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. 2025 Jan 30;18(3):486–497. doi: 10.1002/aur.3313

Cortical Thickness Differences in Autistic Children With and Without Intellectual Disability

Derek S Andrews 1,✉, Andrew J Dakopolos 1, Joshua K Lee 1, Brianna Heath 1, Devani Cordero 2, Marjorie Solomon 1, David G Amaral 1, Christine Wu Nordahl 1
PMCID: PMC11928918  PMID: 39887572

ABSTRACT

Of the 1 in 36 individuals in the United States who are diagnosed with autism spectrum disorder, nearly 40% also have intellectual disability (ID). The cortex has been widely implicated in neural processes underlying autistic behaviors as well as intellectual ability. Thus, neuroimaging features such as cortical thickness are of particular interest as a possible biomarkers of the condition. However, neuroimaging studies often fail to include autistic individuals with ID. As a result, there are few studies of cortical thickness in autistic individuals across the entire range of intellectual abilities. This study used MRI to evaluate cortical thickness in young autistic children (n = 88, mean age 5.37 years) with a large range of intellectual ability (IQ 19–133) as well as nonautistic, nondevelopmentally delayed (referred to here as typically developing [TD]) peers (n = 53, mean age 5.29 years). We first investigated associations between full scale IQ and cortical thickness in both autistic and TD children. Autistic children had significant negative associations (i.e., thinner cortex, higher IQ) in bilateral entorhinal cortex, right fusiform gyrus, superior, middle and inferior temporal gyri, and right temporal pole that were not present in TD children. Significantly thicker cortex was also observed in these regions for autistic children with ID (i.e., IQ ≤ 70) compared with those without. Last, given the reported correspondence between the severity of autism symptoms and intellectual ability, we compared cortical thickness associations with both IQ and ADOS Calibrated Severity Scores and found these patterns overlapped to a significant degree across the cortex.

Keywords: autism, cortical thickness, intellectual disability, IQ, MRI

Summary.

  • Cortical thickness provides one indication of how well the brain functions.

  • We used MRI to examine cortical thickness in young autistic children with and without intellectual disability.

  • We found that autistic children with and without intellectual disability have different relationships between cortical thickness and IQ.

  • We also found substantial overlap between brain maps of cortical thickness associated with intellectual ability and autism symptom severity.

  • This provides evidence for a link between IQ and autism severity.

1. Introduction

Autism spectrum disorder (ASD or autism) is a neurodevelopmental condition characterized by challenges in social communication and the presence of repetitive or restricted behaviors (American Psychiatric Association 2013). Among autistic individuals, intellectual disability (ID) is a common co‐occurring condition defined by significant limitations in both intellectual functioning and adaptive behavior (Schalock, Luckasson, and Tassé 2021). Recent estimates suggest that approximately 38% of the now 1 in 36 children in the United States diagnosed with autism also have ID (Maenner 2023). To date, neuroimaging studies have largely failed to include autistic individuals with ID due to the additional accommodations needed to acquire high quality MRI scans within this population (Nordahl et al. 2008, 2016, 2022). Thus, despite evidence that alterations in cortical structure are associated with intellectual ability in the general population (7, 11, 12), conditions associated with ID (Quach et al. 2021), and autism (8–10), studies of the associations between intellectual ability and cortical thickness in autistic children across the entire IQ range are extremely limited (Bedford et al. 2020). Accordingly, it is unknown whether autistic children with ID have additional, different, or greater cortical thickness alterations than autistic children without ID. Understanding how the cortex develops in relation to IQ in autism might reveal whether progressive and regressive cortical developmental processes (e.g., dendritic growth and axonal and dendritic pruning) differ between autistic individuals with and without ID. This knowledge has the potential to guide future clinical research into efficacious interventions and treatments that may be specific to autistic individuals with ID.

Autism is associated with a broad spectrum of intellectual ability, ranging from gifted individuals to those with profound ID. From a diagnostic perspective, disentangling autism and ID can be challenging due to common, intersecting behavioral characteristics (Thurm et al. 2019). For example, challenges in the domains of language and social communication, a core criteria of autism diagnosis, are commonly observed in individuals with ID. Accordingly, there is a strong behavioral relation between IQ and intensity of autism characteristics. Given the significant intersections between IQ, ID, and autism symptom severity (Courchesne et al. 2019; Mottron and Bzdok 2020; Thurm et al. 2019), we sought to determine to what extent autistic individuals with and without ID demonstrate similar or different neural phenotypes and to what degree IQ and autism symptom severity are associated at the neural systems level.

