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Objectives: Children with externalizing disorders commonly show emotion dysregulation and callous-unemotional (CU) traits. However, it is unclear whether emotion dysregulation and CU traits share underlying neurobiology that can be predictive of psychosocial treatment outcomes. In this preliminary study, we examined neural correlates of externalizing psychopathology dimensions and their prediction of treatment outcomes., Methods: We analyzed a pilot sample of 17 children with an externalizing disorder (9-12 years; 10.45 ± 1.02) who underwent structural magnetic resonance imaging (MRI) before participating in a 15-week psychosocial group intervention targeting conduct problems. We examined cross-sectional associations between emotion dysregulation or CU traits and cortical thickness (anterior cingulate cortex [ACC] and insula) and amygdala volume at baseline. We then examined whether the pre-treatment brain-behaviour relationships were linked to reduction in conduct problems post-treatment., Results: Lower ACC and insula thickness as well as amygdala volume was associated with greater levels of emotion dysregulation and CU traits at baseline (pre-treatment, r  = |0.36-0.61|). There was a significant three-way interaction between emotion dysregulation/CU traits, left insula/right rostral ACC, and treatment (pre/post; β = -1.01 to 3.6). Overall, greater baseline insular and rostral ACC thickness was related to reductions in conduct problems following group-based psychosocial intervention regardless of baseline emotion dysregulation and CU trait levels., Conclusions: The results provide preliminary evidence of shared neural signatures underlying both emotion dysregulation and CU traits. Additionally, alterations in frontolimbic brain structure may be useful predictors of pre-treatment associations with externalizing psychopathology dimensions and post-treatment behavioural outcomes., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Externalizing psychopathology in childhood is a predictor of poor outcomes across the lifespan. Children exhibiting elevated externalizing symptoms also commonly show emotion dysregulation and callous-unemotional (CU) traits. Examining cross-sectional and longitudinal neural correlates across dimensions linked to externalizing psychopathology during childhood may clarify shared or distinct neurobiological vulnerability for psychopathological impairment later in life. We used tabulated brain structure and behavioural data from baseline, year 1, and year 2 timepoints of the Adolescent Brain Cognitive Development Study (ABCD; baseline n = 10,534). We fit separate linear mixed effect models to examine whether baseline brain structures in frontolimbic and striatal regions (cortical thickness or subcortical volume) were associated with externalizing symptoms, emotion dysregulation, and/or CU traits at baseline and over a two-year period. The most robust relationships found at the cross-sectional level was between cortical thickness in the right rostral middle frontal gyrus and bilateral pars orbitalis was positively associated with CU traits (β = |0.027-0.033|, p corrected = 0.009-0.03). Over the two-year follow-up period, higher baseline cortical thickness in the left pars triangularis and rostral middle frontal gyrus predicted greater decreases in externalizing symptoms ((F = 6.33-6.94, p corrected  = 0.014). The results of the current study suggest that unique regions within frontolimbic and striatal networks may be more strongly associated with different dimensions of externalizing psychopathology. The longitudinal findings indicate that brain structure in early childhood may provide insight into structural features that influence behaviour over time., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Background: Patients with early psychosis (EP) (within 3 years after psychosis onset) show significant variability, which makes predicting outcomes challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, which limits the development of early interventions., Methods: A data-driven approach, partial least squares correlation, was used across 2 independent datasets to examine multivariate relationships between white matter properties and symptomatology and to identify stable and generalizable signatures in EP. The primary cohort included patients with EP from the Human Connectome Project for Early Psychosis (n = 124). The replication cohort included patients with EP from the Feinstein Institute for Medical Research (n = 78) as part of the MEND (Multimodal Evaluation of Neural Disorders) Project. Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders., Results: In both cohorts, a significant latent component corresponded to a symptom profile that combined negative symptoms, primarily diminished expression, with specific somatic symptoms. Both latent components captured comprehensive features of white matter disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the partial least squares model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use., Conclusions: This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural white matter alterations in EP across diagnoses and datasets, showing strong covariance of these alterations with a unique profile of negative and somatic symptoms. These findings suggest the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objective: Conceptual similarities between depressive and negative symptoms complicate biomarker and intervention development. This study employed a data-driven approach to delineate the neural circuitry underlying depressive and negative symptoms in schizophrenia spectrum disorders (SSDs)., Methods: Data from three studies were analyzed (157 participants with SSDs) to assess brain-behavior relationships: two neuroimaging studies and a randomized trial of repetitive transcranial magnetic stimulation (rTMS). Partial least squares correlation (PLSC) was used to investigate associations between resting-state functional connectivity and depressive and negative symptoms. Secondary analyses of rTMS trial data (active, N=37; sham, N=33) were used to assess relationships between PLSC-derived symptom profiles and treatment outcomes., Results: PLSC identified three latent variables (LVs) relating functional brain circuitry with symptom profiles. LV1 related a general depressive symptom factor with positive associations between and within the default mode network (DMN), the frontoparietal network (FPN), and the cingulo-opercular network (CON). LV2 related negative symptoms (no depressive symptoms) via negative associations, especially between the FPN and the CON, but also between the DMN and the FPN and the CON. LV3 related a guilt and early wakening depression factor via negative rather than positive associations with the DMN, FPN, and CON. The secondary visual network had a positive association with general depressive symptoms and negative associations with guilt and negative symptoms. Active (but not sham) rTMS applied bilaterally to the dorsolateral prefrontal cortex (DLPFC) reduced general depressive but not guilt-related or negative symptoms., Conclusions: The results clearly differentiate the neural circuitry underlying depressive and negative symptoms, and segregated across the two-factor structure of depression in SSDs. These findings support divergent neurobiological pathways of depressive symptoms and negative symptoms in people with SSDs. As treatment options are currently limited, bilateral rTMS to the DLPFC is worth exploring further for general depressive symptoms in people with SSDs.

