Cognitive Heterogeneity In ADHD: Implications For Maths

Individuals with ADHD often exhibit differences in working memory functions, which are crucial for holding and manipulating information in the short term.

These differences can significantly impact various cognitive processes, including mathematical abilities.

Working memory is essential for math tasks such as mental arithmetic, problem-solving, and understanding complex mathematical concepts.

By studying math ability in individuals with ADHD, researchers can better understand the cognitive mechanisms underlying their academic challenges and develop more targeted interventions.

Kanevski, M., Booth, J. N., Stewart, T. M., & Rhodes, S. M. (2024). Cognitive heterogeneity in Attention Deficit Hyperactivity Disorder: Implications for maths. British Journal of Developmental Psychology. https://doi.org/10.1111/bjdp.12517

Key Points

• Cognitive function better predicted math performance than ADHD diagnosis in children referred for ADHD assessment.
• Three distinct executive function (EF) subtypes were identified: Low Working Memory (WM), Low Visuospatial EF, and Relatively Intact EF.
• Children in the Low WM cluster had the lowest math and intelligence scores compared to other groups.
• The Low Visuospatial EF cluster showed intermediate math performance between the other two groups.
• Diagnostic categories (ADHD vs. subclinical ADHD) did not differentiate cognitive functioning or math outcomes.
• Data-driven cognitive profiles were more informative for identifying children at risk for math difficulties than diagnostic status.
• Co-occurring symptoms did not differ significantly between EF clusters, suggesting cognitive vulnerabilities cut across different developmental disorder symptoms.
• The research highlights the importance of considering cognitive heterogeneity in ADHD when developing targeted math interventions.

Rationale

Attention Deficit Hyperactivity Disorder (ADHD) affects 1-2% of children, with an additional 5% experiencing subthreshold symptoms (Czamara et al., 2013; Hong et al., 2014).

Children with ADHD often struggle with math (Capano et al., 2008; DuPaul et al., 2013), but the relationship between ADHD and math performance is not consistent across studies (Capodieci & Martinussen, 2017).

This variability may be due to differences in underlying cognitive abilities (Geary et al., 2007; Kofler et al., 2017).

Executive functions (EF) have been implicated in both ADHD symptoms and math abilities (Biederman et al., 2004; Cragg et al., 2017).

However, there is marked heterogeneity in EF performance among individuals with ADHD (Coghill et al., 2014; Kofler et al., 2019).

This study aimed to explore whether cognitive function, particularly EF profiles, better predicted math performance than ADHD diagnosis in children referred for ADHD assessment.

The research took a transdiagnostic approach, reflecting the common co-occurrences in neurodivergent children, which is often overlooked in ADHD studies (Goulardins et al., 2015).

By using an undiagnosed sample on an ADHD referral pathway, the study ensured participants were drug-naïve and allowed for direct comparison between those who did and did not receive a diagnosis.

Method

Procedure

The study employed a cross-sectional design. Participants completed a comprehensive battery of cognitive assessments, including tests of executive functions, intelligence, and math skills.

Parents completed questionnaires about their child’s ADHD symptoms and co-occurring difficulties.

Sample

44 drug-naïve children (70% boys) aged 6-12 years (M = 101.34 months, SD = 19.39) were recruited from a clinical ADHD referral waiting list at an NHS Child and Adolescent Mental Health Service in Scotland.

All children scored high on ADHD symptom scales. After clinical evaluation, 24 children received an ADHD diagnosis, 15 did not, and 5 were awaiting confirmation.

Measures

• ADHD symptoms: Conners 3-Parent
• Intelligence: Wechsler Abbreviated Scale of Intelligence (WASI-II), British Picture Vocabulary Scale (BPVS-III)
• Executive Functions: Cambridge Neuropsychological Test Automated Battery (CANTAB) tasks, including Stop Signal Task, Intra-Extra Dimensional Task, Spatial Working Memory Task, and Stockings of Cambridge Task
• Verbal Working Memory: Letters Numbers Sequencing task from WISC-V
• Math Achievement: Wechsler Individual Achievement Test (WIAT-III) subtests
• Math Components: Custom tasks assessing factual knowledge, conceptual understanding, and procedural skills

Statistical measures

The study used two main analytical approaches:

1. Comparison of diagnostic groups (ADHD vs. subclinical ADHD) using t-tests and Mann-Whitney U tests
2. Data-driven clustering approach using hierarchical cluster analysis on EF variables, followed by MANOVAs and ANOVAs to compare resulting clusters

Results

Hypothesis 1: Cognitive function will better predict math performance than ADHD diagnosis.

