Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates.

The application of dynamic or time-varying connectivity techniques to neuroimaging data represents a new and complementary method to traditional static (time-averaged) methods, capturing additional patterns of variation in human brain function. Dynamic connectivity and related measures of brain dynamism have been detailed in neurotypical brain function, during human development and across neuropsychiatric disorders, and linked to cognitive control and executive function abilities. Despite this large and growing body of work, little is known about whether sex-related differences are present in dynamic connectivity and brain dynamism, a question pertinent to our understanding of brain function in both health and disease, given the sex bias observed in the prevalence of neuropsychiatric disorders, and well-demonstrated sex-related differences in the performance of certain neurocognitive tasks. We present the first analyses of sex-related effects in dynamic connectivity and brain dynamism referenced to neurocognitive function, in a large sample of sex-, age- and motion-matched subjects in 24- and 51-network whole brain functional parcellations. We demonstrate that sexual dimorphism is present in human dynamic connectivity and in multiple high-order measures of brain dynamism, as well as validating prior work that sex-related differences exist in static intrinsic connectivity. We also provide the first evidence suggesting a link between differential neurocognitive performance by males and females and brain functional dynamics. Reduced dynamism in females, who spend more time in certain brain states and switch states less frequently, may provide a 'stickier' functional substrate associated with slower response inhibition, whereas males exhibit greater dynamic fluidity, change between certain states more often and range over a larger state space, achieving superior performance in mental rotation, which demands an iterative visualization and problem-solving approach. We conclude that sex is an important variable to consider in functional MRI experiments and the analysis of dynamic connectivity and brain dynamism.

Novel in silico multivariate mapping of intrinsic and anticorrelated connectivity to neurocognitive functional maps supports the maturational hypothesis of ADHD.

From childhood to adolescence, strengthened coupling in frontal, striatal and parieto-temporal regions associated with cognitive control, and increased anticorrelation between task-positive and task-negative circuits, subserve the reshaping of behavior. ADHD is a common condition peaking in adolescence and regressing in adulthood, with a wide variety of cognitive control deficits. Alternate hypotheses of ADHD emphasize lagging circuitry refinement versus categorical differences in network function. However, quantifying the individual circuit contributions to behavioral findings, and relative roles of maturational versus categorical effects, is challenging in vivo or in meta-analyses using task-based paradigms within the same pipeline, given the multiplicity of neurobehavioral functions implicated. To address this, we analyzed 46 positively-correlated and anticorrelated circuits in a multivariate model in resting-state data from 504 age- and gender-matched youth, and created a novel in silico method to map individual quantified effects to reverse inference maps of 8 neurocognitive functions consistently implicated in ADHD, as well as dopamine and hyperactivity. We identified only age- and gender-related effects in intrinsic connectivity, and found that maturational refinement of circuits in youth with ADHD occupied 3-10x more brain locations than in typical development, with the footprint, effect size and contribution of individual circuits varying substantially. Our analysis supports the maturational hypothesis of ADHD, suggesting lagging connectivity reorganization within specific subnetworks of fronto-parietal control, ventral attention, cingulo-opercular, temporo-limbic and cerebellar sub-networks contribute across neurocognitive findings present in this complex condition. We present the first analysis of anti-correlated connectivity in ADHD and suggest new directions for exploring residual and non-responsive symptoms.

Quantitative Volumetric Imaging and Clinical Outcome Characterization of Symptomatic Concussion in 10- to 14-Year-Old Adolescent Athletes.

