cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15154/1528635
title: Multimodal neural correlates of childhood psychopathology

description: Background: The complexity of neurodevelopmental changes that occur during typical and atypical neurodevelopment, particularly during late childhood and early adolescence, advocate for multidimensional approaches to better understand the risk of developing psychopathology.

Methods: Using partial least squares analysis, a multivariate data-driven approach, we simultaneously examined structural and functional brain patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development (ABCD) dataset, a large community-based cohort of typically developing children aged 9-11 years old (Discovery sample: N=3,504; Replication sample: N=1,747). We combined multiple MRI-based measures of brain structure (i.e., cortical surface area, thickness, volume) and resting-state functional connectivity, while psychopathology in children was characterized using parent-reported Child Behavior Checklist scores. Post hoc analyses in smaller subsamples incorporated diffusion MRI measures of fiber architecture (i.e., fractional anisotropy, mean diffusivity) and explored functional connectivity during tasks tapping into executive and reward processes. 

Results: Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining the most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and behavior, as well as the risk for psychopathology. Our findings were validated in a replication sample and were robust to several control analyses.

Conclusion: Our study identified several dimensions of psychopathology alongside their structural and functional neural substrates. These findings may help in better understanding the biological mechanisms underpinning dimensions of psychopathology during development, which might be used to identity vulnerability markers.
DOI: 10.15154/1528639
title: A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder

description: Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.
DOI: 10.15154/1528640
title: A novel neighborhood rough set-based feature selection method and its application to biomarker identification of schizophrenia

description: Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.
DOI: 10.15154/1528648
title: Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition

description: Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman’s H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.
DOI: 10.15154/1528653
title: Association of adverse prenatal exposure burden with child psychopathology in the Adolescent Brain Cognitive Development (ABCD) Study

description: Objective: Numerous adverse prenatal exposures have been individually associated with risk for psychiatric illness in the offspring. However, such exposures frequently co-occur, raising questions about their cumulative impact. We evaluated effects of cumulative adverse prenatal exposure burden on psychopathology risk in school-aged children.

Methods: Using baseline surveys from the U.S.-based Adolescent Brain and Cognitive Development (ABCD) Study (7,898 non-adopted, unrelated children from 21 sites, age 9–10, and their primary caregivers), we examined 8 retrospectively-reported adverse prenatal exposures in relation to caregiver-reported total and subscale Child Behavior Checklist (CBCL) scores. We also assessed cumulative effects of these factors on CBCL total as a continuous measure, as well as on odds of clinically significant psychopathology (CBCL total ≥60), in both the initial set and a separate ABCD sample comprising an additional 696 sibling pairs. Analyses were conducted before and after adjustment for 14 demographic and environmental covariates.

Results: In minimally and fully adjusted models, 6 exposures (unplanned pregnancy; maternal alcohol, marijuana, and tobacco use early in pregnancy; pregnancy complications; and birth complications) independently associated with significant but small increases in CBCL total score. Among these 6, none increased the odds of crossing the threshold for clinically significant symptoms by itself. However, odds of exceeding this threshold became significant with 2 exposures (OR = 1.86, 95% CI 1.47–2.36), and increased linearly with each level of exposure (OR = 1.39, 95% CI 1.31–1.47), up to 3.53-fold for ≥4 exposures versus none. Similar effects were observed in confirmatory analysis among siblings. Within sibling pairs, greater discordance for exposure load associated with greater CBCL total differences, suggesting that results were not confounded by unmeasured family-level effects.

Conclusion: Children exposed to multiple common, adverse prenatal events showed dose-dependent increases in broad, clinically significant psychopathology at age 9–10. Fully prospective studies are needed to confirm and elaborate upon this pattern.
DOI: 10.15154/1528655
title: Longitudinally stable, brain-based predictive models mediate the relationships between childhood cognition and socio-demographic, psychological and genetic factors. 

description: Cognitive abilities are one of the major transdiagnostic domains in the National Institute of Mental Health's Research Domain Criteria (RDoC). Following RDoC's integrative approach, we aimed to develop brain-based predictive models for cognitive abilities that (a) are developmentally stable over years during adolescence and (b) account for the relationships between cognitive abilities and socio-demographic, psychological and genetic factors. For this, we leveraged the unique power of the large-scale, longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (n ~ 11 k) and combined MRI data across modalities (task-fMRI from three tasks: resting-state fMRI, structural MRI and DTI) using machine-learning. Our brain-based, predictive models for cognitive abilities were stable across 2 years during young adolescence and generalisable to different sites, partially predicting childhood cognition at around 20% of the variance. Moreover, our use of ‘opportunistic stacking’ allowed the model to handle missing values, reducing the exclusion from around 80% to around 5% of the data. We found fronto-parietal networks during a working-memory task to drive childhood-cognition prediction. The brain-based, predictive models significantly, albeit partially, accounted for variance in childhood cognition due to (1) key socio-demographic and psychological factors (proportion mediated = 18.65% [17.29%–20.12%]) and (2) genetic variation, as reflected by the polygenic score of cognition (proportion mediated = 15.6% [11%–20.7%]). Thus, our brain-based predictive models for cognitive abilities facilitate the development of a robust, transdiagnostic research tool for cognition at the neural level in keeping with the RDoC's integrative framework.
DOI: 10.15154/1528658
title: Gray matter volumetric correlates of dimensional impulsivity traits in children: Sex differences and heritability

