Introduction
Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are among the most common childhood neurodevelopmental conditions, typically emerging in the first years of life (APA, 2013). ASD is defined by persistent deficits in social communication and interaction, alongside restricted and repetitive patterns of behavior, interests, or activities. In contrast, ADHD is characterized by pervasive patterns of inattention, hyperactivity, and impulsivity that are inconsistent with developmental expectations (APA, 2013). While their diagnostic criteria are distinct, increasing evidence points to substantial overlap in genetic architecture (Rommelse et al., Reference Rommelse, Franke, Geurts, Hartman and Buitelaar2010) and shared alterations in brain structure and functional connectivity (Norman et al., Reference Norman, Sudre, Bouyssi-Kobar, Jiao, Gligorovic, Jean, White and Shaw2025). Early motor and language delays are common in both ASD and ADHD (Johnson, Gliga, Jones, & Charman, Reference Johnson, Gliga, Jones and Charman2014), supporting a shift toward viewing them as part of a neurodevelopmental continuum, with traits distributed dimensionally across the population (Constantino & Todd, Reference Constantino and Todd2003; Lubke et al., Reference Lubke, Hudziak, Derks, Van and Boomsma2009).
Early-life motor and language development are a sensitive window into central nervous system maturation, and disruptions in these domains often represent the first observable signs of atypical development (Iverson, Reference Iverson2010). In ASD, delays in the attainment of gross and fine motor milestones, such as walking and hand coordination as well as language impairments, are consistently among the earliest predictors of later diagnosis (Johnson, Gliga, Jones, & Charman, Reference Johnson, Gliga, Jones and Charman2014; Jones et al., Reference Jones, Gliga, Bedford, Charman and Johnson2013; Lord, Elsabbagh, Baird, & Veenstra-Vanderweele, Reference Lord, Elsabbagh, Baird and Veenstra-Vanderweele2018; Serdarevic et al., Reference Serdarevic, Tiemeier, Jansen, Alemany, Xerxa, Neumann, Robinson, Hillegers, Verhulst and Ghassabian2019). Longitudinal studies have shown that delays in walking and early expressive language (Kuo et al., Reference Kuo, Van Der Merwe, Fu, Carey, Talkowski, Bishop and Robinson2022), as well as declining fine motor performance over the first three years of life (Landa, Gross, Stuart, & Faherty, Reference Landa, Gross, Stuart and Faherty2012), are predictive of ASD. These delays are also linked to the emergence of social communication difficulties (Bhat, Landa, & Galloway, Reference Bhat, Landa and Galloway2011). Maternal reports of fine motor difficulties by six months predict both formal diagnoses and elevated autistic traits in population-based samples (Bolton, Golding, Emond, & Steer, Reference Bolton, Golding, Emond and Steer2012). In addition to milestone delays, specific motor abnormalities, such as atypical gait, altered posture, and poor coordination, are frequently reported in young children with ASD (Ganai, Ratne, Bhushan, & Venkatesh, Reference Ganai, Ratne, Bhushan and Venkatesh2025; Singh et al., Reference Singh, Pathak, Ganai, Bhushan and Subramanian2024). Language development is similarly affected, particularly among infants at familial risk, who often exhibit delays in both expressive and receptive domains (Prelock & Nelson, Reference Prelock and Nelson2012). Recent evidence has further linked gross motor development and receptive language abilities to variation in autistic traits in the general population (Li et al., Reference Li, Pasco, Ali, Johnson, Jones and Charman2023), underscoring the relevance of these domains as early behavioral indicators of neurodevelopmental divergence.
In contrast to ASD, the relationship between early motor and language delays and ADHD is less consistent (Havmoeller, Thomsen, & Lemcke, Reference Havmoeller, Thomsen and Lemcke2018). Although many children with ADHD exhibit motor difficulties, including impairments in both gross and fine motor coordination (Kaiser, Schoemaker, Albaret, & Geuze, Reference Kaiser, Schoemaker, Albaret and Geuze2014), findings related to developmental milestone timing are more heterogeneous. Some studies report delayed walking and motor deficits in infancy (Gurevitz, Geva, Varon, & Leitner, Reference Gurevitz, Geva, Varon and Leitner2012), while others find associations between earlier milestone attainment and later ADHD symptoms (Gui et al., Reference Gui, Hollowell, Wigdor, Morgan, Hannigan, Corfield, Odintsova, Hottenga, Wong, Pool, Cullen, Wilson, Warrier, Eilertsen, Andreassen, Middeldorp, St Pourcain, Bartels, Boomsma and Ronald2025; Lemcke et al., Reference Lemcke, Parner, Bjerrum, Thomsen and Lauritsen2016). In a longitudinal community-based cohort, motor, but not language, abilities in the first year of life predicted ADHD symptoms in adolescence (Jaspers et al., Reference Jaspers, De Winter, Buitelaar, Verhulst, Reijneveld and Hartman2012). Nevertheless, early expressive and receptive language difficulties have also been implicated in later ADHD traits (Gurevitz, Geva, Varon, & Leitner, Reference Gurevitz, Geva, Varon and Leitner2012), suggesting that developmental delays may contribute to ADHD along more variable and nuanced trajectories than those typically observed in ASD.
Neuroimaging studies provide further insight into the shared neural substrates of early motor and language development. Resting-state functional connectivity (rsFC) analyses in infants and toddlers have shown that these domains develop in parallel, involving overlapping interactions among large-scale brain networks, including the default mode, salience, visual, and fronto-parietal networks (Bruchhage et al., Reference Bruchhage, Ngo, Schneider, D’Sa and Deoni2020). Gross motor abilities in infancy have been linked to rsFC in sensorimotor and cerebellar regions, as well as to connectivity between the motor network and the default mode network (DMN) by 12 months (Marrus et al., Reference Marrus, Eggebrecht, Todorov, Elison, Wolff, Cole, Gao, Pandey, Shen, Swanson, Emerson, Klohr, Adams, Estes, Zwaigenbaum, Botteron, McKinstry, Constantino, Evans and Pruett2018). By 24 months, this extends to include the dorsal attention and cingulo-opercular networks, mirroring the growing complexity of motor control and attentional integration. Notably, infants with familial risk for ASD show altered motor-DMN connectivity at 12 months and disrupted integration of attention and control networks by 24 months, potentially underlying both early motor delays and broader developmental divergence. In ADHD, altered rsFC between the primary motor cortex and frontal or subcortical structures, such as the inferior frontal cortex, insula, and basal ganglia, has been associated with motor coordination and planning difficulties (McLeod, Langevin, Goodyear, & Dewey, Reference McLeod, Langevin, Goodyear and Dewey2014). These findings implicate disrupted functional network development as a plausible mechanism for early emerging motor and language differences in both conditions.
