A systematic review of behavioral and neurobiological profiles associated with coexisting attention-deficit/hyperactivity disorder and developmental coordination disorder

Abstract

Attention-deficit/hyperactivity disorder (ADHD) and developmental coordination disorder (DCD) co-occur in approximately 50% of cases. This study aimed to characterize the behavioral, cognitive, and neurobiological profiles of co-occurring ADHD and DCD diagnoses by mapping, synthesizing, and providing a critical appraisal of the existing literature. A systematic search was conducted across four databases (MEDLINE, PsycINFO, Embase, and Scopus) to identify studies comparing a coexisting ADHD+DCD diagnosis to ADHD and DCD alone. From 2353 screened articles, 15 behavioral and 10 neuroimaging studies were included. Collectively, these studies suggest that the comorbid ADHD+DCD presentation constitutes a more severe phenotype characterized by neurocognitive differences associated with both conditions. Despite sharing some common neural features, our findings support the separate etiology hypothesis indicating that neural network alterations in individuals with ADHD+DCD represent a unique neural pattern rather than a sum of ADHD and DCD characteristics. Considering the heterogeneity inherent to both ADHD and DCD, future studies should involve rigorous and comprehensive assessment procedures to delineate how different subtypes of each diagnosis relate to distinct performance characteristics.

1. Introduction

Neurodevelopmental disorders (NDDs) impact millions of children, unfold early in life, and encompass a broad spectrum of symptoms across multiple domains of functioning (Thapar et al., 2017). Although each condition is defined by a distinct constellation of features, NDDs are often comorbid with one another, posing challenges in treatment planning and care (American Psychiatric Association, 2013). Attention-deficit/hyperactivity disorder (ADHD) and developmental coordination disorder (DCD) are among the most common NDDs, with ADHD present in approximately 7% (Polanczyk et al., 2014; Sayal et al., 2018) and DCD in 6% of school-age children (APA, 2000; Zwicker et al., 2012). While ADHD is characterized by inattention, hyperactivity and impulsivity, children with DCD exhibit difficulties with motor control, including motor planning, coordination, and learning (APA, 2013; World Health Organization, 2019). In addition, children diagnosed with either ADHD or DCD are at higher risk for anxiety, depression, lower academic achievement, and less favorable psychosocial outcomes in adulthood (Cairney, 2015; Omer et al., 2019; Rasmussen and Gillberg, 2000; Sibley et al., 2010).

The rate of ADHD and DCD co-occurrence is around 50% (Blank et al., 2012; Farran et al., 2020; Gillberg et al., 2004). Despite this, clinicians have only recently recognized that motor difficulties in children with ADHD are not simply secondary to attention and behavior dysregulation (Sergeant et al., 2006). As a result, the intersection of ADHD and DCD has largely been understudied. The concept termed “deficits in attention, motor control and perception” (DAMP) was introduced in the 1970 s in Scandinavia to describe the overlap between ADHD and DCD (Gillberg, 2003; Gillberg and Rasmussen, 1982). Stemming from clinical observations of children with combined symptomatology, the DAMP concept highlights the link between perceptual abilities and difficulties in attentional and motor domains. The role of perceptual timing is especially intriguing given that both DCD and ADHD have high comorbidity rates with other NDDs associated with perceptual vulnerabilities, including autism spectrum disorder and dyslexia (Arnett et al., 2018; Falter and Noreika, 2014; Gomez and Sirigu, 2015; Goswami, 2011; Lense et al., 2021; Noreika et al., 2013; Pranjić et al., 2023). Nonetheless, the question of whether ADHD and DCD have shared or separate neurobiological mechanisms continues to be debated (Goulardins et al., 2015; Sergeant et al., 2006). In the current review, we summarize the extant literature related to the co-occurrence between ADHD and DCD in the context of these competing hypotheses.

Considering that children with co-occurring ADHD and DCD tend to have more severe outcomes (Pitcher et al., 2003; Visser, 2003), understanding the performance features that characterize the comorbid presentation could have important clinical implications. In addition, relatively little neuroimaging research has directly compared DCD and ADHD (Bishop, 2010), and the relationship between the two has predominantly been studied from an ADHD perspective (for a review of existing neuroimaging evidence on either ADHD or DCD, see: Brown-Lum and Zwicker, 2015; Bush, 2011; Cortese et al., 2012; Langevin et al., 2014; Norman et al., 2016; Parlatini et al., 2023; Sutcubasi et al., 2020; Wilson et al., 2013, 2017). Studies focusing on treatment of motor difficulties in ADHD have been synthesized in recent reviews (Kaiser et al., 2015; Kleeren et al., 2023). Kleeren and colleagues (2023) found positive effects of motor-based interventions on motor skills in children with ADHD, and Kaiser and colleagues (2015) demonstrated positive effects of ADHD medication on motor performance in 28–67% of children. Considering that a proportion of children with ADHD continued to display motor difficulties while on medication, Kaiser and colleagues argue that motor control challenges in ADHD cannot only be attributed to inattention. Altogether, the effects of medication on both attentional and motor difficulties indicate that ADHD and DCD share some common neural circuits; however, the link between the two remains unclear. In an attempt to explain the etiology of comorbidity, several conceptual frameworks have been proposed. The shared etiology hypothesis suggests that NDDs occur on a continuum of symptoms with varying levels of severity, rooted in common neurobiological mechanisms (Gilger and Kaplan, 2001; Gillberg, 2010; Moreno-De-Luca et al., 2013). By contrast, the separate etiology hypothesis asserts that neurocognitive mechanisms differ for each NDD, despite the existence of some shared correlates (Goulardins et al., 2015; Kangarani-Farahani et al., 2020). Goulardins and colleagues (2015) compared the features of ADHD-only and DCD-only and found support for the separate etiology hypothesis, suggesting that ADHD and DCD have separate profiles and may require different treatment methods. Still, it remains unknown whether motor difficulties in ADHD are a part of the ADHD phenotype or if they reflect comorbidity (Goulardins et al., 2017). The same question applies in the context of attentional problems in DCD.

To date, no studies have systematically examined the behavioral and neurobiological characteristics of coexisting ADHD and DCD (ADHD+DCD) diagnoses by mapping, synthesizing, and providing a critical appraisal of the existing evidence. The current review aims to fill this knowledge gap by comparing behavioral, cognitive and neuroscientific correlates across ADHD+DCD, ADHD-only, and DCD-only presentations. In an attempt to explain the factors underlying this co-occurrence, we tested the three competing hypotheses. We hypothesize that the shared etiology hypothesis will be supported if ADHD, DCD, and the comorbid ADHD+DCD presentation share common behavioral and neurocognitive correlates. In contrast, the separate etiology hypothesis will be supported if ADHD and DCD present with unique neurocognitive and behavioral profiles, and the ADHD+DCD presentation constitutes a more severe phenotype characterized by neurocognitive differences associated with both disorders. Finally, it is plausible that the combined ADHD+DCD condition presents a distinct disorder. Therefore, our third hypothesis is that ADHD, DCD, and ADHD+DCD may each exhibit unique neurobiological profiles.

