Neurodevelopmental conditions are a broad group of disorders that typically emerge during childhood and can persist throughout life as a chronic condition. They are highly heterogeneous and the co-occurrence of multiple neurodevelopmental conditions within the same individual is common (Francés et al., 2022). Despite this, neurodevelopmental conditions have rarely been examined collectively and estimates of their prevalence among people under the age of 18 vary considerably across individual conditions and studies. For example, a review by Francés et al., (2022) found that estimates for the prevalence of individual neurodevelopmental conditions ranged from 4.70% (Fleming et al., 2020) to 88.50% (Kita et al., 2020).
Neurodevelopmental conditions were formally introduced as a diagnostic category in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5 (American Psychological Association, 2013)). This category encompasses conditions including intellectual disability, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and specific learning disorder (Francés et al., 2022). The DSM-5 is one of the most widely used and influential diagnostic systems and is used by clinicians to classify difficulties, guide intervention and determine who may require additional support. At its core, however, it is a categorical system. Individuals are assigned to discrete diagnostic groups based on whether they meet a specified set of criteria. This categorical diagnostic approach alone does not capture the full range of a child’s strengths, difficulties and support needs.
This categorical diagnostic approach alone does not capture the full range of a child’s strengths, difficulties and support needs.
In an Annual Research Review published in The Journal of Child Psychology and Psychiatry, Astle et al., (2022) identified three fundamental challenges associated with conventional diagnostic boundaries such as those used by the DSM-5.
The first concerns the use of diagnostic thresholds. Categorical systems necessarily draw a boundary between those who meet criteria for a diagnosis and those who do not. Yet the difficulties experienced by children do not necessarily conform to such clear boundaries. A child may experience substantial symptoms and impairment while falling just below the threshold required for a formal diagnosis. As access to treatment and additional support can depend on meeting these criteria, children with considerable needs may struggle to receive appropriate support. Their experiences may also be less visible in research that focuses primarily on clinically defined groups.
The second challenge is heterogeneity. A single diagnostic label can encompass children with different profiles of strengths and difficulties, including differences in the nature, severity and impact of their symptoms. Two children with the same diagnosis may therefore have very different experiences and support needs. This variability can make it difficult to identify mechanisms that are genuinely shared across children with the same diagnosis and may also complicate decisions about which interventions are most appropriate.
The third issue is that some diagnosis-based studies exclude participants with co-occurring conditions, potentially limiting their applicability to children with more complex presentations. Neurodevelopmental difficulties frequently overlap, yet research studies tend to recruit participants according to a specific diagnostic category while excluding those with known co-occurring conditions. This means that this research focuses on isolated diagnostic presentations, despite co-occurring conditions being more frequent. For example, in a sample of youth with a clinical diagnosis of autism and referred to a specialised program for autism (N = 140), 76% also had a lifetime diagnosis of ADHD (Joshi et al., 2017). Further, learning disability has been reported in 44% of children with parent-reported diagnosed ADHD in US children (Pastor & Reuben, 2008). Consequently, a substantial proportion of children at neurodevelopmental risk, particularly those presenting with complex or overlapping difficulties, may be excluded from research. The resulting evidence base can therefore disproportionately represent relatively “pure” diagnostic presentations, limiting how well research findings apply to children with more complex presentations.
A single diagnostic label can encompass children with different profiles of strengths and difficulties, including differences in the nature, severity and impact of their symptoms.
Together, these limitations have encouraged researchers to explore transdiagnostic approaches, which move beyond the assumption that neurodevelopmental difficulties are best understood as a set of discrete categories. Instead, these approaches examine dimensions of behaviour, cognition and neurobiology that may cut across conventional diagnostic boundaries.
One example comes from Kushki et al. (2019), who recruited children diagnosed with ADHD, autism or obsessive-compulsive disorder (OCD), alongside a comparison group of children without these diagnoses. Rather than simply comparing each diagnostic group with the comparison group, the researchers allowed patterns within the data to determine how children were grouped. Using clustering techniques, a machine learning technique used to organise data based on similarities, they identified groups based on characteristics such as inattention, social skills and brain structure, rather than on existing diagnostic labels. There was little overlap between these data-driven groups and those defined by conventional diagnoses. Children who shared a diagnosis did not necessarily resemble one another most closely, while children with different diagnoses could show similar profiles across behavioural and neurobiological measures (Kushki et al., 2019). Studies such as this demonstrate that recruiting children according to formal diagnoses can still be valuable, but also show why those diagnoses should not necessarily be assumed to represent the most meaningful way of differentiating between children. Instead, looking beyond diagnostic boundaries may reveal the diverse and overlapping difficulties experienced by children with neurodevelopmental needs.
Looking beyond diagnostic boundaries may reveal the diverse and overlapping difficulties experienced by children with neurodevelopmental needs.
These clustering techniques have also been used to demonstrate the heterogeneity within a shared diagnostic label. Stevens et al., (2019) found that amongst children meeting the same diagnostic criteria for autism, five distinct subgroups were identified, each with distinct symptom profiles. These clusters represented different degrees of severity across several developmental domains, including cognitive, language, social, adaptive, academic, executive function, motor and play skills (Stevens et al., 2019). This study revealed that developmental difficulties vary significantly across individuals diagnosed with the same condition, highlighting subgroups that may benefit from different types and level of support.
