Identification of neurobehavioural symptom groups based on shared brain mechanisms

Voxelwise Multivariate Analysis of Brain-Psychosocial Associations in Adolescents Reveals 6 Latent Dimensions of Cognition and Psychopathology

BACKGROUND

Adolescence heralds the onset of considerable psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxelwise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges.

METHODS

We obtained voxelwise gray matter density and psychosocial variables from the baseline (ages 9-10 years) Adolescent Brain Cognitive Development (ABCD) Study cohort (N = 11,288) and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting.

RESULTS

We found 6 latent dimensions, 4 of which pertain specifically to mental health. The mental health dimensions were related to overeating, anorexia/internalizing, oppositional symptoms (all ps < .002) and attention-deficit/hyperactivity disorder symptoms (p = .03). Attention-deficit/hyperactivity disorder was related to increased and internalizing symptoms related to decreased gray matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms were related to increased gray matter in a noradrenergic nucleus. Internalizing symptoms were related to increased and oppositional symptoms to reduced gray matter density in the insular, cingulate, and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus gray matter in attention-deficit/hyperactivity disorder and reduced putamen gray matter in oppositional/conduct problems. Voxelwise gray matter density generated stronger brain-psychosocial correlations than brain parcellations.

CONCLUSIONS

Voxelwise brain data strengthen latent dimensions of brain-psychosocial covariation, and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing symptoms are associated with opposite gray matter changes in similar cortical and subcortical areas.

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.

A framework for a brain-derived nosology of psychiatric disorders

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.

[The German Center for Mental Health : Innovative translational research to promote prevention, targeted intervention and resilience]

BACKGROUND

Due to the high disease burden, the early onset and often long-term trajectories mental disorders are among the most widespread diseases with growing significance. The German Center for Mental Health (DZPG) was established to enhance research conditions and expedite the translation of clinically relevant findings into practice.

OBJECTIVE

The aim of the DZPG is to optimize mental healthcare in Germany, influence modifiable social causes and to develop best practice models of care for vulnerable groups. It seeks to promote mental health and resilience, combat the stigmatization associated with mental disorders, and contribute to the enhancement of treatment across all age groups.

MATERIAL AND METHODS

The DZPG employs a translational research program that accelerates the translation of basic research findings into clinical studies and general practice. University hospitals and outpatient departments, other university disciplines, and extramural research institutions are working together to establish a collaboratively coordinated infrastructure for accelerated translation and innovation.

RESEARCH PRIORITIES

The research areas encompass 1) the interaction of somatic and mental risk and resilience factors and disorders across the lifespan, 2) influencing relevant modifiable environmental factors and 3) based on this personalized prevention and intervention.

CONCLUSION

The DZPG aims to develop innovative preventive and therapeutic tools that enable an improvement in care for individuals with mental disorders. It involves a comprehensive integration of experts with experience at all levels of decision-making and employs trilogue and participatory approaches in all research projects.

Evaluation of behavioral variance/covariance explained by the neuroimaging data through a pattern-based regression

Neuroimaging data have been widely used to understand the neural bases of human behaviors. However, most studies were either based on a few predefined regions of interest or only able to reveal limited vital regions, hence not providing an overarching description of the relationship between neuroimaging and behaviors. Here, we proposed a voxel-based pattern regression that not only could investigate the overall brain-associated variance (BAV) for a given behavioral measure but could also evaluate the shared neural bases between different behaviors across multiple neuroimaging data. The proposed method demonstrated consistently high reliability and accuracy through comprehensive simulations. We further implemented this approach on real data of adolescents (IMAGEN project, n = 2089) and adults (HCP project, n = 808) to investigate brain-based variances of multiple behavioral measures, for instance, cognitive behaviors, substance use, and psychiatric disorders. Notably, intelligence-related scores showed similar high BAVs with the gray matter volume across both datasets. Further, our approach allows us to reveal the latent brain-based correlation across multiple behavioral measures, which are challenging to obtain otherwise. For instance, we observed a shared brain architecture underlying depression and externalizing problems in adolescents, while the symptom comorbidity may only emerge later in adults. Overall, our approach will provide an important statistical tool for understanding human behaviors using neuroimaging data.

Effects of 3-month dapagliflozin on left atrial function in treatment-naïve patients with type 2 diabetes mellitus: Assessment using 4-dimensional echocardiography

BACKGROUND

The sodium-glucose transporter-2 (SGLT-2) inhibitor dapagliflozin can improve left ventricular (LV) performance in patients with type 2 diabetes mellitus (T2DM). However, the effects on left atrial (LA) function in treatment-naïve T2DM patients remain unclear. The aim of our study was 1) to investigate the effects of 3-month treatment with dapagliflozin on LA function in treatment-naïve patients with T2DM using 4-dimensional automated LA quantification (4D Auto LAQ) and 2) to explore linked covariation patterns of changes in clinical and LA echocardiographic variables.