2. Methods

The systematic review was performed in compliance with the Joanna Briggs Institute methodological framework (Aromataris and Munn, 2020), following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Hutton et al., 2015; Page et al., 2021). The protocol was registered with the Open Science Framework on April 19th, 2023 and is available online at https://doi.org/10.17605/OSF.IO/7BGJP. We addressed the following questions: (1) Do children with a co-occurring ADHD+DCD diagnosis exhibit greater task-related performance difficulties compared to children with ADHD or DCD alone? (2) What neural substrates underlie a combined ADHD+DCD compared to a singular ADHD or DCD diagnosis?

2.1. Eligibility criteria

Articles were eligible if they: (1) included participants with coexisting ADHD+DCD diagnoses in addition to a group of participants with ADHD or DCD alone (i.e., ADHD+DCD and ADHD, ADHD+DCD and DCD, or ADHD, ADHD+DCD, and DCD); (2) included participants who had at least one diagnosis confirmed using DSM criteria (i.e., ADHD or DCD, or both); (3) involved children (< 18 years); (4) were published in English; and (5) were categorized as original research. Articles were excluded if they: (1) focused on comparing ADHD or DCD to other comorbid NDD presentations (e.g., ADHD+ASD); (2) included an additional comparison group other than ADHD, DCD, ADHD+DCD, or control groups; (3) included only parent or teacher questionnaires to characterize both the ADHD and DCD sample; (4) were review papers; or (5) were epidemiological studies (i.e., assessed distribution and determinants rather than task-based performance). Publication year was not considered as an inclusion/exclusion criterion, as we aimed to capture all existing research. However, we recognize that the terminology used to describe attentional and motor difficulties consistent with ADHD and DCD has varied in the last few decades. For example, the term “developmental coordination disorder” was introduced in 1994, and the contemporary concept of “ADHD” has been in use since 2000 (APA, 2000; Lange et al., 2010), resulting in a wide variation in terminology used prior to those years. Therefore, studies where the inclusion criteria were not consistent with the current DSM guidelines for at least one diagnosis were excluded.

2.2. Information sources and search strategy

The search was carried out across four databases including the MEDLINE (Ovid), Embase (Ovid), PsycINFO, and Scopus, for articles published before April 1, 2023. Additionally, we examined reference lists of selected studies and papers on a similar topic to ensure we capture current and emerging evidence. The search strategy was developed and performed by two authors (MP and NR) following the PICO (population, intervention, comparison, outcomes) framework. To define the population, we combined the terms related to attention-deficit/hyperactivity disorder (e.g., “ADHD”, “hyperkinetic syndrome”, “attention deficit”) and developmental coordination disorder (e.g., “DCD”, “motor skill disorder”, “dyspraxia”). Groups that involved children with a singular diagnosis of either ADHD or DCD or typically developing peers were considered the comparison. The intervention and the outcome were not specified, in order to capture all relevant publications regardless of the method (e.g., behavioral or neural), task, or outcome domain (e.g., cognitive, motor, psychosocial). Subject headings were adapted for each database. Table 1 provides an example of the search strategy performed in MEDLINE (see Supplementary Table S1 for the full electronic search strategy).

Table 1.

Search strategy used for selection of scientific articles in MEDLINE.

Database	MEDLINE (Ovid)
Limits	Language: “English”, Age: “Children < 18”, Publication Type: “Article”
Search Query	Developmental coordination disorder		AND Attention-deficit/hyperactivity disorder
	Subject Headings	Keywords (combined with ‘OR’)	Subject Headings	Keywords (combined with ‘OR’)
	exp Motor Skills Disorders/	developmental coordination disorder.mp. DCD.mp. motor skill* disorder.mp. dyspraxi* .mp. motor coordination difficult* .mp. (developmental coordination adj3 (disorder* or problem* or dysfunction* or difficult* or impairment* or deficit*)).mp.	exp Attention deficit and disruptive behavior disorders/exp Attention deficit disorder with hyperactivity/	ADHD.mp. attention deficit hyperactivity disorder.mp. (hyperkin* adj3 (deficit* or disorder* or syndrome)).mp. (attention adj1 (deficit* or disorder* or problem*)).mp.
Results	375

2.3. Screening and data extraction

All titles, abstracts, and full-text publications were screened by two independent reviewers (MP and NR) for inclusion and exclusion criteria; any discrepancies were resolved through discussion. The appropriate filtering processes of the search results were done via Covidence. Relevant studies were further evaluated and inconsistencies among the reviewers regarding study selection were discussed and resolved by consensus. Four authors (AK, KM, NR, SP) systematically and independently extracted the following information from the included papers using a shared data extraction form: publication details, sample characteristics (i.e., age range, sex, sample size, inclusion criteria, control group), study design, performance tasks, outcomes measures, and key findings. The lead author (MP) screened all the eligible studies and verified the correctness of the extracted information. Data from the selected behavioral and neuroimaging studies were collated, summarized, and reported separately. Given the diversity of the tasks used, behavioral studies were characterized according to the outcome domain to determine whether performance characteristics are task-specific. Meta-analyses could not be undertaken due to the heterogeneity of screening tools, tasks, functional domains, and outcome measures.

2.4. Study risk of bias assessment

For each study, two independent reviewers assessed the risk of bias following the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for analytical cross-sectional studies (Moola et al., 2017). The checklist evaluates the following eight areas: inclusion criteria, description of study participants and the setting, validity of diagnostic assessments, control group comparison, validity and reliability of the outcome measures used, analysis features, confounding factors, and discussion quality (see Supplementary Table S2). The possible answers were “yes,” “no,” “unclear”, or “not applicable”. A numeric score was assigned to each answer (e.g., “yes” = 1, “no,” “unclear”, or “not applicable” = 0) and, after the responses from both reviewers were compared and discussed, a summary score was calculated. Papers with four or fewer “yes” responses were classified as having a high risk of bias (i.e., poor quality), those with five or six “yes” responses as having a moderate risk of bias (i.e., average quality), and those with seven or more “yes” responses as having low risk of bias (i.e., high quality). After each reviewer had rated all the articles independently, the inter-rater reliability (Cohen’s kappa) was assessed and indicated a strong level of agreement (k = .82).

3. Results

A total of 2353 sources were identified from searches of four electronic databases (Fig. 1). Based on the title and the abstract, 1243 articles were excluded and a total of 96 sources remained for full-text screening. Of these, 70 studies were excluded for the following reasons: (1) included the wrong patient population (n = 47), e.g., did not include a combined ADHD+DCD group and/or included participants with another NDD; (2) employed a study design that did not allow for task-related performance comparisons between the combined ADHD+DCD group and ADHD or DCD groups alone (n = 24). In total, 15 behavioral and 10 neuroimaging studies were considered eligible for this review and are summarized in Table 2 and Table 3, respectively. Behavioral results were reported by the outcome domain, including cognitive (n = 3), psychosocial (n = 2), and motor domains (n = 8), and two intervention studies related to handwriting and cognitive-motor training. Neuroimaging findings involved nine magnetic resonance imaging (MRI) studies and one single photon emission computed tomography (SPECT) study. Twenty of 25 studies (80%) employed a cross-sectional design. In terms of risk of bias assessment, 52% of studies were classified as having low risk of bias (i.e., high quality), 44% as having moderate risk (i.e., average quality), and 4% as having high risk of bias (i.e., low quality). Neuroimaging studies received higher ratings than behavioral, with eight studies being classified as high quality and two as average quality, while five behavioral studies were rated as high quality, nine as average, and one as low quality. The following sections provide the synthesis of the included behavioral and neuroimaging findings.

