Measuring strengths and weaknesses in dimensional psychiatry

Social effort discounting reveals domain-general and social-specific motivation components

BACKGROUND

Social motivation is crucial for healthy interpersonal connections and is impaired in a subset of the general population and across many psychiatric disorders. However, compared to nonsocial (e.g., monetary) motivation, social motivation has been understudied in quantitative behavioral work, especially regarding willingness to exert social effort. We developed a novel social effort discounting task, paired with a monetary task to examine motivational specificity. We expected social task performance would relate to general motivation also show selective relationships with self-reported avoidance tendencies and with sociality.

METHODS

An analyzed sample of 397 participants performed the social and nonsocial effort discounting task online, along with self-report measures of various aspects of motivation and psychiatric symptomatology.

RESULTS

Social and nonsocial task motivation correlated strongly (rho=0.71 p<0.001). Both social and nonsocial task motivation related similarly to self-reported general motivation (social ß=0.16; nonsocial ß=0.13) and to self-reported approach motivation (social ß=0.14; nonsocial ß=0.11), with this common effect captured by a significant main effect across social and nonsocial conditions. Significant condition interaction effects supported a selective relationship of social task motivation with self-reported sociality and also with avoidance motivation.

CONCLUSIONS

Our novel social effort discounting task revealed both domain-general and social-specific components of motivation. In combination with other measures, this approach can facilitate further investigation of common and dissociable neurobehavioral mechanisms, in order to better characterize normative and pathological variation and develop personalized interventions targeting specific contributors to social impairment.

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.

Assessment of affective dysregulation in children: development and evaluation of a semi-structured interview for parents and for children

BACKGROUND

Children with affective dysregulation (AD) show an excessive reactivity to emotionally positive or negative stimuli, typically manifesting in chronic irritability, severe temper tantrums, and sudden mood swings. AD shows a large overlap with externalizing and internalizing disorders. Given its transdiagnostic nature, AD cannot be reliably and validly captured only by diagnostic categories such as disruptive mood dysregulation disorder (DMDD). Therefore, this study aimed to evaluate two semi-structured clinical interviews-one for parents and one for children.

METHODS

Both interviews were developed based on existing measures that capture particular aspects of AD. We analyzed internal consistencies and interrater agreement to evaluate their reliability. Furthermore, we analyzed factor loadings in an exploratory factor analysis, differences in interview scores between children with and without co-occurring internalizing and externalizing disorders, and associations with other measures of AD and of AD-related constructs. The evaluation was performed in a screened community sample of children aged 8-12 years (n = 445). Interrater reliability was additionally analyzed in an outpatient sample of children aged 8-12 years (n = 27).

RESULTS

Overall, internal consistency was acceptable to good. In both samples, we found moderate to excellent interrater reliability on a dimensional level. Interrater agreement for the dichotomous diagnosis DMDD was substantial to perfect. In the exploratory factor analysis, almost all factor loadings were acceptable. Children with a diagnosis of disruptive disorder, attention-deficit/hyperactivity disorder, or any disorder (disruptive disorder, attention-deficit/hyperactivity disorder, and depressive disorder) showed higher scores on the DADYS interviews than children without these disorders. The correlation analyses revealed the strongest associations with other measures of AD and measures of AD-specific functional impairment. Moreover, we found moderate to very large associations with internalizing and externalizing symptoms and moderate to large associations with emotion regulation strategies and health-related quality of life.

CONCLUSIONS

The analyses of internal consistency and interrater agreement support the reliability of both clinical interviews. Furthermore, exploratory factor analysis, discriminant analyses, and correlation analyses support the interviews' factorial, discriminant, concurrent, convergent, and divergent validity. The interviews might thus contribute to the reliable and valid identification of children with AD and the assessment of treatment responses.

TRIAL REGISTRATION

ADOPT Online: German Clinical Trials Register (DRKS) DRKS00014963. Registered 27 June 2018.

Evaluation of a new intrinsic and extrinsic motivation scale in youth with psychosis spectrum symptoms

BACKGROUND

Impairment in intrinsic motivation (IM), the drive to satisfy internal desires like mastery, may play a key role in disability in psychosis. However, we have limited knowledge regarding relative impairments in IM compared to extrinsic motivation (EM) or general motivation (GM), in part due to limitations in existing measures.