METHODS

4D Auto LAQ was used to evaluate LA volumes, longitudinal and circumferential strains in treatment-naïve T2DM patients at baseline, at follow-up, and in healthy control (HC). Sparse canonical correlation analysis (sCCA) was performed to capture the linked covariation patterns between changes in clinical and LA echocardiographic variables within the treatment-naïve T2DM patient group.

RESULTS

This study finally included 61 treatment-naïve patients with T2DM without cardiovascular disease and 39 healthy controls (HC). Treatment-naïve T2DM patients showed reduced LA reservoir and conduit function at baseline compared to HC, independent of age, sex, BMI, and blood pressure (LASr: 21.11 ± 5.39 vs. 27.08 ± 5.31 %, padjusted = 0.017; LAScd: -11.51 ± 4.48 vs. -16.74 ± 4.51 %, padjusted = 0.013). After 3-month treatment with dapagliflozin, T2DM patients had significant improvements in LA reservoir and conduit function independent of BMI and blood pressure changes (LASr: 21.11 ± 5.39 vs. 23.84 ± 5.74 %, padjusted < 0.001; LAScd: -11.51 ± 4.48 vs. -12.75 ± 4.70 %, padjusted < 0.001). The clinical and LA echocardiographic parameters showed significant covariation (r = 0.562, p = 0.039). In the clinical dataset, changes in heart rate, insulin, and BMI were most associated with the LA echocardiographic variate. In the LA echocardiographic dataset, changes in LAScd, LASr, and LASr_c were most associated with the clinical variate.

CONCLUSION

Compared with HC, treatment-naïve patients with T2DM had lower LA function, and these patients benefited from dapagliflozin administration, particularly in LA function.

Timing of lifespan influences on brain and cognition

Modifiable risk and protective factors for boosting brain and cognitive development and preventing neurodegeneration and cognitive decline are embraced in neuroimaging studies. We call for sobriety regarding the timing and quantity of such influences on brain and cognition. Individual differences in the level of brain and cognition, many of which present already at birth and early in development, appear stable, larger, and more pervasive than differences in change across the lifespan. Incorporating early-life factors, including genetics, and investigating both level and change will reduce the risk of ascribing undue importance and causality to proximate factors in adulthood and older age. This has implications for both mechanistic understanding and prevention.

Reduced volume of the left cerebellar lobule VIIb and its increased connectivity within the cerebellum predict more general psychopathology one year later via worse cognitive flexibility in children

Predicting the risk for general psychopathology (the p factor) requires the examination of multiple factors ranging from brain to cognitive skills. While an increasing number of findings have reported the roles of the cerebral cortex and executive functions, it is much less clear whether and how the cerebellum and cognitive flexibility (a core component of executive function) may be associated with the risk for general psychopathology. Based on the data from more than 400 children aged 6-12 in the Children School Functions and Brain Development (CBD) Project, this study examined whether the left cerebellar lobule VIIb and its connectivity within the cerebellum may prospectively predict the risk for general psychopathology one year later and whether cognitive flexibility may mediate such predictions in school-age children. The reduced gray matter volume in the left cerebellar lobule VIIb and the increased connectivity of this region to the left cerebellar lobule VI prospectively predicted the risk for general psychopathology and was partially mediated by worse cognitive flexibility. Deficits in cognitive flexibility may play an important role in linking cerebellar structure and function to the risk for general psychopathology.

Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience: A Review

Importance

Climate change, pollution, urbanization, socioeconomic inequality, and psychosocial effects of the COVID-19 pandemic have caused massive changes in environmental conditions that affect brain health during the life span, both on a population level as well as on the level of the individual. How these environmental factors influence the brain, behavior, and mental illness is not well known.

Observations

A research strategy enabling population neuroscience to contribute to identify brain mechanisms underlying environment-related mental illness by leveraging innovative enrichment tools for data federation, geospatial observation, climate and pollution measures, digital health, and novel data integration techniques is described. This strategy can inform innovative treatments that target causal cognitive and molecular mechanisms of mental illness related to the environment. An example is presented of the environMENTAL Project that is leveraging federated cohort data of over 1.5 million European citizens and patients enriched with deep phenotyping data from large-scale behavioral neuroimaging cohorts to identify brain mechanisms related to environmental adversity underlying symptoms of depression, anxiety, stress, and substance misuse.

Conclusions and Relevance

This research will lead to the development of objective biomarkers and evidence-based interventions that will significantly improve outcomes of environment-related mental illness.