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. LV 1 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). LV 1 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., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2024 Massachusetts Institute of Technology.)

Background: Early Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions., Methods: A data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders., Results: In both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use., Conclusions: This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

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., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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 (r tet =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., Competing Interests: Declaration of Competing Interest Other authors report no related funding support, financial or potential conflicts of interest., (Copyright © 2023. Published by Elsevier Inc.)

Early-life adversity has profound consequences for youth neurodevelopment and adjustment; however, experiences of adversity are heterogeneous and interrelated in complex ways that can be difficult to operationalize and organize in developmental research. We sought to characterize the underlying dimensional structure of co-occurring adverse experiences among a subset of youth (ages 9-10) from the Adolescent Brain Cognitive Development (ABCD) Study (N = 7115), a community sample of youth in the United States. We identified 60 environmental and experiential variables that reflect adverse experiences. Exploratory factor analysis identified 10 robust dimensions of early-life adversity co-occurrence, corresponding to conceptual domains such as caregiver substance use and biological caregiver separation, caregiver psychopathology, caregiver lack of support, and socioeconomic disadvantage / neighborhood lack of safety. These dimensions demonstrated distinct associations with internalizing problems, externalizing problems, cognitive flexibility, and inhibitory control. Non-metric multidimensional scaling characterized qualitative similarity among the 10 identified dimensions. Results supported a nonlinear three-dimensional structure representing early-life adversity, including continuous gradients of "perspective", "environmental uncertainty", and "acts of omission/commission". Our findings suggest that there are distinct dimensions of early-life adversity co-occurrence in the ABCD sample at baseline, and the resulting dimensions may have unique implications for neurodevelopment and youth behavior., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Canonical Correlation Analysis (CCA) and Partial Least Squares Correlation (PLS) detect associations between two data matrices based on computing a linear combination between the two matrices (called latent variables; LVs). These LVs maximize correlation (CCA) and covariance (PLS). These different maximization criteria may render one approach more stable and reproducible than the other when working with brain and behavioural data at the population-level. This study compared the LVs which emerged from CCA and PLS analyses of brain-behaviour relationships from the Adolescent Brain Cognitive Development (ABCD) dataset and examined their stability and reproducibility., Methods: Structural T1-weighted imaging and behavioural data were accessed from the baseline Adolescent Brain Cognitive Development dataset ( N > 9000, ages = 9-11 years). The brain matrix consisted of cortical thickness estimates in different cortical regions. The behavioural matrix consisted of 11 subscale scores from the parent-reported Child Behavioral Checklist (CBCL) or 7 cognitive performance measures from the NIH Toolbox. CCA and PLS models were separately applied to the brain-CBCL analysis and brain-cognition analysis. A permutation test was used to assess whether identified LVs were statistically significant. A series of resampling statistical methods were used to assess stability and reproducibility of the LVs., Results: When examining the relationship between cortical thickness and CBCL scores, the first LV was found to be significant across both CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV 1 from the CCA model found that covariation of CBCL scores was linked to covariation of cortical thickness. LV 1 from the PLS model identified decreased cortical