Machine learning (ML) has developed classifiers differentiating patient groups despite concerns regarding diagnostic reliability. An alternative strategy, used here, is to develop a functional classifier (hyperplane) (e.g. distinguishing the neural responses to received reward v. received punishment in typically developing (TD) adolescents) and then determine the functional integrity of the response (reward response distance from the hyperplane) in adolescents with externalizing and internalizing conditions and its associations with symptom clusters.

Two hundred and ninety nine adolescents (mean age = 15.07 ± 2.30 years, 117 females) were divided into three groups: a training sample of TD adolescents where the Support Vector Machine (SVM) algorithm was applied (N = 65; 32 females), and two test groups– an independent sample of TD adolescents (N = 39; 14 females) and adolescents with a psychiatric diagnosis (major depressive disorder (MDD), generalized anxiety disorder (GAD), attention deficit hyperactivity disorder (ADHD) & conduct disorder (CD); N = 195, 71 females).

SVM ML analysis identified a hyperplane with accuracy = 80.77%, sensitivity = 78.38% and specificity = 88.99% that implicated feature neural regions associated with reward v. punishment (e.g. nucleus accumbens v. anterior insula cortices). Adolescents with externalizing diagnoses were significantly less likely to show a normative and significantly more likely to show a deficient reward response than the TD samples. Deficient reward response was associated with elevated CD, MDD, and ADHD symptoms.

Distinguishing the response to reward relative to punishment in TD adolescents via ML indicated notable disruptions in this response in patients with CD and ADHD and associations between reward responsiveness and CD, MDD, and ADHD symptom severity.

ADHD symptoms are associated with emotional problems such as depressive and anxiety symptoms from early childhood to adulthood, with the association increasing with age. A shared aetiology and/or a causal relationship could explain their correlation. In the current study, we explore these explanations for the association between ADHD symptoms and emotional problems from childhood to adulthood.

Data were drawn from the Twins Early Development Study (TEDS), including 3675 identical and 7063 non-identical twin pairs. ADHD symptoms and emotional symptoms were reported by parents from childhood to adulthood. Self-report scales were included from early adolescence. Five direction of causation (DoC) twin models were fitted to distinguish whether associations were better explained by shared aetiology and/or causal relationships in early childhood, mid-childhood, early adolescence, late adolescence, and early adulthood. Follow-up analyses explored associations for the two subdomains of ADHD symptoms, hyperactivity-impulsivity and inattention, separately.

The association between ADHD symptoms and emotional problems increased in magnitude from early childhood to adulthood. In the best-fitting models, positive genetic overlap played an important role in this association at all stages. A negative causal effect running from ADHD symptoms to emotional problems was also detected in early childhood and mid-childhood. When distinguishing ADHD subdomains, the apparent protective effect of ADHD symptoms on emotional problems in childhood was mostly driven by hyperactivity-impulsivity.

Genetic overlap plays an important role in the association between ADHD symptoms and emotional problems. Hyperactivity-impulsivity may protect children from emotional problems in childhood, but this protective effect diminishes after adolescence.

Type 2 diabetes (T2D) is a global health burden, more prevalent among individuals with attention deficit hyperactivity disorder (ADHD) compared to the general population. To extend the knowledge base on how ADHD links to T2D, this study aimed to estimate causal effects of ADHD on T2D and to explore mediating pathways.

We applied a two-step, two-sample Mendelian randomization (MR) design, using single nucleotide polymorphisms to genetically predict ADHD and a range of potential mediators. First, a wide range of univariable MR methods was used to investigate associations between genetically predicted ADHD and T2D, and between ADHD and the purported mediators: body mass index (BMI), childhood obesity, childhood BMI, sedentary behaviour (daily hours of TV watching), blood pressure (systolic blood pressure, diastolic blood pressure), C-reactive protein and educational attainment (EA). A mixture-of-experts method was then applied to select the MR method most likely to return a reliable estimate. We used estimates derived from multivariable MR to estimate indirect effects of ADHD on T2D through mediators.

Genetically predicted ADHD liability associated with 10% higher odds of T2D (OR: 1.10; 95% CI: 1.02, 1.18). From nine purported mediators studied, three showed significant individual mediation effects: EA (39.44% mediation; 95% CI: 29.00%, 49.73%), BMI (44.23% mediation; 95% CI: 34.34%, 52.03%) and TV watching (44.10% mediation; 95% CI: 30.76%, 57.80%). The combination of BMI and EA explained the largest mediating effect (53.31%, 95% CI: −1.99%, 110.38%) of the ADHD–T2D association.