OBJECTIVE: Prior work suggests that younger athletes may be more vulnerable to postconcussive syndrome. We investigated measures of clinical outcome and quantitative volumetric imaging in 10- to 14-year-old adolescent athletes to better understand the impact of concussion on this younger population. SETTING: Outpatient clinics. PARTICIPANTS: Ten- to 14-year-old symptomatic pediatric sports concussion patients and typically developing active controls. DESIGN: Prospective, observational multiclinic study. MAIN MEASURES: Demographics, magnetic resonance imaging, clinical assessments (neurocognitive function, postconcussive symptoms, mental health symptoms, quality of life). RESULTS: Neuropsychological performance was comparable between groups while symptoms of mental health were discriminating and comprised the top regression model describing factors related to overall health behavior impairment. Concussion patients had smaller total brain volume as well as total intracranial volume in comparison with controls even though there was no difference on measures of natural development (age, height, weight, education, gender, and handedness). CONCLUSIONS: Findings indicate that 10- to 14-year-old concussion patients symptomatic at 1 month more likely exhibit mental health symptoms impairing health behavior than cognitive dysfunction. There may be a vulnerability for those with smaller brain volumes at the time of the exposure. The study provides new data to support further investigation into risk factors for prolonged symptoms in this younger athlete population.

RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.

Predicting individual and population risk for disease outcomes and identifying persons at elevated risk is a key prerequisite for targeting interventions to improve health. However, current risk stratification tools for the common, chronic diseases that develop over the lifecourse and represent the majority of disease morbidity, mortality and healthcare costs are aging and achieve only moderate predictive performance. In some common, highly morbid conditions such as mental illness no risk stratification tools are yet available. There is an urgent need to improve predictive performance for chronic diseases and understand how cumulative, multifactorial risks aggregate over time so that intervention programs can be targeted earlier and more effectively in the disease course. Chronic diseases are the end outcomes of multifactorial risks that increment over years and represent cumulative, temporally-sensitive risk pathways. However, tools in current clinical use were constructed in older data and utilize inputs from a single data collection step. Here, we present RiskPath, a multistep deep learning method for temporally-sensitive biomedical risk prediction tailored for the constraints and demands of biomedical practice that achieves very strong performance and full translational explainability. RiskPath delineates and quantifies cumulative multifactorial risk pathways and allows the user to explore performance-complexity tradeoffs and constrain models as required by clinical use cases. Our results highlight the potential for developing a new generation of risk stratification tools and risk pathway mapping in time-dependent diseases and health outcomes by leveraging powerful timeseries deep learning methods in the wealth of biomedical data now appearing in large, longitudinal open science datasets.

Revisiting the relationship between autism and schizophrenia: toward an integrated neurobiology.

Schizophrenia and autism have been linked since their earliest descriptions. Both are disorders of cerebral specialization originating in the embryonic period. Genetic, molecular, and cytologic research highlights a variety of shared contributory mechanisms that may lead to patterns of abnormal connectivity arising from altered development and topology. Overt behavioral pathology likely emerges during or after neurosensitive periods in which resource demands overwhelm system resources and the individual's ability to compensate using interregional activation fails. We are at the threshold of being able to chart autism and schizophrenia from the inside out. In so doing, the door is opened to the consideration of new therapeutics that are developed based upon molecular, synaptic, and systems targets common to both disorders.

Predicting new onset thought disorder in early adolescence with optimized deep learning implicates environmental-putamen interactions.

Background: Thought disorder (TD) is a sensitive and specific marker of risk for schizophrenia onset. Specifying factors that predict TD onset in adolescence is important to early identification of youth at risk. However, there is a paucity of studies prospectively predicting TD onset in unstratified youth populations. Study Design: We used deep learning optimized with artificial intelligence (AI) to analyze 5,777 multimodal features obtained at 9-10 years from youth and their parents in the ABCD study, including 5,014 neural metrics, to prospectively predict new onset TD cases at 11-12 years. The design was replicated for all prevailing TD cases at 11-12 years. Study Results: Optimizing performance with AI, we were able to achieve 92% accuracy and F1 and 0.96 AUROC in prospectively predicting the onset of TD in early adolescence. Structural differences in the left putamen, sleep disturbances and the level of parental externalizing behaviors were specific predictors of new onset TD at 11-12 yrs, interacting with low youth prosociality, the total parental behavioral problems and parent-child conflict and whether the youth had already come to clinical attention. More important predictors showed greater inter-individual variability. Conclusions: This study provides robust person-level, multivariable signatures of early adolescent TD which suggest that structural differences in the left putamen in late childhood are a candidate biomarker that interacts with psychosocial stressors to increase risk for TD onset. Our work also suggests that interventions to promote improved sleep and lessen parent-child psychosocial stressors are worthy of further exploration to modulate risk for TD onset.

Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy.

Introduction: The externalizing disorders of attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD) are common in adolescence and are strong predictors of adult psychopathology. While treatable, substantial diagnostic overlap complicates intervention planning. Understanding which factors predict the onset of each disorder and disambiguating their different predictors is of substantial translational interest. Materials and methods: We analyzed 5,777 multimodal candidate predictors from children aged 9-10 years and their parents in the ABCD cohort to predict the future onset of ADHD, ODD, and CD at 2-year follow-up. We used deep learning optimized with an innovative AI algorithm to jointly optimize model training, perform automated feature selection, and construct individual-level predictions of illness onset and all prevailing cases at 11-12 years and examined relative predictive performance when candidate predictors were restricted to only neural metrics. Results: Multimodal models achieved ~86-97% accuracy, 0.919-0.996 AUROC, and ~82-97% precision and recall in testing in held-out, unseen data. In neural-only models, predictive performance dropped substantially but nonetheless achieved accuracy and AUROC of ~80%. Parent aggressive and externalizing traits uniquely differentiated the onset of ODD, while structural MRI metrics in the limbic system were specific to CD. Psychosocial measures of sleep disorders, parent mental health and behavioral traits, and school performance proved valuable across all disorders. In neural-only models, structural and functional MRI metrics in subcortical regions and cortical-subcortical connectivity were emphasized. Overall, we identified a strong correlation between accuracy and final predictor importance. Conclusion: Deep learning optimized with AI can generate highly accurate individual-level predictions of the onset of early adolescent externalizing disorders using multimodal features. While externalizing disorders are frequently co-morbid in adolescents, certain predictors were specific to the onset of ODD or CD vs. ADHD. To our knowledge, this is the first machine learning study to predict the onset of all three major adolescent externalizing disorders with the same design and participant cohort to enable direct comparisons, analyze >200 multimodal features, and include many types of neuroimaging metrics. Future study to test our observations in external validation data will help further test the generalizability of these findings.

Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence.

Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known about how to robustly identify youth at risk to target intervention efforts known to improve outcomes. Barriers to knowledge have included obtaining robust predictions while simultaneously analyzing large numbers of different types of candidate predictors. In a new, large, transdiagnostic youth sample and multidomain high-dimension data, we used 160 candidate predictors encompassing neural, prenatal, developmental, physiologic, sociocultural, environmental, emotional and cognitive features and leveraged three different machine learning algorithms optimized with a novel artificial intelligence meta-learning technique to predict individual cases of anxiety, depression, attention deficit, disruptive behaviors and post-traumatic stress. Our models tested well in unseen, held-out data (AUC ≥ 0.94). By utilizing a large-scale design and advanced computational approaches, we were able to compare the relative predictive ability of neural versus psychosocial features in a principled manner and found that psychosocial features consistently outperformed neural metrics in their relative ability to deliver robust predictions of individual cases. We found that deep learning with artificial neural networks and tree-based learning with XGBoost outperformed logistic regression with ElasticNet, supporting the conceptualization of mental illnesses as multifactorial disease processes with non-linear relationships among predictors that can be robustly modeled with computational psychiatry techniques. To our knowledge, this is the first study to test the relative predictive ability of these gold-standard algorithms from different classes across multiple mental health conditions in youth within the same study design in multidomain data utilizing >100 candidate predictors. Further research is suggested to explore these findings in longitudinal data and validate results in an external dataset.

Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.