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach

BACKGROUND

Existing robust, pervasive device-based systems developed in recent years to detect depression require data collected over a long period and may not be effective in cases where early detection is crucial. Additionally, due to the requirement of running systems in the background for prolonged periods, existing systems can be resource inefficient. As a result, these systems can be infeasible in low-resource settings.

OBJECTIVE

Our main objective was to develop a minimalistic system to identify depression using data retrieved in the fastest possible time. Another objective was to explain the machine learning (ML) models that were best for identifying depression.

METHODS

We developed a fast tool that retrieves the past 7 days' app usage data in 1 second (mean 0.31, SD 1.10 seconds). A total of 100 students from Bangladesh participated in our study, and our tool collected their app usage data and responses to the Patient Health Questionnaire-9. To identify depressed and nondepressed students, we developed a diverse set of ML models: linear, tree-based, and neural network-based models. We selected important features using the stable approach, along with 3 main types of feature selection (FS) approaches: filter, wrapper, and embedded methods. We developed and validated the models using the nested cross-validation method. Additionally, we explained the best ML models through the Shapley additive explanations (SHAP) method.

RESULTS

Leveraging only the app usage data retrieved in 1 second, our light gradient boosting machine model used the important features selected by the stable FS approach and correctly identified 82.4% (n=42) of depressed students (precision=75%, F1-score=78.5%). Moreover, after comprehensive exploration, we presented a parsimonious stacking model where around 5 features selected by the all-relevant FS approach Boruta were used in each iteration of validation and showed a maximum precision of 77.4% (balanced accuracy=77.9%). Feature importance analysis suggested app usage behavioral markers containing diurnal usage patterns as being more important than aggregated data-based markers. In addition, a SHAP analysis of our best models presented behavioral markers that were related to depression. For instance, students who were not depressed spent more time on education apps on weekdays, whereas those who were depressed used a higher number of photo and video apps and also had a higher deviation in using photo and video apps over the morning, afternoon, evening, and night time periods of the weekend.

CONCLUSIONS

Due to our system's fast and minimalistic nature, it may make a worthwhile contribution to identifying depression in underdeveloped and developing regions. In addition, our detailed discussion about the implication of our findings can facilitate the development of less resource-intensive systems to better understand students who are depressed and take steps for intervention.

Neurodevelopmental risk and adaptation as a model for comorbidity among internalizing and externalizing disorders: genomics and cell-specific expression enriched morphometric study

BACKGROUND

Comorbidity is the rule rather than the exception for childhood and adolescent onset mental disorders, but we cannot predict its occurrence and do not know the neural mechanisms underlying comorbidity. We investigate if the effects of comorbid internalizing and externalizing disorders on anatomical differences represent a simple aggregate of the effects on each disorder and if these comorbidity-associated cortical surface differences relate to a distinct genetic underpinning.

METHODS

We studied the cortical surface area (SA) and thickness (CT) of 11,878 preadolescents (9-10 years) from the Adolescent Brain and Cognitive Development Study. Linear mixed models were implemented in comparative and association analyses among internalizing (dysthymia, major depressive disorder, disruptive mood dysregulation disorder, agoraphobia, panic disorder, specific phobia, separation anxiety disorder, social anxiety disorder, generalized anxiety disorder, post-traumatic stress disorder), externalizing (attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder) diagnostic groups, a group with comorbidity of the two and a healthy control group. Genome-wide association analysis (GWAS) and cell type specificity analysis were performed on 4468 unrelated European participants from this cohort.

RESULTS

Smaller cortical surface area but higher thickness was noted across patient groups when compared to controls. Children with comorbid internalizing and externalizing disorders had more pronounced areal reduction than those without comorbidity, indicating an additive burden. In contrast, cortical thickness had a non-linear effect with comorbidity: the comorbid group had no significant CT differences, while those patient groups without comorbidity had significantly higher thickness compare to healthy controls. Distinct biological pathways were implicated in regional SA and CT differences. Specifically, CT differences were associated with immune-related processes implicating astrocytes and oligodendrocytes, while SA-related differences related mainly to inhibitory neurons.

CONCLUSION

The emergence of comorbidity across distinct clusters of psychopathology is unlikely to be due to a simple additive neurobiological effect alone. Distinct developmental risk moderated by immune-related adaptation processes, with unique genetic and cell-specific factors, may contribute to underlying SA and CT differences. Children with the highest risk but lowest resilience, both captured in their developmental morphometry, may develop a comorbid illness pattern.