Higher order processes involved in both intellectual ability and behaviors associated with autism (e.g., social interaction) heavily involve the neocortex, making imaging features such as cortical thickness of particular interest in studies seeking to identify neural correlates of both intelligence and autism. Several neuroimaging studies have identified associations between cortical thickness and intellectual ability in typical development (Shaw et al. 2006; Sowell et al. 2004; Zhao et al. 2022), even suggesting shared genetic components between the two (Brans et al. 2010). Cortical regions associated with IQ scores include the anterior cingulate, orbitofrontal, dorsolateral, and medial prefrontal cortices (Misaki et al. 2012; Zhao et al. 2022). In adults, thicker cortex is associated with increased intellectual ability (Brans et al. 2010), while in younger children, thinner cortex is associated with greater intellectual ability (Shaw et al. 2006; Sowell et al. 2004; Zhao et al. 2022). These opposing age‐related patterns can likely be attributed to developmental processes underlying cortical maturation.

Lifespan MRI models of the developing brain indicate that average cortical thickness across the cortex increases prenatally and immediately postnataly, reaching peak thickness between 1.5 and 2 years of age. This is followed by a rapid thinning during early childhood prior to more protracted thinning across adolescence and adulthood (Bethlehem et al. 2022; Rutherford et al. 2021; Vidal‐Pineiro et al. 2020). The preceding is true of average cortical thickness. However, longitudinal evidence of cortical maturation during the first 2 years of life shows cortical regions have distinct developmental trajectories and that several regions continue to increase in thickness beyond 2 years (Wang et al. 2019). Neurobiological processes contributing to cortical maturation including; intracortical myelination, the maturation and remodeling of dendritic trees, axonal innervation and collateralization, and vascularization, are all likely to influence macroscopic measures of cortical thickness (Vidal‐Pineiro et al. 2020). Furthermore, in addition to typical developmental trajectories and the biological programs underpinning these, it is important to note that cortical thickness remains sensitive to both experience and environmental factors throughout the lifespan (Habibi et al. 2020; Noble et al. 2015; Sun et al. 2020).

The relationship between cortical thickness and intellectual ability differs across development. Longitudinal evidence suggests a transition from a negative to positive association during middle childhood in typical development (TD), approximately between the ages of 7 and 10 years (Shaw et al. 2006; Sowell et al. 2004). One of the largest imaging studies of IQ to date leveraged the Adolescent Brain Cognitive Development (ABCD) cohort to identify cross sectional associations of cortical thickness with general intelligence (estimated from the NIH Toolbox Cognition battery (Luciana et al. 2018)) during this transition phase (i.e., 7–10 years of age). Higher scores on cognitive tasks were associated with thinner cortex in the rostral portion of the anterior cingulate, and the dorsolateral and medial prefrontal cortices and thicker cortex in the orbitofrontal and postcentral primary sensory cortices in 9–11 year olds (Zhao et al. 2022).

Numerous studies have identified structural differences in cortical thickness related to autism, and it is increasingly understood that there is a high degree of interindividual variability in brain measures among autistic individuals (Ecker et al. 2021; Segal et al. 2023; Zabihi et al. 2019). For example, large cross‐sectional studies have indicated both increased and decreased cortical thickness in autism across multiple cortical regions. These differences are often age dependent and vary across developmental stages (Bedford et al. 2020; Ecker et al. 2021; Nunes et al. 2020; van Rooij et al. 2018). One study, that did include a small proportion of autistic individuals with IQ below 80, found cortical thickness differences associated with autism varied significantly according to intellectual ability, with the largest differences observed in those with average intelligence (i.e., IQ = 100) (Bedford et al. 2020). Another study of autistic adolescent males with IQs above ID cutoffs (i.e., IQ 85–143) identified different patterns of associations between IQ and cortical thickness in autistic vs. nonautistic participants. This study found higher IQ in non‐autistic participants was associated with thicker orbitofrontal, postcentral, superior temporal, and ventral occipital cortices, while autistic individuals had no, or negative, associations between cortical thickness and IQ in these regions (Misaki et al. 2012). This finding raises the possibility that autistic adolescents exhibit different associations between cortical thickness and intelligence than their non‐autistic peers.