Artificial intelligence and machine learning techniques have proved fertile methods for attacking difficult problems in medicine and public health. These techniques have garnered strong interest for the analysis of the large, multi-domain open science datasets that are increasingly available in health research. Discovery science in large datasets is challenging given the unconstrained nature of the learning environment where there may be a large number of potential predictors and appropriate ranges for model hyperparameters are unknown. As well, it is likely that explainability is at a premium in order to engage in future hypothesis generation or analysis. Here, we present a novel method that addresses these challenges by exploiting evolutionary algorithms to optimize machine learning discovery science while exploring a large solution space and minimizing bias. We demonstrate that our approach, called integrated evolutionary learning (IEL), provides an automated, adaptive method for jointly learning features and hyperparameters while furnishing explainable models where the original features used to make predictions may be obtained even with artificial neural networks. In IEL the machine learning algorithm of choice is nested inside an evolutionary algorithm which selects features and hyperparameters over generations on the basis of an information function to converge on an optimal solution. We apply IEL to three gold standard machine learning algorithms in challenging, heterogenous biobehavioral data: deep learning with artificial neural networks, decision tree-based techniques and baseline linear models. Using our novel IEL approach, artificial neural networks achieved ≥ 95% accuracy, sensitivity and specificity and 45-73% R 2 in classification and substantial gains over default settings. IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. This approach offers significant flexibility, enlarges the solution space and mitigates bias that may arise from manual or semi-manual hyperparameter tuning and feature selection and presents the opportunity to select the inner machine learning algorithm based on the results of optimized learning for the problem at hand.

Sex/gender differences in the neural substrate of long-term memory.

Sex/gender-related differences in neurocognitive task performance and their neural correlates have long been of substantial research interest. Spets & Slotnick's robust study joins a growing body of evidence that significant sex/gender differences exist in long term memory and neurocognition more broadly. In addition to fundamental differences in the neural substrate, hormonal cycles, divergent neurodevelopmental trajectories, sex versus gender identification and sociocultural and educational influences are likely important factors. Building upon these findings, future studies in larger sample sizes should carefully measure these potential modulating and/or confounding variables in order to provide a nuanced picture of sex/gender-related differences in brain function.

Sex-related differences in brain dynamism at rest as neural correlates of positive and negative valence system constructs.

Clinical anxiety and depression are the most prevalent mental illnesses, likely representing maladaptive expressions of negative valence systems concerned with conditioned responses to fear, threat, loss, and frustrative nonreward. These conditions exhibit similar, striking sex/gender-related differences in onset, incidence, and severity for which the neural correlates are not yet established. In alarge sample of neurotypical young adults, we demonstrate that intrinsic brain dynamism metrics derived from sex-sensitive models of whole-brain network function are significantly associated with valence system traits. Surprisingly, we found that greater brain dynamism is strongly positively correlated to anxiety and depression traits in males, but almost wholly decoupled from traits for important cognitive control and reappraisal strategies associated with positive valence. Conversely, intrinsic brain dynamism is strongly positively coupled to drive, novelty-seeking and self-control in females with only rare or non-significant directional negative correlation with anxiety and depression traits. Our results suggest that the dynamic neural correlates of traits for valence, anxiety and depression are significantly different in males/men and females/women. These findings may relate to the known sex/gender-related differences in cognitive reappraisal of emotional experiences and clinical presentations of anxiety and depression, with potential relevance to gold standard therapies based on enhancing cognitive control strategies.

KSADS-COMP Perspectives on Child Psychiatric Diagnostic Assessment and Treatment Planning.

Gibbons et al.1 demonstrated the utility of computerized adaptive tests (CATs) based on multidimensional item response theory for the assessment of depression, anxiety, mania/hypomania, attention-deficit/hyperactivity disorder, conduct disorder, oppositional defiant disorder, and suicidality in children and adolescents. The Kiddie-Computerized Adaptive Test (K-CAT) demonstrated good convergent validity, test-retest reliability, and diagnostic concordance with diagnoses derived using the paper-and-pencil Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS) child psychiatric interview.