Genetic evidence supports both shared and distinct developmental mechanisms across ASD and ADHD. Polygenic risk scores (PRSs) for each condition have been linked to variation in early motor and language development, including pragmatic communication and neuromotor coordination (Martin et al., Reference Martin, Hamshere, Stergiakouli, O’Donovan and Thapar2014; Serdarevic et al., Reference Serdarevic, Tiemeier, Jansen, Alemany, Xerxa, Neumann, Robinson, Hillegers, Verhulst and Ghassabian2019). A recent large-scale cohort study found that higher ADHD PRSs were associated with earlier attainment of walking milestones, while higher ASD PRSs were associated with delays in walking (Hannigan et al., Reference Hannigan, Askeland, Ask, Tesli, Corfield, Ayorech, Magnus, Njølstad, Øyen, Stoltenberg, Andreassen, Ronald, Smith, Reichborn-Kjennerud and Havdahl2021). These results suggest that the timing of motor milestone attainment reflects underlying genetic susceptibility to neurodevelopmental conditions, even in the general population. Taken together, both rsFC and PRS findings point to biologically rooted, partly overlapping pathways that give rise to behavioral variability in early development.
Given the substantial overlap in neurodevelopmental mechanisms underlying ASD and ADHD, early motor and language milestones may serve as transdiagnostic indicators of neurodevelopmental risk. Yet, most existing studies have examined autism traits and ADHD symptoms in isolation, limiting our understanding of shared early-life predictors and the pathways through which diagnostic specificity emerges. Only a small number of investigations have adopted a dimensional framework that captures trait variability across the population, and fewer still have linked early behavioral development to brain-based measures in non-clinical adolescent cohorts. As a result, the potential of early motor and language milestones to index pluripotent neurodevelopmental risk across diagnostic boundaries remains underexplored.
The present study addresses this gap by leveraging a dimensional, population-based approach to examine how early motor and language milestone attainment relates to later autism traits and ADHD symptoms. We assessed a comprehensive set of early developmental behaviors, including rolling, sitting, walking, speech development, and motor coordination, and their associations with both behavioral outcomes and rsFC. We then tested whether patterns of rsFC mediated the relationship between early milestones and later traits. By jointly evaluating behavioral and neural markers across domains, this study aims to identify early-life pathways of neurodevelopmental divergence, clarify the nature of transdiagnostic risk, and provide insight into mechanisms of developmental differentiation between ASD and ADHD.
Methods
Participants
Participants were drawn from the Adolescent Brain Cognitive Development (ABCD) Study, ‘Curated Annual Release 4.0’ (https://nda.nih.gov/study.html?id=1299). The ABCD Study is an ongoing, longitudinal, multi-site investigation of brain development and health in youth, with data collected from over 11,000 children representing a diverse demographic background (Casey et al., Reference Casey, Cannonier, Conley, Cohen, Barch, Heitzeg, Soules, Teslovich, Dellarco, Garavan, Hanlon, McQueeny, Schubel, Castaneda, Fontaine, Wallace, Snider, Bhave, Flack and Dale2018). For the present study, participants with missing data were excluded. The final analytic sample comprised 6,663 participants (Table 1). Unless otherwise specified, only baseline assessments were used. Written informed consent was obtained from parents or legal guardians after a full explanation of the study procedures, and assent was obtained from all child participants. The ABCD Study was approved by the central Institutional Review Board at the University of California, San Diego, as well as by the institutional review boards of each participating site (Casey et al., Reference Casey, Cannonier, Conley, Cohen, Barch, Heitzeg, Soules, Teslovich, Dellarco, Garavan, Hanlon, McQueeny, Schubel, Castaneda, Fontaine, Wallace, Snider, Bhave, Flack and Dale2018).
Sample characteristics (N = 6,663)
Table 1. Long description
The table consists of two columns: Characteristic and n or M (S D) or %.
* Male/female: 3,437/3,226.
* Age in years: 9.92 (0.63).
* Household income:
- Less than or equal to $50,000: 1,710.
- Greater than or equal to $50,000 to less than $100,000: 1,787.
- Greater than or equal to $100,000: 2,692.
- Do not know and refuse to answer: 474.
* Race/Ethnicity:
- White: 3,667.
- Black: 817.
- Hispanic: 1,385.
- Asian: 121.
- Other: 673.
* Autism traits: 14.18 (3.87).
* A D H D symptoms: 2.84 (3.41).
* Roll over (attained/delayed): 5,599 (84.1) / 1,064 (15.9).
* Sit without assistance (attained/delayed): 6,336 (95.1) / 327 (4.90).
* Walk without assistance (attained/delayed): 6,422 (96.38) / 241 (3.62).
* Gross motor development: 2.60 (0.80).
* Speech development: 2.70 (0.99).
* Coordination: 0.13 (0.37).
Measures
Autism traits and ADHD symptoms
Autism traits were assessed using the abbreviated version of the Social Responsiveness Scale, Second Edition (Constantino & Gruber, Reference Constantino and Gruber2011). The Short Social Responsiveness Scale (S-SRC) was administered in year 1, with parents responding to items on a four-point scale ranging from ‘Not true’ (1) to ‘Almost always true’ (4). The total score of the S-SRC was calculated by summing all items. ADHD symptoms were measured using the Attention Problems subscale from the Child Behavior Checklist (Achenbach & Ruffle, Reference Achenbach and Ruffle2000). The total score on this subscale was used as a continuous measure of ADHD symptom severity.
Developmental assessments
Developmental milestone data were obtained through the Developmental History Questionnaire (DHQ), completed retrospectively by the child’s biological mother. The questionnaire assessed the age in months at which the child achieved key milestones, including roll over, sit without assistance, and walk without assistance. Two items assessing gross motor and speech development were rated on a five-point scale ranging from 1 (‘much earlier’) to 5 (‘much later’) relative to peers. Milestone ages were binarized based on the Centers for Disease Control and Prevention (CDC) developmental guidelines, which represent the median age (50th percentile) by which children typically acquire these skills: roll over by 4 months, sit without assistance by 9 months, and walk without assistance by 18 months (Zubler et al., Reference Zubler, Wiggins, Macias, Whitaker, Shaw, Squires, Pajek, Wolf, Slaughter, Broughton, Gerndt, Mlodoch and Lipkin2022). Gross motor and speech development scores were treated as continuous variables. An additional indicator of motor development, the item ‘poorly coordinated or clumsy’ from the CBCL, was included, with higher scores reflecting greater coordination difficulties (Achenbach & Ruffle, Reference Achenbach and Ruffle2000).