Fig. 1.

Selection of sources of evidence (PRISMA 2020 flow diagram).

Table 2.

Key characteristics and critical appraisal of behavioral studies.

Author (s)	Groups (n)	Age	Variable (s)	Key findings	Etiology	Quality
Cognitive performance
Kanevski et al. (2023)	ADHD (18) ADHD+DCD (25) TDC N/A	6–12	CANTAB, WASI-II, WISC-IV, WIAT-III	ADHD + DCD ↓ visuospatial working memory Cognition and maths: comparable	Separate	High (7)
Loh et al. (2011)	ADHD (14) DCD (11) ADHD+DCD (11) TDC (26)	9–13	WISC-IV subtests (VIQ, PRI, PSI)	DCD and ADHD+DCD ↓ perceptual reasoning (visuospatial) ability than ADHD alone ADHD + DCD ↓ processing speed	Separate	High (7)
Norrelgen et al. (1999)	ADHD (9) ADHD+DCD symptoms (13) TDC (19)	8–15	Speech discrimination Phonological working memory	ADHD + DCD symptoms (DAMP) ↓ phonological working memory than ADHD Speech discrimination test: comparable	Separate	High (7)
Psychosocial performance
Dewey and Volkovinskaia (2018)	ADHD (9) DCD (9) ADHD+DCD (10) TDC (16)	11–18	KIDSCREEN-52, PRQ A semi-structured interview (N = 24)	Overall HRQoL scores: comparable ADHD + DCD ↓ HRQoL mood and emotion, environment, physical well-being ADHD+DCD ↑ peer victimization	Separate	Average (6)
Missiuna et al. (2014)	ADHD (31) DCD (68) ADHD+DCD (54) TDC (91)	10–14	CDI, SCARED	ADHD + DCD ↑ anxiety Depressive symptoms ADHD + DCD ↑ [parent] ; DCD and ADHD + DCD ↑ [self-report]	Separate	High (8)
Motor performance
Baerg et al. (2011)	DCD (32) DCD+ADHD symptoms (30) TDC (48)	12–13	Physical activity using accelerometry (step count, AEE)	Females: DCD+ADHD symptoms ↑ step count than DCD or TDC Males: DCD and DCD + ADHD symptoms ↓ step count than TDC	Separate	Average (6)
Izadi-Najafabadi, Gunton et al. (2022)	DCD (37) DCD+ADHD (41) TDC N/A	8–12	CO-OP intervention COPM, PQRS, BOT-2	Improved motor skills: DCD and ADHD+DCD Transfer of motor learning: DCD only	Separate	Average (6)
Jucaite et al. (2003)	ADHD (11) DCD (12) ADHD+DCD (13) TDC (26) [11 YC]	8–11 YC 5–7	Lifting task: anticipatory postural adjustments (grip force, load force)	ADHD+DCD ↑ grip force and ↓ amplitude DCD ↓ timing of postural adjustments	Separate	Average (6)
Lee et al. (2013)	ADHD (15) ADHD+DCD (8) TDC (38)	6–11	Tracking task (fine motor fluency) Pursuit task (motor flexibility)	ADHD+DCD ↓ overall difficulties ADHD symptom severity related to unsmooth movement performance	Separate	Average (6)
Lewis et al. (2008)	ADHD (14) DCD (15) ADHD+DCD (14) TDC (15)	8–12	Motor imagery: real and imagined pointing movements (VGPT)	DCD ↓ imagined movements ADHD and ADHD + DCD comparable to TDC but slower	Shared	Average (6)
Licari and Larkin (2008)	ADHD (13) DCD (13) ADHD+DCD (10) TDC (15)	6–8	Associated movements (ZNA, the Fog Test) MAND (NDI score)	DCD and ADHD + DCD ↑ associated movements than ADHD and TDC	Separate	Average (6)
Pereira et al. (2000)	ADHD (9) ADHD+DCD symptoms (15) TDC (25)	9–15	Grip-lift task (motor-memory) Force plate task (anticipatory control)	ADHD+DCD symptoms ↑ variability of grip force output (anticipation and production)	Separate	Average (5)
Pitcher et al. (2002)	ADHD (49) ADHD+DCD (55) TDC (31)	7–12	Finger-tapping task (reaction time, peak force, inter-tap interval)	ADHD and ADHD + DCD ↑ variability and inter-tap intervals ADHD + DCD ↑ reaction times and force control variability	Separate	Poor (4)
Puyjarinet et al. (2022)	ADHD (4) DCD+ADHD (4) TDC N/A	7–10	PRO-PEN intervention Handwriting evaluation (BHK)	Handwriting improvement: ADHD and ADHD + DCD [immediate and three months post intervention]	Shared	Average (6)
Williams et al. (2013)	ADHD (14) DCD (10) ADHD+DCD (16) TDC (18)	7–12	Motor imagery (VGPT) Hand rotation (response time, accuracy)	DCD ↓ imagined movements DCD and ADHD + DCD ↓ hand rotation accuracy	Shared	High (8)

Note: ADHD = attention-deficit/hyperactivity disorder; AEE = activity energy expenditure; BHK = Concise Evaluation Scale for Children’s Handwriting; BOT-2 = The Bruininks–Oseretsky Test of Motor Proficiency–Second Edition; CANTAB = Cambridge Neuropsychological Test Automated Battery; CDI = Children’s Depression Inventory (Child and Parent versions); CO-OP = Cognitive Orientation to daily Occupational Performance; COPM = Canadian Occupational Performance Measure; DAMP = Deficits in Attention, Motor Control and Perception; DCD = developmental coordination disorder; Fog test = the test of non-homologous associated movements; HRQoL = Health-Related Quality of Life; KIDSCREEN-52 = Health Questionnaire for Children and Young People (measures 10 HRQoL dimensions); MAND = McCarron Assessment of the Neuromuscular Development (MAND); NDI = Neuromuscular Developmental Index score; PQRS = Performance Quality Rating Scale; PRI = Perceptual Reasoning Index; PRO-PEN = Psychomotor handwriting training program; PRQ = The Peer Relations Questionnaire for Children; PSI = Processing Speed Index; SCARED = Childhood Anxiety and Related Emotional Disorders; TDC = typically developing controls; VGPT = visually guided pointing task; VIQ = Verbal Intelligence Quotient; WASI-II = Wechsler Abbreviated Scale of Intelligence-Second Edition; WIAT-III = Wechsler Individual Achievement Test-III; WISC-IV = Wechsler Intelligence Scale for Children-IV; YC = young controls; ZNA = Zurich Neuromotor Assessment.