METHODS

Here we address this gap using a novel Trait Intrinsic and Extrinsic Motivation self-report scale in a sample of n = 243 participants including those with schizophrenia, psychosis-risk, and healthy controls. Each of the 7 IM and 6 EM items used a 7-point Likert scale assessing endorsement of dispositional statements. Bifactor analyses of these items yielded distinct IM, EM, and GM factor scores. Convergent and discriminant validity were examined in relation to General Causality Orientation Scale (GCOS-CP) and Quality of Life 3-item IM measure (QLS-IM). Utility was assessed in relation to psychosis-spectrum (PS) status and CAINS clinical amotivation.

RESULTS

IM and EM showed acceptable inter-item consistency (IM: α = 0.88; EM: α = 0.66); the bifactor model exhibited fit that varied from good to borderline to inadequate depending on the specific fit metric (SRMR = 0.038, CFI = 0.94, RMSEA = 0.106 ± 0.014). IM scores correlated with established IM measures: GCOS-CP Autonomy (rho = 0.38, p < 0.01) and QLS-IM (rho = 0.29, p < 0.01). Supporting discriminant validity, IM did not correlate with GCOS-CP Control (rho = -0.14, p > 0.05). Two-year stability in an available longitudinal subset (n = 35) was strong (IM: rho = 0.64, p < 0.01; EM: rho = 0.55, p < 0.01). Trait IM was lower in PS youth (t = 4.24, p < 0.01), and correlated with clinical amotivation (rho = -0.36, p < 0.01); EM did not show significant clinical associations.

CONCLUSIONS

These results demonstrate the clinical relevance of IM in psychosis risk. They also provide preliminary support for the reliability, validity and utility of this new Trait IM-EM scale, which addresses a measurement gap and can facilitate identification of neurobehavioral and clinical correlates of IM deficits.

Probing the overarching continuum theory: data-driven phenotypic clustering of children with ASD or ADHD

The clinical validity of the distinction between ADHD and ASD is a longstanding discussion. Recent advances in the realm of data-driven analytic techniques now enable us to formally investigate theories aiming to explain the frequent co-occurrence of these neurodevelopmental conditions. In this study, we probe different theoretical positions by means of a pre-registered integrative approach of novel classification, subgrouping, and taxometric techniques in a representative sample (N = 434), and replicate the results in an independent sample (N = 219) of children (ADHD, ASD, and typically developing) aged 7-14 years. First, Random Forest Classification could predict diagnostic groups based on questionnaire data with limited accuracy-suggesting some remaining overlap in behavioral symptoms between them. Second, community detection identified four distinct groups, but none of them showed a symptom profile clearly related to either ADHD or ASD in neither the original sample nor the replication sample. Third, taxometric analyses showed evidence for a categorical distinction between ASD and typically developing children, a dimensional characterization of the difference between ADHD and typically developing children, and mixed results for the distinction between the diagnostic groups. We present a novel framework of cutting-edge statistical techniques which represent recent advances in both the models and the data used for research in psychiatric nosology. Our results suggest that ASD and ADHD cannot be unambiguously characterized as either two separate clinical entities or opposite ends of a spectrum, and highlight the need to study ADHD and ASD traits in tandem.

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.

Diagnostic instruments for the assessment of disruptive mood dysregulation disorder: a systematic review of the literature

Disruptive mood dysregulation disorder (DMDD) involves non-episodic irritability and frequent severe temper outbursts in children. Since the inclusion of the diagnosis in the DSM-5, there is no established gold-standard in the assessment of DMDD. In this systematic review of the literature, we provide a synopsis of existing diagnostic instruments for DMDD. Bibliographic databases were searched for any studies assessing DMDD. The systematic search of the literature yielded K = 1167 hits, of which n = 110 studies were included. The most frequently used measure was the Kiddie Schedule for Affective Disorders and Schizophrenia DMDD module (25%). Other studies derived diagnostic criteria from interviews not specifically designed to measure DMDD (47%), chart review (7%), clinical diagnosis without any specific instrument (6%) or did not provide information about the assessment (9%). Three structured interviews designed to diagnose DMDD were used in six studies (6%). Interrater reliability was reported in 36% of studies (ranging from κ = 0.6-1) while other psychometric properties were rarely reported. This systematic review points to a variety of existing diagnostic measures for DMDD with good reliability. Consistent reporting of psychometric properties of recently developed DMDD interviews, as well as their further refinement, may help to ascertain the validity of the diagnosis.