Imaging acquisition, preprocessing, and resting-state functional connectivity
Imaging data were acquired as part of the ABCD Study, using harmonized protocols across multiple sites (Casey et al., Reference Casey, Cannonier, Conley, Cohen, Barch, Heitzeg, Soules, Teslovich, Dellarco, Garavan, Hanlon, McQueeny, Schubel, Castaneda, Fontaine, Wallace, Snider, Bhave, Flack and Dale2018). Details of image acquisition, preprocessing, and quality control for rsFC are provided in the Supplementary Materials. Briefly, this study utilized preprocessed resting-state fMRI data provided by the ABCD Study (Hagler et al., Reference Hagler, Hatton, Cornejo, Makowski, Fair, Dick and Peltier2019). Cortical parcellation was based on the Gordon atlas, which delineates functionally distinct cortical networks (Gordon et al., Reference Gordon, Laumann, Adeyemo, Huckins, Kelley and Petersen2014), while subcortical segmentation was performed using FreeSurfer’s automated subcortical atlas (Fischl et al., Reference Fischl, Salat, Busa, Albert, Dieterich, Haselgrove, van der Kouwe, Killiany, Kennedy, Klaveness, Montillo, Makris, Rosen and Dale2002). In total, 55 cortical within- and between-network connectivity measures and 50 cortical–subcortical connectivity measures were derived per participant. These included within- and between-network rsFC of the auditory (AN), cingulo-opercular (CO), default mode (DMN), salience (SAL), fronto-parietal (FPN), ventral attention (VAN), somatomotor hand (SMH), visual (VIS), dorsal attention (DAN), and somatomotor mouth (SMM) networks (Figure 1). Subcortical regions of interest included the averaged left and right cerebellar cortex, thalamus, caudate nucleus, putamen, and nucleus accumbens.
Resting-state functional connectivity (rsFC) networks.
Figure 1. Long description
A series of four anatomical brain diagrams illustrating functional networks. From left to right, the panels show the left hemisphere lateral view, left hemisphere medial view, right hemisphere medial view, and right hemisphere lateral view. Each brain is divided into a mosaic of small parcels colored according to their network affiliation.
Below the brain maps is a legend with eleven color-coded categories.
* Red represents the Auditory Network A N.
* Blue represents the Cingulo Opercular Network C O.
* Green represents the Default Mode Network D M N.
* Orange represents the Dorsal Attention Network D A N.
* Purple represents the Fronto-Parietal Network F P N.
* Yellow represents the Salience Network S A L.
* Pink represents the Somatomotor Hand Network S M H.
* Brown represents the Somatomotor Mouth Network S M M.
* Cyan represents the Ventral Attention Network V A N.
* Teal represents the Visual Network V I S.
* Grey areas represent regions not assigned to these specific networks.
Statistical analysis
All analyses were conducted using R (R Core Team, 2025). We first fitted separate linear mixed-effects (LME) models to examine associations between six early developmental measures: roll over, sit without assistance, walk without assistance, gross motor development, speech development, and coordination, and two behavioral outcomes of autism traits and ADHD symptoms. Models were implemented with the lmerTest package (Kuznetsova, Brockhoff, & Christensen, Reference Kuznetsova, Brockhoff and Christensen2017). Age, sex, income, and race/ethnicity (as unordered factors) were included as fixed-effect covariates, and study site and family ID were modeled as random effects to account for clustering. When modeling autism traits, ADHD symptoms were included as a covariate; conversely, when modeling ADHD symptoms, autism traits were included as a covariate. All continuous predictors were standardized prior to analysis. Statistical significance was set at p < 0.05, and false discovery rate (FDR) correction using the Benjamini–Hochberg procedure (Benjamini & Hochberg, Reference Benjamini and Hochberg1995) was applied to adjust for multiple comparisons across developmental predictors.
Subsequently, a series of LME models examined associations between resting-state functional connectivity (rsFC) metrics and the six developmental variables, with each rsFC measure treated as a separate outcome. Models included fixed effects for age, average framewise displacement (motion), sex, income, and race/ethnicity, with site and family ID as random effects. Continuous variables were standardized, and significance was defined at p < 0.05 with FDR correction applied to account for multiple comparisons.
Finally, Bayesian multilevel mediation analyses were conducted to test whether specific rsFC measures mediated the relationships between early developmental milestones and behavioral outcomes. Models included the same fixed covariates and site and family ID as random effects. When modeling autism traits, ADHD symptoms were included as a covariate; conversely, when modeling ADHD symptoms, autism traits were included as a covariate. Analyses were performed using the brms package for Bayesian hierarchical modeling (Bürkner, Reference Bürkner2017), and mediation effects were summarized with bayestestR (Makowski, Ben-Shachar, & Lüdecke, Reference Makowski, Ben-Shachar and Lüdecke2019). Continuous variables were standardized to improve model convergence and interpretability. Mediation was considered meaningful when the 95% credible interval (CrI) of the indirect effect did not include zero. Indirect effects and proportion mediated were estimated and reported accordingly.
Results
Associations between developmental measures and autism traits and ADHD symptoms
Associations between early developmental measures and later behavioral outcomes are summarized in Table 2. Delayed speech development (β = 0.11, 95% CI [0.08, 0.13], FDR-p < 0.0001) and poor coordination (β = 0.12, 95% CI [0.09, 0.14], FDR-p < 0.0001) showed significant association with elevated autism traits. In contrast, later attainment of walk without assistance (β = 0.14, 95% CI [0.03, 0.26], FDR-p = 0.03), and poor coordination (β = 0.26, 95% CI [0.24, 0.28], FDR-p < 0.0001) were associated with higher ADHD symptomatology. More advanced gross motor development was inversely associated with ADHD symptoms (β = − 0.04, 95% CI [−0.06, −0.01], FDR-p = 0.006).
Associations between developmental measures and autism traits and ADHD symptoms
Table 2. Long description
The table is organized into two primary sections: Autism traits and A D H D symptoms. Each section evaluates six variables: Roll over, Sit without assistance, Walk without assistance, Gross motor development, Speech development, and Coordination. Statistical columns include beta, 95 percent C I, S E, t, d.f., p, and F D R-p.
For Autism traits:
* Roll over: beta 0.02, p 0.52.
* Sit without assistance: beta 0.01, p 0.99.
* Walk without assistance: beta 0.06, p 0.29.
* Gross motor development: beta 0.01, p 0.64.
* Speech development: beta 0.11, p less than 0.0001.