These findings suggest a potentially causal, positive relationship between ADHD liability and T2D, with mediation through higher BMI, more TV watching and lower EA. Intervention on these factors may thus have beneficial effects on T2D risk in individuals with ADHD.

To quantify the proportion of referrals sent to Crumlin Cardiology Department for cardiac screening prior to commencement or modifying attention deficit hyperactivity disorder medication and assess the number detected with a clinically significant abnormality.

A prospective audit was performed over a 6-month period, from November 2021 to April 2022 inclusive. Referrals sent via outpatient department triage letters, electrocardiogram dept. email, and walk-in electrocardiogram service were screened for those pertaining to commencing or modifying medication for children with attention deficit hyperactivity disorder. Each referral was coded against National Institute for Health and Care Excellence guidelines to determine the degree of clinical details given. Reported abnormalities, recommended management, and correspondence were recorded.

Ninety-one referrals were received during the 6-month audit period. More than half lacked a clinical indication for referral (53/91, 58.2%), with fewer than one third (26/91, 28.5%) meeting National Institute for Health and Care Excellence criteria for referral for cardiology. Eighty (80/91) referrals had clinical outcomes available for review (missing outpatient department information and age outside of service range accounted for eleven referrals with unavailable clinical outcomes). Of the eighty clinically reviewed referrals, seventy-two (72/80, 90%) were reported as normal with no cardiology follow up required. Eight referrals (8/80, 10%) were reviewed in the Cardiology Outpatient Department prior to commencement or modifying attention deficit hyperactivity disorder medication. Of these, only one (1/80 1%) had a clinically significant abnormality which was a potential contraindication to attention deficit hyperactivity disorder medication use, and this referral was appropriate as per National Institute for Health and Care Excellence guidelines.

Routine screening prior to attention deficit hyperactivity disorder medication prescription in the absence of clinical indications (as per National Institute for Health and Care Excellence) contributed to delays in medication initiation among young people with attention deficit hyperactivity disorder. Unnecessary referrals have resource implications for cardiology clinical team. Improved adherence to National Institute for Health and Care Excellence guidelines would provide benefits for patients and clinicians.

Attention deficit hyperactivity disorder (ADHD) is increasingly diagnosed in adults. People with intellectual disability have higher rates of ADHD yet there is little evidence on the presentation and pharmacological treatment of ADHD in this population or how this differs from the general population.

Retrospective cohort study using data from electronic health records. Adults with intellectual disability newly diagnosed with ADHD between 2007 and 2022 were matched to adults with ADHD without intellectual disability and their clinical features and treatments were compared.

A total of 159 adults with ADHD and intellectual disability and 648 adults with ADHD without intellectual disability formed the dataset. Adults with intellectual disability had higher rates of psychiatric co-morbidity and spent more time under mental health services than those without intellectual disability. They were more likely to have recorded agitation, aggression, hostility, and mood instability, and less likely to have poor concentration recorded in the 12 months prior to the diagnosis of ADHD. Following diagnosis, people with intellectual disability were significantly less likely to be prescribed any medication for ADHD than controls without intellectual disability (adjusted odds ratio 0.60, 95% confidence interval 0.38–0.91), and were less likely to be prescribed stimulants (27.7% v 46.0%, p < 0.001).

The presence of behaviors that challenge in adults with intellectual disability may indicate co-occurring ADHD. Further work to define the safety and efficacy of medication for ADHD in adults with intellectual disability is needed to understand differences in prescription rates and to avoid inequities in care outcomes.

Attention-deficit hyperactivity disorder (ADHD) is highly heritable, though environmental factors also play a role. Prenatal maternal stress is suggested to be one such factor, including exposure to highly distressing events that could lead to post-traumatic stress disorder (PTSD). The aim of this study is to investigate whether prenatal maternal PTSD is associated with offspring ADHD.

A register-based retrospective cohort study linking 553 766 children born in Sweden during 2006–2010 with their biological parents. Exposure: Prenatal PTSD. Outcome: Offspring ADHD. Logistic regression determined odds ratios (ORs) with 95% confidence intervals (CIs) for ADHD in the offspring. Adjustments were made for potential covariates, including single parenthood and possible indicators of heredity measured as parental ADHD and maternal mental disorders other than PTSD. Subpopulations, excluding children with indicators of heredity, were investigated separately.

In the crude results, including all children, prenatal PTSD was associated with offspring ADHD (OR: 1.79, 95% CI: 1.37–2.34). In children with indicators of heredity, the likelihood was partly explained by it. Among children without indicators of heredity, PTSD was associated with offspring ADHD (OR: 2.32, 95% CI: 1.30–4.14), adjusted for confounders.

