Whole Brain White Matter Tract Deviation and Idiosyncrasy From Normative Development in Autism and ADHD and Unaffected Siblings Link With Dimensions of Psychopathology and Cognition
Publication: American Journal of Psychiatry
Abstract
Objective:
The heterogeneity of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) preclude definitive identification of neurobiomarkers and biological risks. High clinical overlap suggests multifaceted circuit-level alterations across diagnoses, which remains elusive. This study investigated whether individuals with ADHD or ASD and their unaffected siblings constitute a spectrum of neurodevelopmental conditions in terms of white matter etiology.
Methods:
Sex-specific white matter tract normative development was modeled from diffusion MRI of 626 typically developing control subjects (ages 5–40 years; 376 of them male). Individualized metrics estimating white matter tract deviation from the age norm were derived for 279 probands with ADHD, 175 probands with ASD, and their unaffected siblings (ADHD, N=121; ASD, N=72).
Results:
ASD and ADHD shared diffuse white matter tract deviations in the commissure and association tracts (rho=0.54; p<0.001), while prefrontal corpus callosum deviated more remarkably in ASD (effect size=−0.36; p<0.001). Highly correlated deviance patterns between probands and unaffected siblings were found in both ASD (rho=0.69; p<0.001) and ADHD (rho=0.51; p<0.001), but only unaffected sisters of ASD probands showed a potential endophenotype in long-range association fibers and projection fibers connecting prefrontal regions. ADHD and ASD shared significant white matter tract idiosyncrasy (rho=0.55; p<0.001), particularly in tracts connecting prefrontal regions, not identified in either sibling group. Canonical correlation analysis identified multiple dimensions of psychopathology/cognition across categorical entities; autistic, visual memory, intelligence/planning/inhibition, nonverbal-intelligence/attention, working memory/attention, and set-shifting/response-variability were associated with distinct sets of white matter tract deviations.
Conclusions:
When conceptualizing neurodevelopmental disorders as white matter tract deviations from normative patterns, ASD and ADHD are more alike than different. The modest white matter tract alterations in siblings suggest potential endophenotypes in these at-risk populations. This study further delineates brain-driven dimensions of psychopathology/cognition, which may help clarify within-diagnosis heterogeneity and high between-diagnosis co-occurrence.
Despite distinct diagnostic core symptoms between autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), emerging evidence suggests clinical and cognitive overlaps (1). Inattention and impulsivity are common presentations in ASD (1); autistic-like social deficits are frequently reported in ADHD (1). Cognitively, both disorders are associated with executive dysfunction involving attention, working memory, and planning (1). Both ASD and ADHD show strong familial tendencies with high heritability (2, 3). Unaffected siblings, the at-risk populations, tend to show subtle levels in symptoms and executive and attention dysfunction (2, 3). Neuroimaging features, such as brain endophenotypes and brain-behavior relationships, for individuals with ASD or ADHD, together with their unaffected siblings, will inform etiological insight, targeted intervention plans, and mental health promotion.
The behavioral and cognitive overlaps among ASD and ADHD could be a result of shared genetic vulnerability (4), which may be mirrored by similar abnormal brain network organization. White matter fiber connections, as quantified by diffusion MRI, constitute the brain structural architecture (5), which plays a vital role in facilitating functional network formation (6) and gray matter structural coupling (7), but few neuroimaging studies of overlapping/distinct brain alterations between ASD and ADHD have been focused on this area (8). For example, atypical white matter organization in the corpus callosum (CC) and superior longitudinal fasciculus (SLF) have been identified in both disorders, based on separate comparisons with typically developing control subjects (8). However, studies directly comparing white matter profiles in ASD, ADHD, and typically developing control subjects cannot replicate these findings (9, 10). In addition to methodological variability, these diagnoses include widely heterogeneous phenotypes, extending from “discrete disorders” to “quantitative spectrums” (2, 3). Combined with the emerging evidence of idiosyncratic brain alterations (11–13), “patients’ averages” adopted by conventional case-control approaches obscure within-group variations, leading to inconsistent results (8–10). Furthermore, some of this inconsistency might be explained by age and sex confounders. The case-control design tends to address this issue by including people within narrow age ranges to avoid within-ASD/ADHD heterogeneity (8–10), resulting in nonrepresentative samples. Normative modeling is a valuable paradigm shift to derive personalized brain metrics (11–13) that quantify atypicality in ASD or ADHD as individual deviations in developmental trajectories relative to typically developing control norms. Such an approach could yield individualized neurobiological fingerprints (11) that would account for the inherent heterogeneity and the nonlinear age and sex confounders and permit statistical inference at the individual level. Previous studies have demonstrated that normative modeling parses heterogeneous alterations in brain morphometry in people with ASD (14) and ADHD (15), respectively. To our knowledge, no published study has investigated atypical whole-brain major white matter tracts in individuals with ASD or ADHD and their unaffected siblings as an extreme of typically developing control norms.
Considering that the current categorical nosology may not carve the boundaries that capture fundamental mechanisms, another approach to address biological heterogeneity is to adopt the Research Domain Criteria (RDoC) framework to investigate brain-behavior relationships beyond diagnostic entities (16). However, neural substrates that directly associate with multidimensional autism-related and ADHD-related symptoms alongside cognitive executive functions cutting across clinically defined diagnostic entities and at-risk populations have never been investigated.
This study leveraged normative modeling to investigate categorically distinct and common patterns of the altered white matter pathways in a large, well-characterized cohort of individuals with ADHD or ASD and their unaffected siblings with high-quality single-scanner diffusion MRI data. The main hypothesis was that individuals with ADHD or ASD and at-risk populations (siblings) would constitute a spectrum of neurodevelopmental conditions, with the respective clinically labeled groups lying distinctly along a brain-driven continuum. Concurrently, these clinical groups would lack clear-cut categorical boundaries, which would be reflected by more similar, rather than different, white matter deviations relative to the norm across clinically labeled groups. The first part of the study derived deviation and idiosyncrasy patterns for each clinical group. Atypical white matter tract organization was defined both as mean differences in individuals’ deviation from sex-specific normative developmental trajectories and interindividual variations (“idiosyncrasy”) (12, 13). Between-group comparison revealed the similarity and distinction between ADHD and ASD. The sex and age-group issues and potential endophenotypes were specifically investigated. The second part of the study was to delineate linked dimensions of symptoms/cognition and white matter tract fingerprints, as quantified by the degree of deviation in each white matter tract, cutting across categorical entities (16), reflecting the potential shared underlying neural mechanism.