Dynamic connectivity and the effects of maturation in youth with attention deficit hyperactivity disorder.

The analysis of time-varying connectivity by using functional MRI has gained momentum given its ability to complement traditional static methods by capturing additional patterns of variation in human brain function. Attention deficit hyperactivity disorder (ADHD) is a complex, common developmental neuropsychiatric disorder associated with heterogeneous connectivity differences that are challenging to disambiguate. However, dynamic connectivity has not been examined in ADHD, and surprisingly few whole-brain analyses of static functional network connectivity (FNC) using independent component analysis (ICA) exist. We present the first analyses of time-varying connectivity and whole-brain FNC using ICA in ADHD, introducing a novel framework for comparing local and global dynamic connectivity in a 44-network model. We demonstrate that dynamic connectivity analysis captures robust motifs associated with group effects consequent on the diagnosis of ADHD, implicating increased global dynamic range, but reduced fluidity and range localized to the default mode network system. These differentiate ADHD from other major neuropsychiatric disorders of development. In contrast, static FNC based on a whole-brain ICA decomposition revealed solely age effects, without evidence of group differences. Our analysis advances current methods in time-varying connectivity analysis, providing a structured example of integrating static and dynamic connectivity analysis to further investigation into functional brain differences during development.

Multilevel Mapping of Sexual Dimorphism in Intrinsic Functional Brain Networks.

Differences in cognitive performance between males and females are well-described, most commonly in certain spatial and language tasks. Sex-related differences in cognition are relevant to the study of the neurotypical brain and to neuropsychiatric disorders, which exhibit prominent disparities in the incidence, prevalence and severity of symptoms between men and women. While structural dimorphism in the human brain is well-described, controversy exists regarding the existence and degree of sex-related differences in brain function. We analyzed resting-state functional MRI from 650 neurotypical young adults matched for age and sex to determine the degree of sexual dimorphism present in intrinsic functional networks. Multilevel modeling was pursued to create 8-, 24-, and 51-network models of whole-brain data to quantify sex-related effects in network activity with increasing resolution. We determined that sexual dimorphism is present in the majority of intrinsic brain networks and affects ∼0.5-2% of brain locations surveyed in the three whole-brain network models. It is particularly common in task-positive control networks and is pervasive among default mode networks. The size of sex-related effects varied by network but can be moderate or even large in size. Female > male effects were on average larger, but male > female effects spread across greater network territory. Using a novel methodology, we mapped dimorphic locations to meta-analytic association test maps derived from task fMRI, demonstrating that the neurocognitive footprint of intrinsic neural correlates is much larger in males. All results were replicated in a motion-matched sub-sample. Our findings argue that sex is an important biological variable in human brain function and suggest that observed differences in neurocognitive performance have identifiable intrinsic neural correlates.

Longitudinal Clinical and Neuroimaging Evaluation of Symptomatic Concussion in 10- to 14-year-old Youth Athletes.

This study longitudinally assessed 10- to 14-year-old patients with sports and recreational concussion (n = 22) who remained symptomatic 3 to 4weeks post-injury compared with typically developing controls (n = 24). Examination by multi-modal magnetic resonance imaging (MRI) and multi-domain clinical outcome measures was completed at 1-month and 6-months post-injury. Concussion patients showed evidence of improvement by 6-month follow-up in domains of cognitive function, whereas measures of psychological health were less resolved with patients exhibiting sustained symptoms of depression, behavior impairment, and concussion symptoms. Quantitative neuroimaging measures identified measures indicative of chronic injury with regional reductions observed by both volumetric segmentation and white matter fractional anisotropy (FA) from diffusion tensor imaging (DTI). Volumetric reductions (p < 0.01) were observed in the middle anterior and posterior portions of the corpus callosum, and right caudal anterior cingulate cortex of patients, although none held after strict correction. Examination of the FA data identified significant reductions in the left middle frontal gyrus white matter (p = 0.0003). Linear regression analysis on the 6-month depression outcome variable using the initial clinical, demographic, and imaging measures identified the top predictive models to include concussion diagnosis, and initial symptoms of depression, concussion symptoms, and sleep impairment with additional contribution from other measures of mental health, behavior impairment, and quality of life depending on the model (adjusted r-squared = 0.69 indicating strong predictive ability). This study supports further inclusion of mental health rehabilitation and imaging supplementing traditional cognitive rehabilitation strategies employed in these young athletes.