Result: Supported. The diagnostic approach (ADHD vs. subclinical ADHD) did not differentiate cognitive functioning or math outcomes. In contrast, data-driven EF clusters showed significant differences in math performance.

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Hypothesis 2: Data-driven EF clusters will show distinct profiles in math performance.

Result: Supported. Three EF clusters were identified:

1. Low Working Memory: Lowest math and intelligence scores
2. Low Visuospatial EF: Intermediate math performance
3. Relatively Intact EF: Highest math and intelligence scores

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Hypothesis 3: EF clusters will not differ significantly in ADHD symptoms or co-occurring disorders.

Result: Supported. No significant differences were found between clusters in ADHD symptoms or co-occurring disorder symptoms.

Insight

This study provides compelling evidence that cognitive profiles, particularly executive function patterns, are more informative than diagnostic categories in predicting math performance among children referred for ADHD assessment.

The identification of three distinct EF clusters (Low Working Memory, Low Visuospatial EF, and Relatively Intact EF) highlights the cognitive heterogeneity within ADHD and its impact on academic outcomes.

The findings extend previous research by demonstrating that working memory difficulties may pose the greatest risk for math problems in children with ADHD symptoms.

This suggests that interventions targeting working memory could be particularly beneficial for improving math performance in this population.

The study also underscores the limitations of categorical diagnostic approaches in understanding academic difficulties in ADHD.

The lack of significant differences between children diagnosed with ADHD and those with subclinical symptoms in terms of cognitive functioning and math outcomes challenges the utility of strict diagnostic cutoffs in educational contexts.

Future research could explore the stability of these EF clusters over time and investigate whether targeted interventions based on a child’s EF profile lead to improved academic outcomes.

Additionally, examining how these cognitive profiles relate to other academic domains beyond math could provide a more comprehensive understanding of learning difficulties in ADHD.

Strengths

This study had several methodological strengths, including:

• Comprehensive assessment of executive functions, including both verbal and visuospatial components
• Inclusion of specific math component tasks (factual, conceptual, procedural) in addition to standardized achievement measures
• Use of a transdiagnostic approach, including children with co-occurring symptoms
• Recruitment of drug-naïve participants, eliminating potential medication effects
• Combination of categorical (diagnostic) and dimensional (data-driven) approaches to group comparison

Limitations

This study also had several methodological limitations, including:

• Small sample size, limiting the number of variables that could be included in the cluster analysis
• Reliance on parent-reported symptoms for ADHD and co-occurring difficulties
• Lack of a typically developing control group for comparison
• Cross-sectional design, preventing conclusions about causality or developmental trajectories
• Potential confounding effects of numerical processing in the verbal working memory task

These limitations restrict the generalizability of the findings and highlight the need for larger, longitudinal studies to confirm and extend the results.

Implications

The results have significant implications for both clinical practice and educational interventions:

1. Diagnostic reassessment: The study challenges the utility of strict diagnostic categories in predicting academic difficulties, suggesting a need for more nuanced, cognitively-informed approaches to assessment and intervention planning.
2. Targeted interventions: Identifying a child’s EF profile could help tailor educational support more effectively. For example, children in the Low Working Memory cluster might benefit most from interventions specifically targeting working memory skills.
3. Early identification: Cognitive profiling could potentially identify children at risk for math difficulties earlier than traditional diagnostic approaches, allowing for more timely interventions.
4. Transdiagnostic approach: The findings support the value of considering cognitive profiles across diagnostic boundaries, which could lead to more personalized and effective interventions for neurodivergent children.
5. Educational policy: The results suggest that educational support should be based on cognitive needs rather than diagnostic labels, which could inform policy decisions about resource allocation and intervention strategies in schools.