Table 3.

Key characteristics and critical appraisal of neuroimaging studies.

Author (s)	Groups (n)	Age	Variable (s)	Key findings	Etiology	Quality
Izadi-Najafabadi et al. (2021a)	DCD (21) DCD + ADHD (19) TDC N/A	8–12	CO-OP intervention COPM, PQRS, BOT-2 rs-MRI: functional connectivity	DCD: ↑ motor function, ↑ FC DMN and the right pregenual ACC; 3 months post: ↑ FC DAN and the precentral gyrus DCD + ADHD: ↑ motor function, no FC changes	Separate	Average (6)
Izadi-Najafabadi and Zwicker (2021b)	DCD (28) DCD + ADHD (25) TDC N/A	8–12	CO-OP intervention MRI DTI: white matter diffusion	DCD: ↑ motor function, changes in WM tracts post CO-OP and post 3 months DCD + ADHD: ↑ motor function, no WM changes	Separate	High (7)
Langevin et al. (2014a)	ADHD (23) DCD (9) ADHD + DCD (23) TDC (26)	8–17	MRI DTI: white matter integrity in the corpus callosum, SLF, and cingulum	ADHD: ↓ frontal corpus callosum DCD: ↓ parietal corpus callosum, left SLF ADHD + DCD: ↓ frontal and parietal corpus callosum	Separate	High (8)
Langevin et al. (2014b)	ADHD (10) DCD (14) DCD + ADHD (10) TDC (14)	8–17	MRI: cortical thickness	DCD + ADHD: distinct and more widespread cortical thinning in the frontotemporal, parietal, and occipital regions	Distinct	High (7)
McLeod et al. (2014)	ADHD (21) DCD (7) DCD + ADHD (18) TDC (23)	8–17	rs-MRI: functional connectivity of the M1	ADHD, DCD, DCD + ADHD: ↓ FC within motor circuitry relative to TDC ADHD + DCD: additional alterations in FC between M1 and sensory networks	Shared and separate	High (8)
McLeod et al. (2016)	ADHD (19) DCD (6) ADHD + DCD (14) TDC (21)	8–17	rs-MRI: functional connectivity of the primary and SM1 cortices	ADHD, DCD, ADHD + DCD: atypical FC between SM1, basal ganglia, and cerebellum ADHD + DCD: atypical FC associated with higher order sensory processing	Shared and separate	High (7)
Rohr et al. (2023)	ADHD (35) DCD (21) ADHD + DCD (28) TDC (31)	7–17	rs-MRI: functional connectivity across the prefrontal, limbic, and striatal regions BRIEF	ADHD (ADHD+DCD): ↑ behavioral regulation problems associated with reductions in FC within prefrontal pathways and visual reward pathways	Separate	High (8)
Shaw et al. (2016)	ADHD (42) DCD (22) DCD + ADHD (41) TDC (65)	4–17	MRI: four latent neuroanatomic variables mapped onto the MABC domains	DCD and DCD + ADHD: atypical ↓ in the volumes of the cerebral cortex and the cerebellum, but not significantly different	Shared and separate	High (8)
Thornton et al. (2018)	DCD (9) ADHD (20) ADHD + DCD (18) TDC (20)	8–17	Go/Nogo fMRI: BOLD responses	ADHD, DCD, TDC: no difference ADHD, DCD, ADHD + DCD: no difference ADHD + DCD vs. TDC: ↑ errors and ↓ BOLD in M1 and sensory cortices	Shared	High (7)
Yeh et al. (2012)	ADHD (10) ADHD + DCD (5) TDC N/A	15–18	SPECT: rCBF response to MPH	ADHD + DCD: rCBF at baseline ↓ frontal lobes and ↑ posterior lobes, after MPH ↓ occipital lobe ADHD: rCBF at baseline ↑ frontal lobes and ↓ posterior lobes, after MPH ↑ occipital lobe	Separate	Average (6)

Note: ACC = anterior cingulate cortex; ADHD = attention-deficit/hyperactivity disorder; BOLD = blood-oxygen-level-dependent signal; BOT-2 = The Bru-ininks–Oseretsky Test of Motor Proficiency–Second Edition; BRIEF = Behavior Rating Inventory of Executive Function (Parent Report); CO-OP = Cognitive Orientation to daily Occupational Performance; COPM = Canadian Occupational Performance Measure; DAN = dorsal attention networks; DCD = developmental coordination disorder; DMN = default mode networks; DTI = diffusion tensor imaging; FC = functional connectivity; fMRI = functional magnetic resonance imaging; M1 = primary motor cortex; MABC = Movement Assessment Battery for Children; MPH = methylphenidate; MRI = magnetic resonance imaging; PQRS = Performance Quality Rating Scale; rCBF = regional cerebral blood flow; rs-MRI = resting-state magnetic resonance imaging; SLF = superior longitudinal fasciculus; SM1 = sensory-motor cortex; SPECT = single photon emission computed tomography; TDC = typically developing controls; WM = white matter.

3.1. Behavioral studies

3.1.1. Cognitive performance

Three studies examined cognitive profiles, including verbal intelligence, perceptual reasoning, and processing speed (Loh et al., 2011), speech discrimination and phonological working-memory (Norrelgen et al., 1999), and visuospatial working memory capacity (Kanevski et al., 2023) of children with co-occurring ADHD+DCD compared to those with DCD or ADHD alone.

Loh et al. (2011) compared the cognitive abilities in 62 children with ADHD, DCD, ADHD+DCD, and a control group using the verbal intelligence quotient, perceptual reasoning index, and processing speed index from the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV; Wechsler, 2003). They found that children with combined ADHD+DCD or DCD-alone had significantly lower perceptual reasoning scores compared to both the ADHD and control groups. A recent study by Kanevski et al. (2023) found similar results regarding visuospatial processing abilities among children with ADHD and a combined ADHD+DCD group (aged 6–12 years). They examined whether the presence of co-occurring DCD affects the cognitive and math performance in children with ADHD by employing a battery of tests to assess children’s intellectual functioning (WASI-II), cognitive performance (CANTAB and WISC-IV), and math attainment (WIAT-III). Although the ADHD and ADHD+DCD groups could not be differentiated based on overall math performance, the ADHD+DCD group performed worse on a visuospatial working memory task. Given that visuospatial processing is an important element of math performance, Kanevski and colleagues proposed that poorer visuospatial working memory may be a distinct characteristic of DCD. These two studies offer evidence of different cognitive profiles for children with ADHD and children with DCD, by showing that difficulties in visuospatial ability may underlie DCD but not ADHD. However, the findings by Kanevski and colleagues (2023) should be interpreted with caution considering both of their groups scored high on parent-reported symptoms of Oppositional Defiant Disorder and Conduct Disorder. A study by Norrelgen and colleagues (1999) examined phonological working memory and speech discrimination between 8 and 15-years-old boys with ADHD versus ADHD with “motor difficulties”. Phonological working memory was tested using two- to five-syllabic non-word pairs with contrasting vowels. While the performance on the working memory test declined with increasing stimulus lengths (i.e., two-to-five-syllabic stimuli) across all the participants, children with ADHD combined with motor difficulties exhibited significantly greater performance difficulties, possibly due to higher sensitivity to working memory load.