* Coordination: beta 0.12, p less than 0.0001.
For A D H D symptoms:
* Roll over: beta 0.05, p 0.08.
* Sit without assistance: beta 0.09, p 0.08.
* Walk without assistance: beta 0.14, p 0.01.
* Gross motor development: beta minus 0.04, p 0.002.
* Speech development: beta 0.02, p 0.06.
* Coordination: beta 0.26, p less than 0.0001.
Note: S E stands for standard error, d.f. for degrees of freedom, and F D R for false discovery rate.
Note: SE, standard error; d.f., degrees of freedom; FDR, false discovery rate.
Associations between developmental measures and resting-state functional connectivity
Significant associations between early developmental milestones and rsFC are detailed in Supplementary Materials (Supplementary Table S1). The milestone roll over was significantly associated with multiple rsFC measures. Positive associations were observed with connectivity between the auditory and somatomotor hand networks (AN–SMH; β = 0.11, 95% CI = [0.04, 0.18], FDR-p = 0.009), auditory and somatomotor mouth networks (AN–SMM; β = 0.09, 95% CI = [0.02, 0.16], FDR-p = 0.0443), within the auditory network (AN; β = 0.08, 95% CI = [0.01, 0.15], FDR-p = 0.0467), somatomotor hand network (SMH; β = 0.10, 95% CI = [0.03, 0.17], FDR-p = 0.0108), and between the somatomotor hand and mouth networks (SMH–SMM; β = 0.11, 95% CI = [0.04, 0.18], FDR-p = 0.0128). In contrast, roll over was negatively associated with connectivity between the cingulo-opercular and visual networks (CO–VIS; β = −0.11, 95% CI = [−0.18, −0.04], FDR-p = 0.0155), the fronto-parietal and somatomotor mouth networks (FPN–SMM; β = −0.12, 95% CI = [−0.19, −0.05], FDR-p = 0.0070), and between the somatomotor hand and putamen (SMH–Put; β = −0.10, 95% CI = [−0.17, −0.04], FDR-p = 0.0179).
Walk without assistance was associated with reduced connectivity between the somatomotor hand and salience networks (SMH–SAL; β = −0.20, 95% CI = [−0.33, −0.06], FDR-p = 0.0128).
Gross motor development showed both positive and negative associations with rsFC. Negative associations were identified for connectivity between the auditory and somatomotor hand (AN–SMH; β = −0.04, 95% CI = [−0.07, −0.01], FDR-p = 0.0212), auditory and somatomotor mouth (AN–SMM; β = −0.06, 95% CI = [−0.09, −0.03], FDR-p = 0.0002), somatomotor hand and mouth (SMH–SMM; β = −0.04, 95% CI = [−0.06, −0.01], FDR-p = 0.0263), within the somatomotor hand network (SMH; β = −0.04, 95% CI = [−0.07, −0.02], FDR-p = 0.0091), and auditory network (AN; β = −0.03, 95% CI = [−0.06, 0.00], FDR-p = 0.0467). Positive associations emerged with connectivity between the somatomotor hand and salience networks (SMH–SAL; β = 0.05, 95% CI = [0.02, 0.08], FDR-p = 0.0042), the somatomotor hand and caudate (SMH–Caud; β = 0.05, 95% CI = [0.02, 0.08], FDR-p = 0.0014), and the salience network and cerebellar cortex (SAL–Cer; β = 0.04, 95% CI = [0.01, 0.07], FDR-p = 0.0291).
Speech development was negatively associated with connectivity between the auditory network and caudate (AN–Caud; β = −0.04, 95% CI = [−0.07, −0.02], FDR-p = 0.0119), between the visual network and putamen (VIS–Put; β = −0.04, 95% CI = [−0.07, −0.02], FDR-p = 0.0120), and within-network connectivity of auditory network (AN; β = −0.03, 95% CI = [−0.05, 0.00], FDR-p = 0.0467).
Lastly, coordination was associated with a distributed pattern of network-level alterations. Negative associations were observed with connectivity between the auditory and somatomotor hand networks (AN–SMH; β = −0.03, 95% CI = [−0.05, −0.01], FDR-p = 0.0321), salience network and nucleus accumbens (SAL–Acc; β = −0.03, 95% CI = [−0.06, −0.01], FDR-p = 0.0467), the cingulo-opercular network (CO; β = −0.04, 95% CI = [−0.06, −0.02], FDR-p = 0.001), the default mode network (DMN; β = −0.03, 95% CI = [−0.06, −0.01], FDR-p = 0.0129), the somatomotor hand network (SMH; β = −0.03, 95% CI = [−0.05, 0.00], FDR-p = 0.0408), and the auditory network (AN; β = −0.03, 95% CI = [−0.05, 0.00], FDR-p = 0.0467). Positive associations were observed between the coordination and connectivity between the default mode and dorsal attention networks (DMN–DAN; β = 0.03, 95% CI = [0.01, 0.06], FDR-p = 0.0189), auditory and visual networks (AN–VIS; β = 0.03, 95% CI = [0.01, 0.06], FDR-p = 0.0362).
Mediation results
There was negligible evidence that rsFC measures exhibited meaningful indirect effects mediating the associations between early developmental measures and neurodevelopmental traits. Detailed results are provided in the Supplementary Materials (Supplementary Tables S2 and S3 for autism traits and ADHD symptoms, respectively). Key mediation effects are summarized in Figures 2 and 3 for autism traits and ADHD symptoms, respectively, while rsFC networks are visualized in Figure 4 (cortical networks) and Figure 5 (cortical–subcortical connectivity).
Indirect (mediation) effects of resting-state functional connectivity linking early developmental measures and autism traits. Note: AN, auditory network; DMN, default mode network; FPN, fronto-parietal network; SMH, somatomotor hand network; SMM, somatomotor mouth network; Caud, caudate nucleus.
Figure 2 Long description
Each of the nine panels follows a triangular mediation structure. The predictor is in a blue box on the left, the mediator is in a red-outlined box at the top, and the outcome is in a grey box on the right. All panels use Autism Traits as the outcome.
* Top-Left: Roll Over to F P N–S M M–F C to Autism Traits. Path A beta equals -0.08. Path B beta equals -0.02. Path C beta equals 0.078. Path A B beta equals 0.001.
* Top-Middle: Speech Development to A N–F C to Autism Traits. Path A beta equals -0.04. Path B beta equals -0.01. Path C beta equals 0.115. Path A B beta equals 0.0005.
* Top-Right: Gross Motor Development to A N–S M M–F C to Autism Traits. Path A beta equals -0.07. Path B beta equals -0.02. Path C beta equals 0.078. Path A B beta equals 0.001.
