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Scientific Reports volume 12, Article number: 20151 (2022)
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Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder, with only a small proportion of people obtaining optimal outcomes. We do not know if children with ASD exhibit abnormalities in the white matter (WM) microstructure or if this pattern would predict ASD prognosis in a longitudinal study. 182 children with ASD were recruited for MRI and clinical assessment; 111 completed a four-year follow-up visit (30 with optimal outcomes, ASD−; 81 with persistent diagnosis, ASD+). Additionally, 72 typically developing controls (TDC) were recruited. The microstructural integrity of WM fiber tracts was revealed using tract-based spatial statistics (TBSS) and probabilistic tractography analyses. We examined the neuroimaging abnormality associated with ASD and its relationship to ASD with optimal outcome. The ASD+ and TDC groups were propensity score matched to the ASD− group in terms of age, gender, and IQ. TBSS indicated that children with ASD exhibited abnormalities in the superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), and extending to the anterior thalamic radiation (ATR) and cingulum; whereas the ASD+ group showed more severe abnormalities than the ASD- group. Probabilistic tractography analysis revealed that ASD+ group exhibited lower Fractional Anisotropy (FA) of the left superior thalamic radiation (STR L) than ASD− group, and that FA value of the STR L was a significant predictor of optimal outcome (EX(B), 6.25; 95% CI 2.50—15.63; p < 0.001). Children with ASD showed significant variations in SLF_L and STR_L, and STR_L was a predictor of ‘ASD with optimal outcome’. Our findings may aid in comprehension of the mechanisms of ‘ASD with optimal outcome’.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder, characterized by deficits in social interaction, social communication, and restricted, repetitive patterns of behavior and interests1. The prevalence of ASD is estimated to be 1–2%2. With considerable long-term impairments across multiple domains3, the recommended treatment for ASD is behavioral training, while medication treatment utilized mostly to control comorbid disorders4. Currently, there is no specific treatment for the core symptoms of ASD.
The etiologies of ASD have been extensively studied2,5. ASD is regarded as a spectrum term that encompasses a plethora of possibly complex etiopathogenic processes6. Brain structure and function abnormalities have been implicated in the neurology of ASD7,8. The diffusion MRI (dMRI) study using tract-based spatial statistics (TBSS) analyses revealed that ASD is associated with several long fiber tracts9,10. The fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were all commonly used dMRI metrics. The most usually used metric was FA. Typically, a breakdown in WM integrity resulted with a decreased FA11. TBSS is a voxel-based methodology that provides a quick and automated means of assessing diffusion data12; nevertheless, determining a differentiation between adjacent, differently oriented fiber bundles with similar FA values is challenging, which results in an undesired loss of tract anatomical topology. Probabilistic tractography is beneficial for dealing with complex fiber architectures, such as crossing fibers and signal noise13. Previous tractography studies on ASD have shown abnormal WM integrity in the cingulum14, thalamus pathway15, and in the association fibers, including: superior longitudinal fasciculus(SLF)16,17, inferior longitudinal fasciculus(ILF)18,19, inferior frontal-occipital fasciculus (IFOF)20, uncinate fasciculus (UF)20. The breakdown in WM integrity typically generally resulted in a lower FA21, which is sensitive to fiber coherence, myelination, and fiber density. In addition, other dMRI metrics also measure axonal integrity and are sensitive to alteration of brain tissues and axonal damage22. Several meta-analyses reviewed the dMRI studies of ASD. One study found that ASD suffers from reduced FA in the right occipito-temporal, inferior occipital and lingual gyrus, fusiform and inferior temporal gyrus23; Another study indicated that the uncinate fasciculus(part of SLF) and frontal and temporal thalamic projections were responsible for complex socio-emotional functioning of ASD24; and the splenium of corpus callosum and the cerebral peduncle were found to be associated with ASD25. Above all, brain WM microstructure abnormality (impaired WM fiber integrity) has been confirmed in ASD; yet, no consensus on the exact tract has been obtained. Diverse ASD subgroups may exhibit distinct brain structural abnormalities, as well as distinct cognitive profiles, clinical profiles, and prognoses.
Individuals with a history of ASD who, over time, no longer meet the diagnostic criteria for ASD are referred to as ‘ASD with optimal outcome’26,27. The ‘optimal outcome’ was defined as the absence of significant autism symptoms and a normal range of social functioning; nevertheless, additional issues, such as impaired cognition or susceptibility to other mental diseases, may still persist. The developmental trajectory of children with ASD between the ages of two and fourteen years might be divided into six distinct categories, according to a large longitudinal study (n = 6975)28. The reported stability rates for ASD vary greatly, when socially adapted functioning is chosen as a criterion29,30,31,32. 3–25% of the children with ASD lose their ASD diagnosis later in life, according to Helt et al.33. The diagnosis of ASD remains persistent, and only a tiny proportion of children with a history of ASD have obtained the optimal outcomes, according to various longitudinal studies26. At the present, understanding about the mechanisms underlying the ‘optimal outcome’ is inadequate.