Intellectual Disability and Psychotropic Medications.

CASE: Andrew is a 17-year-old male with trisomy 21, commonly known as Down syndrome, and accompanying severe intellectual disability who presents to your primary care office with his father for the first time to establish care and assistance with transition. Andrew has a history of a complete atrioventricular canal that was repaired as an infant and poorly controlled infantile spasms. Currently, he struggles with constipation, esophageal strictures, medullary nephrocalcinosis, urinary retention, sleep dysregulation, G-tube dependency, and hip dysplasia.Andrew walked at 11 to 12 years of age. Currently, he ambulates on his feet at home and in a wheelchair out in the community. He is nonverbal but can imprecisely sign for "more" and understands a few words. His father reports that his main concern is long-standing nonsuicidal self-injury (NSSI) and aggression. His self-injury consists of head banging against hard objects such as concrete floors and biting or scratching himself to the point of bleeding. Over the past 13 years, he has been prescribed over 10 different psychotropic medications, including various typical and atypical antipsychotics, selective serotonin reuptake inhibitors, benzodiazepines, mood stabilizers, and alpha agonists, all of which were discontinued because of the perception of undesirable side effects or lack of efficacy. His current medications include aripiprazole, olanzapine, levetiracetam, clorazepate, and trazodone. To rule out causes of irritability, you order a brain and spine magnetic resonance imaging, metabolic testing (for causes of NSSI such as Lesch-Nyhan), an autoimmune workup (for causes of pain or inflammation such as juvenile idiopathic arthritis), and hearing/vision testing, which are all normal. Previous testing by subspecialists (he is followed by gastroenterology, sleep medicine, orthopedics, nephrology, neurology, cardiology, and psychiatry) included normal renal ultrasound and no clear sources of gastrointestinal pain. However, key providers are spread among multiple institutions and do not regularly communicate.Andrew lives with his parents, who are highly educated and very dedicated to his health and wellness. His mother travels frequently for work, and his father is Andrew's full-time caregiver. Despite remaining ostensibly positive, his father reports significant caregiver burnout and fatigue.Over the next several months, Andrew continues to experience worsening NSSI necessitating medication changes despite active involvement in applied behavior analysis therapy. During this time, he presents to the emergency department multiple times for irritability and self-injury. On examination, he is aggressive, irritable, has bruises on his forehead and scratches on his skin, and has intermittent vertical gaze deviation that was noticeable to parents. The rest of his physical and neurological examination was unremarkable and revealed no asymmetry, clonus, hyperreflexia, or changes in muscle tone. While examining his extremities, joints, and abdomen, there was no obvious source of pain.What are your next steps? How would you support this family, both in the immediate management of his self-injury and long-term care needs for this medically and behaviorally complex adolescent?

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis.

For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.

Psychiatric assessment of severe presentations in autism spectrum disorders and intellectual disability.

Children with autism spectrum and related disorders and intellectual disability are not protected from the experience of psychiatric illnesses. Many factors can contribute to exacerbation of existing behavioral symptoms or to the emergence of new psychiatric problems. The psychiatric assessment must thus take into account a range of possible etiologic or contributory factors. The approach outlined in this article highlights the value of assessing 4 broad domains, including diagnostic (genetic) factors, medical considerations, developmental influences, and environmental factors. Examples of how the consideration of each of these domains may inform the diagnostic formulation are highlighted.