Prenatal maternal PTSD is associated with offspring ADHD regardless of indicators of heredity, such as parental ADHD or maternal mental disorder other than PTSD. The association is partly explained by heredity and socioeconomic factors. If replicated in other populations, preferably using a sibling design, maternal PTSD could be identified as a risk factor for ADHD.

Neurobiological and cognitive theories implicate deficits in executive function (EF) as a core facet of both depressive disorders and attention-deficit/hyperactivity disorder (ADHD), but empirical investigations inconsistently support this conclusion. Despite recognition of the likely bi-directional relationship of EF deficits to depression and ADHD, respectively, the extent to which comorbid depression might impact EF in adults remains unclear, considering more of the literature has examined children and adolescents. This study examined performance differences on EF measures in clinically-referred adults diagnosed with ADHD or a non-ADHD primary psychopathological condition in the presence/absence of comorbid depression.

This cross-sectional study included data from 404 adults referred for neuropsychological evaluation at a Midwestern academic medical center. In total, 343 met DSM-5 diagnostic criteria for ADHD (ADHD-all group:164 Predominantly Inattentive presentation [ADHD-I] and 179 Combined presentation [ADHD-C]) and 61 met criteria for a non-ADHD primary psychopathological condition (psychopathology group: 31 mood disorder, 17 anxiety disorder, and 13 posttraumatic stress disorder) when assessed via semi-structured clinical interview. All patients completed the Beck Depression Inventory-Second Edition (BDI-II) and five EF tests: Letter Fluency, Trail Making Test-Part B (Trails-B), Stroop Color and Word Test Color-Word trial (SCWT CW); and WAIS-IV Working Memory Index (WMI). Oneway MANOVAs assessed for significant EF differences between groups with high (BDI-II greater than or equal to 20) or low (BDI-II less than or equal to 19) depressive symptoms.

When group diagnosis (ADHD-all vs. psychopathology) was examined in the context of high or low depression, a significant difference in EF performance emerged between groups, F(12, 1042.72)=2.44, p<.01, Wilk's A=.93, partial n2=.02, with univariate analyses indicating a significant difference in FAS-T between at least two of the groups (F(3, 397)=3.92 , p< .01, partial n2=.03). Tukey's HSD Test for multiple comparisons found that the mean value of FAS-T was significantly different between the ADHD-high depression and ADHD-low depression groups (p=.046 , 95% CI = [5.81, -.04]) as well as between the ADHD-low depression and psychopathology-high depression groups (p=.05, 95% CI = [-8.89, .00]). A one-way MANOVA examining differences between groups when distinguishing ADHD by subtype revealed a statistically significant difference in EF performance between groups, F(20, 1301)=1.85, p<.05, Wilk's A=.91, partial n2=.02, with univariate analyses indicating a statistically significant difference in FAS-T between at least two of the groups (F(5, 395) = 2.39 , p<.05, partial n2 = .03). However, Tukey's HSD Test for multiple comparisons found that the mean value of FAS-T was not significantly different between any of the groups.

Overall, results indicate that clinically-referred patients with ADHD perform comparably on tests of EF regardless of the presence or absence of comorbid depression. These findings have implications for conceptualizing EF weaknesses in neuropsychological profiles for individuals with ADHD and suggest examining factors beyond comorbid depression.

The prevalence of ADHD diagnoses more than doubled in VA settings between 2009 and 2016 (Hale et al., 2020). However, attentional difficulties are not exclusive to ADHD and can also be seen in non-neurodevelopmental disorders, including depression, anxiety, substance use, and PTSD (Marshall et al., 2018, Suhr et al., 2008). Further, patients can easily feign symptoms of ADHD with few available instruments for accurate detection (Robinson & Rogers, 2018). Given the significant symptom overlap and rising rates of reported ADHD among Veterans, accurate detection of feigned ADHD is essential.

This study examined the utility of the experimental Dissimulation ADHD scale (Ds-ADHD; Robinson & Rogers, 2018) on the MMPI-2, in detecting feigned ADHD presentation within a mixed sample of Veterans.

In this retrospective study, 173 Veterans (Mage = 36.18, SDage = 11.10, Medu = 14.01, SDedu = 2.11, 88% male, 81% White, and 17% Black) were referred for neuropsychological evaluation of ADHD that included the MMPI-2 and up to 10 PVTs. Participants were assigned to a credible group (n=146) if they passed all PVTs or a non-credible group (n=27) if they failed two or more PVTs. Group assignment was also clinically confirmed. The Ds-ADHD was used to differentiate groups who either had credible or non-credible performance on cognitive measures. Consistent with Robinson and Rogers’ study, “true” answers (i.e., erroneous stereotypes) were coded as 1 and “false” answers were coded as 2, creating a 10- to 20-point scale. Lower scores were associated with a higher likelihood of a feigned ADHD presentation.

