Kimberly L. H. Carpenter

The benefit of directly comparing autism and schizophrenia for revealing mechanisms of social cognitive impairment

Autism and schizophrenia share a history of diagnostic conflation that was not definitively resolved until the publication of the DSM-III in 1980. Though now recognized as heterogeneous disorders with distinct developmental trajectories and dissociative features, much of the early nosological confusion stemmed from apparent overlap in certain areas of social dysfunction. In more recent years, separate but substantial literatures have accumulated for autism and schizophrenia demonstrating that abnormalities in social cognition directly contribute to the characteristic social deficits of both disorders. The current paper argues that direct comparison of social cognitive impairment can highlight shared and divergent mechanisms underlying pathways to social dysfunction, a process that can provide significant clinical benefit by informing the development of tailored treatment efforts. Thus, while the history of diagnostic conflation between autism and schizophrenia may have originated in similarities in social dysfunction, the goal of direct comparisons is not to conflate them once again but rather to reveal distinctions that illuminate disorder-specific mechanisms and pathways that contribute to social cognitive impairment.

Psychological and Psychosocial Impairment in Preschoolers With Selective Eating

OBJECTIVE: We examined the clinical significance of moderate and severe selective eating (SE). Two levels of SE were examined in relation to concurrent psychiatric symptoms and as a risk factor for the emergence of later psychiatric symptoms. Findings are intended to guide health care providers to recognize when SE is a problem worthy of intervention. METHODS: A population cohort sample of 917 children aged 24 to 71 months and designated caregivers were recruited via primary care practices at a major medical center in the Southeast as part of an epidemiologic study of preschool anxiety. Caregivers were administered structured diagnostic interviews (the Preschool Age Psychiatric Assessment) regarding the child’s eating and related self-regulatory capacities, psychiatric symptoms, functioning, and home environment variables. A subset of 188 dyads were assessed a second time ∼24.7 months from the initial assessment. RESULTS: Both moderate and severe levels of SE were associated with psychopathological symptoms (anxiety, depression, attention-deficit/hyperactivity disorder) both concurrently and prospectively. However, the severity of psychopathological symptoms worsened as SE became more severe. Impairment in family functioning was reported at both levels of SE, as was sensory sensitivity in domains outside of food and the experience of food aversion. CONCLUSIONS: Findings suggest that health care providers should intervene at even moderate levels of SE. SE associated with impairment in function should now be diagnosed as avoidant/restrictive food intake disorder, an eating disorder that encapsulates maladaptive food restriction, which is new to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

Attention deficits in schizophrenia--preliminary evidence of dissociable transient and sustained deficits.

Attention deficits are considered to be fundamental in patients with schizophrenia. During attention tasks, patients with schizophrenia have been shown to display increased brain activity in some neuroimaging studies but reduced brain activity in others. These conflicting findings may be due to some study designs primarily eliciting transient engagement of attention and other study designs primarily eliciting sustained engagement of attention. In the present study, ten males with schizophrenia and fourteen age-matched, male controls performed a visual selective attention task. A mixed block/event-related fMRI design was used, allowing for separate analysis of transient and sustained phases of attention. Results revealed that the schizophrenia group made significantly fewer correct responses and displayed a significantly slower mean response time than the control group. Voxel-wise random effects analyses revealed that both groups displayed activation in regions considered to constitute a core attentional network including the anterior cingulate gyrus, dorsolateral prefrontal cortex, insula and inferior parietal sulcus. Region of Interest (ROI) analyses revealed that across the entire sequence of task and non-task blocks, the schizophrenia group displayed a greater percentage of active voxels than controls in many ROIs. However, during transient periods most pertinent to task performance, the schizophrenia group displayed a lower percentage of active voxels than controls. These results help to explain contrasting findings across previous studies and suggest that attention deficits displayed by patients with schizophrenia are more likely to reflect deficits in modulating brain activity in response to variations in transient, attention demanding stimuli, rather than deficits in sustained attention.