Methods
Participants
Participants with a clinical diagnosis of ADHD (ages 7–40 years) or ASD (ages 9–35 years) and their unaffected siblings (ages 6–40 years) were recruited from the outpatient clinic of the Department of Psychiatry, National Taiwan University Hospital, Taipei, from 2010 to 2018. All clinical diagnoses were made by senior child psychiatrists who are experienced in mental health issues of neurodevelopmental disorders across the life span; diagnoses were made per DSM-IV-TR and were confirmed by the Autism Diagnostic Interview–Revised (17) for ASD diagnoses and the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Epidemiologic Version (18) for ADHD and other psychiatric diagnoses. Autism symptoms were assessed by parent report on the Autism Spectrum Quotient (19) and the Social Responsiveness Scale (20), and ADHD-related symptoms were evaluated by parent report on the Swanson, Nolan, and Pelham Rating Scale, version IV (SNAP-IV) (21) (Table 1). Typically developing control subjects (ages 5–40 years), recruited from schools or through advertisements, were clinically evaluated by psychiatrists to ensure that they were free of any psychiatric diagnoses. Individuals with a history of other major psychiatric disorders, intellectual disability, or major medical or neurological disorders or lesions were excluded. Details of recruitment and assessments are provided in the Supplementary Methods section in the online supplement .
| Characteristic or Measure | ADHD Probands (N=279) | ADHD Unaffected Siblings (N=121) | ASD Probands (N=175) | ASD Unaffected Siblings (N=72) | Typically Developing Control Subjects (N=626) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | N | % | N | % | |
| Male | 205 | 73.5 | 56 | 46.3 | 158 | 90.3 | 38 | 52.8 | 376 | 60.1 |
| Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | |
| Age (years) | 18.03 | 8.3 | 18.50 | 7.8 | 16.35 | 4.2 | 16.39 | 5.5 | 20.16 | 8.5 |
| Swanson, Nolan, and Pelham rating scale, version IV | ||||||||||
| Inattentive | 17.7 | 5.8 | 6.3 | 5.4 | 14.9 | 6.5 | 5.8 | 5.2 | 5.6 | 4.6 |
| Hyperactive-impulsivity | 12.0 | 6.7 | 3.4 | 4.6 | 10.1 | 6.5 | 3.9 | 5.0 | 2.9 | 3.7 |
| Oppositionality | 10.5 | 6.0 | 4.8 | 4.9 | 8.9 | 5.6 | 6.4 | 5.4 | 3.2 | 3.5 |
| Total score | 29.7 | 10.7 | 9.7 | 9.3 | 25.0 | 11.4 | 9.7 | 9.5 | 8.6 | 7.6 |
| Autism Spectrum Quotient (4-point Likert scale) | ||||||||||
| Social skill | 24.1 | 5.1 | 20.2 | 5.5 | 29.6 | 5.4 | 18.9 | 5.4 | 20.1 | 4.8 |
| Attention switching | 25.5 | 3.1 | 22.2 | 4.2 | 28.7 | 4.4 | 20.9 | 4.2 | 22.8 | 4.2 |
| Attention to details | 22.4 | 3.9 | 24.3 | 4.3 | 25.6 | 5.1 | 23.1 | 4.5 | 24.7 | 4.4 |
| Communication | 24.0 | 4.8 | 18.1 | 4.9 | 29.4 | 4.6 | 17.2 | 5.0 | 18.8 | 4.6 |
| Imagination | 23.6 | 4.2 | 20.1 | 4.2 | 27.2 | 4.4 | 18.3 | 4.1 | 20.5 | 4.0 |
| Total score | 119.6 | 14.2 | 104.9 | 14.8 | 140.4 | 15.0 | 98.3 | 14.9 | 106.9 | 15.3 |
| Autism Spectrum Quotient (dichotomous scale) | ||||||||||
| Social skill | 4.7 | 2.6 | 2.8 | 2.6 | 6.9 | 2.4 | 2.1 | 2.3 | 3.2 | 2.6 |
| Attention switching | 5.5 | 1.7 | 3.9 | 2.0 | 6.7 | 2.1 | 3.2 | 2.1 | 4.6 | 2.0 |
| Attention to details | 3.9 | 2.1 | 4.9 | 2.1 | 5.6 | 2.3 | 4.1 | 2.0 | 5.0 | 2.1 |
| Communication | 4.6 | 2.4 | 1.9 | 2.1 | 6.9 | 2.1 | 1.6 | 2.0 | 2.4 | 2.0 |
| Imagination | 4.2 | 2.0 | 2.7 | 2.0 | 6.0 | 2.0 | 2.2 | 1.6 | 2.8 | 1.7 |
| Total score | 22.9 | 6.7 | 16.3 | 6.3 | 32.0 | 7.0 | 13.3 | 6.3 | 18.0 | 6.8 |
| Social Responsiveness Scale | ||||||||||
| Social communication | 18.0 | 10.5 | 7.1 | 7.3 | 37.6 | 14.3 | 10.2 | 8.9 | 11.7 | 9.4 |
| Stereotyped behaviors | 9.0 | 5.4 | 3.5 | 3.7 | 19.5 | 8.1 | 4.2 | 4.8 | 5.4 | 5.0 |
| Social awareness | 19.3 | 5.2 | 13.8 | 7.3 | 20.4 | 5.0 | 10.0 | 5.5 | 12.5 | 5.9 |
| Social emotion | 6.0 | 3.0 | 3.3 | 2.8 | 12.0 | 4.7 | 3.8 | 3.4 | 4.8 | 3.7 |
| Total score | 52.4 | 20.0 | 27.7 | 16.7 | 89.5 | 27.7 | 28.3 | 19.6 | 34.4 | 19.8 |
| Intelligence test | ||||||||||
| Verbal IQ | 106.8 | 11.2 | 106.2 | 9.9 | 101.1 | 18.6 | 108.8 | 11.7 | 109.3 | 10.8 |
| Performance IQ | 106.9 | 13.5 | 109.1 | 13.1 | 100.8 | 18.2 | 107.1 | 13.3 | 110.0 | 12.5 |
| Full IQ | 106.6 | 12.0 | 107.5 | 11.0 | 100.9 | 18.4 | 108.4 | 11.8 | 110.0 | 11.0 |
| Image quality measures | ||||||||||
| Signal-to-noise ratio | 27.2 | 2.8 | 27.9 | 3.2 | 27.4 | 3.4 | 28.4 | 3.1 | 27.2 | 3.1 |
| Signal dropout counts | 9.0 | 12.4 | 7.6 | 11.8 | 11.1 | 15.5 | 10.7 | 16.6 | 6.8 | 11.1 |
a
ADHD=attention deficit hyperactivity disorder; ASD=autism spectrum disorder.