Preliminary analyses revealed no significant group differences in age, education, race, or gender (ps > .05). An ANOVA indicated a significant difference between groups (F[1, 171] = 10.44, p = .001; Cohen’s d = .68) for Ds-ADHD raw scores; Veterans in the non-credible group reported more “erroneous stereotypes” of ADHD (M raw score = 13.33, SD = 2.20) than those in the credible group (M = 14.82, SD = 2.20). A ROC analysis indicated AUC of .691 (95% CI = .58 to .80). In addition, a cut score of <12 resulted in specificity of 91.8% and sensitivity of 18.5%, whereas a cut score of <13 resulted in specificity of 83.6% and sensitivity of 44.4%.

The Ds-ADHD scale demonstrated significant differences between credible and non-credible respondents in a real-world setting. Previously, this scale has primarily been studied within laboratory settings. Further, results indicate a cut score of <12 could be used in order to achieve adequate specificity (i.e., >90%), which were similar findings to a study examining SVT-based groups (Winiarski et al., 2023). These results differ slightly from prior research by Robinson and Rogers (2018), who indicated a cut score of <13 based on the initial simulation-based study. In similar clinical settings, where there are high rates of psychiatric comorbidity, a cut score of <12 may prove clinically useful. However, this cut-score was associated with low sensitivity within this mixed Veteran sample. Further research should focus on replicating findings within other clinical settings, including ones with larger non-credible samples.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder commonly associated with relative impairments on processing speed, working memory, and/or executive functioning. Anxiety commonly co-occurs with ADHD and may also adversely affect these cognitive functions. Additionally, language status (i.e., monolingualism vs bilingualism) has been shown to affect select cognitive domains across an individual’s lifespan. Yet, few studies have examined the potential effects of the interaction between anxiety and language status on various cognitive domains among people with ADHD. Thus, the current study investigated the effects of the interaction of anxiety and language status on processing speed, working memory, and executive functioning among monolingual and bilingual individuals with ADHD.

The sample comprised of 407 consecutive adult patients diagnosed with ADHD. When asked about their language status, 67% reported to be monolingual (English). The Mean age of individuals was 27.93 (SD = 6.83), mean education of 15.8 years (SD = 2.10), 60% female, racially diverse with 49% Non-Hispanic White, 22% Non-Hispanic Black, 13% Hispanic/Latinx, 9% Asian/Pacific Islander, and 6% other race/ethnicity. Processing speed, working memory, and executive function were measured via the Wechsler Adult Intelligence Scale-Fourth Edition Processing Speed Index, Working Memory Index, and Trail Making Test B, respectively. Anxiety was measured via the Beck Anxiety Inventory (BAI). Three separate linear regression models examined the interaction between anxiety (moderator) and cognition (processing speed, working memory, and executive function) on language. Models included sex/gender and education as covariates with Processing Speed Index and Working Memory Index as the outcomes. Age, sex/gender, and education were used as covariates when Trail Making Test B was the outcome.

Monolingual and bilingual patients differed in mean age (p < .05) but did not differ in level of anxiety, education, or sex/gender. Overall, anxiety was not associated with processing speed, working memory, and executive function. However, the interaction between anxiety and language status was significantly associated with processing speed (ß = -0.37, p < .05), and executive functioning (ß = 0.82, p < .05). No associations were found when anxiety was added as a moderator for the associations between language and working memory.

This study found that anxiety moderated the relationship between language status and select cognitive domains (i.e., processing speed and executive functioning) among individuals with ADHD. Specifically, anxiety had a greater association on processing speed and executive functioning performance for bilinguals rather than monolinguals. Future detailed studies are needed to better understand how anxiety modifies the relationship between language and cognitive performance outcomes over time amongst a linguistically diverse sample.

Youth with attention-deficit/hyperactivity disorder (ADHD), characterized by symptoms of inattention and hyperactivity, often experience challenges with emotion regulation (ER) and/or emotional lability/negativity (ELN).1-3 Prior work has shown that difficulties with ER and ELN among young children contribute to lower academic achievement.4-6 To date, research examining associations between ADHD and academic achievement have primarily focused on the roles of inattentive symptoms and executive functioning.7-8 However, preliminary work among youth with ADHD suggests significant associations between disruptions in emotional functioning and poor academic outcomes.9-10 The current study will examine associations between ER, ELN, and specific subdomains of academic achievement (i.e., reading, spelling, math) among youth with and without ADHD.