description: Previous research investigated the cerebral volumetric correlates of impulsivity largely in moderate‐sized samples and few have examined the distinct correlates of dimensions of impulsivity, sex differences, or heritability of the correlates. Here, we performed voxel‐based morphometry analysis of data (n = 11,474; 5,452 girls, 9–10 years) curated from the Adolescent Brain Cognition Development project. In a linear regression with all five UPPS‐P subscores as regressors and age in months, total intracranial volume, study site, and scanner model as covariates, higher levels of lack of premeditation, and sensation seeking were correlated with larger cortical and subcortical gray matter volumes (GMVs). In contrast, higher positive urgency was correlated with smaller GMVs in many of the same regions. The dimensional impulsivity traits also involved distinct volumetric correlates, with, for instance, sensation seeking and positive urgency specifically implicating bilateral caudate head/mid‐cingulate cortex and bilateral lateral orbitofrontal cortex/left precentral gyrus, respectively. Boys relative to girls scored higher in all impulsivity dimensions. Girls relative to boys showed significantly stronger positive and negative correlations between sensation seeking and insula, putamen, and inferior frontal gyrus (IFG) GMVs and between positive urgency and cingulate cortex, insula, and IFG GMVs, respectively. With a subsample of twins, the dimensional impulsivity traits were weakly to moderately heritable in both girls and boys, and the GMV correlates were highly heritable in girls and boys combined. These findings collectively suggest shared and nonshared as well as sex differences in the cerebral volumetric bases of dimensional impulsivity traits and may facilitate research of externalizing psychopathology in children.
DOI: 10.15154/1528659
title: Gray matter volumetric correlates of attention deficit and hyperactivity traits in emerging adolescents

description: Previous research has demonstrated reduction in cortical and subcortical, including basal ganglia (BG), gray matter volumes (GMV) in individuals with attention deficit hyperactivity disorder (ADHD), a neurodevelopmental condition that is more prevalent in males than in females. However, the volumetric deficits vary across studies. Whether volumetric reductions are more significant in males than females; to what extent these neural markers are heritable and relate to cognitive dysfunction in ADHD remain unclear. To address these questions, we followed published routines and performed voxel-based morphometry analysis of a data set (n = 11,502; 5,464 girls, 9–10 years) curated from the Adolescent Brain Cognition Development project, a population-based study of typically developing children. Of the sample, 634 and 2,826 were identified as monozygotic twins and dizygotic twins/siblings, respectively. In linear regressions, a cluster in the hypothalamus showed larger GMV, and bilateral caudate and putamen, lateral orbitofrontal and occipital cortex showed smaller GMVs, in correlation with higher ADHD scores in girls and boys combined. When examined separately, boys relative to girls showed more widespread (including BG) and stronger associations between GMV deficits and ADHD scores. ADHD traits and the volumetric correlates demonstrated heritability estimates (a2) between 0.59 and 0.79, replicating prior findings of the genetic basis of ADHD. Further, ADHD traits and the volumetric correlates (except for the hypothalamus) were each negatively and positively correlated with N-back performance. Together, these findings confirm volumetric deficits in children with more prominent ADHD traits. Highly heritable in both girls and boys and potentially more significant in boys than in girls, the structural deficits underlie diminished capacity in working memory and potentially other cognitive deficits in ADHD.
DOI: 10.15154/1528660
title: Sex-different interrelationships of rs945270, cerebral gray matter volumes, and attention deﬁcit hyperactivity disorder: a region-wide study across brain