* Middle-Left: Gross Motor Development to S M H–Caud–F C to Autism Traits. Path A beta equals -0.06. Path B beta equals -0.02. Path C beta equals 0.077. Path A B beta equals 0.0007.
* Middle-Middle: Gross Motor Development to S M H–F C to Autism Traits. Path A beta equals -0.04. Path B beta equals -0.01. Path C beta equals 0.077. Path A B beta equals 0.0005.
* Middle-Right: Gross Motor Development to S M H–S M M–F C to Autism Traits. Path A beta equals -0.03. Path B beta equals -0.01. Path C beta equals 0.077. Path A B beta equals 0.00023.
* Bottom-Left: Gross Motor Development to A N–F C to Autism Traits. Path A beta equals -0.05. Path B beta equals -0.02. Path C beta equals 0.077. Path A B beta equals 0.0007.
* Bottom-Middle: Coordination to A N–F C to Autism Traits. Path A beta equals -0.08. Path B beta equals -0.01. Path C beta equals 0.321. Path A B beta equals 0.001.
* Bottom-Right: Coordination to D M N–F C to Autism Traits. Path A beta equals -0.06. Path B beta equals -0.01. Path C beta equals 0.320. Path A B beta equals 0.0007.
All paths include 95 percent C r I values. Initialisms include F P N for Fronto-Parietal Network, S M M for Somatomotor Mouth, F C for Functional Connectivity, A N for Auditory Network, S M H for Somatomotor Hand, Caud for Caudate, and D M N for Default Mode Network.
Indirect (mediation) effects of resting-state functional connectivity linking early developmental measures and ADHD symptoms. Note: AN, auditory network; DMN, default mode network; SAL, salience network; SMH, somatomotor hand network; DAN, dorsal attention network; Cer, cerebellum cortex; Caud, caudate nucleus.
Figure 3 Long description
A multi-panel figure containing eight mediation diagrams arranged in three rows. Each diagram follows a standard structure: a blue box on the left (independent variable), a red-outlined box at the top (mediator), and a grey box on the right (dependent variable, labeled A D H D Symptoms in all panels). Arrows represent Path A (left to top), Path B (top to right), Path C (left to right), and Path A B (the indirect effect).
Row 1:
* Panel 1: Speech Development to A N–Caud–F C to A D H D Symptoms. Path A beta = -0.05. Path B beta = -0.03. Path C beta = 0.019. Path A B beta = 0.001.
* Panel 2: Gross Motor Development to A N–S M H–F C to A D H D Symptoms. Path A beta = -0.03. Path B beta = -0.02. Path C beta = 0.005. Path A B beta = 0.0005.
* Panel 3: Gross Motor Development to S A L–Cer–F C to A D H D Symptoms. Path A beta = 0.05. Path B beta = 0.03. Path C beta = 0.004. Path A B beta = 0.001.
Row 2:
* Panel 4: Gross Motor Development to S M H–Caud–F C to A D H D Symptoms. Path A beta = 0.05. Path B beta = 0.02. Path C beta = 0.005. Path A B beta = 0.0009.
* Panel 5: Gross Motor Development to S M H–F C to A D H D Symptoms. Path A beta = -0.04. Path B beta = -0.01. Path C beta = 0.005. Path A B beta = 0.0005.
* Panel 6: Gross Motor Development to S M H–S A L–F C to A D H D Symptoms. Path A beta = 0.06. Path B beta = 0.03. Path C beta = 0.004. Path A B beta = 0.002.
Row 3:
* Panel 7: Coordination to D M N–D A N–F C to A D H D Symptoms. Path A beta = 0.07. Path B beta = 0.03. Path C beta = 0.686. Path A B beta = 0.002.
* Panel 8: Coordination to D M N–F C to A D H D Symptoms. Path A beta = -0.07. Path B beta = -0.02. Path C beta = 0.687. Path A B beta = 0.001.
All paths include 95% C r I values in brackets. Initialisms include A N (Auditory Network), S M H (Sensorimotor Hand), S A L (Salience), Cer (Cerebellum), D M N (Default Mode Network), D A N (Dorsal Attention Network), and F C (Functional Connectivity).
Cortical resting-state functional connectivity mediation networks linking developmental milestones to neurodevelopmental traits. Note: AN, auditory network; DMN, default mode network; DAN, dorsal attention network; FPN, fronto-parietal network; SMH, somatomotor hand network; SMM, somatomotor mouth network; SAL, salience network.
Figure 4. Long description
The figure consists of nine panels labeled A through I, each containing four brain surface views (lateral and medial for both hemispheres) highlighting specific functional networks.
* Panel A: Roll Over to F P N-S M M-F C to Autism Traits. Highlights the Auditory Network (A N) in red and Fronto-Parietal Network (F P N) in purple.
* Panel B: Speech and Gross Motor Development to A N-F C to Autism Traits. Highlights A N in red.
* Panel C: Gross Motor Development to A N-S M M-F C to Autism Traits. Highlights A N in red and Somatomotor-Mouth Network (S M M) in brown.
* Panel D: Gross Motor Development to S M H-S M M-F C to Autism Traits. Highlights Somatomotor-Hand Network (S M H) in pink and S M M in brown.
* Panel E: Gross Motor Development to A N-S M H-F C to A D H D Symptoms. Highlights A N in red and S M H in pink.
* Panel F: Gross Motor Development to S M H-S A L-F C to A D H D Symptoms. Highlights S M H in pink and Salience Network (S A L) in yellow.
* Panel G: Coordination to D M N-D A N-F C to A D H D Symptoms. Highlights Default Mode Network (D M N) in green and Dorsal Attention Network (D A N) in orange.
* Panel H: Coordination to D M N-F C to Autism Traits or A D H D Symptoms. Highlights D M N in green.
* Panel I: Gross Motor Development to S M H-F C to Autism Traits or A D H D Symptoms. Highlights S M H in pink.
Each panel uses arrows to indicate the flow from a developmental milestone through functional connectivity (F C) of specific networks to a clinical trait.
Cortical–subcortical resting-state functional connectivity (rsFC) mediation networks linking developmental milestones to neurodevelopmental traits. Note: AN, auditory network; SMH, somatomotor hand network; SAL, salience network; Cer, cerebellum cortex; Caud, caudate nucleus.
Figure 5. Long description
The diagram consists of three horizontal panels labeled A, B, and C.
Panel A is titled Speech Development yields A N dash Caud dash F C yields A D H D Symptoms. On the left, four cortical surface views show the Auditory Network (A N) highlighted in red. To the right, a coronal cross-section and a sagittal cross-section highlight the caudate nucleus in red. A legend identifies the red square as Auditory Network (A N) and a red line as caudate.