Forty-six youth (52% male; Mage=9.52 years; 76.1% Hispanic/Latino; 21 with ADHD) and their parents were recruited as part of an ongoing study. Parents completed the Disruptive Behavior Disorders Rating Scale11 and Emotion Regulation Checklist12 about their child. Youth completed the Wechsler Abbreviated Scale of Intelligence-II13 and three subtests [Spelling (SP), Numerical Operations (NO), Word Reading (WR)] of the Wechsler Individual Achievement Test-III.14 Univariate analysis of variance assessed differences in emotional functioning and academic achievement among youth with and without ADHD. Correlation and regression analyses were conducted to examine the association between emotional factors and the three subtests of academic achievement.

Youth with ADHD exhibited significantly higher ELN (M=30.7, SD=8.7) compared to their peers (M=23.2, SD=5.8), when controlling for child age, sex, and diagnoses of conduct disorder and/or oppositional defiant disorder [F(1,41)=8.96, p<.01, ŋp2=.18]. With respect to ER, youth with (M=24.8, SD=4.2) and without ADHD (M=25.8, SD=4.3) did not differ [F(1,41)=.51, p=.48]. Surprisingly, within this sample, ADHD diagnostic status was not significantly associated with performance on any of the academic achievement subtests [WR: F(1,41)=.29, p=.59; NO: F(1,41)=.91, p=.35; SP: F(1,41)=2.14, p=.15]. Among all youth, ER was significantly associated with WR (r=.31, p=.04) and SP (r=.35, p=.02), whereas ELN was associated with performance on NO (r=-.30, p=.04). When controlling for child age, sex, IQ, and ER within the full sample, higher ELN was associated with lower scores on the NO subtest (b=-.56, SE=.26, p=.04). The associations between higher ER and WR scores (b=1.12, SE=.51, p=.03), as well as higher ER and SP scores (b=1.47, SE=.56, p=.01), were significant when controlling for child age and sex, but not ELN and IQ (p=.73 and p=.64, respectively).

As expected, youth with ADHD had higher ELN, although they did not differ from their peers in terms of ER. Results identified distinct associations between ER and higher reading/spelling performance, as well as ELN and lower math performance across all youth. Thus, findings suggest that appropriate emotional coping skills may be most important for reading and spelling, while emotional reactivity appears most salient to math performance outcomes. In particular, ELN may be a beneficial target for intervention, especially with respect to improvement in math problem-solving skills. Future work should account for executive functioning skills, expand the academic achievement domains to include fluency and more complex academic skills, and assess longitudinal pathways within a larger sample.

Childhood obesity is a serious health epidemic affecting the world today. Children who are obese earlier in life are more likely to stay obese and have an increased risk of poorer health outcomes later in life, such as diabetes and cardiovascular diseases. Obesity is also associated with deficits in executive function. Executive function (EF) is comprised of several distinct but interrelated abilities including working memory, planning, inhibition, and flexibility. Prior research suggests that obesity drives brain changes which implicate executive function structures. Our aim is to examine the relationship between childhood obesity and executive function in children with neurodevelopmental disorders.

These data are from an ongoing study on neural and behavioral phenotypes of executive functioning in children with developmental disabilities, primarily Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD). Only study participants with complete BMI and BRIEF data were included in these analyses (n = 184). 134 representing (72.8%) of the participants were Male, 49 representing (26.6%) were Female, and 1 representing (.5%) were Gender nonconforming. 50 representing (27.2%) of the participants were between 8-9 years, 55 representing (29.9%) were between 10-11 years, and 80 representing (43.0%) were between 12-13 years. Average age was 11 years. 11 representing (6.0%) of the participants were underweight, 115 representing (62.5%) were healthy, 29 representing (15.8%) were overweight, and 29 representing (15.8%) were obese. Average BMI was 19.0, ranging from 13.2 to 36.3. 106 representing (57.6%) of the participants identified as White, 65 representing (35.3%) identified as BIPOC (2 Asian, 31 Hispanic/Latinx, 32 Black) and 13 representing (4.4%) identified as other/unspecified. 114 representing (61.9%) of the participants had a diagnosis of ADHD, ASD, or comorbid ASD and ADHD, 70 representing (38.1%) had a diagnosis of other. Average FSIQ-2 score was 106.98. Parents were asked to complete the Behavior Rating Inventory of Executive Function (BRIEF-2) and the Inhibit, Shift, Working Memory (WM), Planning, and Global Executive Composite (GEC) scales were used as the dependent measure in analyses. BMI (kg/mA2) was calculated based on CDC 2000 growth charts and classified into 4 mutually exclusive categories—underweight, healthy, overweight, and obese. There was a prediction that higher BMI would be associated with lower executive function.