Critical for the rationale of the current study, the relationship between cortical thickness and IQ has not yet been investigated during early childhood in autistic individuals across the entire range of intellectual ability level, including a substantial proportion of autistic children with IQs ≤ 70. Accordingly, the generalizability of existing neuroimaging finding for autistic individuals with ID is an open question and basic questions regarding possible neuroanatomical differences and associations with IQ between autistic children with and without ID remain unanswered. To address this, in this study, we evaluated cortical thickness in a cohort of young autistic children (mean age 5.2 years) across a large range of intellectual ability (full scale IQ range 19–133). First, we investigated how associations between full scale IQ and cortical thickness differ between autistic and nonautistic, nondevelopmentally delayed (referred to here as typically developing [TD]) children. Based on previous research, and given the age of our sample, we hypothesized that autistic and TD children would exhibit negative associations between cortical thickness and IQ that differed regionally and by degree. Second, we directly compared cortical thickness between autistic children with IQ scores either in the range of ID (i.e., IQ ≤ 70) or above ID cutoffs (i.e., IQ > 70). Given previously noted negative associations between IQ and cortical thickness in children, we hypothesized that autistic children with ID would have thicker cortex in regions associated with intelligence compared with autistic children without ID. We further hypothesized that associations of cortical thickness with IQ would differ across the cortex between autistic children with and without ID. Last, given the reported correspondence between the severity of autism symptoms and intellectual ability (Courchesne et al. 2019; Dennis et al. 2009; Mottron and Bzdok 2020; Thurm et al. 2019), we directly tested how these two measures may be similarly associated with cortical thickness. We hypothesized that there would be substantial overlap in cortical thickness associations with autism symptoms and intellectual ability.

2. Materials and Methods

2.1. Participants, Autism Spectrum Disorder, and Intellectual Disability Criteria

This study included 88 autistic (60 males, 28 females) and 53 nonautistic, nondevelopmentally delayed (referred to as TD; 27 males, 26 females) child participants (Table 1). All participants were required to be native English speakers, ambulatory, have no contraindications for MRI, no suspected vision or hearing problems, and no known genetic disorders or neurological conditions (e.g., Fragile X, Down Syndrome, epilepsy, etc.). At time of enrollment, TD children were screened for autism using the Social Communication Questionnaire (scores < 11) and Developmental Quotient > 70 on the Mullen Scales of Early Learning (MSEL) (Mullen 1995; Rutter, Bailey, and Lord 2003). Sex assigned at birth was utilized to categorize sex in this study. Participants were enrolled in the ongoing, longitudinal UC Davis MIND Institute Autism Phenome Project (APP), which includes the Girls with Autism Imaging of Neurodevelopment (GAIN) study, launched to increase representation of females within the larger APP (Nordahl et al. 2022). These ongoing longitudinal studies enroll participants between the ages of 2–4 (Time 1), with follow up timepoints annually for 2 years (Times 2, 3) as well as in middle childhood (ages 8–12, Time 4) and adolescence (ages 16–18, Time 5). In the present study, we utilized a cross‐sectional subset of individuals from the APP/GAIN cohort who had undergone a structural MRI scan of sufficient quality to complete the Freesurfer preprocessing pipeline (Fischl 2012) in addition to an IQ assessment at the third APP timepoint (Time 3 mean age = 64.11 months/5.23 years). Time 3 data were selected because IQ is more stable at this age compared with Time 1 (Solomon et al. 2018, 2023) and the sample size of available MRI data in autistic children with ID is largest at this timepoint (data collection is ongoing at later Time points). For further details of the APP including additional publications that include overlapping samples, see (Nordahl et al. 2022). All research was approved by the UC Davis Institutional Review Board and informed consent and assent was acquired.

TABLE 1.

Sample characteristics.

ASD (total) ASD‐noID ASD ID TD
n 88 55 33 53
M/F 60/28 37/18 23/10 27/26
Age 64.49 (52.8–78.3, 5.27) 64.39 (54.6–75.8, 5.02) 64.66 (53–78, 5.75) 63.48 (51.1–79.6, 6.42)
FSIQ 79.78 (19–133, 32.37) 102.18 (71–133, 15.43) 42.44 (19–67, 12.15) 114.22 (85–134, 10.61)
ADOS CSS 7.29 (4–10, 1.72) 6.76 (4–10, 1.74) 8.18 (6–10, 1.28) —
VABS ABC* 77.02 (47–115, 64.49) 84.46 (54–115, 12.57) 64.07 (47–114, 13.47) 111.70 (90–134, 12.71)
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Abbreviations: ASD = Autism spectrum disorder, noID = full scale IQ > 70, ID = full scale IQ ≤ 70, TD = nonautistic typically developing, n = sample size, M/F = male/female, values given as mean (range, standard deviation), FSIQ = full scale IQ, ADOS = autism diagnostic observation schedule, CSS = calibrated severity score, VABS ABC = *VABS ABC scores were not available for 14 ASD participants (8 ASD‐noID, 6 ASD ID).