The study was approved by the Research Ethics Committee of National Taiwan University Hospital, Taipei, and written informed consent was provided by all participants and by the parents of participants who were under age 18.
Image Acquisition and Analysis
All MRI scans were conducted in the same 3-T scanner (TIM Trio, Siemens, Erlangen, Germany) with a 32-channel phased-array head coil. The diffusion-weighted imaging protocol, specifically diffusion spectrum imaging (DSI) with 102 diffusion-encoding directions with a maximal b-value of 4,000 s/mm2 (22), was acquired using a single-shot spin-echo echo-planar imaging sequence with twice-refocused diffusion-sensitive gradients to reduce distortion caused by eddy currents (23). DSI data with low signal-to-noise ratio (SNR) or high signal dropout counts, a proxy of in-scanner motion levels henceforth, were excluded from further analyses. Categorical groups showed comparable distributions in SNR and signal dropout counts (see Figure S1 in the online supplement ). The 45 major white matter tracts (see Table S1 in the online supplement ) were built in an open-source DSI template (24) through deterministic streamline-based tractography (25) with multiple regions of interest defined in the automated anatomical labeling atlas (26). White matter microstructural property was sampled in the native space along each white matter tract and estimated by diffusion indices, including generalized fractional anisotropy (GFA) derived from mean-apparent propagator models (27) (the main results) and complemented by diffusivity measures, including mean diffusivity, radial diffusivity, and axial diffusivity (28) (see the Supplementary Results section in the online supplement ). DSI acquisition, quality control, preprocessing, and tractography are detailed in the Supplementary Methods section in the online supplement .
Statistical Analysis, Standardized Normative Models, and Brain-Behavior Relationships
Sex-specific normative models (see Figures S2 and S3 in the online supplement ) for 45 white matter tracts were developed from the 626 typically developing control subjects (376 of them male; ages 5–40 years; mean=20.16 years, SD=8.5). A moving average approach (see Table S2 in the online supplement ) was used to obtain unbiased mean and variance estimates for diffusion metrics in each age range. Diffusion metrics were transformed into Z-scores, yielding individualized tract-alteration profiles (see Figure S4 in the online supplement ), representing the normalized deviation of each white matter tract concerning the population mean matched in age and sex. The derived Z-scores of clinical groups and typically developing control subjects were not correlated with age, SNR, or in-scanner motion levels (see Figure S5 and S6 in the online supplement ). The construction of normative models is detailed in the Supplementary Methods section in the online supplement .
For categorical comparisons, a one-sample t test was computed on the basis of GFA Z-score for each group to identify significant diagnostic group deviations from the typically developing sex-age norm. A two-sample t test was calculated to compare differences in derivative measures of normative modeling between groups (e.g., ASD children versus ASD adults, ASD versus ADHD, and so on). Stratified analyses for children and adults and by sex were implemented to specify the effects of these two critical factors (2, 3) on Z-scores. F-tests of GFA-Z variability were performed for each group to determine brain idiosyncrasy. The false discovery rate was employed to correct for multiple comparisons of 45 white matter tracts for each clinical group of interest, separately for deviation and idiosyncrasy metrics. The effect size of the one-sample t test and the F-ratio of F-tests for the 45 white matter tracts reflect the extent of white matter tract deviation and idiosyncrasy profile for each group, respectively. Spearman’s correlation (rho) of the white matter tract deviation and idiosyncrasy profiles were estimated between groups, respectively, as these white matter profiles (45 values in each metric) were not normally distributed according to the Kolmogorov-Smirnov test. High correlations of white matter tract profiles between groups indicate similarity regardless of absolute values. The details regarding data distributions and the rationale for the statistical test selection are provided in the Supplementary Methods section in the online supplement .
For dimensional brain-behavior relationships cutting across diagnostic boundaries (9, 10), canonical correlation analysis (CCA) (see the Supplementary Methods section in the online supplement ), which connects two variable sets by identifying linear combinations of variables that maximally correlate (29), was modeled to determine canonical sets of brain-behavior relationships between diffusion Z-scores and clinical and cognitive measures. CCA identifies multiple orthogonal “modes” of population covariation between diffusion indices and clinical measures (29). The relative “importance” of each original variable in a mode is reflected by the canonical loadings, the linear correlation between the canonical variate and the original variables in this set. We computed two CCAs separately for brain-symptom and brain-cognition relationships. Autistic symptoms were assessed by the Autism Spectrum Quotient and the Social Responsiveness Scale, and ADHD-related symptoms were evaluated with the SNAP-IV (Table 1). Cognition was assessed by Wechsler intelligence scores, Conners’ Continuous Performance Test (30), and the Cambridge Neuropsychological Test Automated Battery (31) (see Tables S3 and S4 in the online supplement ). These symptom/cognitive measures extensively cover cognitive and social systems in the RDoC framework (16).
The study workflow is depicted in Figure 1.