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Effects of urban living environments on mental health in adults

Urban-living individuals are exposed to many environmental factors that may combine and interact to influence mental health. While individual factors of an urban environment have been investigated in isolation, no attempt has been made to model how complex, real-life exposure to living in the city relates to brain and mental health, and how this is moderated by genetic factors. Using the data of 156,075 participants from the UK Biobank, we carried out sparse canonical correlation analyses to investigate the relationships between urban environments and psychiatric symptoms. We found an environmental profile of social deprivation, air pollution, street network and urban land-use density that was positively correlated with an affective symptom group (r = 0.22, Pperm < 0.001), mediated by brain volume differences consistent with reward processing, and moderated by genes enriched for stress response, including CRHR1, explaining 2.01% of the variance in brain volume differences. Protective factors such as greenness and generous destination accessibility were negatively correlated with an anxiety symptom group (r = 0.10, Pperm < 0.001), mediated by brain regions necessary for emotion regulation and moderated by EXD3, explaining 1.65% of the variance. The third urban environmental profile was correlated with an emotional instability symptom group (r = 0.03, Pperm < 0.001). Our findings suggest that different environmental profiles of urban living may influence specific psychiatric symptom groups through distinct neurobiological pathways.

Comparing the stability and reproducibility of brain-behaviour relationships found using Canonical Correlation Analysis and Partial Least Squares within the ABCD Sample

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). LV1 from the CCA model found that covariation of CBCL scores was linked to covariation of cortical thickness. LV1 from the PLS model identified decreased cortical thickness linked to lower CBCL scores. There was limited evidence of stability or reproducibility of LV1 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.

Depression-related phenotypes at early stages of Aβ and tau accumulation in inducible Alzheimer's disease mouse model: Task-oriented and concept-driven interpretations

Depression is highly prevalent in Alzheimer Disease (AD); however, there is paucity of studies that focus specifically on the assessment of depression-relevant phenotypes in AD mouse models. Conditional doxycycline-dependent transgenic mouse models reproducing amyloidosis (TetOffAPPsi) and/or tau (TetOffTauP301L) pathology starting at middle age (6 months) were used in this study. As AD patients can experience depressive symptoms relatively early in disease, testing was conducted at early, pre-pathology stages of Aβ and/or tau accumulation (starting from 45 days of transgenes expression). Tau-related differences were detected in the Novelty Suppressed Feeding task (NSF), whereas APP-related differences were observed predominantly in measures of the Open Field (OF) and Forced Swim tasks (FST). Effects of combined production of Aβ and tau were detected in immobility during the 1st half of the Tail Suspension task (TST). These data demonstrate that results from different tasks are difficult to reconcile using task/variable-centered interpretations in which a single task/variable is assigned an ad-hoc meaning relevant to depression. An alternative, concept-oriented, approach is based on multiple variables/tests, with an understanding of their possible inter-dependence and utilization of statistical approaches that handle correlated data sets. The existence of strong correlations within and between some of the tasks supported utilization of factor analyses (FA). FA explained a similar amount of variability across the genotypes (∼80%) and identified two factors stable across genotypes and representing motor activity and anxiety measures in OF. In contrast, variables related to FST, TST, and NSFT did not demonstrate a structure of factor loadings that would support the existence of a single integral factor of "depressive state" measured by these tasks. In addition, factor loadings varied between genotypes, indicating that genotype-specific between-task correlations need to be considered for interpretations of findings in any single task. In general, this study demonstrates that utilization of multiple tasks to characterize behavioral phenotypes, an approach that is finally gaining more widespread adoption, requires a step of data integration across different behavioral tests for appropriate interpretations.

On the complex and dimensional relationship of maternal posttraumatic stress disorder during early childhood and child outcomes at school-age

BACKGROUND

Several studies have shown associations between maternal interpersonal violence-related posttraumatic stress disorder (PTSD), child mental health problems, and impaired socioemotional development. However, the existing literature lacks evidence linking constellations of risk factors such as maternal interpersonal-violence-related PTSD, psychopathology, and interactive behavior with toddlers and outcome measures at school-age.

METHODS

This study involved a prospective, longitudinal investigation of 62 mothers and examined the relationship between maternal variables measured when children were in early childhood (mean age 27 months), and child outcomes when children were school-age (age mean = 83.2 months) while retaining a focus on the context of maternal PTSD. To identify and weigh associated dimensions comparatively, we employed sparse canonical correlation analysis (sCCA) aimed at associating dimensions of a dataset of 20 maternal variables in early childhood with that of more than 20 child outcome variables (i.e., child psychopathology, life-events, and socioemotional skills) at school-age.

RESULTS

Phase 1 variables with the highest weights were those of maternal psychopathology: PTSD, depressive and dissociative symptoms, and self-report of parental stress. The highest weighted Phase 2 child outcome measures were those of child psychopathology: PTSD, anxiety, and depressive symptoms as well as peer bullying and victimization.