Diagnosis of ASD was confirmed at study Time 1 using the Autism Diagnostic Observation Schedule‐Generic (ADOS‐G) (Lord et al. 2000) or ADOS‐2 (Lord et al. 2012), as well as the Autism Diagnostic Interview‐Revised (ADI‐R) (Lord, Rutter, and Le Couteur 1994) and DSM‐IV‐TR criteria (American Psychiatric Association 1994). ADOS Calibrated Severity Scores (CSS) were utilized to assess autism symptom severity (Gotham, Pickles, and Lord 2009). Intellectual ability (full scale IQ) was assessed using one of the following; Differential Ability Scales (DAS) 2nd Edition School Aged (TD n = 2, ASD n = 1), DAS Early Years (TD n = 51, ASD n = 70), or using the developmental quotient (DQ) as measured using the MSEL (TD n = 0, ASD n = 17) (Elliott 2007; Mullen 1995). Previous studies of autistic children have shown strong construct validity between the DAS and MSEL (Bishop et al. 2011; Farmer, Golden, and Thurm 2016). Assessments of intellectual ability were selected based on children's chronological age, however if participants demonstrated consistent floor‐level performance on the DAS they were administered the MSEL to better align with their developmental level.

Diagnostic criteria for ID requires concurrent deficits in intellectual ability and adaptive functioning skills (Schalock, Luckasson, and Tassé 2021). To form IQ groupings of autistic children with and without ID, we operationally defined ID as having a full‐scale IQ/DQ in the range of ID, that is, a standard score of 70 or below. In line with population estimates, of the 88 autistic participants, 33 (37.5%) had full scale IQ standard scores in the range of ID (ASD‐ID), while 55 (62.5%) had full scale IQ above 70 (ASD‐noID). Vineland Adaptive Behavior Scales were administered via parent report questionnaire to assess adaptive functioning within the sample, but due to missing data (n = 8 ASD‐noID, 6 ASD‐ID) were not utilized as ID grouping criteria. Table 1. See Supporting Information: Methods and Figures S1–S4 for additional details.

2.2. MRI Acquisition, Preprocessing, and Quality Control

All MRI scanning was performed at the Imaging Research Center, UC Davis, Sacramento using a 3 Tesla Siemens Magnetom Trio MR system (Erlangen, Germany) with an 8‐channel head coil. MRI scanning was performed during natural, nocturnal sleep without sedation (Nordahl et al. 2008). High resolution T1 images were acquired using an MPRAGE sequence (1 mm3 resolution, TR = 2170 ms, TE = 4.86 ms, 256 × 256 × 192 mm FOV, 8:46 acquisition time). Image distortion associated with the scanner was controlled for by scanning a calibration phantom (ADNI MAGPHAM, The Phantom Laboratory) at the end of each MRI session and subsequently applying distortion correction to each MPRAGE image (Image Owl Inc., Greenwich, NY; http://www.imageowl.com/).

Individual estimates of cortical thickness were calculated based on cortical surface reconstructions of structural MRI scans using Freesurfer v7.1.1 (Fischl 2012; Fischl and Dale 2000). These methods have been extensively described elsewhere (Dale, Fischl, and Sereno 1999; Fischl, Sereno, and Dale 1999), and results validated histologically (Cardinale et al. 2014; Rosas et al. 2002). All surface reconstructions were visually inspected for quality and, when appropriate, manual edits were performed to improve reconstruction quality. See Supporting Information: Methods for additional details.

2.3. Statistical Analysis

Associations between IQ and cortical thickness were evaluated using the SurfStat toolbox (www.math.mcgill.ca/keith/surfstat/) for MATLAB (R2022a, The Mathworks, Massachusetts). Parameter estimates for the main effects of IQ were obtained by regression of separate general linear models within the autistic and TD groups for each vertex i, with sex and age as covariates, that is:

Yi=β0+β1IQ+β2Age+β3Sex+εi

where ε i was the residual error. Effects of interest were estimated from coefficient β 1 normalized by the corresponding standard error. Differences in the association of IQ with cortical thickness between the autistic and nonautistic groups were directly assessed by combining the two groups and adding terms for the main effect of diagnosis and a two‐way interaction of diagnostic group‐by‐IQ to the above model, that is:

Yi=β0+β1Diagnosis+β2IQ+β3Age+β4Sex+β5Diagnosis×IQ+εi

where ε i is the residual error. Effects of interest were estimated from coefficient β 5− normalized by the corresponding standard error.

To evaluate differences in cortical thickness within the autism group between those individuals with (ASD‐ID) and without ID (ASD‐noID), a model was fit including IQ grouping as a fixed effect factor, with sex and age as covariates that is:

Yi=β0+β1ASDIQGroup+β2Age+β3Sexεi

where ε i is the residual error. Effects of interest were estimated from coefficient β 1− normalized by the corresponding standard error. Corrections for multiple comparisons (i.e., Type I errors) were performed using random field theory (RFT) based cluster analysis for non‐isotropic images at a cluster‐threshold of p < 0.05 (two‐tailed) (Worsley et al. 1999). All clusters incorporating over 100 vertices are described.

