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Published in final edited form as: Biol Psychiatry. 2020 Oct 29;89(5):424–432. doi: 10.1016/j.biopsych.2020.10.013

A Longitudinal Study of White Matter Development in Relation to Changes in Autism Severity Across Early Childhood

Derek Sayre Andrews a, Joshua K Lee a, Danielle Jenine Harvey b, Einat Waizbard-Bartov a, Marjorie Solomon a, Sally J Rogers a, Christine Wu Nordahl a,*, David G Amaral a,*
PMCID: PMC7867569  NIHMSID: NIHMS1642197  PMID: 33349451

Abstract

Background:

Cross-sectional diffusion-weighted MRI studies suggest that young autistic children have alterations in white matter structure that differ from older autistic individuals. However, it is unclear if these differences result from atypical neurodevelopment or sampling differences between young and older cohorts. Furthermore, the relationship between altered white matter development and longitudinal changes in autism symptoms is unknown.

Methods:

Using longitudinal diffusion-weighted MRI acquired over 2-3 timepoints between the ages of approximately 2.5-7 years in 125 children with autism and 69 typically developing controls, we directly tested the hypothesis that individuals with autism have atypical white matter development across childhood. Additionally, we sought to determine whether changes in white matter diffusion parameters were associated with longitudinal changes in autism severity.

Results:

Children with autism were found to have slower development of fractional anisotropy (FA) in the cingulum bundle, superior longitudinal fasciculus, internal capsule, and splenium of the corpus callosum. Furthermore, in the sagittal stratum, autistic individuals who increased in severity over time had a slower developmental trajectory of FA compared to individuals who decreased in severity. In the uncinate fasciculus individuals who decreased in severity also had greater increases in FA with age.

Conclusions:

These longitudinal findings indicate that previously reported differences in diffusion-weighted MRI measures between younger and older autism cohorts are attributable to an atypical developmental trajectory of white matter. Differences in white matter development between individuals who increased, remained stable, or decreased in autism severity suggest that these functional differences are associated with fiber development in the autistic brain.

Keywords: imaging, autism, longitudinal, diffusion, white matter, development

Introduction

Autism spectrum disorder (ASD) is an etiologically and phenotypically heterogenous neurodevelopmental condition characterized by core deficits in social communication and restricted, repetitive behaviors (1). It has been theorized that ASD symptoms are associated with atypical neural connectivity (2). Diffusion weighted MRI (DW-MRI) has been a valuable method to study structural properties of white matter in ASD. A growing body of literature suggests that young children and older individuals with ASD show opposite relationships between DW-MRI measures of white matter microstructure when compared to typical development (TD) (3–11). However, longitudinal studies needed to confirm this atypical white matter development in autism are limited (6,8,12,13). Furthermore, the relationships between altered white matter development and longitudinal changes in autism symptoms are unclear.

Of current available methods, DW-MRI represents one of the best ways to investigate microstructural properties of white matter in-vivo. Diffusion tensor-based metrics, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), measure structural variance attributable to several underlying axonal properties including density and diameter, orientation, tortuosity, membrane permeability, and myelin content (14). While white matter tracts studied and implicated in autism vary to some degree from study to study, the directionality of DW-MRI results is generally consistent. Studies of older children, adolescents and adults report decreased measures of FA in autism (15,16), with comparatively few exceptions (17,18). In contrast, a smaller body of literature reports increases in FA in autism during early childhood (3–10). Longitudinal studies are needed to determine if differences between cross-sectional DW-MRI findings in early childhood and older ASD samples are related to altered white matter development or are attributable to methodological artefacts (e.g. cross-sectional participant sampling differences, head motion).