CONCLUSIONS

sCCA revealed that trauma-related concepts in mothers were significantly and reliably associated with child psychopathology and other indicators of risk for intergenerational transmission of violence and victimization. The results highlight the dimensional and multifaceted nature-both for mothers as well as children-of the intergenerational transmission of violence and associated psychopathology.

Striatal connectopic maps link to functional domains across psychiatric disorders

Transdiagnostic approaches to psychiatry have significant potential in overcoming the limitations of conventional diagnostic paradigms. However, while frameworks such as the Research Domain Criteria have garnered significant enthusiasm among researchers and clinicians from a theoretical angle, examples of how such an approach might translate in practice to understand the biological mechanisms underlying complex patterns of behaviors in realistic and heterogeneous populations have been sparse. In a richly phenotyped clinical sample (n = 186) specifically designed to capture the complex nature of heterogeneity and comorbidity within- and between stress- and neurodevelopmental disorders, we use exploratory factor analysis on a wide range of clinical questionnaires to identify four stable functional domains that transcend diagnosis and relate to negative valence, cognition, social functioning and inhibition/arousal before replicating them in an independent dataset (n = 188). We then use connectopic mapping to map inter-individual variation in fine-grained topographical organization of functional connectivity in the striatum-a central hub in motor, cognitive, affective and reward-related brain circuits-and use multivariate machine learning (canonical correlation analysis) to show that these individualized topographic representations predict transdiagnostic functional domains out of sample (r = 0.20, p = 0.026). We propose that investigating psychiatric symptoms across disorders is a promising path to linking them to underlying biology, and can help bridge the gap between neuroscience and clinical psychiatry.

A cross-cohort replicable and heritable latent dimension linking behaviour to multi-featured brain structure

Identifying associations between interindividual variability in brain structure and behaviour requires large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of-cohort replication. Moreover, the influence of nature vs nurture on brain-behaviour associations should be analysed. We analysed associations between brain structure (grey matter volume, cortical thickness, and surface area) and behaviour (spanning cognition, emotion, and alertness) using regularized canonical correlation analysis and a machine learning framework that tests the generalisability and stability of such associations. The replicability of brain-behaviour associations was assessed in two large, independent cohorts. The load of genetic factors on these associations was analysed with heritability and genetic correlation. We found one heritable and replicable latent dimension linking cognitive-control/executive-functions and positive affect to brain structural variability in areas typically associated with higher cognitive functions, and with areas typically associated with sensorimotor functions. These results revealed a major axis of interindividual behavioural variability linking to a whole-brain structural pattern.

Canonical Correlation Analysis and Partial Least Squares for Identifying Brain-Behavior Associations: A Tutorial and a Comparative Study

Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful multivariate methods for capturing associations across 2 modalities of data (e.g., brain and behavior). However, when the sample size is similar to or smaller than the number of variables in the data, standard CCA and PLS models may overfit, i.e., find spurious associations that generalize poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar to or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimizing the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and Alzheimer's Disease Neuroimaging Initiative (both of n > 500). We use both low- and high-dimensionality versions of these data (i.e., ratios between sample size and variables in the range of ∼1-10 and ∼0.1-0.01, respectively) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial.

Association between mental health and executive dysfunction and the moderating effect of urban-rural subpopulation in general adolescents from Shangrao, China: a population-based cross-sectional study

OBJECTIVES

To examine the association between mental health and executive dysfunction in general adolescents, and to identify whether home residence and school location would moderate that association.

DESIGN

A population-based cross-sectional study.

SETTING

A subsample of the Shanghai Children's Health, Education, and Lifestyle Evaluation-Adolescents project. 16 sampled schools in Shangrao city located in downstream Yangtze River in southeast China (December 2018).

PARTICIPANTS

1895 adolescents (48.8% male) which were divided into three subpopulations: (A) adolescents who have urban hukou (ie, household registration in China) and attend urban schools (UU, n=292); (B) adolescents who have rural hukou and attend urban schools (RU, n=819) and (C) adolescents who have rural hukou and attend rural schools (RR, n=784).

MEASURES

The Depression Anxiety and Stress Scale-21 was used to assess adolescent mental health symptoms, and the Behaviour Rating Inventory of Executive Function (parent form) was applied to measure adolescent executive dysfunction in nature setting.

RESULTS

Mental health symptoms were common (depression: 25.2%, anxiety: 53.0%, stress: 19.7%) in our sample, and the prevalence rates were lower among UU adolescents than those among the RR and RU, with intersubgroup differences in screen exposure time explaining most of the variance. We found the three types of symptoms were strongly associated with executive dysfunction in general adolescents. We also observed a marginal moderating effect of urban-rural subgroup on the associations: UU adolescents with depression (OR 6.74, 95% CI 3.75 to 12.12) and anxiety (OR 5.56, 95% CI 1.86 to 16.66) had a higher executive dysfunction risk when compared with RR youths with depression (OR 1.93, 95% CI 0.91 to 4.12) and anxiety (OR 1.80, 95% CI 1.39 to 2.33), respectively.