The Neural Circuitry of Autism

Autism is a complex neurodevelopmental disorder, characterized by deficits in social emotional, and language domains, as well as repetitive restrictive behaviors. The vast heterogeneity of the clinical and behavioral symptoms has made it rather difficult to delineate the neural circuitry affiliated with these domains of dysfunction. The current review aims at broadly outlining the latest research into the neurobiology and neural circuitry underlying the core domains of deficits in autism. We further discuss new avenues of research that can further our understanding of the dimensions of this complex disorder.

Preschool anxiety disorders predict different patterns of amygdala-prefrontal connectivity at school-age.

Objective In this prospective, longitudinal study of young children, we examined whether a history of preschool generalized anxiety, separation anxiety, and/or social phobia is associated with amygdala-prefrontal dysregulation at school-age. As an exploratory analysis, we investigated whether distinct anxiety disorders differ in the patterns of this amygdala-prefrontal dysregulation. Methods Participants were children taking part in a 5-year study of early childhood brain development and anxiety disorders. Preschool symptoms of generalized anxiety, separation anxiety, and social phobia were assessed with the Preschool Age Psychiatric Assessment (PAPA) in the first wave of the study when the children were between 2 and 5 years old. The PAPA was repeated at age 6. We conducted functional MRIs when the children were 5.5 to 9.5 year old to assess neural responses to viewing of angry and fearful faces. Results A history of preschool social phobia predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces. Preschool generalized anxiety predicted less functional connectivity between the amygdala and dorsal prefrontal cortices in response to fearful faces. Finally, a history of preschool separation anxiety predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces and greater school-age functional connectivity between the amygdala and dorsal prefrontal cortices to angry faces. Conclusions Our results suggest that there are enduring neurobiological effects associated with a history of preschool anxiety, which occur over-and-above the effect of subsequent emotional symptoms. Our results also provide preliminary evidence for the neurobiological differentiation of specific preschool anxiety disorders.

Use of a Digital Modified Checklist for Autism in Toddlers – Revised with Follow-up to Improve Quality of Screening for Autism

Objectives To assess changes in quality of care for children at risk for autism spectrum disorders (ASD) due to process improvement and implementation of a digital screening form. Study design The process of screening for ASD was studied in an academic primary care pediatrics clinic before and after implementation of a digital version of the Modified Checklist for Autism in Toddlers – Revised with Follow‐up with automated risk assessment. Quality metrics included accuracy of documentation of screening results and appropriate action for positive screens (secondary screening or referral). Participating physicians completed pre‐ and postintervention surveys to measure changes in attitudes toward feasibility and value of screening for ASD. Evidence of change was evaluated with statistical process control charts and χ2 tests. Results Accurate documentation in the electronic health record of screening results increased from 54% to 92% (38% increase, 95% CI 14%‐64%) and appropriate action for children screening positive increased from 25% to 85% (60% increase, 95% CI 35%‐85%). A total of 90% of participating physicians agreed that the transition to a digital screening form improved their clinical assessment of autism risk. Conclusions Implementation of a tablet‐based digital version of the Modified Checklist for Autism in Toddlers – Revised with Follow‐up led to improved quality of care for children at risk for ASD and increased acceptability of screening for ASD. Continued efforts towards improving the process of screening for ASD could facilitate rapid, early diagnosis of ASD and advance the accuracy of studies of the impact of screening.