Taken together, these results are consistent with the separate etiology hypothesis. However, only Loh et al. (2011) compared the combined group with both singular diagnoses; therefore, more research is needed to delineate the characteristics of cognitive performance in children with coexisting diagnosis compared to ADHD and DCD alone.

3.1.2. Psychosocial performance

Two studies examined the impact of a combined versus singular diagnosis on quality of life (Dewey and Volkovinskaia, 2018) and levels of psychological distress (Missiuna et al., 2014) among adolescents with ADHD and/or DCD.

Dewey and Volkovinskaia (2018) used self-report questionnaires to assess health-related quality of life (HRQoL) and peer relationships among adolescents with ADHD+DCD, ADHD, DCD, and controls (ages 11–18). While total HRQoL scores did not differ between groups, adolescents with co-occurring ADHD+DCD reported greater challenges than their peers on a number of subscales. Specifically, the ADHD+DCD group had significantly higher scores on the peer victimization scale and lower scores on the school environment subscale than the controls and a DCD group, and lower scores (i.e., more symptoms) on the mood and emotions subscale than adolescents with DCD only. The ADHD group most closely resembled a combined group, suggesting that the ADHD symptomatology may be contributing to the observed group differences. However, limited conclusions can be drawn given a small sample size of approximately 10 participants per group. In addition, 24 participants completed a semi-structured interview which provided further insights into their individual experiences and perceptions and indicated that children with co-occurring ADHD+DCD were at highest risk of experiencing peer victimization and marginalization, although all three diagnostic groups described themselves as less socially successful and less likely to participate in team sports compared to controls.

Another study that involved adolescents with ADHD+DCD, ADHD, DCD, and controls (N = 244, ages 10–14) focused on levels of psychological distress through self- and parent-reported questionnaires (Missiuna et al., 2014). Self-report revealed significantly more depressive symptoms among the combined ADHD+DCD and DCD groups compared to controls. Additionally, parent-report indicated that the prevalence of clinically significant depressive symptoms in young adolescents with DCD+ADHD was nearly 15 times greater than that in controls, and three-to-five times more common than youth with DCD or ADHD alone. Interestingly, adolescents with ADHD reported experiencing only slightly higher levels of anxiety and depression symptoms than controls, and fewer symptoms than the DCD group.

Overall, these findings add to the literature concerning secondary impacts of neurodevelopmental difficulties on internalizing symptoms and overall quality of life and suggest an additive effect wherein the combined ADHD+DCD presentation is associated with broader psychological distress compared to ADHD or DCD alone, thus supporting the separate etiology hypothesis. In line with previous research by Campbell et al. (2012), these findings also point to a specific link between motor difficulties and depression, suggesting that motor challenges alone can be a risk factor for psychological distress, while ADHD symptomatology may be associated with poorer peer relations.

3.1.3. Motor performance

Motor control encompasses a wide range of movements that are commonly assessed within three domains: fine motor, gross motor, and balance. We identified 10 studies that examined motor performance in children with a combined ADHD+DCD diagnosis compared to those with ADHD or DCD alone. A variety of motor measurements were completed, including finger tapping and lifting, anticipatory postural adjustments, physical activity, and motor imagery, as well as the effects of handwriting and cognitive-motor interventions on motor performance.

Pitcher and colleagues (2002) examined manual dexterity in 7–12-year-old boys with ADHD, ADHD+DCD, and a control group using a finger-tapping task. Measuring reaction time and force control, they found that boys with ADHD and ADHD+DCD displayed greater variability and longer inter-tap intervals than the comparison group. However, only those in a combined ADHD+DCD group had slower reaction times and more variability in force control, suggesting that those challenges cannot be attributed to inattentive symptomatology alone. A study by Pereira et al. (2000) included a similar sample of boys who performed a grip-lift task involving motor-memory representations and a force plate task that involved anticipatory parametric control of force output. Again, their results showed that only boys with ADHD who also had reported motor difficulties had pronounced challenges when anticipating and producing grip force output. Both studies suggest that force control vulnerabilities are related to motor dysfunction or the additive effect of ADHD and DCD symptoms and cannot be explained by core ADHD symptoms alone. More recently, Lee and colleagues (2013) used tracking and pursuit tasks to evaluate fine motor fluency and flexibility, respectively. Their findings align with the abovementioned studies (Pitcher et al., 2002; Pereira et al., 2000) in that children with co-occurring ADHD and DCD diagnosis or symptoms displayed greater difficulties compared to children with ADHD alone, providing further evidence for the separate etiology hypothesis. However, these studies did not include a group of children with DCD-only; thus, a comprehensive comparison between motor and attention difficulties alone and their combined effects is not possible.

Two studies examined motor imagery abilities in children with ADHD, DCD, ADHD+DCD, and controls through tasks involving real and imagined movements (Lewis et al., 2008; Williams et al., 2013). Lewis et al. (2008) used a visually guided pointing task (VGPT) which required the participants (ages 8–12) to execute or imagine executing movements between targets of varying size as quickly and accurately as possible. They found that only children with DCD alone had pronounced difficulties in generating imagined movements. Williams and colleagues (2013) employed a hand rotation task in addition to the VGPT and revealed similar results: children with DCD-only and ADHD+DCD were less accurate in identifying directions of hand images, whereas only children with DCD without ADHD displayed more difficulties in a VGPT task compared to other groups. These studies are in line with a well-established internal modelling hypothesis (Adams et al., 2014) suggesting that vulnerabilities in motor imagery may be a characteristic specific to DCD rather than a by-product of ADHD inattentive symptoms. Furthermore, this indicates that a combined diagnosis does not lead to more severe outcomes; on the contrary, an ADHD diagnosis may be a protective factor in the context of motor imagery vulnerabilities.

Licari and Larkin (2008) employed several fine and gross motor tasks to assess contralateral and bilateral associated movements in 6–8-year-old boys. Associated movements (AMs) are involuntary movements that accompany voluntary actions across varying contexts. They found that children with DCD and ADHD+DCD exhibited more AMs than children without movement difficulties, while children with ADHD alone performed similarly to controls. Anticipatory postural adjustments are another important feature of motor control as they enable coordinated and efficient performance by stabilizing the body’s posture prior to movement execution. Jucaite and colleagues (2003) employed a lifting task under four different weight conditions to examine anticipatory postural adjustments in children with ADHD, DCD, ADHD+DCD, and aged-matched controls. Although children with ADHD or DCD alone displayed minor to mild difficulties with postural adjustments, children in the ADHD+DCD group differed most from the controls by having significantly higher grip forces and smaller amplitude postural adjustments, indicating that combined symptomatology contributes to a higher degree and severity of balance challenges.