FIGURE 1. Flow diagram for a study of white matter tract deviation and idiosyncrasy in ASD and ADHDa
a Data were analyzed from 676 typically developing control subjects (376 of them male), free of psychiatric disorders, neurological disorders, substance abuse, or organic brain lesions (panel A) and 454 probands (attention deficit hyperactivity disorder [ADHD], N=279; autism spectrum disorder [ASD], N=175) and their unaffected siblings (ADHD, N=121; ASD, N=72) with high-quality diffusion MRI images (panel B). Symptom assessment (panel C) of ADHD-related symptoms was conducted via the Swanson, Nolan, and Pelham Teacher and Parent Rating Scale, version IV, and ASD-related symptoms via the Autism Spectrum Quotient (AQ) and the Social Responsiveness Scale (SRS). Neuropsychological tests include Conners’ Continuous Performance Test for attention performance and the Cambridge Neuropsychological Test Automated Battery for a wide range of executive functions. Wechsler Intelligence Scales were included for cognitive assessment. The diffusion spectrum imaging protocol (panel D) used 102 diffusion-encoding directions with a maximal b value of 4,000 s/mm2. Images with low signal-to-noise ratio or high signal dropout were discarded and rescanned. In panel E, mean apparent propagator MRI reconstruction used Hermite functions as basis functions, whose higher-order terms are orthogonal corrections to the Gaussian approximation. The derived generalized fractional anisotropy (GFA) is particularly advantageous for complex tissue environments and provides more information compared with FA. Diffusion indices were sampled in voxels of 45 main white matter tracts. In panel F, sex-specific normative models were built for each white matter tract from 626 cognitively healthy participants. In panel G, diffusion indices for each white matter tract were transformed into Z-scores. Each white matter tract is represented by four-numbered tuples (GFA Z-score, mean diffusivity Z-score, radial diffusivity Z-score, axial diffusivity Z-score). Personalized white matter tract profile provides information on white matter tract–specific deviations for each subject compared with an age- and sex-matched healthy cohort. In panel H, normative models have eliminated the nonlinear confounding effects of age and sex, providing group comparison by average brain analysis. With an individualized Z-score profile, the interindividual variation could be quantitatively assessed, providing evidence for idiosyncratic white matter alterations. In panel I, the mean deviance and idiosyncratic white matter tract patterns were compared between ADHD and ASD probands and siblings with Spearman’s correlation. In panel J, dimensional analysis beyond category boundaries provides insight into brain-symptom and brain-function relationships, according to the Research Domain Criteria framework.
Results
Participants
MRI data for 58 individuals with ASD and 27 of their siblings, 183 individuals with ADHD and 14 of their siblings, and 96 typically developing control subjects were excluded because of unsatisfactory image quality. After quality control, the final analysis included 456 probands (ADHD, N=279; ASD, N=175), their unaffected siblings (ADHD, N=121; ASD, N=72), and 626 typically developing control subjects for the normative models (Table 1; see also Table S5 in the online supplement ). Individuals with ASD presented intermediate levels in inattention, hyperactivity-impulsivity, and opposition-defiance, between individuals with ADHD and unaffected siblings of probands with ASD or ADHD (p<0.001). Individuals with ADHD formed an intermediate group between individuals with ASD and unaffected siblings of ASD or ADHD probands in terms of autistic symptoms (p<0.001). Unaffected siblings of ASD or ADHD probands had comparable levels of ADHD and ASD symptoms. None of the participants in the ADHD group had a diagnosis of ASD, based on the psychiatrist’s diagnostic interview. In the ASD group, 26 (14.9%) participants also met DSM-IV-TR diagnostic criteria for ADHD, and the other 149 were diagnosed with ASD but without ADHD.
Shared and Distinct Tract Alterations in ADHD and ASD
All reported results were false discovery rate corrected for multiple comparisons. Both disorders exhibited widespread GFA-Z reduction compared with those in the age norm (negative GFA-Z) across association (axons connecting cortical areas within the same hemisphere) and projection tracts (tracts connecting the cortex with subcortical areas), alongside the CC (Figure 2A; see also Figure S7 and Results S1-1 and S1-2, in the online supplement ). Regarding projection fibers, frontostriatal circuits and thalamic radiations were altered in both disorders. Regarding the CC, alterations were more widespread in ASD, whereas only the splenium of the CC was altered in ADHD. Two-sample t tests showed that compared with ADHD, individuals with ASD had significantly lower GFA-Z in the CC interconnecting prefrontal cortices (p<0.001; effect size=−0.36) (see Result S6-1 in the online supplement ).
FIGURE 2. Mean deviance and idiosyncratic white matter tract patterns of ADHD and ASD probands and their unaffected siblingsa
a Panels A and B present group average and variance brain maps, respectively, of generalized fractional anisotropy (GFA) Z-score profiles of probands with attention-deficit hyperactivity disorder (ADHD), probands with autism spectrum disorder (ASD), and their unaffected siblings. Scores that did not pass multiple Benjamini-Hochberg testing (false discovery rate corrected p≥0.05) were masked. The color gradient encodes the effect size of the Z-scores. Panel C shows the correlation between GFA Z-score mean deviation (effect size of one-sample t test) of ADHD and ASD. The significant correlation is demonstrated by Spearman’s correlation coefficient (rho=0.54). Panel D shows a significant correlation (rho=0.55) between GFA Z-score heterogeneity/idiosyncrasy (F-ratio between probands and healthy people) of ADHD and ASD. Panel E shows a significant correlation (rho=0.69) between GFA Z-score mean deviation of ASD and ASD unaffected siblings. Panel F shows no correlation between ASD and ASD siblings in terms of GFA Z-score heterogeneity. Panel G shows a significant correlation (rho=0.51) between GFA Z-score mean deviation of ADHD probands and ADHD siblings. Panel H shows no correlation between ADHD probands and their siblings in terms of GFA Z-score heterogeneity/idiosyncrasy. Panel I shows a significant correlation (rho=0.29) between GFA Z-score mean deviation of ADHD siblings and ASD siblings. Panel J shows no correlation between ADHD siblings and ASD siblings in terms of GFA Z-score heterogeneity/idiosyncrasy.