IQ Modulates Coupling Between Diverse Dimensions of Psychopathology in Children and Adolescents

OBJECTIVE

Correlations between cognitive ability and psychopathology are well recognized, but prior research has been limited by focusing on individuals with intellectual disability, single-diagnosis psychiatric populations, or few measures of psychopathology. Here, we quantify relationships between full-scale IQ and multiple dimensions of psychopathology in a diverse care-seeking population, with a novel focus on differential coupling between psychopathology dimensions as a function of IQ.

METHOD

A total of 70 dimensional measures of psychopathology, plus IQ and demographic data, were collated for 2,752 children and adolescents from the Healthy Brain Network dataset. We first examined univariate associations between IQ and psychopathology, and then characterized how the correlational architecture of psychopathology differs between groups at extremes of the IQ distribution.

RESULTS

Associations with IQ vary in magnitude between different domains of psychopathology: IQ shows the strongest negative correlations with attentional and social impairments, but is largely unrelated to affective symptoms and psychopathy. Lower IQ is associated with stronger coupling between internalizing problems and aggression, repetitive behaviors, and hyperactivity/inattentiveness.

CONCLUSION

Our analyses reveal that variation in general cognitive ability is associated not only with significant and selective shifts in severity of psychopathology, but also in the coupling between different dimensions of psychopathology. These findings have relevance for the clinical assessment of mental health in populations with varying IQ, and may also inform ongoing efforts to improve the measurement of psychopathology and to understand how relationships between cognition and behavior are reflected in brain organization.

DIVERSITY & INCLUSION STATEMENT

We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure sex balance in the selection of non-human subjects. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science.

Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research

Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.

Functional connectivity directionality between large-scale resting-state networks across typical and non-typical trajectories in children and adolescence

Mental disorders often emerge during adolescence and have been associated with age-related differences in connection strengths of brain networks (static functional connectivity), manifesting in non-typical trajectories of brain development. However, little is known about the direction of information flow (directed functional connectivity) in this period of functional brain progression. We employed dynamic graphical models (DGM) to estimate directed functional connectivity from resting state functional magnetic resonance imaging data on 1143 participants, aged 6 to 17 years from the healthy brain network (HBN) sample. We tested for effects of age, sex, cognitive abilities and psychopathology on estimates of direction flow. Across participants, we show a pattern of reciprocal information flow between visual-medial and visual-lateral connections, in line with findings in adults. Investigating directed connectivity patterns between networks, we observed a positive association for age and direction flow from the cerebellar to the auditory network, and for the auditory to the sensorimotor network. Further, higher cognitive abilities were linked to lower information flow from the visual occipital to the default mode network. Additionally, examining the degree networks overall send and receive information to each other, we identified age-related effects implicating the right frontoparietal and sensorimotor network. However, we did not find any associations with psychopathology. Our results suggest that the directed functional connectivity of large-scale resting-state brain networks is sensitive to age and cognition during adolescence, warranting further studies that may explore directed relationships at rest and trajectories in more fine-grained network parcellations and in different populations.

Predicting the future of neuroimaging predictive models in mental health

Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and "predict" topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data).

A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.

Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms

OBJECTIVE

Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health.

METHODS

We used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n = 1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance.

RESULTS

Our model was able to predict age in the independent test samples, with a model performance of r = 0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN.

DISCUSSION

Our findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.

Cerebral perfusion in depression: Relationship to sex, dehydroepiandrosterone sulfate and depression severity

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disease burden and shows a marked sexual dimorphism. Previous studies reported changes in cerebral perfusion in MDD, an association between perfusion and dehydroepiandrosterone sulfate (DHEAS) levels, and large sex differences in perfusion. This study examines whether perfusion and DHEAS might mediate the link between sex and depressive symptoms in a large, unmedicated community sample.