Panel B is titled Gross Motor Development yields S A L dash Cer dash F C yields A D H D Symptoms. On the left, four cortical surface views show the Salience Network (S A L) highlighted in yellow. To the right, the coronal and sagittal cross-sections highlight the cerebellum cortex in green. A legend identifies the yellow square as Salience Network (S A L) and a green line as cerebellum cortex.
Panel C is titled Gross Motor Development yields S M H dash Caud dash F C yields Autism Traits forward slash A D H D Symptoms. On the left, four cortical surface views show the Somatomotor Hand Network (S M H) highlighted in pink. To the right, the coronal and sagittal cross-sections highlight the caudate nucleus in red. A legend identifies the pink square as Somatomotor Hand Network (S M H) and a red line as caudate.
All panels include a gray line labeled N A in the legend area.
For autism traits, connectivity between the FPN–SMM mediated 1.49% of the association with roll over (indirect effect: β = 0.001, 95% CrI = [0.00, 0.004]). Connectivity within the AN mediated 0.43% of the association between speech development and autism traits (indirect effect: β = 0.0005, 95% CrI = [0.000, 0.002]).
For gross motor development, connectivity between the AN–SMM connectivity mediated 1.90% of the association with autism traits (indirect effect: β = 0.001, 95% CrI = [0.00, 0.003]). Connectivity between the SMH–Caud mediated 1.54% (indirect effect: β = 0.001, 95% CrI = [0.001, 0.000]), while connectivity between the SMH–SMM mediated 0.30% (indirect effect: β = 0.00023, 95% CrI = [0.000, 0.001]). Within-network connectivity of the SMH (indirect effect: β = 0.00049, 95% CrI = [0.000, 0.002]) and AN (indirect effect: β = 0.00066, 95% CrI = [0.00, 0.002]) mediated 0.63% and 0.86% of the association between gross motor development and autism traits, respectively.
For coordination within-network connectivity of AN (indirect effect: β = 0.001, 95% CrI = [0.00, 0.004]) and DMN (indirect effect: β = 0.00074, 95% CrI = [0.00, 0.003]) mediated 0.32% and 0.23% of the association with autism traits, respectively.
For ADHD symptoms, connectivity between the AN–Caud mediated 6.70% of the association with speech development (indirect effect: β = 0.001, 95% CrI = [0.000, 0.003]). For gross motor development connectivity between the AN–SMH mediated 10.61% of the association with ADHD symptoms (indirect effect: β = 0.00048, 95% CrI = [0.000, 0.002]). Connectivity between the SAL–Cer mediated 28.54% (indirect effect: β = 0.001, 95% CrI = [0.000, 0.003]), connectivity between the SMH–Caud mediated 17.65% (indirect effect: β = 0.0009, 95% CrI = [0.000, 0.002]), and connectivity between the SMH–SAL mediated 44.32% (indirect effect: β = 0.002, 95% CrI = [0.000, 0.004]). Within-network connectivity of the SMH connectivity mediated 11.30% of the association between gross motor development and ADHD symptoms (indirect effect: β = 0.00053, 95% CrI = [0.000, 0.002]).
For coordination, connectivity between the DMN–DAN mediated 0.32% (indirect effect: β = 0.002, 95% CrI = [0.000, 0.005]) of the association with ADHD symptoms, while within-network connectivity of the DMN (indirect effect: β = 0.001, 95% CrI = [0.000, 0.004]) mediated 0.19%.
When examining the path coefficients, several small but credible associations were observed between early developmental predictors, rsFC measures, and neurodevelopmental traits. Inverse associations were observed between roll over and connectivity between the FPN–SMM (β = −0.08, 95% CrI = [−0.15, −0.02]), gross motor development and connectivity between the AN–SMM (β = −0.07, 95% CrI = [−0.10, −0.04]), gross motor development and within-network connectivity of the SMH (β = −0.04, 95% CrI = [−0.07, −0.01]), and coordination and connectivity within the AN (β = −0.08, 95% CrI = [−0.15, −0.01]).
A small positive association was observed between gross motor development and connectivity between the SMH–Caud (β = 0.06, 95% CrI = [0.03, 0.08]). In addition, a negative association was observed between connectivity between the AN–SMM and autism traits (β = −0.02, 95% CrI = [−0.04, −0.00]).
Similarly, several inverse associations were observed for ADHD symptoms, including between speech development and AN–Caud connectivity (β = −0.05, 95% CrI = [−0.07, −0.03]), gross motor development and AN–SMH connectivity (β = −0.03, 95% CrI = [−0.06, −0.01]), and coordination and within-network connectivity of the DMN (β = −0.07, 95% CrI = [−0.13, −0.01]).
Positive associations were also observed between predictors and rsFC measures for ADHD symptoms, including between gross motor development and connectivity between the SAL–Cer (β = 0.05, 95% CrI = [0.02, 0.08]) and SMH–Caud (β = 0.05, 95% CrI = [0.03, 0.08]), as well as between coordination and connectivity between the DMN–DAN (β = 0.07, 95% CrI = [0.01, 0.13]).
Lastly, several associations were observed between rsFC measures and ADHD symptoms. Connectivity between the AN–Caud was inversely associated with ADHD symptoms for speech development (β = −0.03, 95% CrI = [−0.05, −0.01]). Positive associations with ADHD symptoms were observed for connectivity between the SAL–Cer (β = 0.03, 95% CrI = [0.01, 0.05]), SMH–SAL (β = 0.03, 95% CrI = [0.01, 0.05]) for gross motor development, and DMN–DAN for coordination (β = 0.03, 95% CrI = [0.01, 0.06]).
Discussion
In this study, we examined how early motor and language development relate to autism traits and ADHD symptoms, and whether intrinsic functional connectivity mediates these associations. We observed both shared and condition-specific patterns, highlighting early developmental markers as indicators of neurodevelopmental vulnerability.
Delayed speech and poor motor coordination were associated with elevated autism traits, whereas delayed walking without assistance and gross motor delay were more specifically linked to higher ADHD symptoms. Poor motor coordination predicted both autism traits and ADHD symptoms, suggesting a shared developmental liability. In contrast, speech delays were more strongly related to autism traits, underscoring early language acquisition as a salient precursor, while delays in walking indexed attentional and regulatory difficulties characteristic of ADHD. These patterns align with evidence that motor coordination deficits span both conditions, reflecting shared disruptions in cerebellar and cortico-striatal systems implicated in motor control, temporal processing, and executive regulation (Fliers et al., Reference Fliers, Rommelse, Vermeulen, Altink, Buschgens, Faraone, Sergeant, Franke and Buitelaar2008; Fournier et al., Reference Fournier, Hass, Naik, Lodha and Cauraugh2010; Lemcke et al., Reference Lemcke, Parner, Bjerrum, Thomsen and Lauritsen2016; Posar & Visconti, Reference Posar and Visconti2022). At the same time, the partially distinct milestone associations support a model in which early deviations confer transdiagnostic vulnerability that differentiates over development.