A one-way ANOVA revealed a statistically significant difference between groups (F(3,180) = 3.649, p = .014). A Tukey post hoc test revealed more Shift problems in the obese group (74.55 ± 11.7) compared to the overweight group (65.79 ± 11.6, p = .026). There was no statistically significant difference between the underweight/healthy and obese groups (p = .999/p = .054). There was no statistically significant difference in mean T-scores for the Inhibit, WM, Planning, or GEC scales.

Childhood obesity and executive function deficits are significant risk factors for adult health outcomes. Obesity and elevated executive function T-scores for flexibility are related in a group of children with neurodevelopmental disorders. Future investigation will explore the role of cortical thickness and medication in these data.

Accurate identification of Attention-Deficit/Hyperactivity Disorder (ADHD) is complicated by possible secondary gain, overlap of symptoms with psychiatric disorders, and face validity of measures (Suhr et al., 2011; Shura et al., 2017). To assist with diagnostic clarification, an experimental Dissimulation ADHD scale (Ds-ADHD; Robinson & Rogers, 2018) on the MMPI-2 was found to distinguish credible from non-credible respondents defined by Performance Validity Test (PVT)-based group assignment in Veterans (Burley et al., 2023). However, symptom and performance validity have been understood as unique constructs (Van Dyke et al., 2013), with Symptom Validity Tests (SVTs) more accurately identifying over-reporting of symptoms in ADHD (White et al., 2022). The current study sought to evaluate the effectiveness of the Ds-ADHD scale using an SVT, namely the Infrequency Index of CAARS (CII; Suhr et al., 2011), for group assignment within a mixed sample of Veterans.

In this retrospective study, 187 Veterans (Mage = 36.76, SDage = 11.25, Medu = 14.02, SDedu = 2.10, 83% male, 19% black, 78% white) were referred for neuropsychological evaluation of ADHD and administered a battery that included internally consistent MMPI-2 and CAARS profiles. Veterans were assigned to a credible group (n=134) if CII was <21 or a non-credible group (n=53) if CII was >21. The Ds-ADHD scale was calculated for the MMPI-2. Consistent with Robinson and Rogers (2018), “true” answers (i.e., erroneous stereotypes) were coded as 1 and “false” answers were coded as 2, creating a 10- to 20-point scale. Lower scores were associated with a higher likelihood of a feigned ADHD presentation.

Analyses revealed no significant differences in age, education, race, or gender (ps > .05) between credible and non-credible groups. An ANOVA indicated a significant difference between groups (F[1,185] = 24.78, p <.001; Cohen’s d = 0.80) for Ds-ADHD raw scores. Veterans in the non-credible group reported more “erroneous stereotypes” of ADHD (M raw score = 13.23, SD = 2.10) than those in the credible group (M = 14.94, SD = 2.13). A ROC analysis indicated AUC of .72 (95% CI = .64 to .80). In addition, a Ds-ADHD cut score of <12 resulted in specificity of 94.5% and sensitivity of 22.6%, whereas a cut score of <13 resulted in specificity of 85.8% and sensitivity of 50.9%. When analyzing other CII cut scores recommended in the literature, results were essentially similar. Specifically, analyses were repeated when group assignment was defined by cut score of CII<18 and by removing an intermediate group (CII = 18 to 21; n=24).

The Ds-ADHD scale demonstrated significant differences between credible and non-credible respondents in a Veteran population. Results suggest a cut score of <12 had adequate specificity (.95) with low sensitivity (.23). This is consistent with findings using PVTs for group assignment that indicated a cut score of <12 had adequate specificity (.92) with low sensitivity (.19; Burley et al., 2023). Taken together, findings suggest that the Ds-ADHD scale demonstrates utility in the dissociation of credible from non-credible responding. Further research should evaluate the utility of the scale in other clinical populations.

Typical evaluations of adult ADHD consist of behavior self-report rating scales, cognitive or intellectual functioning measures, and specific measures designed to measure attention. Boone (2009) suggested monitoring continuous effort is essential throughout psychological assessments. However, very few research studies have contributed to malingering literature on the ADHD population. Many studies have reported the adequate use of symptom validity tests, which assess effortful performance in ADHD evaluations (Jasinski et al., 2011; Sollman et al., 2010; Schneider et al., 2014). Because of the length of ADHD assessments, individuals are likely to become weary and tired, thus impacting their performance. This study investigates the eye movement strategies used by a clinical ADHD population, non-ADHD subjects, and malingering simulators when playing a common simple visual search task.