The age subgroup analysis (see Figure S8 and Result S2 in the online supplement ) revealed that children in both the ADHD and ASD groups showed widespread negative deviations in association tracts. Children with ADHD showed no deviation of the CC, whereas children with ASD showed fewer alterations in projection tracts. Adults with ADHD only showed alteration in the CC-prefrontal cortex (PFC), and adults with ASD showed deviation in the arcuate fasciculus, frontal aslant tract, cingulum body, auditory radiation, and anterior CC. However, child-adult two-sample t tests were insignificant across both disorders (see Result S7 in the online supplement ). Two-way analysis of variance (ANOVA) revealed no tracts with significant diagnosis-by-age group interactions (see Result S9-1 in the online supplement ).
The sex subgroup analysis revealed widespread negative deviations in females, rather than males, with ADHD (see Figure S9 and Results S3-1 and S3-2 in the online supplement ). Specifically, two-sample t tests showed eight tracts with lower GFA-Z in females than males: the CC (splenium, prefrontal, and parietal cortex), the left SLF II and III, and the left and right prefronto-striatal and the right inferior fronto-occipital fasciculus (see Result S8-1 in the online supplement ). In contrast, profound alterations were found in males, rather than females, with ASD (see Figure S9 and Results S3-5 and S3-6 in the online supplement ). However, the sample size of females with ASD was too small (N=17) for statistical inference. Two-way ANOVA revealed no tracts with significant diagnosis-by-sex interactions (see Result S9-2 in the online supplement ).
Idiosyncrasy of Tract-Deviation Profiles in ADHD and ASD
F-tests revealed five scattered tracts with significantly increased GFA-Z variability in individuals with ADHD (Figure 2B; see also Figure S10 and Result S4-1 in the online supplement ). Individuals with ASD exhibited 14 tracts with significantly increased GFA-Z variability, especially in limbic circuits (fornix, cingulum body), PFC-subcortical connections, CC (PFC, sensorimotor, and parietal cortex), and SLF (see Result S4-2 in the online supplement ). In the age subgroup analysis (see Figure S11 in the online supplement ), adults with ADHD, but not children with ADHD, showed significantly increased GFA-Z variability, particularly in the SLF, projection fibers, and CC (sensorimotor, and parietal cortex) (see Results S5-1 and S5-2 in the online supplement ). Notably, children with ADHD showed deviation without idiosyncrasy, and adults with ADHD showed the reverse (see Figures S8 and S11 in the online supplement). As for ASD, both adults and children showed increased variability in multiple white matter tracts (see Figure S11 and Results S5-3 and S5-4 in the online supplement ).
The Comorbidity of ADHD and ASD
As reported above, the ASD group comprised individuals with comorbid ADHD and individuals without ADHD (the pure ASD group). Individuals with or without comorbid ADHD were compared in terms of the deviation pattern and idiosyncrasy pattern (see Figures S14 and S15 in the online supplement ). Because the sample size of the comorbid group was less than 30, the central limit theorem does not apply. The normality of GFA Z-score of “comorbid group” was proven by Kolmogorov-Smirnov normality test. The deviation and idiosyncrasy patterns between “all individuals with ASD” and “individuals with ASD without ADHD” were similar, while “individuals with ASD and ADHD” did not show much significance, but the effect size and F ratio were comparable to those of the other two groups (see Figure S14 and S15 in the online supplement ). High correlations of white matter tract patterns between all individuals with pure ASD and those with comorbid ADHD were shown in terms of deviation pattern (rho=0.54) and idiosyncrasy pattern (rho=0.66) (see Figure S16 in the online supplement ). Two-sample t test showed no significant difference in white matter tract integrity between the two ASD groups (pure or comorbid ADHD).
Unaffected Siblings of ASD or ADHD Probands
No white matter atypical deviation was observed in unaffected siblings of ADHD probands. In contrast, unaffected siblings shared negative GFA deviations with their ASD probands in the SLF II and III, frontal aslant tract, inferior fronto-occipital fasciculus, auditory radiations, and tracts linking prefrontal cortex (frontostriatal circuit, thalamic radiations, CC) (Figure 2A; see also Figure S7 in the online supplement ). Notably, the observed deviations were largely driven by sisters rather than brothers (see Figure S9 and Results S3-7 and S3-8 in the online supplement ). Unaffected siblings of ASD probands presented increased GFA-Z variability in the arcuate fasciculus and the CC parietal and sensorimotor areas (see Figure S10 and Result S4-4 in the online supplement ).
Between-Group Correlations of White Matter Profiles
Figure 2 presents mean deviance and idiosyncratic white matter tract patterns of ADHD and ASD probands and their unaffected siblings. Significant correlations of white matter tract deviation profiles (rho=0.54, Figure 2C) and idiosyncrasy (rho=0.55, Figure 2D) were found between ADHD and ASD. Both ASD (rho=0.69, Figure 2E) and ADHD (rho=0.51, Figure 2G) probands showed significant correlations of deviation profiles, but not idiosyncrasy (Figure 2F, 2H), with their unaffected siblings. Unaffected siblings of ADHD probands showed weak correlations (rho=0.29; p=0.025, Figure 2I) of white matter deviation, but not idiosyncrasy (Figure 2J), with unaffected siblings of ASD probands.
Sensitivity Analysis Based on Subsamples With Higher Image Quality
We conducted a sensitivity analysis to test the stability of the main results by removing the participants in the final samples (Table 1) whose imaging was in the 5% or 10% with the worst quality (lowest SNRs or largest signal dropout counts). The Z-score mean deviation pattern and idiosyncrasy pattern (see Figures S12 and S13 in the online supplement ) were calculated for subsamples with higher image quality and compared with the original values calculated with the full sample. The results were highly correlated with the original ones, with the correlations of deviation and idiosyncrasy patterns over 0.90 and 0.80, respectively. Together with the stringent quality control step (see the Supplementary Methods section in the online supplement ) and the fact that there were no correlations between GFA-Z and motion/SNR (see Figure S6 in the online supplement ), the findings from the sensitivity analysis indicate that the image quality had a negligible bearing on the main results.