description: Previous genome-wide association studies (GWAS) reported that the allele C of rs945270 of the kinectin 1 gene (KTN1) most significantly increased the gray matter volume (GMV) of the putamen and modestly regulated the risk for attention deficit hyperactivity disorder (ADHD). On the other hand, ADHD is known to be associated with a reduction in subcortical and cortical GMVs. Here, we examined the interrelationships of the GMVs, rs945270 alleles, and ADHD symptom scores in the same cohort of children. With data of rs945270 genotypes, GMVs of 118 brain regions, and ADHD symptom scores of 3372 boys and 3129 girls of the Adolescent Brain Cognition Development project, we employed linear regression analyses to examine the pairwise correlations adjusted for the third of the three traits and other relevant covariates, and examine their mediation effects. We found that the major allele C of rs945270 modestly increased risk for ADHD in males only when controlling for the confounding effects of the GMV of any one of the 118 cerebral regions (0.026 ≤ p ≤ 0.059: Top two: left and right putamen). This allele also significantly increased putamen GMV in males alone (left p = 2.8 × 10−5, and right p = 9.4 × 10−5; α = 2.1 × 10−4) and modestly increased other subcortical and cortical GMVs in both sexes (α < p < 0.05), whether or not adjusted for ADHD symptom scores. Both subcortical and cortical GMVs were significantly or suggestively reduced in ADHD when adjusted for rs945270 alleles, each more significantly in females (3.6 × 10−7 ≤ p < α; Top two: left pallidum and putamen) and males (3.5 × 10−6 ≤ p < α), respectively. Finally, the left and right putamen GMVs reduced 14.0% and 11.7% of the risk effects of allele C on ADHD, and allele C strengthened 4.5% (left) and 12.2% (right) of the protective effects of putamen GMVs on ADHD risk, respectively. We concluded that the rs945270-GMVs-ADHD relationships were sex-different. In males, the major allele C of rs945270 increased risk for ADHD, which was compromised by putamen GMVs; this allele also but only significantly increased putamen GMVs that then significantly protected against ADHD risk. In females, the top two GMVs significantly decreasing ADHD risk were left pallidum and putamen GMVs. Basal ganglia the left putamen in particular play the most critical role in the pathogenesis of ADHD.
DOI: 10.15154/1528662
title: The relationship between Misuse/Abuse of Treatment and Quality of Life among Adults 18 and Over with Bipolar Disorder by Race/Ethnicity

description: The objective of the study was to determine the relationship of misuse/abuse of pharmacological treatment and the quality of life by race/ethnicity for adults 18 and over with bipolar disorder. This was an analysis of secondary data from the Step BD project, which is a longitudinal controlled trial. Analysis was of baseline data and included 2576 subjects over the age of 18 with data for all variables of interest. The main independent variable was treatment adherence, based on reported adherence to prescribed medications. Non-adherence was defined as taking <75% of prescribed medications. Outcomes included quality of life, depression, and hopelessness, as assessed by the Quality of Life Enjoyment and Satisfaction Questionnaire, Montgomery-Asberg Depression Rating Scale, and Beck Hopelessness Scale, respectively. Descriptive and bivariate statistics were used to describe the sample, and multivarible regression analyses were used to determine the association between treatment adherence and outcomes, correcting for sociodemographics, co-morbid physical and mental health conditions, and use of psychosocial services. The majority of the sample identified as White, with approximately 5% identifying as Black/African American and 5% identifying as Hispanic. 89% of participants were adherent to prescription medication. Adherence did not differ by racial/ethnic group. In multivariable analysis, poor medication adherence was associated with a poorer quality of life score only among White patients (p=0.0014). Medication adherence was not significantly associated with hopelessness or depression scores in any racial/ethnic groups. Overall, our results provide limited evidence for associations between treatment adherence and quality of life and negative outcomes. These findings highlight the need for treatment strategies beyond pharmaceutical medication to improve the lives of people living with bipolar disorder, particularly those of minoritized racial/ethnic groups.
DOI: 10.15154/1528663
title: Genetic liability to major psychiatric disorders contributes to multi-faceted quality of life outcomes in children and adults

description: Importance
Psychiatric disorders are highly heritable. Under a liability threshold model, an important question arises as to what extent genetic liability for psychiatric disorders relates to, and possibly impacts on, different aspects of quality of life in the general population. 
Objective
To characterize the link between psychiatric genetic liability and diverse aspects of quality of life in childhood and adulthood.
Design, setting, and participants
The study utilized data from two population-based cohorts, namely preadolescent children from the Adolescent Brain Cognitive Development (ABCD) study (N=4,645) and white British adults from the UK Biobank study (N=377,664). 
Main outcomes and measures
The genetic liabilities to seven major psychiatric disorders were quantified by a set of polygenic scores (PGSs) derived from the largest genome-wide association studies to date. General linear models assessed associations between PGSs and the estimated latent quality of life factors, controlling for age, sex, site, genotyping batch, plate, and genetic ancestry.
Results
In each cohort, we estimated three latent factors indexing distinct but correlated quality of life domains (i.e., educational performance and cognition (Edu), physical health (Hea), and peer experience (Peer) in ABCD cohort; social economic status (SES), physical health (Hea), and social well-being (Soc) in UK Biobank), in addition to a general factor that captured the covariances between the three latent factors (QoL). In the ABCD cohort, ADHD-PGS was significantly associated with Edu, Peer, and general QoL (β=-0.094~-0.140, p<7.81×10-9) factors. In UK Biobank, all examined disorder PGSs were significantly associated with the general QoL factor and at least one first-order subdomain, with ADHD-PGS (β=-0.056~-0.096, p<5.91×10-186) and MDD-PGS (β=-0.042~-0.066, p<3.63×10-125) showing the largest effects. 
Conclusions and relevance
The present study reveals an inverse relationship between psychiatric genetic liabilities and multiple quality of life metrics, with ADHD-associated genetic risk being the main contributor in both children and adults, and MDD additionally showing effects in adults. All effect sizes observed were small, as expected. Understanding potential real-world outcomes of disorder-related genetic risks in the general population could have significant implications for healthcare, research, and society as a whole.