thickness linked to lower CBCL scores. There was limited evidence of stability or reproducibility of LV 1 for both CCA and PLS. When examining the relationship between cortical thickness and cognitive performance, there were 6 significant LVs for both CCA and PLS ( p < .01). The first LV showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001)., Conclusion: CCA and PLS identify different brain-behaviour relationships with limited stability and reproducibility when examining the relationship between cortical thickness and parent-reported behavioural measures. However, both methods identified relatively similar brain-behaviour relationships that were stable and reproducible when examining the relationship between cortical thickness and cognitive performance. The results of the current study suggest that stability and reproducibility of brain-behaviour relationships identified by CCA and PLS are influenced by characteristics of the analyzed sample and the included behavioural measurements when applied to a large pediatric dataset., Competing Interests: Funding, Disclosures, and conflict of interest: HN has received funding from the CAMH Discovery Fund, Ontario Graduate Scholarship, Fulbright Canada, and currently receives funding from the Canadian Institutes of Health Research (CIHR) Doctoral Award. CH current receives funding from the National Institute of Mental Health (NIMH), The CAMH Foundation, Natural Sciences and Engineering Research Council of Canada (NSERC). ANV currently receives funding from the NIMH, CIHR, Canada Foundation for Innovation, CAMH Foundation, and University of Toronto. M-CL receives funding from CIHR, the Academic Scholars Award from the Department of Psychiatry, University of Toronto, and CAMH Foundation. ALW currently receives funding from CIHR, Brain Canada Foundation, and NSERC. ARM currently receives funding from NSERC and CIHR research grant. SHA currently receives funding from the NIMH, CIHR, the Academic Scholars Award from the Department of Psychiatry, University of Toronto, and the CAMH Foundation. Other authors report no related funding support, financial or potential conflicts of interest.

Background: Externalizing and internalizing behaviors contribute to clinical impairment in children with neurodevelopmental disorders (NDDs). Although associations between externalizing or internalizing behaviors and cortico-amygdalar connectivity have been found in clinical and non-clinical pediatric samples, no previous study has examined whether similar shared associations are present across children with different NDDs., Methods: Multi-modal neuroimaging and behavioral data from the Province of Ontario Neurodevelopmental Disorders (POND) Network were used. POND participants aged 6-18 years with a primary diagnosis of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or obsessive-compulsive disorder (OCD), as well as typically developing children (TDC) with T1-weighted, resting-state fMRI or diffusion weighted imaging (DWI) and parent-report Child Behavioral Checklist (CBCL) data available, were analyzed (total n = 346). Associations between externalizing or internalizing behavior and cortico-amygdalar structural and functional connectivity indices were examined using linear regressions, controlling for age, gender, and image-modality specific covariates. Behavior-by-diagnosis interaction effects were also examined., Results: No significant linear associations (or diagnosis-by-behavior interaction effects) were found between CBCL-measured externalizing or internalizing behaviors and any of the connectivity indices examined. Post-hoc bootstrapping analyses indicated stability and reliability of these null results., Conclusions: The current study provides evidence towards an absence of a shared linear relationship between internalizing or externalizing behaviors and cortico-amygdalar connectivity properties across a transdiagnostic sample of children with different primary NDD diagnoses and TDC. Different methodological approaches, including incorporation of multi-dimensional behavioral data (e.g., task-based fMRI) or clustering approaches may be needed to clarify complex brain-behavior relationships relevant to externalizing/internalizing behaviors in heterogeneous clinical NDD populations., (© 2022. The Author(s).)

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process., (Copyright © 2021. Published by Elsevier Inc.)