CONCLUSIONS

Rural adolescents experienced more mental health symptoms, whereas urban individuals with mental health problems had a higher executive dysfunction risk.

Common and Distinct Neural Patterns of Attention-Deficit/Hyperactivity Disorder and Borderline Personality Disorder: A Multimodal Functional and Structural Meta-analysis

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) have partially overlapping symptom profiles and are highly comorbid in adults. However, whether the behavioral similarities correspond to shared neurobiological substrates is not known.

METHODS

An overlapping meta-analysis of 58 ADHD and 66 BPD whole-brain articles incorporating observations from 3401 adult patients and 3238 healthy participants was performed by seed-based d mapping. Brain maps were subjected to meta-analytic connectivity modeling and data-driven functional decoding analyses to identify associated neural circuit alterations and relations to behavioral dimensions.

RESULTS

Both groups exhibited hypoactivated abnormalities in the left inferior parietal lobule, and altered clusters of the bilateral superior temporal gyrus were disjunctive in ADHD and BPD. No overlapping structural abnormalities were found. Multimodal alterations of ADHD were located in the right putamen and of BPD in the left orbitofrontal cortex.

CONCLUSIONS

The transdiagnostic neural bases of ADHD and BPD in temporoparietal circuitry may underlie overlapping problems of behavioral control, while disorder-specific substrates in frontostriatal circuitry may account for their distinguishing features in motor and emotion domains, respectively.

An electroencephalography connectome predictive model of major depressive disorder severity

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.

Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data

BACKGROUND

Adolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level.

METHODS

A subsample of a multisite longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into the following: 1) those developing a diagnosis of major depressive disorder or subthreshold major depressive disorder and 2) healthy control subjects. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental, and structural magnetic resonance imaging) at age 14 years were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (n = 407). The features contributing the highest to the prediction were validated in an independent hold-out sample (three independent IMAGEN sites; n = 137).

RESULTS

The area under the receiver operating characteristic curve for predicting depression onset ranged between 0.70 and 0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events, and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression, with an area under the receiver operating characteristic curve between 0.68 and 0.72 in the independent validation sample.

CONCLUSIONS

This study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical characteristics, life events, personality traits, and brain structure variables.

Linking interindividual variability in brain structure to behaviour

What are the brain structural correlates of interindividual differences in behaviour? More than a decade ago, advances in structural MRI opened promising new avenues to address this question. The initial wave of research then progressively led to substantial conceptual and methodological shifts, and a replication crisis unveiled the limitations of traditional approaches, which involved searching for associations between local measurements of neuroanatomy and behavioural variables in small samples of healthy individuals. Given these methodological issues and growing scepticism regarding the idea of one-to-one mapping of psychological constructs to brain regions, new perspectives emerged. These not only embrace the multivariate nature of brain structure-behaviour relationships and promote generalizability but also embrace the representation of the relationships between brain structure and behavioural data by latent dimensions of interindividual variability. Here, we examine the past and present of the study of brain structure-behaviour associations in healthy populations and address current challenges and open questions for future investigations.

Nighttime lights, urban features, household poverty, depression, and obesity

Nighttime Light Emission (NLE) is associated with diminished mental and physical health. The present study examines how NLE and associated urban features (e.g., air pollution, low green space) impact mental and physical wellbeing. We included 200,393 UK Biobank Cohort participants with complete data. The study was carried out in two steps. In Step1, we assessed the relationship between NLE, deprivation, pollution, green space, household poverty and mental and physical symptoms. In Step2, we examined the role of NLE on environment-symptom networks. We stratified participants into high and low NLE and used gaussian graphical model to identify nodes which bridged urban features and mental and physical health problems. We then compared the global strength of these networks in high vs low NLE. We found that higher NLE associated with higher air pollution, less green space, higher economic and neighborhood deprivation, higher household poverty and higher depressed mood, higher tiredness/lethargy and obesity (Rtraining_mean = 0.2624, P training_mean < .001; Rtest_mean = 0.2619, P test_mean < .001). We also found that the interaction between environmental risk factors and mental, physical problems (overall network connectivity) was higher in the high NLE network than in the low NLE network (t = 0.7896, P < .001). In areas with high NLE, economic deprivation, household poverty and waist circumference acted as bridge factors between the key urban features and mental health symptoms. In conclusion, NLE, urban features, household poverty and mental and physical symptoms are all interrelated. In areas with high NLE, urban features associate with mental and physical health problems at a greater magnitude than in areas with low NLE.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12144-022-02754-3.