To our knowledge, only four studies have longitudinally evaluated white matter development in young children with ASD. One study of high-risk infant siblings found that infants who were later diagnosed with ASD had both higher FA values at 6 months and slower development of FA from 6-24 months compared to ASD negative infants (6). Another longitudinal study of 12-48 month old toddlers reported a slower developmental course of FA in ASD compared to TD (8). This trajectory was characterized by higher FA values in the youngest autistic children with an apparent inflection around 30-40 months leading to comparative decreases in FA by 3-4 years of age. Two additional DW-MRI studies reported on an accelerated longitudinal sample that covered a large age range (3-41 years) (12,13). One of these studies reported significant differences in the developmental trajectory of FA within the corpus callosum marked by atypically high FA values among young autistic children that decreased with age, intersecting the TD trajectory around 7 years of age before plateauing and resulting in decreases in FA compared to TD across adolescences and adulthood (12). The other study characterized the longitudinal development of the internal capsule and reported early decreases in FA in ASD that later “catch up” to TD cases with age (13). Thus, collectively these studies provide some longitudinal evidence that young children with ASD have both early increases in and slower development of FA measures compared to TD (8,12), but are not fully consistent (13).

Beyond clarifying differences in white matter development between ASD and TD, a critical question to resolve is whether altered white matter development is associated with significant clinical differences across subsets of individuals with ASD. While reports find that a majority of autistic individuals have a stable level of severity over time, significant numbers of individuals are reported to either decrease (improve) or increase (worsen) in autism symptom severity across childhood (19). For example, we recently reported that while 54.4% of children with ASD have a stable level of severity from approximately 2.5-7 years of age, 28.8% decrease and 16.8% increase significantly over this period (20).

In the current study, we sought to characterize the developmental trajectory of white matter structure in ASD compared to TD from approximately 2.5-7 years of age. We utilized longitudinal DW-MRI acquired during natural nocturnal sleep at 2-3 timepoints and linear mixed effects modeling to evaluate longitudinal measures of FA, MD, RD and AD within several white matter tracts previously indicated in ASD. Our primary aim was to test the hypothesis that young children with ASD have altered white matter neurodevelopment characterized by increased FA during early life and a slower developmental trajectory of FA resulting in decreased FA compared to TD at older ages. A secondary aim was to investigate the relationship of white matter development with baseline autism severity and between three defined groups of autism severity change (i.e. increasing, stable, and decreasing severity) across early childhood.

Methods and Materials

Participants

This study included 125 individuals with autism and 69 TD controls (Figure 1, Table 1). Participants were enrolled in either the ongoing UC Davis Medical Investigation of Neurodevelopmental Disorders Institute Autism Phenome Project (APP) or Girls with Autism: Imaging of Neurodevelopment (GAIN) studies. The longitudinal design of these studies includes MRI scanning at three timepoints; enrollment/baseline at 24-42 months of age and annual follow up for two additional time points. All participants were required to be native English speakers, ambulatory, have no contraindications for MRI, no suspected vision or hearing problems or known genetic disorders or neurological conditions. An ASD diagnosis was confirmed by research reliable, trained clinical psychologists using the Autism Diagnostic Observation Schedule-Generic (ADOS-G) (21) or ADOS-2 (22), the Autism Diagnostic Interview-Revised (ADI-R) (23) and DSM-IV-TR criteria (1). ADOS calibrated severity scores were calculated to allow comparison of autism severity across participants tested with different ADOS modules (19). The Mullen Scales of Early Learning (MSEL) (24) were used to assess developmental quotient (DQ) at enrollment. TD children were excluded if they did not fall within two standard deviations on the MSEL. The current study included all individuals in the APP/GAIN cohorts who had successfully completed structural and diffusion weighted MRI scans with sufficient quality at two or three timepoints. Previous DW-MRI studies have utilized subgroups of the currently described sample (10,25,26). All aspects of the study protocol were approved by the University of California Davis Institutional Review Board. For additional details of sample ascertainment, inclusion criteria, and demographics see supplementary methods and Table S1.

Figure 1: Longitudinal Cohort.

Figure 1:

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A) Individual’s timepoints are plotted according to diagnosis and sex. To investigate relationships between white matter development and changes in autism severity B) a subsample of autistic participants who completed ADOS assessment at study time 1 and 3 were defined. This sample consisted of three groups 1) decreased severity (DS), 2) stable severity (SS), and 3) increased severity (IS). ASD=autism spectrum disorder, TD=‘typical development’.