A total of 153 college students participated in this study. To be placed in the ADHD group, a participant must endorse four or more symptoms on the ASRS (N = 37). To be placed in the non-ADHD, participants should have endorsed no ADHD symptoms (N = 43). Participants that did not meet the above criteria for ADHD and not-ADHD were placed in an Indeterminate group and were not included in the analysis. A total of 20 participants were instructed to fake symptoms related to ADHD during the session. A total of twelve Spot the Difference images were used as the visual picture stimuli. Sticky by Tobii Pro (2020) was used for the collection of eye-movement data was utilized. Sticky by Tobii Pro is an online self-service platform that combines online survey questions with an eye-tracking webcam, allowing participants to see images from their home computers.

Results indicated on the participants classified as Malingering had a significantly Visit Count (M = 17.16; SD= 4.99) compared to the ADHD(M = 12.53; SD= 43.92) and not-ADHD groups (M =11.51; SD=3.23). Results also indicated a statistically significant Area Under the Curve (AUC) = .784; SE = .067; p -.003; 95% CI = .652-.916. Optimal cutoffs suggest a Sensitivity of 50% with a False Positive Rate of 10%.

Results indicated that eye-tracking technology could help differentiate simulator malingerers from non-malingerers with ADHD. Eye-tracking research’ relates to a patchwork of fields more diverse than the study of perceptual systems. Due to their close relation to attentional mechanisms, the study’s results can provide an insight into cognitive processes related to malingering performance.

Theory suggests that symptoms of Attention-deficit Hyperactivity Disorder (ADHD; e.g., hyperactivity and impulsivity) may be associated with social cognition deficits characterised by fast but erroneous processing of social cues. Despite this, prior research has provided mixed evidence for (a) deficits in social cognition skills and (b) a link between such deficits and poor social outcomes among children with ADHD. We sought to clarify this ambiguity by (a) exploring variation in social cognition skills across a mixed clinical and normative population and (b) examining the demographic, clinical, and dimensional symptom profiles of children presenting with reduced social cognition skills characterised by fast but erroneous processing.

Participants were children and adolescents (N = 1,097) aged 4–18 years (M = 9.02, SD = 2.72) assessed using the Paediatric Evaluation of Emotions Relationships and Socialisation (PEERS), a child-direct, ecologically sensitive measure of social cognition. Latent profile analysis of standardised social cognition scores and response times for incorrect encoding of social cues (error-response times) was used to identify social cognition profiles. Differences between each profile in terms of demographics, clinical profiles, symptom dimensions, and social outcomes were explored.

Four social cognition profiles were identified. Two profiles were identified as being of particular interest: one which captured typically developing children (TDC; n = 727), and another which was characterised by lower social cognition scores and faster error-response times (impulsive responding; n = 201). The remaining profiles captured the response styles of younger participants (n = 152) and children with more pervasive social cognition deficits (n = 17). Comparison of the two profiles of interest revealed a number of statistically significant differences (p < .05). Compared to the TDC group, the impulsive responding group had: higher SDQ scores for hyperactivity, conduct, emotional, and peer problems; lower IQ and prosocial scores, and; greater parent-perceived social function deficits. Children in this group were also more likely to be male and from a lower SES background. Clinically, 18% of children in the impulsive responding group had an ADHD diagnosis, and 14% had at lease one mental health diagnosis other than ADHD.

A large minority of children (~18%) demonstrate social cognition deficits characterised by fast but erroneous processing of social cues. Although the explorative nature of this study does not allow conclusions to be made about the causes of such deficits, it is reasonable to conclude that they are not reducible to clinically significant symptoms of hyperactivity-impulsivity — less than 1/5 of the children in this group had an ADHD diagnosis, and 2/3 of children in this group had no mental health diagnosis at all. Child-direct tools designed to detect individual differences in social cognition skills may be beneficial in identifying individuals who will benefit from social support or interventions aimed at reducing social cognition deficits despite being missed by more traditional screening measures (e.g., clinical diagnoses). Future work should focus on understanding the causal relationships between symptoms of hyperactivity-impulsivity, fast but erroneous processing of social cues, social cognition skills, and social outcomes for this group of children.

Because cognitive resources are limited, models of cognitive control predict that additional control is engaged only if it improves task performance. Increased response caution, which occurs when individuals increase the threshold of information needed before making a decision, is one example of cognitive control adaptation. While previous studies have measured increased response caution via increased reaction time, the diffusion model can be used to derive a boundary separation parameter that directly indexes response caution and eliminates capturing alternative influences on reaction time. This study aims to determine if school-aged children, either with or without ADHD, show adaptive changes in response caution during a set-shifting task. These groups have demonstrated mixed results when analyzing reaction time, so this study utilizes diffusion modeling to measure response caution more directly. The set-shifting task presents switches in a random order such that they cannot be predicted; therefore, increasing response caution is only adaptive following errors, called post-error slowing (PES), but not following switch trials. It is predicted that children will show increased response caution only when adaptive. If child with ADHD adapt their response caution fundamentally differently, then there will be individual differences in change in boundary separation.