References

Primary reference

Kanevski, M., Booth, J. N., Stewart, T. M., & Rhodes, S. M. (2024). Cognitive heterogeneity in Attention Deficit Hyperactivity Disorder: Implications for maths. British Journal of Developmental Psychology. https://doi.org/10.1111/bjdp.12517

Other references

Capano, L., Minden, D., Chen, S. X., Schachar, R. J., & Ickowicz, A. (2008). Mathematical learning disorder in school-age children with attention-deficit hyperactivity disorder. The Canadian Journal of Psychiatry, 53(6), 392-399. https://doi.org/10.1177/070674370805300609

Coghill, D. R., Seth, S., & Matthews, K. (2014). A comprehensive assessment of memory, delay aversion, timing, inhibition, decision making and variability in attention deficit hyperactivity disorder: advancing beyond the three-pathway models. Psychological medicine, 44(9), 1989-2001.

Cragg, L., Keeble, S., Richardson, S., Roome, H. E., & Gilmore, C. (2017). Direct and indirect influences of executive functions on mathematics achievement. Cognition, 162, 12-26. https://doi.org/10.1016/j.cognition.2017.01.014

Czamara, D., Tiesler, C. M., Kohlböck, G., Berdel, D., Hoffmann, B., Bauer, C. P., … & Heinrich, J. (2013). Children with ADHD symptoms have a higher risk for reading, spelling and math difficulties in the GINIplus and LISAplus cohort studies. PloS one, 8(5), e63859. https://doi.org/10.1371/journal.pone.0063859

DuPaul, G. J., Gormley, M. J., & Laracy, S. D. (2013). Comorbidity of LD and ADHD: Implications of DSM-5 for assessment and treatment. Journal of learning disabilities, 46(1), 43-51. https://doi.org/10.1177/0022219412464351

Geary, D. C., Hoard, M. K., Byrd‐Craven, J., Nugent, L., & Numtee, C. (2007). Cognitive mechanisms underlying achievement deficits in children with mathematical learning disability. Child development, 78(4), 1343-1359. https://doi.org/10.1111/j.1467-8624.2007.01069.x

Goulardins, J. B., Marques, J. C., & De Oliveira, J. A. (2017). Attention deficit hyperactivity disorder and motor impairment: A critical review. Perceptual and motor skills, 124(2), 425-440. https://doi.org/10.1177/0031512517690607

Hong, S. B., Dwyer, D., Kim, J. W., Park, E. J., Shin, M. S., Kim, B. N., … & Cho, S. C. (2014). Subthreshold attention-deficit/hyperactivity disorder is associated with functional impairments across domains: a comprehensive analysis in a large-scale community study. European child & adolescent psychiatry, 23, 627-636. https://doi.org/10.1007/s00787-013-0501-z

Kofler, M. J., Irwin, L. N., Soto, E. F., Groves, N. B., Harmon, S. L., & Sarver, D. E. (2019). Executive functioning heterogeneity in pediatric ADHD. Journal of abnormal child psychology, 47, 273-286. https://doi.org/10.1007/s10802-018-0438-2

Keep Learning

Socratic questions for a college class discussion:

1. How might the identification of distinct executive function profiles in ADHD change our approach to diagnosing and treating the disorder?
2. What are the potential benefits and drawbacks of moving away from categorical diagnoses towards more dimensional, cognitively-informed approaches in neurodevelopmental disorders?
3. How could the findings of this study inform the development of more effective math interventions for children with ADHD symptoms?
4. What ethical considerations arise when using cognitive profiles to guide educational interventions, particularly in terms of labeling and resource allocation?
5. How might the cognitive heterogeneity observed in this study relate to the broader debate about the validity and utility of ADHD as a diagnostic category?
6. In what ways could the transdiagnostic approach used in this study be applied to other areas of developmental psychopathology research?
7. How might sociocultural factors influence the relationship between executive functions, ADHD symptoms, and academic performance? How could future research address this?
8. What are the potential long-term implications of identifying and intervening based on cognitive profiles in childhood for academic and career outcomes in adulthood?