White-Matter-Tract-Guided Dimensions of Symptom/Cognition
Each orthogonal CCA mode reflected distinct brain-driven dimensions of symptom/cognition across clinical categories.
Symptom-CCA yielded two dimensions covarying between white matter deviations and psychopathology (first: p<0.001, correlation=0.567 [Figure 3A]; second: p=0.002, correlation=0.536; see Figure S18 in the online supplement ). In the first dimension, deviations of the tracts connecting the prefrontal and temporal cortex, cingulum body, and frontal aslant tract exhibited associations linked with autism symptoms. The second dimension involved the link between tracts connecting the temporal and parietal cortex with the inflexibility of autistic symptoms, especially the unique mannerisms domain of the Social Responsiveness Scale and the attention switching domain of the Autism Spectrum Quotient.
FIGURE 3. The brain-behavior and brain-cognition relationships cutting across diagnostic boundaries in ADHD and ASDa
a The first modes of canonical correlation analysis (CCA) between generalized fractional anisotropy (GFA) Z-scores of neural tracts and (panel A) symptom scores, including the Autism Spectrum Quotient (AQ), the Social Responsiveness Scale (SRS), and the Swanson, Nolan, and Pelham Teacher and Parent Rating Scale, version IV (SNAP-IV), and (panel B) neuropsychological assessment, including the Cambridge Neuropsychological Test Automated Battery (CANTAB) and Conners’ Continuous Performance Test (CCPT). SNAP_INATT, SNAP_HYPER, and SNAP_OPP represent the SNAP-IV subscales of inattention, hyperactivity-impulsivity, and oppositional symptoms, respectively. AQ_SOC, AQ_ATTSW, AQ_ATTDE, AQ_COMM, and AQ_IMAG represent Autism Spectrum Quotient subscales for social skills, attention-switching, attention to detail, communication, and imagination, respectively. SRS_SC, SRS_UM, SRS_SA, and SRS_SE denote the SRS subscales for social communications, unique mannerisms (stereotyped behaviors), social awareness, and social emotion, respectively. For complete white matter tract names, please refer to Table S1 in the online supplement . For a complete list of CCPT and CANTAB items, please refer to Tables S3 and S4, respectively, in the online supplement .
Cognition-CCA yielded five significant dimensions covarying between the white matter GFA-Z and cognitive functions (Figure 3B; see also Figure S19 in the online supplement ). The first dimension (p<0.001; correlation=0.536) involved the link of visual memory and attention alongside processing speed, with deviations mainly in the arcuate fasciculus and fornix. The second dimension (p<0.001; correlation=0.507) comprised the link between cognitive function involving general intelligence, planning, inhibition, and visuospatial (working) memory, and deviations in the cingulum and SLF II and III. The third dimension (p<0.001; correlation=0.479) involved associations of nonverbal intelligence and attention with the frontostriatal circuit-motor and SLF I. The fourth dimension (p=0.002; correlation=0.443) was loaded by working memory, attention, and verbal intelligence, driven by the CC, uncinate fasciculus, stria terminalis, inferior fronto-occipital fasciculus, SLF, arcuate fasciculus, and thalamic radiations. The fifth dimension (p=0.038; correlation=0.430) comprised planning, set shifting, response variability, and verbal IQ, linked with CC-frontoparietal and thalamic radiations.
The link between symptoms-cognition and white matter deviations is summarized in Figure 4. Results based on diffusivity measures, complementary to the preceding GFA findings, are provided in Figures S21–S27 in the online supplement .
FIGURE 4. Potential shared underlying neural mechanisms of ADHD and ASD probands and their unaffected siblingsa
a Potential associations are shown between generalized fractional anisotropy (GFA) Z-scores of neural tracts and (panel A) symptom scores, including the Autism Spectrum Quotient (AQ), the Social Responsiveness Scale (SRS), and the Swanson, Nolan, and Pelham Teacher and Parent Rating Scale, version IV (SNAP-IV), and (panel B) neuropsychological assessment, including the Cambridge Neuropsychological Test Automated Battery (CANTAB) and Conners’ Continuous Performance Test (CCPT). The associations were identified in significant modes of CCA results. Items with high loadings (>0.2) on the significant modes (p<0.05) were selected. The plots show the underlying brain-behavior relationship beyond diagnostic boundaries. SNAP_INATT, SNAP_HYPER, and SNAP_OPP represent the SNAP-IV subscales of inattention, hyperactivity-impulsivity, and oppositional symptoms, respectively. AQ_SOC, AQ_ATTSW, AQ_ATTDE, AQ_COMM, and AQ_IMAG represent AQ subscales for social skills, attention-switching, attention to detail, communication, and imagination, respectively. SRS_SC, SRS_UM, SRS_SA, and SRS_SE denote the SRS subscales for social communications, unique mannerisms (stereotyped behaviors), social awareness, and social emotion, respectively. For complete white matter tract names, please refer to Table S1 in the online supplement . For a complete list of CCPT and CANTAB items, please refer to Tables S3 and S4, respectively, in the online supplement .
Discussion
To our knowledge, this is the first study to apply white matter normative models to derive personalized neural tract profiles, rather than group averages. We found that ASD and ADHD shared similar white matter tract deviations relative to the norm, as well as interindividual variability in the degree of deviation, providing the first evidence of white matter idiosyncrasy in neurodevelopmental disorders. Unaffected siblings of ASD probands, but not those of ADHD probands, shared these deviation patterns to some extent. Dimensionally, we discovered multivariate patterns of white matter deviations that are correlated with multifaceted neurodevelopmental psychopathology and cognition across categorical boundaries.
