Developmental Trajectories of Executive and Verbal Processes in Children with Phenylketonuria

Phenylketonuria (PKU) is a recessive disorder characterized by disruption in the metabolism of the amino acid phenylalanine. Using a verbal fluency task, previous studies demonstrated that word production is reduced in individuals with PKU relative to controls. Beyond word production, verbal fluency output can be scored for clustering and switching, which enable characterization of verbal and executive processes, respectively. The present study is the first to evaluate clustering and switching in PKU within a longitudinal design, thereby elucidating the developmental time course of core cognitive processes. To this end, semantic (animals, food/drink) and phonemic (S words, F words) fluency data were obtained at three longitudinal time-points in children with early- and continuously-treated PKU (n = 23; 11 males, 12 females) and age-matched controls (n = 44; 22 males, 22 females). Age ranged from 7-19 years at the outset of the study; approximately 12-18 months elapsed between each time-point. Word production, clustering, and switching scores were analyzed using hierarchical linear modeling (HLM) to account for longitudinal dependencies and to allow evaluation of the individual contributions of age, group, and the interaction between age and group. Results indicated impairments in frontally-mediated executive processes in children with PKU relative to controls, and these impairments were exacerbated with increasing age. Additionally, children with PKU relied more on verbal processes relative to controls, suggesting that they may use compensatory strategies to overcome executive deficits. Continued efforts to characterize cognitive development in children with PKU will inform our understanding of the disorder across the lifespan

Testing Candidate Cerebellar Presymptomatic Biomarkers for Autism Spectrum Disorder

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed on the basis of social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar-mediated error signaling impairments contribute to the causation of ASD. However, the relationship between infant cerebellar functional connectivity (fcMRI) and later ASD behaviors and outcomes has not been investigated. Such work is critical to establish early (presymptomatic) cerebellar correlates of ASD. Methods: Data from the Infant Brain Imaging Study (n=94, 68 male) were used to evaluate cerebellar fcMRI as a presymptomatic biomarker for ASD. Specifically, brain-behavior associations were analyzed for 6-month cerebellar connections in relation to later (12- and 24-month) ASD behaviors and outcomes using univariate tests of association, multivariate machine learning prediction, and fcMRI enrichment. Univariate and multivariate approaches focused on cerebellar-frontoparietal network (FPN is implicated in error-signaling) and cerebellar-default mode network (DMN is implicated in adult studies of ASD) connections, while enrichment afforded a data-driven test of whole-brain connectivity. Results: Univariate tests of cerebellar-FPN and cerebellar-DMN connections failed to implicate the cerebellum in ASD, despite \u3e 80% power to detect medium-sized effects. Multivariate tests in high-risk infants using cerebellar-FPN and cerebellar-DMN connections similarly failed to achieve above-chance classification accuracy for ASD diagnosis, despite replicating procedures that achieved \u3e 80% positive predictive value in whole-brain data. FcMRI enrichment identified correlates of ASD-associated behaviors in brain networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (CO) and medial visual (mVis) networks. However, post-hoc tests did not support a unique role for cerebellar connectivity within these networks. Conclusions: Contrary to contemporary theories, we failed to observe a relationship between infant cerebellar fcMRI and ASD. Instead—in the first-known application of fcMRI enrichment to temporally lagged, early developmental brain-behavior associations—we identified infant control (FPN, CO), visual, and default mode correlates of later ASD behaviors. Future work may investigate whether connectivity involving these networks prospectively predicts ASD diagnosis, thereby expediting intervention and furthering etiologic understanding

Pretreatment cognitive and neural differences between sapropterin dihydrochloride responders and non-responders with phenylketonuria

Sapropterin dihydrochloride (BH4) reduces phenylalanine (Phe) levels and improves white matter integrity in a subset of individuals with phenylketonuria (PKU) known as “responders.” Although prior research has identified biochemical and genotypic differences between BH4 responders and non-responders, cognitive and neural differences remain largely unexplored. To this end, we compared intelligence and white matter integrity prior to treatment with BH4 in 13 subsequent BH4 responders with PKU, 16 subsequent BH4 non-responders with PKU, and 12 healthy controls. Results indicated poorer intelligence and white matter integrity in non-responders compared to responders prior to treatment. In addition, poorer white matter integrity was associated with greater variability in Phe across the lifetime in non-responders but not in responders. These results underscore the importance of considering PKU as a multi-faceted, multi-dimensional disorder and point to the need for additional research to delineate characteristics that predict response to treatment with BH4