The neuroimaging disparity between ASD with current diagnosis and ASD with optimal outcome has revived renewed interest in retrospective cohort studies. According to one concept, ‘neural normalization’ can account for behavior normalization in ASD; in this scenario, the ASD with optimal outcome should resemble controls34,35. Alternatively, the concept of ‘neural compensation’ has been proposed, in which behavioral normalization is accomplished through the recruitment of alternative brain pathways rather than through the normalization of processing pathways. In this paradigm, brain activation in ASD with optimal outcome would be considerably different from control and current diagnosis ASD36. A reasonable assumption is the ‘residual ASD’ pattern, which shows that the ASD associated with optimal outcome continues to reflect the individual’s ASD background. As a result, brain activation of ‘ASD with optimal outcome’ is comparable to that observed in ASD with current diagnosis36,37. Furthermore, we wondered whether ASD with optimal outcome exhibited distinct neuroimaging pathways from the outset in comparison to ASD with a persistent diagnosis? Previous longitudinal study found that processing speed abnormality of ASD in adulthood might partially attributable to WM microstructural integrity38. The anatomical asymmetry of the fusiform gyrus may have significant consequences for the severity of symptoms in ASD and demonstrates a developmental trajectory distinct from that of controls39. However, no cohort study has explored WM microstructure abnormality associated with ASD with optimal outcome.
The study aimed to explore the neuroimaging differences between ASD with optimal outcome, ASD with persistent diagnosis, and healthy controls. We wondered whether individuals who achieved optimal outcome exhibited a distinct WM microstructure, and whether this pattern might be used to predict clinical behavioral normalization. Our hypothesis were that (i) children with ASD had altered WM fiber integrity in the thalamus pathway, the association fibers, and other brain areas, as evidenced by previous studies; (ii) It was expected that the patients with optimal outcome would exhibit distinct brain WM abnormalities when compared to ASD with persistent diagnosis, and that the abnormalities would be associated with prognoses.
The study was conducted from September 2014 to September 2021, with participants drawn from West China Hospital of Sichuan University and local community. The neuroimaging mechanism of ASD with optimal outcomes is analyzed as part of another study in this collection: The National Natural Science Foundation of China (NO. 81371495). The primary goal of the original research was to explore the biomarkers of ASD, and the current study is a follow-up study involving the same cohort. A total of 254 participants were recruited online and offline (ASD, N = 182; TDC, N = 72). Off-line method was conducted in the West China Hospital outpatient. Our online recruitment advertises were on the main social platforms including WeChat as well as the official accounts of the schools and hospitals involved. All patients were medication-naive and met the criteria of DSM-5. ASD subjects were excluded if they had a history of head injury, other neurodevelopmental or psychiatric disorders, and metabolic or genetic disorders such as Fragile-X Syndrome. TDC had never used psychotropic medications and had no history of neurodevelopmental, cognitive, learning, or neuropsychiatric problems. To confirm that they were typically developing, all of them had extensive testing, including the IQ and psychiatric testing. After a thorough explanation of the study, parents of participants provided the written informed consent for study participation, which was approved by the Medical Ethical Committee of West China Hospital of Sichuan University.
All participants completed the diagnosis at time 1 (September 2014–September 2017): First, the child should be diagnosed by one professional child psychiatrist based on the DSM-5; second, a parent interview and a child assessment should be conducted using the Autism Diagnostic Interview-Revised (ADI-R40) and the Autism Diagnostic Observation Schedule (ADOS41) to confirm ASD diagnosis, as well as the Chinese version of the Schedule for Affective Disorders and Schizophrenia-Parent Long Version (K-SADS-PL) interview to exclude comorbidities; third, after receiving their diagnosis, participants were required to take an IQ test and have an MRI scan. Around four years after registration, participants were requested to take part in a follow-up assessment (time 2, September 2018 to September 2021). In Fig. 1, the workflow is shown. 111 participants completed the DSM-5 and ADOS assessments twice (ASD with optimal outcome, ASD−, N = 30; ASD with persistent diagnosis, ASD+, N = 81). 46 participants did not confirm to the inclusion/exclusion criteria at baseline; 25 children refuse to participate in the follow-up visit. In the present study ‘optimal outcome’ means that individuals who had ‘recovered’ from ASD. We defined optimal outcome as: no longer met diagnostic criteria for ASD after our diagnosis assessment. Data were analyzed in 2021 October.
Study flowchart. ASD− ASD with optimal outcome, ASD+ ASD with persistent symptoms, TDC typically developing controls.
All brain imaging was performed on a 3.0-T imaging system (Philips, Achieva, TX, Best, The Netherlands) at the Tibet Chengban Branch of Sichuan University West China Hospital. Three-dimensional T1 images were acquired using a spoiled gradient recalled echo planar imaging sequence (repetition time, 8.2 ms; echo time, 3.8 ms; flip angle, 7°; slice thickness, 1 mm; field of view, 256 mm × 256 mm; matrix size, 256 × 256; voxel size, 1 × 1 × 1 mm3). dMRI scans were acquired using a two-dimensional diffusion-weighted echo planar imaging sequence [repetition time, 10,295 ms; echo time, 91 ms; field of view, 128 × 128 mm; voxel size, 2 × 2 × 2 mm3; matrix size, 128 × 128; slice thickness = 2.0 mm, gap = 0 mm, slice number = 75, and slice order = interleaved; 32 gradient direction diffusion-weighted images with b = 1000 s/mm2 and one minimally diffusion-weighted scan (the B0 image)].