Unlike previous multigroup studies (9, 10), we determined that both ASD and ADHD presented widespread alterations in the CC and association fibers, but the projection fibers, especially the corticospinal tracts and optic radiations, were less affected. The observed altered tracts are largely consistent with those jointly reported in a systematic review (8). The inconsistency between previous large-scale studies (9, 10) and ours may be partly explained by the lower sensitivity of group averages in the case-control context in detecting disorder-related subtle effects, because of interindividual diversity caused by age and sex confounders (11). This issue could be addressed by normative modeling. The remaining interindividual variability may be reflected by white matter idiosyncrasy in neurodevelopmental disorders. This strength of the normative model may also account for a null result from child-adult comparisons, as the child and adult subgroups appear to deviate to a similar extent relative to the norm, consistent with findings from a recent morphometric study (32). Interestingly, the white matter tracts exhibiting significant neurodevelopmental deviation effects relative to the age and sex norms are largely consistent with the white matter pathways involved in neurotypical sex differences (33, 34). Given the male predominance in neurodevelopmental disorders, our findings indirectly support a notion that sex-differential genetic and hormonal factors may contribute to these neurodevelopmental phenotypes (35).
Despite some distinct patterns based on comparisons with the typically developing control norm, direct between-group comparisons revealed only one tract with more pronounced alterations in ASD than ADHD, namely, the CC-prefrontal cortex. In addition, significant correlations of both mean deviation and idiosyncrasy profiles were found between ADHD and ASD, suggesting that when conceptualizing ASD and ADHD as extreme conditions of white matter tract deviations from the age and sex norms, they share similar patterns, with subtle distinctions.
Our novel findings of white matter tract “idiosyncrasy” suggest interindividual variation in tract alterations, contrary to the notion of the “average ADHD brain” or “average ASD brain.” Specifically, several association tracts connecting the prefrontal and parietal cortex showed statistically increased variability among individuals with ADHD. Unlike in ASD, the literature suggesting brain idiosyncrasy in ADHD is scarce (15). Individuals with ASD had highly varying deviations in the cognitive control part of the frontal and limbic circuitries. Our results complement previous findings of idiosyncratic prefrontal functional activation (13) and connectivity patterns (12) in ASD. Our findings suggest that white matter tract alterations and idiosyncrasy are equally important in understanding both disorders. This scattered and variable pattern of white matter tract deviations from the norm may help explain why heterogeneity is the rule rather than the exception in these neurodevelopmental conditions (2, 3).
With an unaffected sibling design, we not only replicate results from previous small-sample reports (36–38) but also extend the potential endophenotypic white matter candidates to tracts connecting prefrontal regions and tracts involved in sensorimotor integration. This finding supports the notion of indispensable involvement of the prefrontal and sensorimotor circuits in autism and autism symptoms (2, 39). Interestingly, these patterns were largely driven by the sisters of ASD probands. This sex-specific result may reflect female protective effects in a differential liability model of ASD (35). Unaffected sisters could carry more etiological loads, leading to more prominent brain alterations but have phenotypic expression similar to that of their unaffected brothers. Conversely, we did not identify significantly altered tracts in unaffected siblings of ADHD probands based on a categorical model. But together with the finding of a modest correlation in white matter deviation profiles between ADHD-sibling pairs (Figure 2G), this still suggests some degree of familial similarity of this trait, but in a milder form. This inconsistency with previous categorical white matter reports on siblings of ADHD probands may be explained by low-powered studies (40, 41), use of the average-patient approach (11), and potential compensatory brain phenotypes in siblings (42). Together, white matter tract deviation patterns were more alike between ASD probands and their siblings than between ADHD probands and their siblings, largely echoing a previous endophenotypic study investigating intrafamilial relationships of white matter in siblings discordant for ASD (43). This may partially reflect mildly higher estimates of heritability in ASD (2) than ADHD (3). Interestingly, white matter idiosyncrasy is largely not shared between proband-sibling pairs, echoing the notion that brain idiosyncrasy is a “state” marker of neurodevelopmental disorders (12, 13). The findings that unaffected siblings shared some extent of white matter alterations with their probands warrant the development of proactive mental health promotion strategies for these at-risk populations.
ASD and ADHD showed relatively similar patterns in both mean levels and variability of white matter tract deviations relative to the typically developing control norm in terms of the involved tract and high correlations of these individualized measures between both disorders. This somewhat blurred diagnostic boundary is consistent with previous reports of overlapping gray matter morphometric features (44–46), functional networks (47), and white matter tract characteristics (9, 10) between ASD and ADHD. Nonetheless, previous well-powered studies and meta-analysis suggest generally distinct patterns of structural alterations (48–50), resting-state functional brain organization (51), and task-functional MRI activation during cognitive control between ADHD and ASD (49). But no such evidence exists based on white matter diffusion MRI studies. The specific developmental impact on environmental-genetic vulnerabilities (52) may account for this nonuniform picture. Notably, given the white matter fiber connections as the structural architecture of the human cerebral cortex (5), future studies might benefit from mechanistic investigation of structural-functional coupling (53) to advance the understanding of these seemingly inconsistent transdiagnostic traits across MRI modalities. Overall, our data suggest difficulties identifying a single brain hallmark underlying either disorder (15, 16), given the prominence of brain idiosyncrasy. Thus, rather than suggesting disorder-specific tracts, elucidating the white matter tract correlates of neurodevelopmental psychopathology across diagnostic entities could align with the RDoC to add bottom-up brain-driven symptom/cognition dimensions to future nosology (16).
Given the intertwined nature of brain-behavior relationships, it is impossible to find a one-to-one correspondence between tracts and symptom/cognition measures. Instead, we leveraged CCA to enable the disentanglement of correlations and demonstrate orthogonal modes that represent multidimensional relationships with diverse levels of neurodevelopmental psychopathology. These brain-driven dimensions incorporated symptoms across diagnostic categories while remaining congruent with common clinical pictures, that is, ASD and ADHD often coincide but have distinct relationships among core symptom domains. In both modes, the social-communication domain largely covaried inversely with inattention, repetitiveness, and inflexibility (54). However, our data did not find neat convergence (55) between two common tools used to estimate autistic traits, which may be explained by rater differences (56), age ranges (57), inconsistent dimension structures across studies (58), and inclusion of at-risk cohorts here. The first mode showed that deviations of tracts connecting the prefrontal and temporal cortex (CC, uncinate fasciculus), cingulum (body), frontal aslant tract, and perpendicular fasciculus were associated with the social-communication domain (inversely covaried with inattention). The second mode indicated that association tracts and CC connecting temporal and parietal lobes were associated with inflexibility. (For an explanation of what clinical symptoms and cognitive functions may be related to the identified tracts, please refer to the Supplementary Discussion section in the online supplement .) These results not only replicate the univariate correlation between the anterior CC and social impairment (9, 39), especially in the ADHD and ASD samples (9), but also provide neurobiological evidence underpinning neurodevelopmental spectrums encompassing different domains of ASD and ADHD symptoms.