Previous studies using the ABCD dataset have shown that motor abnormalities are associated with depressive symptoms in adolescents. For instance, developmental motor delays and dyscoordination were significantly related to higher depression symptoms but not to psychotic-like experiences (PLEs). In addition, rsFC between motor networks and the striatum and cerebellum was associated with PLEs, whereas motor network-striatal connectivity was linked to depression symptoms (Damme et al., Reference Damme, Park, Walther, Vargas, Shankman and Mittal2022). Similarly, developmental motor delays and dyscoordination have been shown to predict the number of depression symptoms endorsed, suggesting that motor dysfunction may serve as an early marker of depression risk (Damme et al., Reference Damme, Park, Vargas, Walther, Shankman and Mittal2021).
Early motor and language milestones showed heterogeneous associations with rsFC across large-scale cortical and subcortical networks. Delayed rollover was linked to stronger connectivity within auditory and somatomotor networks but weaker integration with control and somatomotor-putamen circuits, consistent with altered network specialization (Wheelock et al., Reference Wheelock, Austin, Bora, Eggebrecht, Melzer, Woodward and Smyser2018). Delayed walking was associated with reduced coupling between somatomotor hand and salience networks, potentially reflecting less efficient coordination between motor execution and attentional monitoring (Menon & Uddin, Reference Menon and Uddin2010). Poor gross motor development was related to reduced integration within sensorimotor systems alongside increased coupling with salience, cerebellar, and subcortical networks, suggesting altered recruitment of motor prediction circuits (Buckner, Reference Buckner2013; Doyon et al., Reference Doyon, Bellec, Amsel, Penhune, Monchi, Carrier, Lehéricy and Benali2009). Delayed speech was linked to weaker auditory network connectivity and reduced integration with visual and striatal regions, consistent with disrupted sensory-subcortical coordination relevant to language acquisition (Friederici, Reference Friederici2012; Kotz & Schwartze, Reference Kotz and Schwartze2010). Poor motor coordination was associated with weaker within-network sensorimotor connectivity, reduced segregation among control, default mode, and attention systems, and increased default mode-attention coupling, indicative of less differentiated large-scale organization (Fair et al., Reference Fair, Cohen, Power, Dosenbach, Church, Miezin, Schlaggar and Petersen2009). Taken together, these findings are consistent with delayed or altered network specialization, characterized by reduced segregation of higher-order systems alongside persistent coupling of primary sensorimotor circuits (Gao, Reference Gao2025). However, whether these alterations reflect delayed maturation, atypical specialization, compensatory plasticity, or inefficient synaptic pruning cannot be determined from cross-sectional connectivity data. Thus, these interpretations remain theoretically informed but provisional.
Resting-state connectivity did not meaningfully mediate associations between early motor and language development and later autism traits or ADHD symptoms. Indirect effects were small and accounted for only a minor proportion of total associations. For autism traits, mediation was sparse and inconsistent. Only a weak pathway linked speech delay to reduced auditory-somatomotor connectivity, while other indirect effects across sensory, motor, and subcortical networks were negligible or mixed. This accords with the marked neurodevelopmental heterogeneity of autism, in which diverse and partially independent neural pathways contribute to behavioral outcomes (Happé, Ronald, & Plomin, Reference Happé, Ronald and Plomin2006; Lombardo, Lai, & Baron-Cohen, Reference Lombardo, Lai and Baron-Cohen2019).
In contrast, ADHD symptoms showed relatively more convergent, though still modest, indirect pathways involving auditory-striatal, salience-cerebellar, sensorimotor-salience, and default mode-dorsal attention interactions. These circuits align with established models implicating cortico-striatal, cerebellar, salience, and attention systems in attentional allocation, motor regulation, and adaptive control (Castellanos & Proal, Reference Castellanos and Proal2012; Cortese et al., Reference Cortese, Aoki, Itahashi, Castellanos and Eickhoff2021; Menon, Reference Menon and Toga2015). Nevertheless, indirect effects were small, indicating that intrinsic connectivity captures only a partial expression of underlying developmental vulnerability rather than serving as a singular mechanistic conduit.
rsFC such as somatomotor-striatal circuits and default mode-dorsal attention interactions are not specific to autism or ADHD but are core components of large-scale control architectures supporting motor regulation, cognitive control, salience detection, and adaptive task switching (Gao, Reference Gao2025). Converging evidence indicates that disruptions in these systems extend beyond individual diagnoses and are implicated in transdiagnostic risk dimensions, including executive dysfunction, attentional dysregulation, and general psychopathology (McTeague et al., Reference McTeague, Huemer, Carreon, Jiang, Eickhoff and Etkin2017). Accordingly, the observed mediation effects may reflect relatively general neurodevelopmental vulnerability circuits that influence multiple cognitive and behavioral domains rather than condition-specific pathways. Within this framework, early motor and language delays may perturb foundational integration and control systems during sensitive developmental windows, thereby shaping broad trajectories of cognitive and behavioral functioning, with phenotypic differentiation emerging over time (Karmiloff-Smith, Reference Karmiloff-Smith1998). Notably, the relatively more convergent indirect pathways observed for ADHD symptoms may reflect the central role of control and attention networks in that phenotype (Castellanos & Proal, Reference Castellanos and Proal2012; Cortese et al., Reference Cortese, Aoki, Itahashi, Castellanos and Eickhoff2021), whereas autism-related outcomes may arise from more distributed and heterogeneous neurodevelopmental perturbations consistent with its marked etiological and neural diversity (Happé, Ronald, & Plomin, Reference Happé, Ronald and Plomin2006; Lombardo, Lai, & Baron-Cohen, Reference Lombardo, Lai and Baron-Cohen2019).