Table 1:

Participant Demographics

Full Sample Males Females
ASD
(n=125)		 TD
(n=69)		 ASD
(n=85)		 TD
(n=38)		 ASD
(n=40)		 TD
(n=31)
DQ 66.03
(22.31)		 107.07
(11.92)		 65.10
(21.32)		 103.69
(12.52)		 68.06
(24.16)		 111.22
(9.83)
ADOS Calibrated Severity 7.26
(1.79)		 - 7.32
(1.75)		 - 7.13
(1.86)		 -
Absolute Movement (mm) 0.46
(0.54)		 0.47
(0.38)		 0.44
(0.42)		 0.46
(0.32)		 0.51
(0.74)		 0.48
(0.45)
Relative Movement (mm) 0.36
(0.10)		 0.37
(0.12)		 0.36
(0.10)		 0.37
(0.10)		 0.36
(0.12)		 0.36
(0.15)
Scans per participant 2.44
(0.50)		 2.41
(0.49)		 2.51
(0.50)		 2.47
(0.51)		 2.30
(0.46)		 2.32
(0.48)
Scans at Time 1 119 60 81 33 38 27
Scans at Time 2 113 60 79 34 34 26
Scans at Time 3 73 46 53 27 20 19
Total number of Scans 305 166 213 94 92 72
Number of participants with three scans 70 41 43 18 12 10
Number of participants with two scans 55 28 42 20 28 21
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Note: Values are given as mean (standard deviation). ASD=Autism Spectrum Disorder, TD=Typically Development, DQ=Mullen Developmental Quotient, ADOS=Autism Diagnostic Observation Schedule. DQ and ADOS scores reflect average at baseline time one assessment.

Image Acquisition, Preprocessing, and Region of Interest Approach

All DW-MRI scans were acquired at the Imaging Research Center, UC Davis, Sacramento using a 3T Siemens Total Imaging Matrix (TIM) Trio MR system (Erlangen, Germany). MRI scanning was performed during natural nocturnal sleep during participant’s usual bedtime hours without sedation (27). Diffusion weighted images were preprocessed with MRtrix3 (www.mrtrix.org) (28) utilizing FSL v5.0.11. A longitudinal registration approach (29) was used to extract diffusion tensor metrics including FA, MD, RD, and AD from twenty white matter regions of interest (ROI) representing ten unique white matter tracts (30) (Table 2). White matter tracts were selected apriori according to previously reported associations in ASD (10,13,15,26,31–36). See supplementary methods for additional detail and summary of head motion protocols.

Table 2:

White Matter Regions of Interest and Diagnosis Effects

FA, Age - by - Diagnosis Effects FA, Time 1 Diagnosis Effects FA, Time 3 Diagnosis Effects
ROI Group White Matter ROI Age Term Coeff. Std. Error DF t p Coeff. Std. Error DF t p Coeff. Std. Error DF t p
1 Middle Cerebellar Peduncle 0.5 −0.1280 0.066 271 −1.92 0.055 −0.0017 0.002 191 −0.65 0.516 0.0032 0.003 191 1.06 0.287
2 Inferior cerebellar peduncle R
Inferior cerebellar peduncle L		 0.5 0.0031 0.001 739 1.70 0.089 0.0024 0.003 191 0.66 0.504 0.0084 0.003 191 2.15 0.032*
3 Superior cerebellar peduncle R
Superior cerebellar peduncle L		 0.5 0.0007 0.001 739 0.36 0.719 0.0030 0.004 191 0.71 0.477 0.0043 0.004 191 0.94 0.345
4 Genu of Corpus Callosum
Body of Corpus Callosum
Splenium of Corpus Callosum		 −0.5 −0.1780 0.080 1207 −2.2 0.027* 0.0012 0.005 191 0.24 0.808 0.0081 0.005 191 1.51 0.13
5 Sagittal stratum R
Sagittal stratum L		 −0.5 −0.0823 0.066 739 −1.25 0.213 0.0072 0.003 191 1.86 0.064 0.0104 0.003 191 2.63 0.009**
6 Cingulum R
Cingulum L		 0.5 0.0042 0.001 739 3.51 0.0004** 0.0072 0.003 191 2.07 0.039* 0.0151 0.003 191 3.97 <0.001**
7 Superior longitudinal fasciculus R
Superior longitudinal fasciculus L		 −0.5 −0.1420 0.049 739 −2.88 0.004** 0.0013 0.003 191 0.43 0.664 0.0068 0.003 191 2.09 0.037*
8 Superior fronto-occipital fasciculus R
Superior fronto-occipital fasciculus L		 0.5 0.0002 0.001 739 0.13 0.893 0.0028 0.004 191 0.65 0.513 0.0032 0.004 191 0.71 0.475
9 Uncinate fasciculus R
Uncinate fasciculus L		 0.5 0.0026 0.001 739 1.80 0.071 0.0086 0.004 191 1.86 0.063 0.0137 0.004 191 2.90 0.004**
10 Internal Capsule R
Internal Capsule L		 −0.5 −0.1390 0.056 739 −2.47 0.0138** 0.0025 0.002 191 0.88 0.375 0.0079 0.002 191 2.78 0.005**
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Note: Region of interests (ROI) were separately modelled according to ten anatomically related groups (ROI Group). R=Right, L=Left. Age term denotes the fractional polynomial term found to provide the best fit for the ROI group. Age-by-diagnosis, and diagnosis effects at the mean age of time 1 and time 3 assessments are given for the linear mixed effects model of fractional anisotropy (FA). DF=degrees of freedom.

*

uncorrected p<0.05

**

false discovery rate corrected p<0.05.

Statistical Analyses

Linear mixed effects modeling was performed using R version 3.6 (R Core Team, 2019). Ten groupings of biologically related ROIs were separately modeled for each DW-MRI feature (Table 2). Each model included diagnosis, sex, pre/post scanner update and ROI as categorical fixed effect factors, age in months, total cerebral volume, mean absolute and relative movement as continuous covariates, and individual as a random effect with age as a random slope. To determine the optimal form of age, a range of polynomials (−3,−2,−1,−0.5,0.5,1,2,3) sufficient to estimate most curvilinear trajectories were fit (37). The factored age term shown to have the lowest log likelihood was then selected to model age for that grouping of ROIs (Table 2).

In addition, all models included an age-by-diagnosis interaction term to test for differences between ASD and TD individuals in the developmental trajectory of DW-MRI measures, a diagnosis-by-ROI term to test for diagnostic differences between grouped ROIs, and an age-by-ROI term to account for potential developmental differences between grouped ROIs. Additionally, main effects of diagnosis were estimated at the mean age of time 1 (37.1 months) and time 3 (63.6 months). Multiple comparisons were corrected using the false discovery rate approach (FDR) (38). Given the primary aim to investigate FA differences, FDR was applied across each term within each DW-MRI feature (n=10). To investigate potential diagnostic sex differences, the significance of adding diagnosis-by-sex-by-age-by-ROI, diagnosis-by-sex-by-age, diagnosis-by-sex-by-ROI, and diagnosis-by-sex interactions to the above model were evaluated in a step-down fashion by maximum likelihood model comparisons using likelihood ratio tests.

Relationship between white matter development and autism severity

Secondary analyses were conducted to investigate associations between white matter development and both baseline calibrated autism severity scores (39) and changes in these scores over time. For the analysis of baseline severity, the fixed factor diagnosis term was substituted with a continuous baseline ADOS severity term. To investigate relationships between white matter development and changes in autism severity over time, a subset of participants with ASD who completed ADOS assessments at both time one and three (n=65♂,30♀) were categorized into one of three groups. Details on how these groups were formulated and associated behavioral differences between them (e.g. IQ) have been described previously (20). In brief, groups were divided according to 1) individuals who decreased in calibrated severity score by 2 or more points (n=17♂,10♀), 2) individuals whose scores changed by one point or less (n=34♂,16♀), and 3) individuals who increased in severity by 2 or more points (n=14♂,4♀) (Figure 1). Significant differences in white matter development between individuals with ASD who decreased, remained stable, or increased in symptom severity were evaluated by substituting a factorial term coding the three ADOS severity change groups for the diagnosis term in the mixed effects model and adding baseline ADOS severity at time of enrollment as a continuous factor.