Overall motor health and well-being can be measured via an individual’s physical activity patterns in the natural environment. Baerg and colleagues (2011) used a lightweight accelerometry device attached to a flexible waist belt to evaluate step count and activity energy expenditure of physical activity patterns during a 7-day period among youth with DCD with and without elevated ADHD symptoms, and controls. Female participants with DCD+ADHD symptoms had increased step count compared to those with DCD only or controls, while no differences were found in activity energy expenditure. In contrast, male participants with DCD or DCD+ADHD symptoms were less physically active than controls. Lastly, among controls, step count and activity energy expenditure were both higher for boys than girls, suggesting that gender may affect physical activity trends.

Finally, two recent studies examined the effectiveness of interventions aimed at improving handwriting (Puyjarinet et al., 2022) or overall motor performance (Izadi-Najafabadi et al., 2022) in children with motor and/or attentional difficulties. Handwriting interventions are of great importance given that handwriting disorders (dysgraphia) occur in almost 60% of children with ADHD (Mayes et al., 2019) and have an ongoing impact on children’s academic and developmental trajectories. Puyjarinet and colleagues (2022) examined the effectiveness of a psychomotor handwriting training program “PRO-PEN” on unmedicated children with ADHD and children with ADHD+DCD (ages 7–10 years) and found improvements in handwriting quality in both groups immediately after and three months post training. However, the results should be interpreted with caution given both groups’ very small sample sizes (n = 4). Izadi-Najafabadi and colleagues (2022) assessed the effectiveness of the Cognitive Orientation to daily Occupational Performance (CO-OP) intervention program in improving motor performance and outcome maintenance three months post-intervention (Polatajko et al., 2001). The results showed significant improvements in motor performance in both children with DCD and DCD+ADHD (ages 8–12) after a 10-week treatment; however, only children with DCD alone showed retention of learned motor skills three months post-treatment.

Collectively, the majority of the included studies provide support for the separate etiology hypothesis. This indicates that, when combined, ADHD+DCD symptomatology forms a unique behavioral profile characterized by broader performance difficulties, with the exception of motor imagery tasks, which may be a specific vulnerability characteristic for DCD alone (Lewis et al., 2008; Williams et al., 2013).

3.2. Neuroimaging studies

Ten neuroimaging studies were considered eligible, nine of which used magnetic resonance imaging (MRI) and one used a single photon emission computed tomography (SPECT; Yeh et al., 2012). MRI techniques included structural MRI, functional MRI (fMRI), and diffusion tensor imaging, and two of these studies involved interventions (IzadiNajafabadi et al., 2021; Izadi-Najafabadi & Zwicker, 2021). Of note, a single group of collaborators from the University of Calgary published six of the studies (Langevin et al., 2014a, 2014b; McLeod et al., 2014, 2016; Rohr et al., 2023; Thornton et al., 2018), using data from a similar cohort of up to 115 8–17-year-olds with co-occurring ADHD+DCD.

Using diffusion tensor imaging (DTI), Langevin et al. (2014a) found significant white matter alterations in the corpus callosum in children with ADHD+DCD. Reductions in white matter integrity were observed in the frontal regions of the corpus callosum among youth with ADHD, in the parietal corpus callosum and the left superior longitudinal fasciculus in children with DCD, and in both frontal and parietal regions of the corpus callosum in children with ADHD+DCD. These microstructural differences corresponded to the performance scores on attentional and motor assessments, suggesting that a different pattern of alterations in the corpus callosum underlies difficulties in both domains. In another study, Langevin and colleagues (2014b) used MRI to assess cortical thickness between the groups. Results showed that children with cooccurring DCD and ADHD had a distinct and more widespread decrease in cortical thickness compared to children with a singular diagnosis of DCD or ADHD. Cortical thinning was particularly pervasive in the frontal, parietal, and temporal lobes, which also correlated with measures of motor and attentional functioning. Therefore, their findings provide support for the hypothesis that ADHD, DCD, and ADHD+DCD each have a unique neurobiological etiology (distinct disorders hypothesis).

Two studies published by McLeod and colleagues (2014, 2016) examined whether the high co-occurrence rates between ADHD and DCD could be explained by disruptions in functional connectivity within and between motor networks using resting-state MRI. The first study (McLeod et al., 2014) revealed that children with DCD, ADHD and ADHD+DCD all have reduced connectivity in the primary motor cortex, striatum, and angular gyrus, while children with co-occurring ADHD+DCD displayed additional alterations in functional connectivity in relation to sensory networks. In the subsequent study (McLeod et al., 2016), the same researchers found that children with DCD, ADHD and ADHD+DCD had atypical functional connectivity in sensory-motor networks both within and between hemispheres, particularly those involving subcortical regions associated with motor planning and execution, such as the basal ganglia and cerebellum. In addition, motor assessment scores of children with DCD alone or ADHD+DCD (but not ADHD-only) correlated with the strength of functional connections between the right primary motor cortex and supplementary motor area, anterior cingulate cortex, and regions supporting visuospatial processing. Collectively, McLeod et al. suggest that, relative to controls, children with ADHD and/or DCD exhibit alterations in functional connectivity within and between areas supporting sensory-motor interactions. Altogether, their findings support the existence of both shared and separate neurobiological substrates.

Another study (Shaw et al., 2016) used MRI in a relatively large sample of children (N = 226) to study the neurobiological overlap between motor coordination problems and ADHD symptoms. The DCD group showed reduction in cerebral cortex and cerebellum volumes compared to controls, but no differences were found between children with DCD versus DCD+ADHD. Moreover, ADHD symptom severity did not moderate the associations between neuroanatomy and motor coordination. Altogether, these results suggest that ADHD is not associated with distinct neuroanatomy from DCD, but also does not add to the degree of brain volume differences. Thus, these results could be consistent with shared or separate etiologies. However, their findings may be task-specific as the neuroanatomic variables were mapped onto a motor skill assessment tool that is primarily used for diagnosing DCD. In a study using single photon emission computed tomography (SPECT), Yeh and colleagues (2012) compared the regional cerebral blood flow (rCBF) response difference to Methylphenidate (MPH) in ten drug-naive adolescents with ADHD-only and five adolescents with ADHD+DCD. Their findings suggest that the two groups have different pathophysiology and rCBF responses to MPH treatment, with greater compromise of attention networks in the ADHD+DCD group. This implies different treatment strategies for the two groups. However, a larger sample size is required to evaluate these findings and determine the effects of long-term MPH treatment on motor skills.

Other studies have focused on associations between neurobiology and behavior regulation among children with ADHD and/or DCD. Rohr and colleagues (2023) examined the correlation between parent report of behavioral regulation and functional connectivity across ten regions of interest using resting-state fMRI. They found that difficulties with behavioral regulation (driven by ADHD symptomatology) corresponded to reductions in functional connectivity within the prefrontal cortex and neural circuits involved in visual reward processes in both ADHD and ADHD+DCD groups compared to DCD-only and control groups, indicating separate etiologies. Laboratory-based behavioral regulation was studied with a motor response inhibition paradigm (i.e., Go/Nogo task) while recording blood-oxygen-level-dependent (BOLD) signal using fMRI (Thornton et al., 2018). Interestingly, the only group differences were found between controls and ADHD+DCD in both behavioral and hemodynamic responses, with increased commission errors and reduced activation in primary motor and sensory areas in the ADHD+DCD children. Their results indicate additive effects of the comorbid condition, providing support for the shared etiology hypothesis. However, these findings should be interpreted with caution given that they did not find the expected performance differences in the ADHD-only group.

