crucial to understand behavioural manifestations of autism. Future research and clinical directions should involve clarification of associated constructs and measurements, demography, mechanisms, impact (including harms and benefits) and tailored support.

Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium.

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.

Sex Differences in Variability of Brain Structure Across the Lifespan.

Several brain disorders exhibit sex differences in onset, presentation, and prevalence. Increased understanding of the neurobiology of sex-based differences in variability across the lifespan can provide insight into both disease vulnerability and resilience. In n = 3069 participants, from 8 to 95 years of age, we found widespread greater variability in males compared with females in cortical surface area and global and subcortical volumes for discrete brain regions. In contrast, variance in cortical thickness was similar for males and females. These findings were supported by multivariate analysis accounting for structural covariance, and present and stable across the lifespan. Additionally, we examined variability among brain regions by sex. We found significant age-by-sex interactions across neuroimaging metrics, whereby in very early life males had reduced among-region variability compared with females, while in very late life this was reversed. Overall, our findings of greater regional variability, but less among-region variability in males in early life may aid our understanding of sex-based risk for neurodevelopmental disorders. In contrast, our findings in late life may provide a potential sex-based risk mechanism for dementia.

BDNF-Dependent Effects on Amygdala-Cortical Circuitry and Depression Risk in Children and Youth.

The brain-derived neurotrophic factor (BDNF) is critical for brain development, and the functional BDNF Val66Met polymorphism is implicated in risk for mood disorders. The objective of this study was to determine how the Val66Met polymorphism influences amygdala-cortical connectivity during neurodevelopment and assess the relevance for mood disorders. Age- and sex-specific effects of the BDNF Val66Met polymorphism on amygdala-cortical connectivity were assessed by examining covariance of amygdala volumes with thickness throughout the cortex in a sample of Caucasian youths ages 8-22 that were part of the Philadelphia Neurodevelopmental Cohort (n = 339). Follow-up analyses assessed corresponding BDNF genotype effects on resting-state functional connectivity (n = 186) and the association between BDNF genotype and major depressive disorder (MDD) (n = 2749). In adolescents, amygdala-cortical covariance was significantly stronger in Met allele carriers compared with Val/Val homozygotes in amygdala-cortical networks implicated in depression; these differences were driven by females. In follow-up analyses, the Met allele was also associated with stronger resting-state functional connectivity in adolescents and increased likelihood of MDD in adolescent females. The BDNF Val66Met polymorphism may confer risk for mood disorders in females through effects on amygdala-cortical connectivity during adolescence, coinciding with a period in the lifespan when onset of depression often occurs, more commonly in females.

Development of short-range white matter in healthy children and adolescents.

Neural communication is facilitated by intricate networks of white matter (WM) comprised of both long and short range connections. The maturation of long range WM connections has been extensively characterized, with projection, commissural, and association tracts showing unique trajectories with age. There, however, remains a limited understanding of age-related changes occurring within short range WM connections, or U-fibers. These connections are important for local connectivity within lobes and facilitate regional cortical function and greater network economy. Recent studies have explored the maturation of U-fibers primarily using cross-sectional study designs. Here, we analyzed diffusion tensor imaging (DTI) data for healthy children and adolescents in both a cross-sectional (n = 78; mean age = 13.04 ± 3.27 years) and a primarily longitudinal (n = 26; mean age = 10.78 ± 2.69 years) cohort. We found significant age-related differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) across the frontal, parietal, and temporal lobes of participants within the cross-sectional cohort. By contrast, we report significant age-related differences in only FA for participants within the longitudinal cohort. Specifically, larger FA values were observed with age in frontal, parietal, and temporal lobes of the left hemisphere. Our results extend previous findings restricted to long range WM to demonstrate regional changes in the microstructure of short range WM during childhood and adolescence. These changes possibly reflect continued myelination and axonal organization of short range WM with increasing age in more anterior regions of the left hemisphere. Hum Brain Mapp 39:204-217, 2018. © 2017 Wiley Periodicals, Inc.

Developmental changes in organization of structural brain networks.

Recent findings from developmental neuroimaging studies suggest that the enhancement of cognitive processes during development may be the result of a fine-tuning of the structural and functional organization of brain with maturation. However, the details regarding the developmental trajectory of large-scale structural brain networks are not yet understood. Here, we used graph theory to examine developmental changes in the organization of structural brain networks in 203 normally growing children and adolescents. Structural brain networks were constructed using interregional correlations in cortical thickness for 4 age groups (early childhood: 4.8-8.4 year; late childhood: 8.5-11.3 year; early adolescence: 11.4-14.7 year; late adolescence: 14.8-18.3 year). Late childhood showed prominent changes in topological properties, specifically a significant reduction in local efficiency, modularity, and increased global efficiency, suggesting a shift of topological organization toward a more random configuration. An increase in number and span of distribution of connector hubs was found in this age group. Finally, inter-regional connectivity analysis and graph-theoretic measures indicated early maturation of primary sensorimotor regions and protracted development of higher order association and paralimbic regions. Our finding reveals a time window of plasticity occurring during late childhood which may accommodate crucial changes during puberty and the new developmental tasks that an adolescent faces.

Disrupted Maturation of White Matter Microstructure After Concussion Is Associated With Internalizing Behavior Scores in Female Children.