METHODS

The sample included 203 healthy volunteers and 79 individuals with past or current MDD. Depression severity was assessed with the Hamilton Depression Scale (HAM-D) and Montgomery-Asberg Depression Rating Scale (MADRS). 3 T MRI perfusion data were collected with a pseudocontinuous arterial spin labelling sequence and DHEAS was measured in serum by LC-MS/MS.

RESULTS

Large sex differences in perfusion were observed (p < 0.001). Perfusion was negatively correlated with DHEAS (r = -0.23, p < 0.01, n = 250) and with depression severity (HAM-D: r = -0.17, p = 0.01, n = 242; partial Spearman correlation, controlling for age and sex), but not with anxiety. A significant sex*perfusion interaction on depression severity was observed. In women, perfusion showed more pronounced negative correlations with depressive symptoms, with absent or, in the case of the MADRS, opposite effects observed in men. A mediation analysis identified DHEAS and perfusion as mediating variables influencing the link between sex and the HAM-D score.

CONCLUSION

Perfusion was linked to depression severity, with the strongest effects observed in women. Perfusion and the neurosteroid DHEAS appear to mediate the link between sex and HAM-D scores, suggesting that inter-individual differences in perfusion and DHEAS levels may contribute to the sexual dimorphism in depression.

Internalizing symptoms, well-being, and correlates in adolescence: A multiverse exploration via cross-lagged panel network models

Internalizing symptoms are the most prevalent mental health problem in adolescents, with sharp increases seen, particularly for girls, and evidence that young people today report more problems than previous generations. It is therefore critical to measure and monitor these states on a large scale and consider correlates. We used novel panel network methodology to explore relationships between internalizing symptoms, well-being, and inter/intrapersonal indicators. A multiverse design was used with 32 conditions to consider the stability of results across arbitrary researcher decisions in a large community sample over three years (N = 15,843, aged 11-12 at Time 1). Networks were consistently similar for girls and boys. Stable trait-like effects within anxiety, attentional, and social indicators were found. Within-person networks were densely connected and suggested mental health and inter/intrapersonal correlates related to one another in similar complex ways. The multiverse design suggested the particular operationalization of items can substantially influence conclusions. Nevertheless, indicators such as thinking clearly, unhappiness, dealing with stress, and worry showed more consistent centrality, suggesting these indicators may play particularly important roles in the development of mental health in adolescence.

Toward a connectivity gradient-based framework for reproducible biomarker discovery

Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent efforts to address this challenge have capitalized on dimensionality reduction techniques applied to resting-state fMRI, identifying principal components of intrinsic connectivity which describe smooth transitions across different cortical systems, so called "connectivity gradients". These gradients recapitulate neurocognitively meaningful organizational principles that are present in both human and primate brains, and also appear to differ among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209 × 2) and the Midnight scan club (n = 9), we tested the following key biomarker traits - reliability, reproducibility and predictive validity - of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (resting-state fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional and multivariate gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment.

Editorial: 'People get ready': Are mental disorder diagnostics ripe for a Kuhnian revolution?

How is practical progress possible in child psychology and psychiatry? How does science advance to promote therapeutic innovation? The importance of the exciting stuff - new insights and ideas, studied using cutting edge and innovative technologies - is self-evident. However, the philosophy of science has shown us that less obvious and more mundane elements are also essential. This is because scientific progress is only possible where attempts to break new ground are solidly anchored in a stable shared framework of assumptions - a metatheory - about the general nature of the phenomenon being studied. This framework defines what questions are considered 'scientific' - questions that it 'makes sense' to ask from a scientific point of view and those that are considered out of bounds (scientists with less subtle minds even considering such to be nonquestions rather than different sorts of questions). Kuhn called this framework a paradigm and the research activity that originates from it, normal science (Kuhn, 1962, The Structure of Scientific Revolutions; Princeton, NJ: Princeton University Press). These frameworks also serve a vital regulatory function because they contain common concepts that embody shared points of reference that allow scientists to communicate with each other to share their ideas, hypotheses and findings (Habermas, 1979, Communication and the evolution of society; Boston: Beacon Press).