Computer vision analysis captures atypical attention in toddlers with autism

To demonstrate the capability of computer vision analysis to detect atypical orienting and attention behaviors in toddlers with autism spectrum disorder. One hundered and four toddlers of 16–31 months old (mean = 22) participated in this study. Twenty-two of the toddlers had autism spectrum disorder and 82 had typical development or developmental delay. Toddlers watched video stimuli on a tablet while the built-in camera recorded their head movement. Computer vision analysis measured participants’ attention and orienting in response to name calls. Reliability of the computer vision analysis algorithm was tested against a human rater. Differences in behavior were analyzed between the autism spectrum disorder group and the comparison group. Reliability between computer vision analysis and human coding for orienting to name was excellent (intra-class coefficient 0.84, 95% confidence interval 0.67–0.91). Only 8% of toddlers with autism spectrum disorder oriented to name calling on >1 trial, compared to 63% of toddlers in the comparison group (p = 0.002). Mean latency to orient was significantly longer for toddlers with autism spectrum disorder (2.02 vs 1.06 s, p = 0.04). Sensitivity for autism spectrum disorder of atypical orienting was 96% and specificity was 38%. Older toddlers with autism spectrum disorder showed less attention to the videos overall (p = 0.03). Automated coding offers a reliable, quantitative method for detecting atypical social orienting and reduced sustained attention in toddlers with autism spectrum disorder.

Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach

Early childhood anxiety disorders are common, impairing, and predictive of anxiety and mood disorders later in childhood. Epidemiological studies over the last decade find that the prevalence of impairing anxiety disorders in preschool children ranges from 0.3% to 6.5%. Yet, less than 15% of young children with an impairing anxiety disorder receive a mental health evaluation or treatment. One possible reason for the low rate of care for anxious preschoolers is the lack of affordable, timely, reliable and valid tools for identifying young children with clinically significant anxiety. Diagnostic interviews assessing psychopathology in young children require intensive training, take hours to administer and code, and are not available for use outside of research settings. The Preschool Age Psychiatric Assessment (PAPA) is a reliable and valid structured diagnostic parent-report interview for assessing psychopathology, including anxiety disorders, in 2 to 5 year old children. In this paper, we apply machine-learning tools to already collected PAPA data from two large community studies to identify sub-sets of PAPA items that could be developed into an efficient, reliable, and valid screening tool to assess a young child’s risk for an anxiety disorder. Using machine learning, we were able to decrease by an order of magnitude the number of items needed to identify a child who is at risk for an anxiety disorder with an accuracy of over 96% for both generalized anxiety disorder (GAD) and separation anxiety disorder (SAD). Additionally, rather than considering GAD or SAD as discrete/binary entities, we present a continuous risk score representing the child’s risk of meeting criteria for GAD or SAD. Identification of a short question-set that assesses risk for an anxiety disorder could be a first step toward development and validation of a relatively short screening tool feasible for use in pediatric clinics and daycare/preschool settings.

QUESTIONNAIRE SIMPLIFICATION FOR FAST RISK ANALYSIS OF CHILDREN'S MENTAL HEALTH

Early detection and treatment of psychiatric disorders on children has shown significant impact in their subsequent development and quality of life. The assessment of psychopathology in childhood is commonly carried out by performing long comprehensive interviews such as the widely used Preschool Age Psychiatric Assessment (PAPA). Unfortunately, the time required to complete a full interview is too long to apply it at the scale of the actual population at risk, and most of the population goes undiagnosed or is diagnosed significantly later than desired. In this work, we aim to learn from unique and very rich previously collected PAPA examples the inter-correlations between different questions in order to provide a reliable risk analysis in the form of a much shorter interview. This helps to put such important risk analysis at the hands of regular practitioners, including teachers and family doctors. We use for this purpose the alternating decision trees algorithm, which combines decision trees with boosting to produce small and interpretable decision rules. Rather than a binary prediction, the algorithm provides a measure of confidence in the classification outcome. This is highly desirable from a clinical perspective, where it is preferable to abstain a decision on the low-confidence cases and recommend further screening. In order to prevent over-fitting, we propose to use network inference analysis to predefine a set of candidate question with consistent high correlation with the diagnosis. We report encouraging results with high levels of prediction using two independently collected datasets. The length and accuracy of the developed method suggests that it could be a valuable tool for preliminary evaluation in everyday care.