Finally, two studies approached the question of shared versus separate etiologies by measuring neurobiological functioning before and following an occupational therapy intervention (CO-OP) in children with DCD+ADHD and DCD-only (Izadi-Najafabadi et al., 2021a; Izadi-Najafabadi and Zwicker, 2021b). Izadi-Najafabadi and colleagues (2021a) found increased functional connectivity between the default mode network (DMN) and the right pregenual anterior cingulate cortex (pACC) in children with DCD-only. This network has been associated with self-regulation (Jokic et al., 2013), supporting the hypothesis that self-regulation mediates motor learning. Additional increases in functional connectivity between the dorsal attention network (DAN) and the precentral gyrus were found in children with DCD-only three months post-treatment, which corresponded with motor skill retention (behavioral data reported in Izadi-Najafabadi et al., 2022). In contrast, children with coexisting DCD+ADHD showed motor improvements post-intervention but these benefits were not maintained three months after post-intervention and were not associated with changes in functional connectivity. Consistent with these findings, their following study (Izadi-Najafabadi and Zwicker, 2021b) found that the CO-OP intervention induced microstructural changes in white matter tracts only in children with DCD alone, and those changes were maintained three months post-intervention. Specifically, changes occurred in regions involved in attention, self-regulation, and white matter structures associated with intra- and inter-hemispheric transfer of motor commands. Therefore, both of these studies align with the hypothesis that ADHD and DCD have separate etiologies. Considering the group differences in behavioral and neural outcomes, modification to the CO-OP intervention may be needed to achieve the same effectiveness in children with co-occurring ADHD symptoms.

Taken together, these emerging findings offer insight into the neurobiological substrates of co-occurring ADHD+DCD. While DCD and ADHD share common substrates, they present unique neurobiological profiles and the combined symptomatology forms a more widespread pattern of neurobiological differences. Therefore, these findings collectively provide support for the separate etiology hypothesis, with two exceptions (Langevin et al., 2014b; Thornton et al., 2018).

4. Discussion

ADHD and DCD are highly comorbid neurodevelopmental disorders whose co-occurrence is not well understood. The current review provides a synthesis of the behavioral and neurobiological characteristics associated with ADHD and DCD. In particular, we evaluate the extant literature in the context of three hypotheses: shared etiology between ADHD and DCD, separate etiologies for ADHD and DCD, and a distinct etiology for the comorbid ADHD+DCD presentation. Altogether, investigations of cognitive, behavioral and neurobiological correlates of these phenotypes support separate etiologies for ADHD and DCD, suggesting that the comorbid ADHD+DCD presentation derives from additive etiological risk and confers a broader, more impaired cognitive-behavioral phenotype (Goulardins et al., 2015; Kangarani-Farahani et al., 2022). First, we discuss the findings related to specific features of DCD-only and ADHD-only, followed by a discussion concerning performance characteristics and neurobiological underpinnings of the ADHD+DCD diagnosis.

4.1. Specific cognitive-behavioral features of the DCD-only diagnosis

While children with the combined ADHD+DCD diagnosis exhibited more severe performance difficulties across motor tasks, the exceptions to these findings were the two studies examining motor imagery (Lewis et al., 2008; Williams et al., 2013). Those studies showed that children with DCD alone exhibit unique challenges on motor imagery tasks compared to children with ADHD alone as well as the combined ADHD+DCD group, implying that ADHD may be a protective factor in this context. These results can be further interpreted in the context of the internal modeling deficit hypothesis (Adams et al., 2014), which postulates that compromised motor control in children with DCD arises from vulnerabilities in generating predictive models of action (Wolpert et al., 1995). Given that internal models are crucial for anticipating movement outcomes, it is not surprising that motor imagery may be an area uniquely impacted in children with DCD.

The presence of DCD symptomatology has also been uniquely associated with increased performance difficulties in visuospatial processing and working memory. Indeed, in a study by Alloway (2011) involving groups with singular ADHD or DCD diagnosis, children with DCD displayed increased difficulties across all memory tests compared to controls and on short-term memory tests (both verbal and visuospatial) compared to children with ADHD. Furthermore, a recent review found weak to strong correlations between executive functions in DCD (Fogel et al., 2023), with difficulties more pronounced on tasks related to working memory, as well as inhibitory control and general executive functioning compared to attention and verbal fluency abilities (Lachambre et al., 2021). These vulnerabilities in working memory are further supported by research linking difficulties in phonological working memory with decreased effectiveness in the retrieval of or the storage of motor commands early in life (Roland, 1984; Barkley, 1997).

Finally, an important outcome of this review is the fact that DCD appears to be uniquely associated with depressive symptoms (Missiuna et al., 2014), possibly owing to worse physical well-being and reduced involvement in peer social activities, like team sports. These secondary effects highlight the need for early identification procedures to prevent the high rates of internalizing symptoms in adolescence.

4.2. Specific cognitive-behavioral features of the ADHD-only diagnosis

The behavioral studies reviewed here suggest that the ADHD-only diagnosis is not associated with more severe cognitive, psychosocial, or motor outcomes than the combined ADHD+DCD diagnosis. In fact, an ADHD diagnosis may be a protective factor for motor imagery abilities. Although motor difficulties in children with ADHD have commonly been attributed to inattention (Fong et al., 2016; Pitcher et al., 2003), studies looking at the impact of ADHD medication on motor performance indicate that only a proportion of treated children show improvements (Kaiser et al., 2015). Likewise, the study by Yeh and colleagues (2012) found contrasting neurobiological responses to stimulant treatment in youth with ADHD versus ADHD+DCD. Specifically, adolescents with ADHD-only had increased rCBF in the occipital lobe in response to methylphenidate treatment, while adolescents with ADHD+DCD showed the opposite pattern. Thus, it remains unclear whether motor difficulties constitute an integral aspect of the ADHD phenotype or if they reflect comorbidity.

ADHD symptomatology could also impact the effectiveness of behavioral interventions for DCD. For example, the CO-OP occupational therapy intervention was less effective for children with combined ADHD+DCD compared to DCD alone (Izadi-Najafabadi et al., 2021a; Izadi-Najafabadi and Zwicker, 2021b). Thus, clinicians working with children with co-occurring ADHD and DCD symptomatology should consider modifying motor-focused interventions to include self-regulatory strategies and allow breaks between tasks.