BACKGROUND: Some children who experience concussions, particularly females, develop long-lasting emotional and behavioral problems. Establishing the potential contribution of preexisting behavioral problems and disrupted white matter maturation has been challenging due to a lack of preinjury data. METHODS: From the Adolescent Brain Cognitive Development cohort, 239 (90 female) children age 12.1 ± 0.6 years who experienced a concussion after study entry at 10.0 ± 0.6 years were compared to 6438 (3245 female) children without head injuries who were age 9.9 ± 0.6 years at baseline and 12.0 ± 0.6 years at follow-up. The Child Behavior Checklist was used to assess internalizing and externalizing behavior at study entry and follow-up. In the children with magnetic resonance imaging data available (concussion n = 134, comparison n = 3520), deep and superficial white matter was characterized by neurite density from restriction spectrum image modeling of diffusion magnetic resonance imaging. Longitudinal ComBat harmonization removed scanner effects. Linear regressions modeled 1) behavior problems at follow-up controlling for baseline behavior, 2) impact of concussion on white matter maturation, and 3) contribution of deviations in white matter maturation to postconcussion behavior problems. RESULTS: Only female children with concussion had higher internalizing behavior problem scores. The youngest children with concussion showed less change in superficial white matter neurite density over 2 years than children with no concussion. In females with concussion, less change in superficial white matter neurite density was correlated with increased internalizing behavior problem scores. CONCLUSIONS: Concussions in female children are associated with emotional problems beyond preinjury levels. Injury to superficial white matter may contribute to persistent internalizing behavior problems in females.

Bifactor models of psychopathology using multi-informant and multi-instrument dimensional measures in the ABCD study.

Background: Due to limitations of categorical definitions of mental illness, there is a need for quantitative empirical investigations of the dimensional structure of psychopathology. Using exploratory bifactor methods, this study investigated a comprehensive and representative structure of psychopathology in children to better understand how psychotic-like experiences (PLEs), autism spectrum disorder (ASD) symptoms, impulsivity, and sensitivity to reward and punishment, may be integrated into extant general factor models of psychopathology. Methods: We used seven child-report and three parent-report instruments capturing diverse mental health symptoms in 11,185 children aged 9-10 from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study. We built on previous modeling frameworks by conducting both split sample and full sample factor analytic approaches that harnessed recent methodological advances in bifactor exploratory structural equation modeling (B-ESEM) to examine a wide range of psychopathology measures not previously integrated into a single analysis. Validity of psychopathology dimensions was examined by investigating associations with sex, age, cognition, imaging measures, and medical service usage. Results: All four factor analytic models showed excellent fit and similar structure within informant. PLEs loaded most highly onto a general psychopathology factor, suggesting that they may reflect non-specific risk for mental illness. ASD symptoms loaded separately from attention/hyperactivity symptoms. Symptoms of impulsivity and sensitivity to reward and punishment loaded onto specific factors, distinct from externalizing and internalizing factors. All identified factors were associated with clinically relevant risk factors, providing preliminary evidence for their construct validity. Conclusion: By integrating diverse child-report and parent-report psychopathology measures for children in the ABCD sample, we deliver data on the quantitative structure of psychopathology for an exceptionally large set of measurements and discuss implications for the field.

A Shared Multivariate Brain-Behavior Relationship in a Transdiagnostic Sample of Adolescents.

BACKGROUND: Internalizing and externalizing psychopathology typically present in early childhood and can have negative implications on general functioning and quality of life. Prior work has linked increased psychopathology symptoms with altered brain structure. Multivariate analysis such as partial least squares correlation can help identify patterns of covariation between brain regions and psychopathology symptoms. This study examined the relationship between gray matter volume (GMV) and psychopathology symptoms in adolescents with various psychiatric diagnoses. METHODS: Structural magnetic resonance imaging data were collected from 490 participants with various internalizing and externalizing diagnoses (197 female/293 male; age = 14.68 ± 2.35 years; IQ = 104.05 ± 13.11). Cortical and subcortical volumes were parcellated using the Desikan-Killiany atlas. Partial least squares correlation was used to identify multivariate linear relationships between GMV and the Strength and Difficulties Questionnaire difficulties domains (emotional, peer, conduct, and hyperactivity issues). Resampling approaches were used to determine significance (permutation test), stability (bootstrap resampling), and reproducibility (split-half resampling) of identified relationships. RESULTS: We found a significant, stable, and largely reproducible dimension that linked lower Strength and Difficulties Questionnaire scores (less impairment) across all difficulties domains with greater widespread GMV (singular value = 1.17, accounts for 87.1% of the covariance; p < .001). This dimension emphasized the relationship between lower conduct problems and greater GMV in frontotemporal regions. CONCLUSIONS: Our results indicate that the most significant and stable brain-behavior relationship in a transdiagnostic sample is a domain-general relationship, linking lower psychopathology symptom scores to greater global GMV. This finding suggests that a shared brain-behavior relationship may be present across adolescents with and without clinically significant psychopathology symptoms.

Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample.

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.

Clinical neuroscience research is going through a translational crisis largely due to the challenges of producing meaningful and generalizable results. Two critical limitations within clinical neuroscience research are the use of univariate statistics and between-study methodological variation. Univariate statistics may not be sensitive enough to detect complex relationships between several variables, and methodological variation poses challenges to the generalizability of the results. We compared two widely used multivariate statistical approaches, canonical correlations analysis (CCA) and partial least squares correlation (PLS), to determine the generalizability and stability of their solutions. We show that the properties of the measures inputted into the analysis likely play a more substantial role in the generalizability and stability of results compared to the specific approach applied (i.e., CCA or PLS).