Accurate prediction of momentary cognition from intensive longitudinal data

Deficits in cognitive performance are implicated in the development and maintenance of psychopathology. Emerging evidence further suggests that within-person fluctuations in cognitive performance may represent sensitive early markers of neuropsychiatric decline. Incorporating routine cognitive assessments into standard clinical care—to identify between-person differences and monitor within-person fluctuations—has the potential to improve diagnostic screening and treatment planning. In support of these goals, it is critical to understand to what extent cognitive performance varies under routine, remote assessment conditions (i.e., momentary cognition) in relation to a wide range of possible predictors. Using data-driven, high-dimensional methods, we ranked strong predictors of momentary cognition and evaluated out-of-sample predictive accuracy. Our approach leveraged innovations in digital technology, including ambulatory assessment of cognition and behavior (1) at scale (n = 122, n = 94 female), (2) in naturalistic environments, and (3) within an intensive longitudinal study design (mean = 25.5 assessments/participant). Reaction time (R2 > .70) and accuracy (.56 > R2 > .35) were strongly predicted by age, between-person differences in mean performance, and time of day. Effects of self-reported, intra-individual fluctuations in environmental (e.g., noise) and internal (e.g., stress) states were also observed. Results provide robust estimates of effect size to characterize sources of cognitive variability, support the identification of optimal windows for psychosocial interventions, and may inform clinical evaluation under remote neuropsychological assessment conditions

Timing of the Diagnosis of Autism in African American Children

ObjectivesAfrican American (AA) children affected by autism spectrum disorder (ASD) experience delays in diagnosis and obstacles to service access, as well as a disproportionate burden of intellectual disability (ID) as documented in surveillance data recently published by the US Centers for Disease Control and Prevention. Our objective in this study was to analyze data from the largest-available repository of diagnostic and phenotypic information on AA children with ASD, and to explore the wide variation in outcome within the cohort as a function of sociodemographic risk and specific obstacles to service access for the purpose of informing a national approach to resolution of these disparities.MethodsParents of 584 AA children with autism consecutively enrolled in the Autism Genetic Resource Exchange across 4 US data collection sites completed event history calendar interviews of the diagnostic odysseys for their children with ASD. These data were examined in relation to developmental outcomes of the children with autism and their unaffected siblings.ResultsThe average age of ASD diagnosis was 64.9 months (±49.6), on average 42.3 months (±45.1) after parents' first concerns about their children's development. The relationship between timing of diagnosis and ASD severity was complex, and ID comorbidity was not predicted in a straightforward manner by familial factors associated with cognitive variation in the general population.ConclusionsThese findings document significant opportunity to expedite diagnosis, the need to further understand causes of ID comorbidity, and the necessity to identify effective approaches to the resolution of disparities in severity-of-outcome for AA children with autism

Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic condition characterized by glucose fluctuations. Laboratory studies suggest that cognition is reduced when glucose is very low (hypoglycemia) and very high (hyperglycemia). Until recently, technological limitations prevented researchers from understanding how naturally-occurring glucose fluctuations impact cognitive fluctuations. This study leveraged advances in continuous glucose monitoring (CGM) and cognitive ecological momentary assessment (EMA) to characterize dynamic, within-person associations between glucose and cognition in naturalistic environments. Using CGM and EMA, we obtained intensive longitudinal measurements of glucose and cognition (processing speed, sustained attention) in 200 adults with T1D. First, we used hierarchical Bayesian modeling to estimate dynamic, within-person associations between glucose and cognition. Consistent with laboratory studies, we hypothesized that cognitive performance would be reduced at low and high glucose, reflecting cognitive vulnerability to glucose fluctuations. Second, we used data-driven lasso regression to identify clinical characteristics that predicted individual differences in cognitive vulnerability to glucose fluctuations. Large glucose fluctuations were associated with slower and less accurate processing speed, although slight glucose elevations (relative to person-level means) were associated with faster processing speed. Glucose fluctuations were not related to sustained attention. Seven clinical characteristics predicted individual differences in cognitive vulnerability to glucose fluctuations: age, time in hypoglycemia, lifetime severe hypoglycemic events, microvascular complications, glucose variability, fatigue, and neck circumference. Results establish the impact of glucose on processing speed in naturalistic environments, suggest that minimizing glucose fluctuations is important for optimizing processing speed, and identify several clinical characteristics that may exacerbate cognitive vulnerability to glucose fluctuations