Leveraging big data to map neurodevelopmental trajectories in pediatric anxiety

Sleep fails to depotentiate amygdala-reactivity to negative emotional stimuli in youth with elevated symptoms of anxiety

Sleep-related problems often precede escalating anxiety in early adolescence. Pushing beyond broad sleep-mental health associations and toward mechanistic theories of their interplay can inform etiological models of psychopathology. Recent studies suggest that sleep depotentiates neural (e.g., amygdala) reactivity during reexposure to negative emotional stimuli in adults. Persistent amygdala reactivity to negative experiences and poor sleep characterize anxiety, particularly at the transition to adolescence. We propose that sleep depotentiates amygdala reactivity in youth but fails to do so among youth with anxiety. Participants (n = 34; 18 males; age, mean [M] = 11.35, standard deviation [SD] = 2.00) recruited from the community and specialty anxiety clinics viewed valenced images (positive, negative, and neutral) across two fMRI sessions (Study, Test), separated by a 10-12-hour retention period of sleep or wake (randomized). Mixed linear models regressed basolateral amygdala (BLA) activation and BLA-medial prefrontal cortex (mPFC) functional connectivity to negative images on Time, Condition, and Anxiety Severity. There were greater reductions in BLA activations to negative target images from Study to Test in the Sleep Condition, which was blunted with higher anxiety (b = -0.065, z = -2.355, p = 0.019). No such sleep- or anxiety-related effects were observed for BLA-mPFC functional connectivity (ps > 0.05). Sleep supports depotentiation of amygdala reactivity to negative stimuli in youth, but this effect is blunted at higher levels of anxiety. Disruptions in sleep-related affective habituation may be a critical, modifiable driver of anxiety.

Extinction Learning Across Development: Neurodevelopmental Changes and Implications for Pediatric Anxiety Disorders

Alterations in extinction learning relate to the development and maintenance of anxiety disorders across the lifespan. While exposure therapy, based on principles of extinction, can be highly effective for treating anxiety, many patients do not show sufficient improvement following treatment. In particular, evidence suggests that exposure therapy does not work sufficiently for up to 40% of children who receive this evidence-based treatment.Importantly, fear learning and extinction, as well as the neural circuitry supporting these processes, undergo dynamic changes across development. An improved understanding of developmental changes in extinction learning and the associated neural circuitry may help to identify targets to improve treatment response in clinically anxious children and adolescents. In this chapter, we provide a brief overview of methods used to study fear learning and extinction in developmental populations. We then review what is currently known about the developmental changes that occur in extinction learning and related neural circuitry. We end this chapter with a discussion of the implications of these neurodevelopmental changes for the characterization and treatment of pediatric anxiety disorders.

Data harnessing to nurture the human mind for a tailored approach to the child

Big data in pediatrics is an ocean of structured and unstructured data. Big data analysis helps to dive into the ocean of data to filter out information that can guide pediatricians in their decision making, precision diagnosis, and targeted therapy. In addition, big data and its analysis have helped in the surveillance, prevention, and performance of the health system. There has been a considerable amount of work in pediatrics that we have tried to highlight in this review and some of it has been already incorporated into the health system. Work in specialties of pediatrics is still forthcoming with the creation of a common data model and amalgamation of the huge "omics" database. The physicians entrusted with the care of children must be aware of the outcome so that they can play a role to ensure that big data algorithms have a clinically relevant effect in improving the health of their patients. They will apply the outcome of big data and its analysis in patient care through clinical algorithms or with the help of embedded clinical support alerts from the electronic medical records. IMPACT: Big data in pediatrics include structured, unstructured data, waveform data, biological, and social data. Big data analytics has unraveled significant information from these databases. This is changing how pediatricians will look at the body of available evidence and translate it into their clinical practice. Data harnessed so far is implemented in certain fields while in others it is in the process of development to become a clinical adjunct to the physician. Common databases are being prepared for future work. Diagnostic and prediction models when incorporated into the health system will guide the pediatrician to a targeted approach to diagnosis and therapy.

Translating Big Data to Clinical Outcomes in Anxiety: Potential for Multimodal Integration

PURPOSE OF THE REVIEW

This review describes approaches to research on anxiety that attempt to link neural correlates to treatment response and novel therapies. The review emphasizes pediatric anxiety disorders since most anxiety disorders begin before adulthood.