2.4. Spatial Similarity Analyses

To assess overall similarity between patterns of associations with IQ across autistic children with and without ID, the similarity between the uncorrected statistical maps of associations between IQ and cortical thickness for the ASD‐noID and ASD‐ID groups were evaluated using spin tests. In brief, the spin test evaluates if spatial correspondence between brain maps is significantly greater than chance levels by generating a null model of overlap using a spatial permutation framework in which cortical maps are permutated through random rotations in a spherical space (Alexander‐Bloch et al. 2018). Here, the pair of main effect maps of full‐scale IQ for ASD‐noID and ID groups were compared (n = 1000 permutations of F maps). Additionally, to test whether associations between cortical thickness and IQ were spatially more similar than chance to associations between cortical thickness and autistic traits, a spin test was conducted comparing uncorrected statistical maps of cortical thickness associations with ADOS calibrated severity scores (ADOS‐CSS) and full‐scale IQ across the full autism sample (n = 1000 permutations of F maps). All spin tests were implemented in MATLAB (R2022a, The Mathworks, Massachusetts).

3. Results

3.1. Participant Characteristics

TD individuals had significantly higher average full‐scale IQ compared with the ASD group as a whole, as well as compared to the ASD group without ID (both p < 0.001). There were no significant differences in age at scan between the ASD and TD groups or between the ASD‐noID and ASD‐ID groups (p > 0.05). Across all children with ASD, full‐scale IQ and ADOS CSS scores were significantly correlated (Pearson's − 0.46, p < 0.001). While there was a higher proportion of males to females in the ASD group (2.13:1) than the TD group (1.18:1) this was not statistically significant (X 2 = 3.46, p = 0.06). As expected, VABS composite scores were significantly lower in the ASD‐ID group compared with the ASD‐noID and TD groups (both p < 0.001). Table 1.

3.2. Main Effects of IQ on Cortical Thickness in Autistic and TD Children

Significant negative associations between cortical thickness and full‐scale IQ were observed in autistic children within two clusters centered in the temporal lobe that included regions of entorhinal cortex bilaterally, and right fusiform gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, and temporal pole (Figure 1A, Table 2). A significant interaction between full‐scale IQ and diagnosis was observed in a single cluster centered within the right posterior cingulate cortex. Within this region, both autistic and TD children showed thinner cortex to be associated with higher IQ. However, in TD children, this negative association was significantly stronger (Figure S5, Table 2). In TD children, associations between thinner cortex and increased IQ (i.e., negative association) were observed within regions consistent with previous research (i.e., middle frontal gyrus, superior frontal gyrus, anterior cingulate cortex), although these associations did not survive multiple comparisons corrections (Figure S6).

FIGURE 1.

FIGURE 1

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Random field theory corrected (top) and uncorrected (bottom) maps indicating cortical regions (A) with a significant negative association between cortical thickness and full‐scale IQ (FSIQ) in autistic (ASD) participants and (B) regions where autistic individuals without ID had significantly thicker cortex compared with autistic individuals with intellectual disability (ID). Maps are shown in both folded and inflated views. Clusters > 100 vertices are visualized.

TABLE 2.

Results summary.

FSIQ ASD
Cluster # p t Vertex Talairach Size Hemisphere Region(s)
1 3.85E−04 −4.079025 160,836 −27 −8 −28 634 Left Entorhinal cortex
2 5.55E−03 −3.182834 11,571 27 −8 −28 1643 Right Superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, temporal pole, entorhinal cortex, fusiform gyrus
FSIQ ASD‐by‐TD
Cluster # p t Vertex Talairach Size Hemisphere Region(s)
1 4.15E−02 2.513206 146,746 5 −6 28 278 Right Posterior cingulate cortex
ASD noID–ASD ID
Cluster # p t Vertex Talairach Size Hemisphere Region(s)
1 3.51E−03 −3.61417 92,025 47 −9 −28 1562 Right Entorhinal cortex, fusiform gyrus, inferior temporal gyrus, middle temporal gyrus, temporal pole
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3.3. Cortical Thickness Differences Between Autistic Children With and Without Intellectual Disability

Direct comparison of cortical thickness between ASD‐noID and ASD‐ID groups revealed thinner cortex in the ASD‐noID group in a single cluster that encompassed portions of the right entorhinal cortex, fusiform gyrus, inferior temporal gyrus, middle temporal gyrus and temporal pole. As depicted in Figure 1, this cluster overlapped with cortical regions indicated as being associated with IQ across the full autism sample (ASD‐noID and ASD‐ID combined) (Figure 1B, Table 2). These differences between ASD‐noID and ASD‐ID in cortical thickness largely overlap and align with the finding that across all autistic children thinner cortex was associated with higher IQ in these regions.