The overall results suggest that early motor and language delays primarily index upstream neurodevelopmental processes, including atypical circuit formation, synaptic pruning, and experience-dependent plasticity unfolding dynamically across infancy and early childhood (Casey et al., Reference Casey, Cannonier, Conley, Cohen, Barch, Heitzeg, Soules, Teslovich, Dellarco, Garavan, Hanlon, McQueeny, Schubel, Castaneda, Fontaine, Wallace, Snider, Bhave, Flack and Dale2018; Johnson, Reference Johnson2011). Because mediation models assume relatively stable intermediary mechanisms, single time-point rsFC measures in middle childhood may underestimate developmental transmission effects, particularly when early perturbations operate through evolving trajectories rather than static network states (Maxwell & Cole, Reference Maxwell and Cole2007; Selig & Preacher, Reference Selig and Preacher2009).
From a developmental cascade perspective, early motor and language milestones scaffold later cognitive, attentional, and social functioning by shaping sensory input, motor exploration, and learning opportunities (Iverson, Reference Iverson2010; Thelen, Reference Thelen2000). Disruptions at these stages likely propagate across distributed and interacting systems rather than through a single intermediate neural phenotype. Accordingly, rsFC measured later in development may reflect downstream or parallel consequences of earlier perturbations, consistent with evidence that large-scale functional networks undergo substantial reorganization across childhood and adolescence (Fair et al., Reference Fair, Cohen, Power, Dosenbach, Church, Miezin, Schlaggar and Petersen2009; Grayson & Fair, Reference Grayson and Fair2017).
The contrast between autism and ADHD mediation patterns further supports a pluripotent model of neurodevelopmental risk, in which early deviations confer broad vulnerability that differentiates over time (Hartmann et al., Reference Hartmann, McGorry, Destree, Amminger, Chanen, Davey, Ghieh, Polari, Ratheesh, Yuen and Nelson2021; Karmiloff-Smith, Reference Karmiloff-Smith1998). Poor motor coordination, associated with both autism traits and ADHD symptoms, may index a shared upstream liability preceding phenotypic divergence. Autism-related outcomes appear more diffuse and weakly mediated, consistent with greater etiological and neural heterogeneity (Happé, Ronald, & Plomin, Reference Happé, Ronald and Plomin2006; Lombardo, Lai, & Baron-Cohen, Reference Lombardo, Lai and Baron-Cohen2019). By contrast, ADHD symptoms involve relatively more convergent alterations in large-scale control and attention systems, yielding somewhat more consistent, though still modest, connectivity-linked pathways (Castellanos & Proal, Reference Castellanos and Proal2012).
Together, these findings suggest that early motor and language delays are best conceptualized as behavioral markers of widespread neurodevelopmental divergence. Later intrinsic connectivity may reflect a downstream or parallel manifestation of dynamic network reorganization rather than a stable intermediary mechanism. Neurodevelopmental risk appears to be expressed through evolving changes in network segregation, integration, and flexibility across childhood, rather than through fixed alterations within isolated circuits (Cui et al., Reference Cui, Li, Xia, Larsen, Adebimpe, Baum, Cieslak, Gur, Gur, Moore, Oathes, Alexander-Bloch, Raznahan, Roalf, Shinohara, Wolf, Davatzikos, Bassett, Fair and Satterthwaite2020; Fair et al., Reference Fair, Cohen, Power, Dosenbach, Church, Miezin, Schlaggar and Petersen2009; Menon, Reference Menon2013).
Beyond their theoretical implications, these findings carry meaningful clinical relevance. Early motor and language delays may serve as accessible, low-cost behavioral markers of broader neurodevelopmental vulnerability, supporting proactive monitoring and risk stratification even in non-clinical or community samples (Ganai, Ratne, Bhushan, & Venkatesh, Reference Ganai, Ratne, Bhushan and Venkatesh2025). Rather than being viewed as isolated delays, such milestones may signal perturbations in foundational sensorimotor and control systems that scaffold later attentional, social, and executive functioning. Although the observed mediation effects were small, such effect sizes are typical in population-level developmental cascade models, in which multiple modest pathways accumulate over time to shape later behavioral outcomes. Clinically, this suggests that early deviations may not map onto a single neural mechanism but instead reflect diffuse, interacting vulnerabilities that unfold dynamically across development. Accordingly, early intervention efforts targeting motor coordination, language enrichment, and attentional regulation during sensitive developmental periods may confer broad downstream benefits, potentially attenuating transdiagnostic risk trajectories before symptom consolidation. Together, these findings support a developmental systems framework in which early behavioral deviations index upstream neurodevelopmental divergence, with later phenotypic differentiation emerging through the dynamic reorganization of neural circuits across childhood and adolescence.
Several limitations should be acknowledged. First, although the findings provide important insights, longitudinal designs are necessary to clarify how early behavioral markers, brain connectivity, and neurodevelopmental traits unfold and interact over time. rsFC and neurodevelopmental traits were assessed concurrently in adolescence, which precludes firm conclusions regarding the temporal precedence of connectivity over behavioral outcomes. Accordingly, the mediation models tested here should be interpreted as statistically consistent with indirect associations rather than as evidence of causal neurodevelopmental pathways, and it remains plausible that trait-related behaviors also influence brain connectivity across development. Second, motor and language milestones were parent-reported, introducing potential recall bias and measurement variability; future studies should incorporate standardized, observational, or clinician-rated assessments. Third, the non-clinical nature of the sample may limit generalizability to clinical populations, highlighting the need for replication in high-risk or diagnosed cohorts. Finally, although mediation effects were small, future research integrating longitudinal neuroimaging, genetic, and environmental data will be critical for disentangling the directionality and mechanisms underlying early developmental risk.
In conclusion, these findings suggest that early motor and language delays may be best conceptualized as behavioral indicators of broad neurodevelopmental divergence rather than condition-specific precursors. Intrinsic connectivity differences observed later in childhood may represent downstream or parallel manifestations of evolving network reorganization, rather than stable intermediary mechanisms linking early developmental perturbations to later traits. From a developmental systems perspective, neurodevelopmental risk appears to unfold through dynamic changes in network segregation, integration, and flexibility across childhood. Early deviations in motor and language development may therefore signal perturbations in foundational sensorimotor and control systems that shape later cognitive, attentional, and social trajectories, with phenotypic differentiation emerging progressively over development.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/S003329172610556X.
Acknowledgments
The first author would like to thank Mitacs and Prof. Nicholas Bock for their support in carrying out this work at McMaster University.
Author contribution
The authors made the following contributions. Conceptualization: U.J.G., B.B.; Data curation: U.J.G., B.B.; Formal Analysis: U.J.G.; Methodology: U.J.G.; Software: U.J.G.; Statical analysis: U.J.G.; Writing – original draft preparation: U.J.G.; Writing – review & editing: U.J.G., B.B.
Funding statement
This research received no specific grant from any funding agency, commercial, or not-for-profit sectors.
Competing interests
The authors declare no conflict of interest regarding this work.
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