Results

Demographics

ASD and TD participants did not significantly differ in the number of DW-MRI timepoints acquired, nor in age across all timepoints, at time one, time two, or time three (p>0.05). At time one, individuals with ASD had significantly lower Mullen full scale, verbal, and non-verbal DQ scores compared to TD controls (p<.0001). Males and females with ASD did not significantly differ in baseline full scale, verbal or non-verbal DQ scores (p>0.05) or baseline ADOS calibrated, social affect, or restricted repetitive behavior severity scores (p>0.05). Across all timepoints, individuals with ASD and TD had no significant differences in either mean relative (p=0.33) or absolute (p=0.90) head motion. See supplement for additional details.

White matter regions with significant differences in developmental trajectory in autism

Significant age-by-diagnosis interactions in FA (FDR p<0.05), indicating differences between individuals with ASD and TD controls in FA development, were observed within the cingulum bundle, superior longitudinal fasciculus, and internal capsule. These tracts showed ‘slower’ FA development in ASD relative to TD controls (Table 2, Figure 2). The corpus callosum showed a significant (FDR p<0.05) diagnosis-by-ROI effect indicating developmental differences between the genu, body, and splenium associated with autism. Within the corpus callosum, the splenium (uncorrected p=0.001, FDR p=0.01) and body (uncorrected p=0.03, FDR p=0.21) both differed from the genu and exemplified the observed slower FA development in ASD, while in the genu the developmental trajectories of FA for ASD and TD individuals appeared similar (Figure 3).

Figure 2: Diagnostic Group by Age Interactions in Measures of Fractional Anisotropy.

Figure 2:

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A) The cingulum bundle, B) superior longitudinal fasciculus, and C) internal capsule showed significant (FDR p<0.05) interaction effects indicating differences in the developmental trajectory of fractional anisotropy (FA) between typically development (TD) and individuals with autism spectrum disorder (ASD).

Figure 3: Fractional Anisotropy Development in the Corpus Callosum.

Figure 3:

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Significant diagnosis-by-ROI effects indicated diagnostic differences between the A) genu, B) body, and C) splenium of the corpus callosum. Within the corpus callosum the splenium (uncorrected p=0.001, FDR p=0.01) and body (uncorrected p=0.03, FDR p=0.21) both differed from the genu and exemplified the observed slower development of FA in ASD while the developmental trajectories of FA for ASD and TD individuals in the genu appear similar.

Across ROIs, the middle and inferior cerebellar peduncles, superior longitudinal fasciculus, internal capsule, and splenium of the corpus callosum were found to have an estimated developmental trajectory that predicted higher FA values in young autistic children, that due to slower development intersected the estimated developmental trajectory of TD children. After FDR correction, no significant differences in FA were observed between ASD and TD individuals at the mean age of the time 1 assessment (37.1 months). However, due to the slower development of FA in ASD, at time 3 (63.6 months) significant decreases in FA in ASD emerged in the sagittal stratum, cingulum, uncinate fasciculus, and internal capsule (Table 2).

Additionally, the uncinate fasciculus showed significant (FDR p<0.05) age-by-diagnosis interactions in measures of AD characterized by initial increases and later decreases in ASD compared to TD controls (Figure S1). No significant age-by-diagnosis interactions were observed for measures of MD or RD. While age-by-diagnosis effects did not reach statistical significance in all tracts for all measures, a consistent developmental trend in measures of FA, MD, RD, and AD could be observed between TD and ASD across a majority of white matter tracts (Figure S1, Table S2). Likelihood model comparisons of the initial model and models with terms defining interactions with sex did not show significant (FDR p>0.05) differences.