3.4. Spatial Similarity in the Association of Full‐Scale IQ With Cortical Thickness Between Autistic Children With and Without Intellectual Disability

To assess the overall similarity between patterns of associations with IQ across autistic children with and without ID, the similarity of the unthresholded statistical maps of full‐scale IQ associations with cortical thickness between ASD‐noID and ASD‐ID groups were evaluated using a spin test of spatial correspondence. As described above, spin tests assess whether two statistical maps differ from each other greater than would be expected by chance. No significant correspondence between these two maps was found (p = 0.24) (Figure 2).

FIGURE 2.

FIGURE 2

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Uncorrected main effect of full‐scale IQ maps for autistic children with (ID) and without (noID) intellectual disability (ID). Spin tests found no spatial correspondence between these two maps above chance level, suggesting that autistic children with and without ID associations between cortical thickness and IQ that do not overlap to a significant degree. Maps are shown in both folded and inflated views. Clusters > 100 vertices are visualized.

3.5. Spatial Similarity Between Associations of ADOS Calibrated Severity Scores and Full‐Scale IQ on Cortical Thickness

To assess the degree of overlap across the cortex between associations of cortical thickness with autism symptom severity and IQ, uncorrected statistical maps of IQ and ADOS CSS associations with cortical thickness across all autistic children (ASD ID and ASD noID combined) were compared. Spin tests revealed significant spatial correspondence between statistical maps depicting the main effect of autism symptoms (ADOS‐CSS) compared with the main effect of full‐scale IQ (p < 0.001), indicating that autism symptom severity and intellectual ability have associations with cortical thickness that overlap more so than would be expected by chance (Figure 3).

FIGURE 3.

FIGURE 3

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Correspondence between cortical thickness associated with full‐scale IQ and autism symptom severity: Uncorrected main effect maps of full‐scale IQ (FSIQ) and ADOS calibrated severity scores (CSS) were found to correspond to a greater degree than would be expected by chance (spin test p < 0.001) indicating that autism symptoms and intellectual ability are largely associated with similar cortical regions. Maps are shown in both folded and inflated views. Clusters > 100 vertices are visualized.

4. Discussion

Very few neuroimaging studies have included autistic individuals across the entire range of intellectual abilities. Here, we examined associations between IQ and cortical thickness in 5–6‐year‐old autistic children with IQs ranging from 19 to 133. We first evaluated the relationship between cortical thickness and full‐scale IQ across all autistic children compared with TD children. We found that thinner right posterior cingulate cortex was associated with higher IQ in both autistic and TD children, although this association was significantly stronger in TD. We then compared cortical thickness between autistic children with and without ID. We found that within regions of the temporal cortex, cortical thickness was negatively associated with IQ in autistic children, but not in TD children. Furthermore, in these same regions of the temporal cortex, autistic children with ID had significantly thicker cortex compared with the autistic children without ID. Additionally, we found that the overall pattern of associations between IQ and cortical thickness across the cortex did not significantly overlap between autistic children with and without ID. Lastly, we found substantial overlap in the patterns of associations of cortical thickness with IQ and autism symptom severity, indicating shared cortical underpinnings of autism symptoms and intellectual ability.

Previous studies have highlighted that cortical associations with intellectual ability are highly age dependent (Karama et al. 2011, 2014; Sowell et al. 2004; Zhao et al. 2022). Accordingly, it is critical to consider the developmental stage of participants when interpreting findings of either thicker or thinner cortex, particularly in relation to intelligence. On average, early life cortical development is characterized by rapid thickening until approximately 2 years (Bethlehem et al. 2022). Subsequent changes in cortical thickness estimates (i.e., cortical thinning) across childhood has been attributed to processes including myelination, maturation and remodeling of dendritic trees, axonal collateralization, and vascularization (Vidal‐Pineiro et al. 2020). Although the resolution of current MRI methods does not allow for the identification of specific mechanisms underlying cortical thickness differences, the current findings should be interpreted in the context of a dynamic interplay between these adaptive neural maturational processes and autism associated factors (e.g., genetic liability). For example, several lines of research have implicated genetic and mechanistic processes associated with autism contribute to alterations in cortical structure, including variants within genes responsible for coding synaptic transmission pathways (Ecker et al. 2021; Romero‐Garcia et al. 2019), and disruption of excitatory‐inhibitory (E‐I) balance of neuronal circuits (Rubenstein and Merzenich 2003; Sohal and Rubenstein 2019). Additional research of specific genetic conditions associated with autism that leverages both animal and organoid models will be useful in identifying the neural mechanisms contributing to macroscopic structural differences within the cortex.