Transdiagnostic approaches can also be applied at an earlier stage of the research process by changing how participants are recruited. Rather than selecting children according to whether they meet criteria for a particular diagnosis, studies can recruit a broader and more heterogeneous group based on the difficulties they experience in everyday life. The Centre for Attention, Learning and Memory (CALM) project at the University of Cambridge adopted this approach by recruiting children who had been referred to specialist educational or clinical services due to difficulties with attention, learning or memory, irrespective of whether they had received a formal diagnosis (Astle et al., 2019). The researchers found that typically, less than 40% of these children had received a formal diagnosis of autism, dyslexia or ADHD (Benhamou, 2022). Research using this cohort has subsequently applied transdiagnostic analytical methods to identify groups of children based on shared cognitive, behavioural or neurobiological characteristics rather than diagnostic labels (Bathelt et al., 2018; Siugzdaite et al., 2020).
Together, these approaches may better capture the diversity of children’s experiences and reduce reliance on diagnostic labels alone.
To conclude, this blog highlighted some of the limitations of traditional diagnostic classification systems for neurodevelopmental conditions, referring to Astle et al., (2022)’s Annual Research Review. These limitations have encouraged researchers to explore transdiagnostic approaches to move beyond conventional diagnostic boundaries. Such approaches can be used to examine whether data-driven groups align with establish diagnoses (Kushki et al., 2019), reveal the considerable heterogeneity that exists within a single diagnostic category (Stevens et al., 2019) and broaden research recruitment to better represent children with complex and overlapping difficulties (Astle et al., 2019). Together, these approaches may better capture the diversity of children’s experiences and reduce reliance on diagnostic labels alone. In practice, this could help shift assessment and support towards identifying a child’s specific strengths and difficulties, rather than making access to support dependent primarily on whether they meet the criteria for a particular diagnosis.
References
- Astle, D. E., Bathelt, J., CALM Team, & Holmes, J. (2019). Remapping the cognitive and neural profiles of children who struggle at school. Developmental Science, 22(1), e12747. https://doi.org/10.1111/desc.12747
- Bathelt, J., Holmes, J., Astle, D. E., Holmes, J., Gathercole, S., Astle, D., Manly, T., & Kievit, R. (2018). Data-Driven Subtyping of Executive Function–Related Behavioral Problems in Children. Journal of the American Academy of Child & Adolescent Psychiatry, 57(4), 252-262.e4. https://doi.org/10.1016/j.jaac.2018.01.014
- Benhamou, E. (2022, February 24). Embracing complexity in neurodevelopment—Part 3. 4D Group. https://www.astlelab.com/post/embracing-complexity-in-neurodevelopment-part-3
- Fleming, M., Salim, E. E., Mackay, D. F., Henderson, A., Kinnear, D., Clark, D., King, A., McLay, J. S., Cooper, S.-A., & Pell, J. P. (2020). Neurodevelopmental multimorbidity and educational outcomes of Scottish schoolchildren: A population-based record linkage cohort study. PLOS Medicine, 17(10), e1003290. https://doi.org/10.1371/journal.pmed.1003290
- Francés, L., Quintero, J., Fernández, A., Ruiz, A., Caules, J., Fillon, G., Hervás, A., & Soler, C. V. (2022). Current state of knowledge on the prevalence of neurodevelopmental disorders in childhood according to the DSM-5: A systematic review in accordance with the PRISMA criteria. Child and Adolescent Psychiatry and Mental Health, 16, 27. https://doi.org/10.1186/s13034-022-00462-1
- Gillberg, C., & Coleman, M. (2000). The biology of the autistic syndromes. Cambridge University Press.
- Joshi, G., Faraone, S. V., Wozniak, J., Tarko, L., Fried, R., Galdo, M., Furtak, S. L., & Biederman, J. (2017). Symptom Profile of ADHD in Youth With High-Functioning Autism Spectrum Disorder: A Comparative Study in Psychiatrically Referred Populations. Journal of Attention Disorders, 21(10), 846–855. https://doi.org/10.1177/1087054714543368
- Kita, Y., Ashizawa, F., & Inagaki, M. (2020). Prevalence estimates of neurodevelopmental disorders in Japan: A community sample questionnaire study. Psychiatry and Clinical Neurosciences, 74(2), 118–123. https://doi.org/10.1111/pcn.12950
- Kushki, A., Anagnostou, E., Hammill, C., Duez, P., Brian, J., Iaboni, A., Schachar, R., Crosbie, J., Arnold, P., & Lerch, J. P. (2019). Examining overlap and homogeneity in ASD, ADHD, and OCD: A data-driven, diagnosis-agnostic approach. Translational Psychiatry, 9(1), 318. https://doi.org/10.1038/s41398-019-0631-2
- Pastor, P. N., & Reuben, C. A. (2008). Diagnosed Attention Deficit Hyperactivity Disorder and Learning Disability: United States, 2004-2006. Data from the National Health Interview Survey. Vital and Health Statistics. Series 10, Number 237. ERIC.
- Siugzdaite, R., Bathelt, J., Holmes, J., & Astle, D. E. (2020). Transdiagnostic Brain Mapping in Developmental Disorders. Current Biology: CB, 30(7), 1245-1257.e4. https://doi.org/10.1016/j.cub.2020.01.078
- Stevens, E., Dixon, D. R., Novack, M. N., Granpeesheh, D., Smith, T., & Linstead, E. (2019). Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning. International Journal of Medical Informatics, 129, 29–36. https://doi.org/10.1016/j.ijmedinf.2019.05.006