Multivariate patterns of brain-behavior associations across the adult lifespan

The nature of brain-behavior covariations with increasing age is poorly understood. In the current study, we used a multivariate approach to investigate the covariation between behavioral-health variables and brain features across adulthood. We recruited healthy adults aged 20–73 years-old (29 younger, mean age = 25.6 years; 30 older, mean age = 62.5 years), and collected structural and functional MRI (s/fMRI) during a resting-state and three tasks. From the sMRI, we extracted cortical thickness and subcortical volumes; from the fMRI, we extracted activation peaks and functional network connectivity (FNC) for each task. We conducted canonical correlation analyses between behavioral-health variables and the sMRI, or the fMRI variables, across all participants. We found significant covariations for both types of neuroimaging phenotypes (ps = 0.0004) across all individuals, with cognitive capacity and age being the largest opposite contributors. We further identified different variables contributing to the models across phenotypes and age groups. Particularly, we found behavior was associated with different neuroimaging patterns between the younger and older groups. Higher cognitive capacity was supported by activation and FNC within the executive networks in the younger adults, while it was supported by the visual networks’ FNC in the older adults. This study highlights how the brain-behavior covariations vary across adulthood and provides further support that cognitive performance relies on regional recruitment that differs between older and younger individuals.

Testing the Stability and Validity of an Executive Dysfunction Classification Using Task-Based Assessment in Children and Adolescents

OBJECTIVE

It is unclear if pediatric executive dysfunction assessed only with cognitive tasks predicts clinically relevant outcomes independently of psychiatric diagnoses. This study tested the stability and validity of a task-based classification of executive function.

METHOD

A total of 2,207 individuals (6-17 years old) from the Brazilian High-Risk Cohort Study participated in this study (1,930 at baseline, 1,532 at follow-up). Executive function was measured using tests of working memory and inhibitory control. Dichotomized age- and sex-standardized performances were used as input in latent class analysis and receiver operating curves to create an executive dysfunction classification (EDC). The study tested EDC's stability over time, association with symptoms, functional impairment, a polymorphism in the CADM2 gene, polygenic risk scores (PRS), and brain structure. Analyses covaried for age, sex, social class, IQ, and psychiatric diagnoses.

RESULTS

EDC at baseline predicted itself at follow-up (odds ratio [OR] = 5.11; 95% CI 3.41-7.64). Participants in the EDC reported symptoms spanning several domains of psychopathology and exhibited impairment in multiple settings, including more adverse school events (OR = 2.530; 95% CI 1.838-3.483). Children in the EDC presented higher attention-deficit/hyperactivity disorder and lower educational attainment PRS at baseline; higher schizophrenia PRS at follow-up; and lower chances of presenting a polymorphism in a gene previously linked to high performance in executive function (CADM2 gene). They also exhibited smaller intracranial volumes and smaller bilateral cortical surface areas in several brain regions.

CONCLUSION

Task-based executive dysfunction is associated with several validators, independently of psychiatric diagnoses and intelligence. Further refinement of task-based assessments might generate clinically useful tools.

Reduced network integration in default mode and executive networks is associated with social and personal optimism biases

An optimism bias refers to the belief in good things happening to oneself in the future with a higher likelihood than is justified. Social optimism biases extend this concept to groups that one identifies with. Previous literature has found that both personal and social optimism biases are linked to brain structure and task-related brain function. Less is known about whether optimism biases are also expressed in resting-state functional connectivity (RSFC). Forty-two participants completed questionnaires on dispositional personal optimism (which is not necessarily unjustified) and comparative optimism (i.e., whether we see our own future as being rosier than a comparison person's future) and underwent a resting-state functional magnetic resonance imaging scan. They further undertook an imaginative soccer task in order to assess both their personal and social optimism bias. We tested associations of these data with RSFC within and between 13 networks, using sparse canonical correlation analyses (sCCAs). We found that the primary sCCA component was positively connected to personal and social optimism bias and negatively connected to dispositional personal pessimism. This component was associated with (a) reduced integration of the default mode network, (b) reduced integration of the central executive and salience networks, and (c) reduced segregation between the default mode network and the central executive network. Our finding that optimism biases are linked to RSFC indicates that they may be rooted in neurobiology that exists outside of concurrent tasks. This poses questions as to what the limits of the malleability of such biases may be.

Neuroimaging in attention-deficit/hyperactivity disorder

PURPOSE OF REVIEW

Neuroimaging research on attention-deficit/hyperactivity disorder (ADHD) continues growing in extent and complexity, although it has yet to become clinically meaningful. We review recent MRI research on ADHD, to identify robust findings, current trends and challenges.