The current study found that across autistic children with and without ID associations between cortical thickness and IQ are developmentally consistent with such associations in slightly older TD children (Zhao et al. 2022). It is notable, however, that the regions associated with IQ in autistic children differ from the frontal cortical and anterior cingulate regions commonly highlighted as being associated with intelligence in TD. Here, autistic children had significant associations with IQ within the bilateral entorhinal cortex, right fusiform and temporal gyri, and temporal pole. While these regions have been implicated in autism (Andrews et al. 2024; Frith et al. 2003; Mundy 2018), they have generally not been highlighted as having associations between their thickness and intelligence. The temporal lobe location of these IQ associations, particularly with the entorhinal cortex, which is a prominent component of the hippocampal formation, may suggest a greater dependence on hippocampally mediated declarative memory systems in intelligence in young autistic children, as opposed to frontal cortical regions implicated in executive functioning (Zhao et al. 2022). Increased hippocampal volumes have been associated with higher full‐scale IQ in TD children and adolescents (ages 8–18 years) (Schumann et al. 2007). Within the APP cohort, we previously found a trend suggesting that faster hippocampal growth from ~2–6 years of age (APP Time 1–Time 3) was associated with higher IQ in autistic boys (Reinhardt et al. 2020). Here, associations of thinner cortex with higher IQ in regions adjacent to the hippocampus could result from autistic children recruiting additional neural resources related to declarative and sematic learning leading to compensatory maturation of hippocampus adjacent cortical regions. Alternatively, these findings may represent downstream effects resulting from disruption of additional neural networks and processes. Future research and longitudinal studies will be critical to determine this and if these differences in ID are more related to alterations in early life neural proliferation or later maturational thinning processes.

It is worth considering the current findings of thicker cortex among autistic children with ID in the context of other neurodevelopmental conditions that are associated with ID. For example, increased cortical thickness has also been noted in children, adolescents and young adults with Down syndrome, the most common genetic cause of ID (Lee et al. 2016; Levman et al. 2019; Parker et al. 2010). Studies of cortical morphology within Fragile X syndrome, the most common monogenic cause of autism, are limited but also suggest increased cortical thickness in the condition (Bartholomay et al. 2024; Meguid et al. 2012). Furthermore, increased cortical thickness has been reported in Williams syndrome, another genetic condition associated with ID (Thompson et al. 2005). Accordingly, the current findings appear to build upon converging evidence that increased cortical thickness may be a marker of ID across several neurodevelopmental conditions. Again, while the current resolution of MRI limits the interpretation of underlying cellular mechanisms associated with increased cortical thickness associations with ID, several monogenic conditions associated with ID implicate disruption to processes underlying cortical maturation including neural migration, proliferation, dendritic spine formation and density (Blayney et al. 2024; Quach et al. 2021; Willemsen et al. 2012). The current findings support the hypothesis that these foundational cortical maturational processes are also disrupted in autistic children with ID.

Another intriguing finding of the current study is the similarity of associations of IQ and autism symptom severity across the cortex. Challenges in disentangling unique associations of autism diagnoses independent of intellectual ability within the brain have been previously discussed (Courchesne et al. 2019; Dennis et al. 2009; Mottron and Bzdok 2020; Thurm et al. 2019). Widely implemented solutions such as inclusion of IQ as a confounding factor in models may potentially conceal neurobiological associations with autism (Dennis et al. 2009). This is an important consideration for neuroimaging studies because autistic samples typically have lower average IQ scores than comparison groups, even in studies that exclude autistic individuals with ID. To our knowledge, no study has previously directly tested the spatial similarity in the cortical regions associated with autism symptoms and intellectual ability. Our findings suggest intrinsic links between intellectual ability and autism characteristics that may not be possible to clearly delineate within the brain. This is supported by genome sequencing studies that have found significant overlap between genes contributing to autism and general developmental delay (Buxbaum et al. 2020; Myers et al. 2020; Satterstrom et al. 2020; Wang et al. 2021) indicating similar etiology in both conditions is likely in many cases.