RECENT FINDINGS

Recent literature illustrates how current treatments for anxiety manifest diverse relations with a range of neural markers. While some studies demonstrate post-treatment normalization of markers in anxious individuals, others find persistence of group differences. For other markers, which show no pretreatment association with anxiety, the markers nevertheless distinguish treatment-responders from non-responders. Heightened error related negativity represents the risk marker discussed in the most depth; however, limitations in measures related to error responding necessitate multimodal and big-data approaches. Single risk markers show limits as correlates of treatment response. Large-scale, multimodal data analyzed with predictive models may illuminate additional risk markers related to anxiety disorder treatment outcomes. Such work may identify novel targets and eventually guide improvements in treatment response/outcomes.

Structural Brain Correlates of Childhood Inhibited Temperament: An ENIGMA-Anxiety Mega-analysis

Temperament involves stable behavioral and emotional tendencies that differ between individuals, which can be first observed in infancy or early childhood and relate to behavior in many contexts and over many years.¹ One of the most rigorously characterized temperament classifications relates to the tendency of individuals to avoid the unfamiliar and to withdraw from unfamiliar people, objects, and unexpected events. This temperament is referred to as behavioral inhibition or inhibited temperament (IT).² IT is a moderately heritable trait¹ that can be measured in multiple species.³ In humans, levels of IT can be quantified from the first year of life through direct behavioral observations or reports by caregivers or teachers. Similar approaches as well as self-report questionnaires on current and/or retrospective levels of IT¹ can be used later in life.

Modelling the neurodevelopmental pathogenesis in neuropsychiatric disorders. Bioactive kynurenines and their analogues as neuroprotective agents-in celebration of 80th birthday of Professor Peter Riederer

Following introduction of the monoamine oxidase type B inhibitor selegiline for the treatment of Parkinson's disease (PD), discovery of the action mechanism of Alzheimer's disease-modifying agent memantine, the role of iron in PD, and the loss of electron transport chain complex I in PD, and development of the concept of clinical neuroprotection, Peter Riederer launched one of the most challenging research project neurodevelopmental aspects of neuropsychiatric disorders. The neurodevelopmental theory holds that a disruption of normal brain development in utero or during early life underlies the subsequent emergence of neuropsychiatric symptoms during later life. Indeed, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and the International Classification of Diseases, 11th Revision categorize autism spectrum disorder and attention deficit hyperactivity disorder in neurodevelopmental disorders (NDDs). More and more evidence, especially from preclinical studies, is revealing that neurodevelopmental pathology is not limited to the diagnostic class above, but also contributes to the development of other psychiatric disorders such as schizophrenia, bipolar disorder, and obsessive-compulsive disorder as well as neurodegenerative diseases such as PD and Huntington's disease. Preclinical animal research is taking a lead in understanding the pathomechanisms of NDDs, searching for novel targets, and developing new neuroprotective agents against NDDs. This narrative review discusses emerging evidence of the neurodevelopmental etiology of neuropsychiatric disorders, recent advances in modelling neurodevelopmental pathogenesis, potential strategies of clinical neuroprotection using novel kynurenine metabolites and analogues, and future research direction for NDDs.

Early Adversity and Development: Parsing Heterogeneity and Identifying Pathways of Risk and Resilience

Adversity early in life is common and is a major risk factor for the onset of psychopathology. Delineating the neurodevelopmental pathways by which early adversity affects mental health is critical for early risk identification and targeted treatment approaches. A rapidly growing cross-species literature has facilitated advances in identifying the mechanisms linking adversity with psychopathology, specific dimensions of adversity and timing-related factors that differentially relate to outcomes, and protective factors that buffer against the effects of adversity. Yet, vast complexity and heterogeneity in early environments and neurodevelopmental trajectories contribute to the challenges of understanding risk and resilience in the context of early adversity. In this overview, the author highlights progress in four major areas-mechanisms, heterogeneity, developmental timing, and protective factors; synthesizes key challenges; and provides recommendations for future research that can facilitate progress in the field. Translation across species and ongoing refinement of conceptual models have strong potential to inform prevention and intervention strategies that can reduce the immense burden of psychopathology associated with early adversity.