Associations between white matter development and ADOS severity

Within the sagittal stratum, autistic individuals who increased in calibrated ADOS severity scores by two or more points from timepoint one to three had a significantly different (uncorrected p=0.001, FDR p=0.01) developmental trajectory in FA compared to the decreased severity group and those that remained stable (uncorrected p=0.02, FDR p=0.27), however, the later finding did not survive FDR p<0.05 correction. Individuals who increased in severity showed a developmental trajectory characterized by early increases in FA with slower development resulting in later FA decreases compared to the stable and decreased severity groups. Trend level differences between the severity change groups were also observed within the uncinate fasciculus where individuals who decreased in ADOS severity showed greater gains in FA compared to those who increased in severity (uncorrected p=0.01, FDR p=0.06) or remained stable (uncorrected p=0.02, FDR p=0.20) (Figure 4). Similar developmental trends between ADOS change groups were observed for FA across many of the tracts studied, but no further effects reached statistical significance (Figure S2, Table S3). No significant associations between FA, MD, RD or AD and baseline ADOS severity or baseline ADOS severity-by-age were observed.

Figure 4: Relationship of Fractional Anisotropy Development and Changes in Autism Severity.

Figure 4:

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A) Within the sagittal stratum, autistic individuals who increased in calibrated ADOS severity scores (IS) significantly differed in the developmental trajectory of fractional anisotropy (FA) measures compared to those who decreased in severity (DS) (uncorrected p=0.001, FDR p=0.01). B) Within the uncinate fasciculus, a trend was observed in which the IS group showed less gains in FA and significantly differed from the DS group (uncorrected p=0.01). Furthermore, in the uncinate fasciculus the DS group showed greater gains in FA and significantly differed from the SS group (uncorrected p=0.02).

Discussion

Our aim was to characterize the development of white matter structure in ASD compared to TD across early childhood. We found the developmental trajectory of FA measures from approximately 2.5-7 in ASD to be slower compared to TD, leading to the emergence of lower FA values in ASD with age. Across early childhood the developmental trajectory of FA in ASD was marked by a transition from increased to decreased FA compared to TD in five white matter tracts. Furthermore, we found significant differences in white matter development between individuals who increased, remained stable, or decreased in measures of autism severity across childhood. Within the sagittal stratum, autistic individuals who increased in severity had a developmental trajectory that predicted early FA increases and slower FA development compared to those who decreased in symptom severity. Taken together these findings: 1) support atypical white matter structure as a neurophenotypic trait in ASD, 2) help establish the developmental course of white matter structure across childhood, and 3) highlight a functional relationship between white matter development and symptom course in ASD.

In the current study we found atypical development of FA measures within several large white matter tracts previously indicated in autism. The corpus callosum has been found to have atypical structure and diffusion properties in ASD (33,34) which have been associated with social deficits in the condition (35) and found to predictive of repetitive behaviors and sensory responsiveness in high risk infants (40). The superior longitudinal fasciculus, a large associative fiber tract that provides frontal-parietal connections, has also been indicated in ASD (50) and correlated with executive functioning, attention processing, and language ability (41). The internal capsule, which connects regions of the prefrontal cortex to the thalamus, basal ganglia, cerebellum and spinal cord, has been associated with both motor deficits (13) and core symptoms in ASD (36). Lastly, the cingulum bundle has been widely implicated in ASD and associated domains such as social cognition, emotion processing, and motor control (32). Additionally, we observed that slower FA development led to the emergence of significantly lower FA measures in ASD by the age of five years in tracts including the internal capsule and cingulum bundle. This suggests that previously reported FA decreases in older children, adolescents and adults with ASD develop across early childhood.