Despite this overlap, it is important to note that evidence suggests that there are compounding effects for individuals with both autism and ID. For example, adaptive behavior skills have been reported to be more impaired in autistic individuals with ID than would be expected based solely on their IQ, that is, compared with individuals with ID only (Duncan and Bishop 2015; Kanne et al. 2011; Pugliese et al. 2015). This additional adaptive functioning‐IQ gap in autistic individuals with ID may be attributable to social communication challenges and repetitive behaviors/restricted interests central to autism (Duncan and Bishop 2015; Kanne et al. 2011; Pugliese et al. 2015). Indeed, functional disabilities are observed across the autistic spectrum, and specific areas of need related to core autism characteristics may unduly reduce functional independence (Thurm et al. 2019). Accordingly, understanding what neural mechanisms shape clinical outcomes in autism and ID remains a critical question. Beyond disentangling effects of IQ and autism within the brain, a central diagnostic question of the extent to which autism characteristics, compared with characteristics of ID, are drivers of functional and cognitive deficits in the population remains an important but elusive distinction within the Diagnostic and Statistical Manual of Mental Disorders 5th ed. (DSM‐5) (American Psychiatric Association 2013; Thurm et al. 2019). This distinction is most apparent in those with severe or profound ID, in which diagnostic measures may fail to sensitively capture or differentiate critical areas of overlap (Courchesne et al. 2019; Thurm et al. 2019).

This study is one of the first to directly investigate differences in brain structure between autistic children with and without ID. However, these findings should be considered within certain limitations of the study. Given the large degree of pheno‐ and genotypic heterogeneity observed across autistic individuals, larger sample sizes and further replication is needed to confirm the current findings and to identify neurophenotypes associated with specific autism associated genetic variants. Here we defined ID as having an IQ/DQ score in the range of ID (IQ ≤ 70), however, diagnostic criteria for ID also includes cutoffs for adaptive functioning which were not included in our analysis due to missing data (Furnier et al. 2024). Future studies that compare individuals with nonsyndromic ID to autistic children with ID could further elucidate neural correlates specific to ID and autism. The above average IQ of our TD group limits our ability to directly compare associations between cortical thickness and IQ across our TD and autism groups since these groups do not have fully overlapping IQ scores. In this initial study, we evaluated a cross‐sectional subset at one time point of our longitudinal study to maximize our sample size. We selected a time point in which IQ scores have mostly stabilized (Solomon et al. 2018, 2023), but neural correlates of IQ are known to change across developmental stages (Sowell et al. 2004). Future studies investigating concurrent neural and intellectual development across time are needed. Additionally, here, we investigated associations with full‐scale IQ as a starting point to begin investigating associations between brain structure and intelligence in autistic children. There is evidence, however, that individuals with autism tend to exhibit relative weaknesses in verbal compared to non‐verbal IQ (Courchesne et al. 2019; Farmer, Golden, and Thurm 2016; Grondhuis et al. 2018). Further studies are needed to identify cortical associations with verbal and nonverbal subscales of IQ. Last, we demonstrated significant spatial correspondence between effects of full‐scale IQ with ADOS CSS, however, future studies would benefit by exploring the correspondence between intellectual ability and autism symptoms using additional more specific quantitative measures of autism symptoms or through the development and implementation of novel measures that more accurately capture the autistic experience.

The present study fills a critical gap in the field by characterizing the association between cortical thickness and IQ in autistic children across a broad range of intellectual ability. Our findings suggest that young autistic children have different patterns of associations between cortical thickness and IQ from their nonautistic, nondevelopmentally delayed peers. Direct comparison of autistic children with and without ID revealed differential patterns of IQ/cortical thickness associations between these groups. These associations are likely dynamic, and longitudinal evaluations are necessary to better understand the developmental time course of cortical thickness and intellectual ability. Moreover, we found substantial overlap in cortical patterns associated with both intellectual ability and autism symptom severity, corroborating behavioral observations that link IQ and autism symptoms. Future work is necessary to further disentangle the interplay between intellectual functioning, autistic characteristics and brain features in individuals with autism. Determining distinct neurobiological markers of autism with and without ID will be a critical step toward predicting individual's outcomes, support needs, and promoting efficacious intervention strategies.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1. Supporting Information.

AUR-18-486-s001.docx (3.3MB, docx)

Acknowledgments

The authors would like to thank the families and children who have participated in the APP and GAIN studies and all members of the research study staff. This research was supported by the National Institute of Mental Health (R01MH127046 [C.W.N.], R01MH128814 [D.G.A., C.W.N.], R01MH103284 [M.S.]), and the National Institute of Child Health and Development (NICHD) (P50 HD093079) its support of the MIND Institute Intellectual and Developmental Disabilities Research Center (P50 HD103526).

Funding: This work was supported by National Institutes of Health, National Institute of Mental Health (R01MH127046 [C.W.N.], R01MH128814 [D.G.A., C.W.N.], R01MH103284 [M.S.]), the National Institute of Child Health and Development (NICHD) (P50 HD093079), and the MIND Institute Intellectual and Developmental Disabilities Research Center (P50 HD103526).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1. Supporting Information.

AUR-18-486-s001.docx (3.3MB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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