Children ages 8-12 with (n=193) and without (n=70) ADHD completed the Navon set-shifting task. Participants saw one of four global shapes made up of local shapes and were asked to identify one or the other based upon the background color. Of the 144 trials, 70 presented a switch between global and local. Trials were presented in the same randomized order for all participants, self-paced, and followed by feedback on correctness. The diffusion model parameters boundary separation (a), drift rate (v), and nondecision time (Ter) were estimated by condition, including a) post-error versus after correct and b) post-switch versus post-same. For PES analyses, only participants with a sufficient number of errors for modeling were included (ADHD n=113, control n=19).

Participants were slower on trials immediately following errors (F(1, 130)=119.76, p<.001, n2=.48) and switches (F(1, 261)=154.93, p<.001, n2=.37). In PES, slowing was attributable to increased boundary separation, F(1, 130)=16.11, p<.001, n2=.11, as well as slower drift rate and longer nondecision time (both p<.01, n2 >.05). However, as predicted, post-switch slowing was only attributable slower drift rate and longer nondecision time (both p<.001, n2 >.10), not increased boundary separation, F(1, 261)=0.77, p=.38, n2<.01. Overall, children with ADHD had slower drift rates (F(1, 261)=4.63, p<.001, n2=.10) and narrower boundary separation (F(1, 261)=10.56, p=.001, n2=.04). However, there were no ADHD x trial-type interactions for PES or post-switch (both p>.33, n2<.01).

School-aged children demonstrated increased response caution following errors, but not following switches. This demonstrates an adaptive use of cognitive control. The diffusion model was crucial in determining this, as reaction time slowed following switches for reasons unrelated to cognitive control. Additionally, although children with ADHD demonstrated slower drift rates and narrower boundary separation overall, they showed no differences when adapting response caution.

Autism Spectrum Disorders (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) are neurodevelopmental disorders with overlapping symptomatology and shared genetic makeup. Numerous previous studies have investigated ASD and ADHD using resting state functional networks. One functional network of particular interest is the Default Mode Network (DMN), as it has been shown to be abnormal in several mental disorders. Previous studies have investigated the DMN in ASD and ADHD separately but reported mixed trends of increased and decreased functional connectivity (FC) in the DMN in ASD and increased FC in ADHD. Additionally, little studies have investigated executive and attentional network dysfunction in the DMN for ASD and ADHD populations. To better understand the shared characteristics between ASD and ADHD, this study analyzed the DMN FC in children with ASD and ADHD.

Archival datasets from Autism Brain Imaging Data Exchange (ABIDE)-I and ADHD-200 datasets were used, with 33 ADHD, 35 ASD, and 32 typically developing (TD) males (ages = 7-17 years). After applying a standard pre-processing pipeline, 11 regions of interest (ROIs) from the Dosenbach-160 atlas were examined with 55 ROI pairs generated for the 100 subjects.

Significant differences were noted between ASD-ADHD groups in attentional networks and executive functioning networks. Specifically, significant Group x VIQ interactions were noted for FC between the following pairs of regions: medial prefrontal cortex - ventromedial prefrontal cortex, anterior cingulate cortex -ventromedial prefrontal cortex, inferior temporal lobe - ventromedial prefrontal cortex, angular -ventromedial prefrontal cortex, angular -anterior cingulate cortex, inferior temporal lobe -ventrolateral prefrontal cortex, angular -superior frontal lobe, and intraparietal sulcus -superior frontal lobe. In the above FC pairs, FC in ADHD was negatively correlated with VIQ, with no correlation for ASD and positive correlation for TD. Previous literature has indicated that ADHD individuals demonstrate increased executive functioning deficits compared to ASD individuals. This study provides evidence at a neural level for these findings by demonstrating decreased FC trends in ADHD in attentional and executive functioning networks compared to ASD individuals. Group and VIQ main effects demonstrated mixed patterns across the three groups, as well as shared decreased FC in attention/executive networks for both ASD and ADHD groups.

In summary, this study found similar findings from previous studies regarding mixed connectivity patterns, as well as shared dysfunction between ASD and ADHD groups. These results help in solidifying the theory that ASD and ADHD share clinical and neural patterns which need to be examined further. Future directions include utilizing more ASD+ADHD comorbid individuals in studies comparing ASD and ADHD FC trends as well as seeking to further understand the neuropsychological and neuroimaging profiles in ASD and ADHD.