Aligning with the RDoC’s cognitive systems, the CCA derived multiple orthogonal cognitive dimensions. The first dimension mainly involved a spectrum from visual memory, planning, processing speed, and visual memory, linking with the arcuate fasciculus, limbic tracts, and optic radiations. The second cognition dimension comprised generalized intelligence, planning, inhibition, and visuospatial (working) memory associated with the SLF and cingulum. The third and fourth dimensions comprised other aspects of attention as well as verbal or nonverbal intelligence, respectively. The last mode reflected the remaining cognitive functions, including set shifting and response variability. (For more detailed discussion of each mode, please refer to the Supplementary Discussion section in the online supplement .) Each dimension was driven by deviations of unique sets of white matter tracts. Specific items selected by CCA that are linked to white matter deviation patterns are quite consistent with results from the previous univariate analysis on brain-cognition relationships (59–61). Nonetheless, the brain-driven multifaceted cognitive function observed here does not accord with the reported fractionated constructs in neurotypical populations (62). This inconsistency may reflect distinct intercorrelations between individual cognitive tasks and symptoms in neurodevelopmental disorders. Besides, different white matter metrics, representing different histology changes (28), may be variably sensitive in detecting different multidimensional brain-behavior relationships, as evidenced in the complementary findings using diffusivity measures (see Figures S24–S27 in the online supplement ). Nonetheless, our multivariate analysis lends new evidence for brain-defined clinical and cognitive spectrums across neurodevelopmental conditions. This study also partly responds to the long-standing question of whether outwardly similar features are underlain by similar or distinct mechanisms among neurodevelopmental disorders (1).
This study has a number of caveats. First, the moving average was selected to construct normative models based on model-fitting procedures. We acknowledge that no optimal method exists, given dynamic trajectories of white matter development. Thorough methodological investigations are warranted. Second, although our sample comprised a wide spectrum and presentations of neurodevelopmental disorders, individuals with intellectual disabilities were not included. This limits the generalizability of our results. Third, the limited number of females with ASD limits the inference of sex-specific effects. Our male-predominant neurodevelopmental sample may also potentially lead to type II error in the sex-by-group interaction. Fourth, our cross-sectional design did not allow inferences of individual trajectories based on dynamic normative statistics. Further, the age range within our neurodevelopmental sample misses the middle-late adulthood and very early developmental stages. Lastly, the unique strength of this study design also limits the possibilities for instant replication of the findings. No available data sets (e.g., the Philadelphia Neurodevelopmental Cohort [63], POND [10], ENIGMA [46, 48]) have ever provided such a cohort simultaneously with universally deep phenotyping including neuropsychological tests, an unaffected sibling design, typically developing control subjects of the broad age range for normative modeling construction, and single-scanner, high-quality advanced diffusion MRI data in one lab.
In summary, we used normative statistics in a large sample of ASD and ADHD probands and their unaffected siblings to model individual heterogeneity in whole-brain major white matter pathways. When neurodevelopmental disorders are conceptualized as white matter deviations from normative patterns, ASD and ADHD are more alike than different. In addition to being purely deviated from the norm, interindividual variability in white matter tract deviations is also remarkable in ASD and ADHD probands, but not in their siblings, suggesting that white matter idiosyncratic patterns are characteristic of neurodevelopmentally disordered states. Echoing the RDoC, our CCA findings demonstrate how specific individual deviation in white matter alterations may contribute to diverse arrays of psychopathology and cognitive functions along a continuum from subtle to strong neurodevelopmentally phenotypical expressions. The modestly shared white matter tract deviations and dimensional brain-behavior relationships in unaffected siblings may point to potential candidates for endophenotypes in these at-risk populations. Moving forward, the determination of ADHD and ASD subtypes may be achieved by data-driven clustering on multimodal personalized Z-score profiles. This endeavor could facilitate more valid brain-behavior estimations, thereby contributing to precision psychiatry.
Acknowledgments
The authors are grateful to all the participants and their parents for their participation and research assistants for data collection.
Footnotes
The data that support the findings of this study are available from the corresponding authors on request.
Drs. Lin, Shang, and Gau were among the investigators of a clinical trial (OP-2PN012-301) supported by Orient Pharma Co. The other authors report no financial relationships with commercial interests.
Supplementary Material
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Received: 8 July 2020
Revision received: 5 September 2020
Revision received: 9 October 2020
Accepted: 17 November 2020
Published online: 17 March 2021
Published in print: August 01, 2021
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Supported by grants to Dr. Gau from the Ministry of Science and Technology, Taiwan (grants NSC96-3112-B-002-033, NSC97-3112-B-002-009, NSC98-3112-B-002-004, NSC99-2627-B-002-015, NSC100-2627-B-002-014, NSC99-2321-B-002-037, NSC100-2321-B-002-015, NSC101-2627-B-002-002, NSC 101-2314-B-002-136-MY3, and NSC101-2321-B-002-079), the National Health Research Institute, Taiwan (grants NHRI-EX97-9407PC, NHRI-EX98-9407PC, NHRI-EX100-10008PI, NHRI-EX101-10008PI, NHRI-EX102-10008PI, NHRI-EX103-10008PI, NHRI-EX104-10404PI, NHRI-EX105-10404PI, and NHRI-EX106-10404PI), National Taiwan University Hospital (grant NTUH101-S1910), and the Chen-Yung Foundation.
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