4.3. Children with coexisting ADHD+DCD exhibit more performance difficulties than children with ADHD or DCD alone with task-related exceptions

Altogether, our findings suggest that children with coexisting ADHD+DCD diagnoses exhibit broader and more severe neuropsychological difficulties compared to children with ADHD or DCD alone, with several task-dependent exceptions. The ADHD+DCD presentation is associated with greater challenges in visuospatial processing, phonological working memory, and fine and gross motor performance. These findings are indicative of an additive effect wherein vulnerabilities associated with each disorder combine to create a broader and more severely impacted phenotype. Our findings are consistent with literature supporting the additive model in the context of co-occurring ADHD and persistent tic disorder (Jurgiel et al., 2022) and ADHD and Tourette syndrome (Termine et al., 2016). Specifically, Jurgiel et al. (2022) found that atypical neural resting-state connectivity combines additively in children with comorbid ADHD and persistent tic disorder, with each diagnosis presenting unique characteristics. Similarly, broader performance difficulties on cognitive tests were reported in children with coexisting ADHD and Tourette syndrome compared to those with a singular diagnosis or healthy controls, with increased difficulties being principally driven by ADHD diagnosis (Termine et al., 2016). Importantly, the studies reviewed in our paper did not clarify the direction of these effects; in other words, greater cognitive difficulties could lead to more severe neurodevelopmental presentations, or conversely, atypical behaviors may interfere with cognitive development.

Studies related to motor behavior showed that children with coexisting ADHD and DCD demonstrate greater performance difficulties across the majority of tasks (Pereira et al., 2000; Pitcher et al., 2002; Jucaite et al., 2003; Williams et al., 2013; Lee et al., 2013), except for motor imagery. While some of the studies found a similar level of difficulty in children with DCD or ADHD alone (Jucaite et al., 2003; Lee et al., 2013), the severity was highest in the combined group. Intervention studies identified in this review indicate that comprehensive intervention programs such as PRO-PEN (Puyjarinet et al., 2022) and CO-OP (Izadi-Najafabadi et al., 2022) may lead to improvements in handwriting quality and overall motor performance in children with both singular and combined conditions, with varying retention effects.

Studies of psychiatric correlates indicate that, children with ADHD and/or DCD are also more likely to experience higher levels of internalizing symptoms (i.e., anxiety and depression; Arim et al., 2015; Omer et al., 2019) than their peers, with the ADHD+DCD presentation associated with the most severe psychiatric symptoms (Dewey and Volkovinskaia, 2018; Missiuna et al., 2014). This is further underscored by a behavioral genetics study that reported significant environmental contributions to depression among youth with ADHD and/or DCD (Piek et al., 2007). As noted above, DCD also appears to explain unique variance in depressive symptoms. Altogether, attentional and motor difficulties appear to have unique and far-reaching impacts on mental health that can be carried from childhood into adulthood.

4.4. Coexisting ADHD+DCD diagnosis forms a unique profile with separate neurobiological patterns and developmental pathways

Studies focusing on the neurobiological mechanisms underlying cooccurrence between ADHD and DCD are emerging and suggest that neural network alterations in individuals with ADHD+DCD represent a unique neural pattern rather than a sum of ADHD and DCD characteristics.

Specifically, the ADHD+DCD phenotype has been associated with alterations in white matter microstructure across frontal and parietal regions of the corpus callosum (Langevin et al., 2014a), atypical functional connectivity patterns within motor networks (McLeod et al., 2014) and between sensory-motor regions (McLeod et al., 2016), as well as distinct patterns of cortical thinning across the frontal, temporal, and parietal lobes (Langevin, 2014b). These differences were not consistently found in comparison groups of children with ADHD-only or DCD-only. Only one neuroimaging study reviewed here provides support for the shared etiology hypothesis. Thornton and colleagues (2018) found comparable behavioral and hemodynamic responses among all three clinical groups; however, their results should be interpreted with caution given the lack of expected performance differences on a Go/Nogo task in the ADHD-only group.

In addition to cross-sectional studies, Izadi-Najafabadi and colleagues (2021a,b) provided novel insights into neuroplastic changes associated with the CO-OP intervention in children with DCD alone or combined with ADHD. A few earlier studies tested the effectiveness of the CO-OP on behavioral levels in children with DCD (Miller et al., 2001; Thornton et al., 2016) and ADHD (Gharebaghy et al., 2015; Green et al., 2008), reporting improvements in motor performance and satisfaction. Interestingly, Izadi-Najafabadi and colleagues found that only children with DCD alone showed changes in functional connectivity (2021a) and white matter (2021b) post-intervention. These changes took place in regions associated with attention networks, self-regulation, motor planning, and inter-hemispheric communication, indicating that adaptations to the intervention protocol may be necessary to yield the same behavioral and neural outcomes in children with coexisting ADHD symptoms.

4.5. Limitations and future directions

When interpreting our findings, several limitations should be noted. First, the inclusion criteria for recruitment and assessment of ADHD and DCD symptoms varied across studies. To overcome the considerable methodological heterogeneity present in the literature and allow comparisons among studies, we only included studies where at least one diagnosis (i.e., ADHD or DCD) had been confirmed using DSM criteria. Nevertheless, the cut-off point on the MABC varied from 5th to 16th percentiles, and differences in ADHD symptom severity and presentation (i.e., hyperactive versus inattentive types), as well as possible gender differences, were rarely reported. Due to these limitations, any gender-specific comparisons could not be extracted from our synthesis. The male to female ratio in pediatric ADHD is about 3:1 in non-referred samples, and is associated with sex differences in genetic as well as cognitive vulnerabilities (Arnett et al., 2015; Ottosen et al., 2019; Rhee et al., 1999). Similarly, patterns of psychiatric comorbidity in ADHD have been shown to differ across sex. Thus, rates, etiology, and functional impairment of ADHD+DCD co-occurrence may also differ by sex. This will be an important line of investigation in future research.

Considering date of publication was not an exclusion criterion in our review, we encountered differences in terminology due to evolving diagnostic guidelines, such as the use of the DAMP concept to define the coexisting ADHD+DCD symptomatology in earlier studies (e.g., Pereira et al., 2000; Norrelgen et al., 1999). Thus, we may have missed some relevant studies. Given the heterogeneity intrinsic to both ADHD and DCD (Sonuga-Barke, 2003), interpretations of our findings should be drawn with caution.

Overall, the research across multiple disciplines supports the separate etiology hypothesis. More research is needed to understand the factors contributing to high rates of comorbidity between the two disorders with different neurocognitive and behavioral profiles. Understanding the neurobiological characteristics is particularly relevant for early intervention and clinical care since the dependence on a behavioral diagnosis is not reliable until school age for both ADHD and DCD. Given the secondary impact of a coexisting diagnosis on psychological distress highlighted in this review, early identification could help prevent detrimental outcomes later in life.

5. Conclusions

ADHD and DCD are common neurodevelopmental disorders with high rates of comorbidity. The combined ADHD+DCD diagnosis is associated with increased cognitive, behavioral, psychiatric, and neurobiological correlates compared to either disorder on its own. Thus, clinical diagnosis and treatment of ADHD and DCD should consider the potential impacts of coexisting symptomatology on the effectiveness of interventions, potential psychiatric and cognitive-academic impairment, and adaptive functioning.