While significant increases in FA were not found at the mean age of time one (37 months), within the splenium of the corpus callosum, superior longitudinal fasciculus, internal capsule and middle and inferior cerebellar peduncles, we observed that the developmental trajectory of FA in ASD was marked by early increases that, due to a slower developmental course, intersected the TD trajectory resulting in later comparative decreases in FA. While not all tracts in the current study indicated a developmental transition from increased to decreased FA in ASD compared to TD, the inflection points observed for tracts that did were between 30-50 months of age, in line with the only previous longitudinal DW-MRI report to focus on this period of development (8). Studies of high-risk infant siblings suggest increased FA in ASD may transition to decreases prior to 24 months in some cases (6) meaning that this effect may have occurred in certain tracts prior to the age covered by this study.

While ASD is believed to first biologically manifest prenatally (42,43) it is increasingly understood that ASD symptoms continue to emerge and change across childhood (44). Prolonged manifestation of the autism phenotype, environmental variability, and individual differences in levels of intervention and intervention susceptibility potentially explain clinically relevant changes in ASD severity over time (19,20). Here we found that compared to other autistic children, individuals who increased in autism symptom severity experienced a concurrent slower development of FA measures in white matter. This developmental relationship was observed across most white matter tracts studied, however after FDR correction only reached statistical significance within the sagittal stratum. The sagittal stratum is a fiber complex that incorporates the inferior longitudinal fasciculus, inferior fronto-occipital fasciculus and posterior thalamic radiation, and connects parietal, occipital, and temporal cortical regions to thalamic and other subcortical structures. This fiber complex has been previously indicated in ASD (45) and functionally linked to visually guided behavior such as non-verbal semantic processing (46) and face-based mentalizing (47). Additionally, within the uncinate fasciculus a trend was observed in which individuals who decreased in autism severity had greater FA gains. The uncinate fasciculus is a medial frontotemporal connection also previously implicated in ASD (31) and in aspects of social interaction, e.g. joint attention (48).

Few other studies have directly investigated relationships between longitudinal changes in white matter structure and autism severity change. One recent study reported that higher frontoparietal network structural connectivity during late childhood/adolescence was predictive of lower symptom load in adolescence and early adulthood (49). Despite covering a later age range, this study is comparable to the current findings in that a relationship between symptom change and white matter structure was observed regardless of baseline severity measures. However, others have reported a positive association between FA and ADOS social communication scores from 12-28 months but a negative association at 37-48 months (8). Collectively these findings suggest a functional relationship between white matter development and core autism symptoms whereby the current results indicate that slower FA development may represent a potential marker of a worsening trajectory of autism symptoms. No significant differences in intervention hours or intensity were observed within the APP/GAIN cohort between the increased, stable, and decreased severity groups (20). However, further study is needed to determine if the underlying processes resulting in atypical white matter structure are a causal factor in ASD symptom severity increases or if altered white matter development in the condition results from other disrupted processes, such as atypical local/distal connectivity efficiency (50), or shared environmental factors.

The maturation of white matter is a dynamic process that involves multiple neurobiological mechanisms including axonal proliferation, pruning, and myelination (51) that may impact DW-MRI measures (14). One potential explanatory factor for previously reported early life increases in FA in ASD is excessive axonal projections and density. Several lines of research indicate accelerated cortical growth in ASD (50,52,53) which would be predicted to result in increased axonal projections (54). Recently, a large study of TD young adults reported FA in the corpus callosum to be more strongly associated with proxy measures of axonal density compared to myelination, although heterogeneity in the spatial distribution of these associations across callosal regions was noted (55). Histological studies of white matter in ASD needed to confirm underlying structural deficits in ASD are limited but do report increased numbers of thin axonal fibers and reduced myelination (56,57). While a majority of myelination occurs prior to the age of five, this process follows a protracted developmental course that extends into adulthood (51). Thus, in ASD one could hypothesize that processes contributing to axonal overgrowth early in life also result in signal delays and metabolic inefficiencies in connecting disparate brain regions (50). These atypical processes could result in less integrated and distributed neural networks, which have been reported in high risk infants with ASD (58), leading to slower development and/or loss of myelin over time.