RECENT FINDINGS

We identified 40 publications between January 2019 and September 2020 reporting or reviewing MRI research on ADHD. Four meta-analyses have presented conflicting results regarding across-study convergence of functional and resting-state functional (fMRI and R-fMRI) studies on ADHD. On the other hand, the Enhancing NeuroImaging Genetics Through Meta-Analysis international consortium has identified statistically robust albeit small differences in structural brain cortical and subcortical indices in children with ADHD versus typically developing controls. Other international consortia are harnessing open-science efforts and multimodal data (imaging, genetics, phenotypic) to shed light on the complex interplay of genetics, environment, and development in the pathophysiology of ADHD. We note growing research in 'prediction' science, which applies machine-learning analysis to identify biomarkers of disease based on big data.

SUMMARY

Neuroimaging in ADHD is still far from informing clinical practice. Current large-scale, multimodal, and open-science initiatives represent promising paths toward untangling the neurobiology of ADHD.

Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study

Adolescence is a period of major brain reorganization shaped by biologically timed and by environmental factors. We sought to discover linked patterns of covariation between brain structural development and a wide array of these factors by leveraging data from the IMAGEN study, a longitudinal population-based cohort of adolescents. Brain structural measures and a comprehensive array of non-imaging features (relating to demographic, anthropometric, and psychosocial characteristics) were available on 1476 IMAGEN participants aged 14 years and from a subsample reassessed at age 19 years (n = 714). We applied sparse canonical correlation analyses (sCCA) to the cross-sectional and longitudinal data to extract modes with maximum covariation between neuroimaging and non-imaging measures. Separate sCCAs for cortical thickness, cortical surface area and subcortical volumes confirmed that each imaging phenotype was correlated with non-imaging features (sCCA r range: 0.30-0.65, all P_FDR < 0.001). Total intracranial volume and global measures of cortical thickness and surface area had the highest canonical cross-loadings (|ρ| = 0.31-0.61). Age, physical growth and sex had the highest association with adolescent brain structure (|ρ| = 0.24-0.62); at baseline, further significant positive associations were noted for cognitive measures while negative associations were observed at both time points for prenatal parental smoking, life events, and negative affect and substance use in youth (|ρ| = 0.10-0.23). Sex, physical growth and age are the dominant influences on adolescent brain development. We highlight the persistent negative influences of prenatal parental smoking and youth substance use as they are modifiable and of relevance for public health initiatives.

Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies

With progress in genome-wide association studies of depression, from identifying zero hits in ~16 000 individuals in 2013 to 223 hits in more than a million individuals in 2020, understanding the genetic architecture of this debilitating condition no longer appears to be an impossible task. The pressing question now is whether recently discovered variants describe the etiology of a single disease entity. There are a myriad of ways to measure and operationalize depression severity, and major depressive disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders-5 can manifest in more than 10 000 ways based on symptom profiles alone. Variations in developmental timing, comorbidity and environmental contexts across individuals and samples further add to the heterogeneity. With big data increasingly enabling genomic discovery in psychiatry, it is more timely than ever to explicitly disentangle genetic contributions to what is likely 'depressions' rather than depression. Here, we introduce three sources of heterogeneity: operationalization, manifestation and etiology. We review recent efforts to identify depression subtypes using clinical and data-driven approaches, examine differences in genetic architecture of depression across contexts, and argue that heterogeneity in operationalizations of depression is likely a considerable source of inconsistency. Finally, we offer recommendations and considerations for the field going forward.

Permutation inference for canonical correlation analysis

Canonical correlation analysis (CCA) has become a key tool for population neuroimaging, allowing investigation of associations between many imaging and non-imaging measurements. As age, sex and other variables are often a source of variability not of direct interest, previous work has used CCA on residuals from a model that removes these effects, then proceeded directly to permutation inference. We show that a simple permutation test, as typically used to identify significant modes of shared variation on such data adjusted for nuisance variables, produces inflated error rates. The reason is that residualisation introduces dependencies among the observations that violate the exchangeability assumption. Even in the absence of nuisance variables, however, a simple permutation test for CCA also leads to excess error rates for all canonical correlations other than the first. The reason is that a simple permutation scheme does not ignore the variability already explained by previous canonical variables. Here we propose solutions for both problems: in the case of nuisance variables, we show that transforming the residuals to a lower dimensional basis where exchangeability holds results in a valid permutation test; for more general cases, with or without nuisance variables, we propose estimating the canonical correlations in a stepwise manner, removing at each iteration the variance already explained, while dealing with different number of variables in both sides. We also discuss how to address the multiplicity of tests, proposing an admissible test that is not conservative, and provide a complete algorithm for permutation inference for CCA.