Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental condition characterized by developmentally extreme and impairing symptoms of inattention and/or hyperactivity/impulsivity. Great interest has emerged in the ways ADHD and its underlying symptom dimensions relate to the development of personality traits. Much extant research on this topic is cross-sectional, relying on self-report measures and male samples. Herein, we present data from a prospective, longitudinal study of a socioeconomically and racially diverse sample of girls, including those with ADHD and a matched neurotypical comparison sample. We examined how parent- and teacher-reported ADHD in middle childhood relate to self-reported Big Five personality traits in adolescence. As expected, childhood ADHD diagnosis prospectively predicted lower self-reported Conscientiousness, lower Agreeableness, and higher Neuroticism in adolescence. With ADHD diagnosis covaried, Inattention (IA) predicted only low Conscientiousness, Hyperactivity/Impulsivity (HI) predicted only low Agreeableness, and neither predicted adolescent Neuroticism. An exploratory moderator analysis showed that family income moderated the effects of IA and HI on the negativity of adolescent self-descriptions of their own personalities, with more pronounced negative effects for girls in families with higher (rather than lower) income. Familial pressures to achieve in higher-income families may be linked to more pronounced negative ramifications of ADHD on personality development.

Background

The 'dysregulation profile' (DP) is a measure of emotional and behavioral dysregulation that may cut across diagnostic boundaries. Siblings of children with autism spectrum disorder (ASD) who do not develop ASD themselves are at risk for atypical outcomes including behavioral challenges and therefore may be a useful population in which to investigate the structure of the DP in preschoolers.

Methods

We sought to examine the factor structure and predictors of the DP in a sample enriched for a wide range of phenotypic variation-36-month-olds with and without family histories of ASD-and to determine whether children with genetic liability for ASD are at risk for a phenotype characterized by elevated dysregulation. Data were collected from 415 children with (n = 253) and without (n = 162) an older sibling with ASD, all without ASD themselves, at 18, 24, and 36 months of age.

Results

Our findings replicate prior reports, conducted in predominantly clinically referred and older samples, supporting the superiority of a bifactor model of the DP in the preschool period compared to the second-order and one-factor models. Examiner ratings were longitudinally and concurrently associated with the DP at 36 months of age. Family history of ASD was associated with higher dysregulation in the Anxious/Depressed dimension.

Conclusions

These findings support the relevance of examining the structure of psychopathology in preschoolers and suggest that examiner observations as early as 18 months of age, particularly of overactivity, may help identify risk for later DP-related concerns. Non-ASD preschoolers with family histories of ASD may be at risk for a phenotype characterized by elevated dysregulation particularly in the Anxious/Depressed dimension by age 3.

Objective: We evaluated trajectories of attention-deficit/hyperactivity (ADHD)-relevant behaviors in a sample of infants at high and low familial risk for ADHD who were prospectively evaluated at 12, 18, and 24 months of age.Method: Participants included 43 infants at risk for ADHD based on family history (i.e., diagnosed first-degree relative) and 40 low-risk infants (i.e., no family history of ADHD). Instances of inattention, out-of-seat, and grabbing behavior were coded from video; analogous constructs were rated by examiners unaware of familial risk status after completing structured standardized assessments with the infants/toddlers. At the end of each study visit, examiners solicited parents' concerns about their child's behavior. Differences in ADHD-related behaviors and parent concerns were examined between 12 and 24 months of age.Results: Infants with an older sibling or parent diagnosed with ADHD were distinguishable from infants with no family history of ADHD as early as 12 months of age based on directly observed and examiner reports of behavior, particularly with respect to hyperactive-impulsive behavior. Parents of infants at familial risk for ADHD also reported significantly more behavior/temperament concerns as early as 12 months of age compared to parents of infants at low risk for ADHD.Conclusions: These findings highlight the ability to detect genetic liability for ADHD by the end of the first year of life, suggesting that well-designed family risk studies of ADHD are feasible and may be clinically valuable. They also suggest the potential for earlier detection of risk for ADHD than has previously been possible.

Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo [Formula: see text], with relative biases [Formula: see text] ([Formula: see text]) and [Formula: see text] (phantom [Formula: see text]) but with higher deviations in ADC at the lowest phantom ADC values. In vivo [Formula: see text] concordance was good, with typical biases of [Formula: see text] to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for [Formula: see text] in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.

BACKGROUND: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. PURPOSE: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. STUDY TYPE: Prospective. POPULATION: Thirty-three patients prospectively imaged prior to prostatectomy. FIELD STRENGTH/SEQUENCE: 3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence. ASSESSMENT: Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). STATISTICAL TEST: Levenes test, P < 0.05 corrected for multiple comparisons was considered statistically significant. RESULTS: The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size. DATA CONCLUSION: We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.