To our knowledge, this study represents the largest longitudinal sample of young children with ASD to participate in a DW-MRI study. Inclusion of individuals with more significant autism related impairments and a relatively large number of autistic females makes the sample more representative of the broader autism population than most imaging studies. However, the number of female participants limited the ability to investigate sex differences between autism symptom change groups. With larger samples this would be an important future direction for additional study. It should also be noted that we report longitudinal findings over a highly dynamic period of early childhood development. However, white matter development is widely accepted to continue throughout adolescents and into early adulthood. Currently observed group differences in white matter development were largely found within large white matter tracts that are believed to undergo most of their development early in life (51). Further longitudinal studies will be needed to identify differences that may emerge at later ages (e.g. in frontal tracts, peripheral white matter). Such studies would be beneficial for understanding the full developmental course of white matter in ASD and its potential relationships with adolescent and adult outcomes.

In the current study we employed an atlas-based approach which limits the definition of certain ROIs to areas of the associated white matter tract that can be discretely dissected. For example, the uncinate fasciculus ROI includes only the fibers linking the frontal and temporal lobes and not the tract’s further projections into frontal and temporal regions where it merges and becomes indistinguishable (using tensor modeling) from other tracts. Given the limitations of tensor based tractography, this approach increases confidence that measures are being taken from the tract of interest and not a conglomerate of interdigitated tracts. However, future studies should consider advanced acquisition sequences such as High Angular Resolution Diffusion Imaging (HARDI) (59) and techniques (60) that more accurately model diffusion within complex fiber orientations. Such studies should also consider utilizing multimodal imaging sequences to concurrently investigate different aspects of white matter structure in order to better address the non-specific relationship of DW-MRI measures with underlying biology (51,55). These future studies would also benefit by investigating multimodal interrelationships between white and grey matter development to provide a more holistic view of brain development in ASD. We should also note that FDR was selected apriori to control for multiple comparisons over a more conservative Bonferroni approach as this study sought to concurrently investigate development across several white matter tracts. However, many of the currently reported findings would have met a similarly applied Bonferroni correction (p<0.005, for 10 comparisons).

We report the largest longitudinal DW-MRI study to date of white matter development in autistic participants across early childhood. Findings of a slower developmental trajectory of FA measures in ASD resulting in emerging decreases in FA with age, suggest that previously reported differences between cross-sectional studies of younger and older autism cohorts are attributable to alterations in dynamic biological processes of white matter development and are not due to sampling differences. Furthermore, significant differences in white matter development between individuals who increased, remained stable, or decreased in autism severity indicate that the structural integrity of connections within the brain are intrinsically related to phenotypic outcomes in autism.

Supplementary Material

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Software; Algorithm Advanced Normalization Tools https://stnava.github.io/ANTs/ https://stnava.github.io/ANTs/
Software; Algorithm R https://www.r-project.org/ https://www.r-project.org/
Software; Algorithm R: nlme package DouglasBatesbates@stat.wisc.edu https://cran.r-project.org/web/packages/nlme/index.html
Other (Hardware) 3T Siemens Total Imaging Matrix Trio MR System General Electric, Erlangen Germany https://www.siemens-healthineers.com/
Other (Assessment) Mullen Scales of Early Learning WPS Publishing https://www.wpspublish.com/mullen-scales-of-early-learning
Other (Assessment) Autism Diagnostic Observation Schedule WPS Publishing https://www.wpspublish.com/ados-2-autism-diagnostic-observation-schedule-second-edition
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Acknowledgements:

The authors would like to the families and children who participated in the GAIN and APP studies and all members of the research study staff. Funding for this study was provided by the National Institute of Mental Health (R01MH104438 [CWN], R01MH103284 [MS], R01MH103371 [DGA]) and the UC Davis MIND Institute. This project was also supported by the MIND Institute Intellectual and Developmental Disabilities Research Center (U54HD079125). DSA was supported by the MIND Institute Autism Research Training Program (T32MH073124).

Footnotes

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Disclosures: DGA is on the scientific advisory board for Stemina Biomarker Discovery and Axial Biotherapeutics. All other authors report no biomedical financial interests or potential conflicts of interest.

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