The dMRI data were processed using FSL42. The data were corrected for motion/eddy currents and non-brain tissues were removed. Scans were excluded if they had rotations or translations larger than 3 mm. Groups were compared on FA, MD, RD, and AD using two approaches provided in FSL: Tract-Based Spatial Statistics (TBSS)12 and Global Probabilistic Tractography13.
Voxelwise analyses were subsequently conducted using TBSS, with mean FA skeleton created by aligning each participant’s FA image to the Montreal Neurological Institute (MNI) 152 space and FA threshed at 0.2 to suppress areas of low fractional anisotropy. Secondary dMRI metrics were analyzed using the mean FA skeleton. Multiple comparison correction was performed using TFCE with identical settings (5000 permutations) in the Randomize program as for the TBSS and further analyses. The p-value threshold for statistical maps was set to 0.05, with TFCE FWE fully corrected.
Separately, after quality control, a probabilistic tractography was fitted for each voxel using BEDPOSTX (the probabilistic distribution of diffusion parameters at each voxel was built up by Bayesian estimation of diffusion parameters). First, based on the protocols described by43,44, we worked to define standard space “seed”, “target”, “stop” and “exclusion” ROIs, and mapped in individual native space based on anatomical landmarks. Then, tractography was performed for each subject and for each structure in native space with final set consisting of 27 tracts45. Low probability voxels were rejected at a threshold of 0.5%, similar to previous study. Only those reconstructed single trajectories present in 70% of the participants in each group were left for subsequent analysis. For each individual separately, we created an average fiber bundle at group level for the 27 major fiber tracts. As a final step, tract density images in subject native space were warped to common space, and then dMRI metrics were exported and averaged across the whole fiber tract for further analysis.
We applied propensity score matching (PSM) to balance the IQ, age, and gender characteristics of the groups in order to avoid bias. We utilized the most common PSM approach of Nearest Neighbor Matching with a 1:1 matching ratio. PSM was performed using the SPSS plug-in psmatching 3.04. After PSM, the categorical and dimensional analysis were performed. A one-way ANOVA was used for each statistic, with post-hoc pairwise group comparisons. The Boneferroni multiple comparison test was employed for multiple comparisons. Pearson correlation was used to explore the association with dMRI metrics and autism symptoms. Multivariate logistic regression analysis was conducted by fitting a logistic regression model to identify neuroimaging risk factors for ASD with ‘optimal outcome’. A statistically significant difference was defined as p < 0.05 (two-tailed).
This study was approved by the ethics committee of Medical Ethical Committee of West China Hospital of Sichuan University, and written informed consent was obtained from all participants in accordance with the Declaration of Helsinki.
Using PSM, ASD+ and TDC groups were matched on age, gender, and IQ to ASD− group. Each group (ASD−, ASD+, and TDC) had a total of 30 participants. (See eFigure 1). As demonstrated in the Table 1, there were no significant differences among groups on age, gender, and IQ (p > 0.05). At baseline, there was a difference in ADIR scores between ASD− and ASD+ (p = 0.003 before matching; p = 0.001 after matching), but not in ADOS scores (p > 0.05). At time 2, the ADOS scores of ASD+ and ASD− individuals were significantly different (p < 0.001) before and after matching. Prior to PSM, the rate of comorbidity was higher in the ASD+ group (16.67%) than in the ASD− group (41.98%). While the results showed that there was no significant difference on the comorbidity rate between ASD+ and ASD− groups after PSM.
We performed TBSS analysis to compare the differences among groups before and after PSMatching. Firstly, prior to PSMatching, the TBSS results showed decreased FA in extensive brain regions (bilateral anterior thalamic radiation (ATR), corticospinal tract (CST), cingulum, inferior fronto-occipital fasciculus, superior longitudinal fasciculus (SLF), and forceps major) in ASD+ versus TDC group. The results also indicated that the ASD− group exhibited reduced FA in the bilateral ATR, CST, cingulum, forceps major; and the right hemisphere of inferior fronto-occipital fasciculus, inferior longitudinal fasciculus (ILF), and SLF, when compared to TDC. However, we did not identify any significant differences in brain area between the ASD− and ASD+ groups, which may be due to confounding variables (unmatched IQ, symptom severity, and comorbidity). (Depicted in eFigure 2) After PSMatching, ANOVA results showed the differential brain areas were SLF, ILF, and extending to ATR, CST, and cingulum (Fig. 2). Children with ASD exhibited reduced FA, and no significant difference between ASD− and ASD+ was found. (Fig. 2a) For MD, results additionally showed increased MD in the SLF, and the left ILF extending to left ATR, CST, and cingulum in ASD+ compared to ASD−. (Fig. 2b) The AD metrics showed no group differences. ASD+ showed increase RD than TDC in the bilateral SLF, ILF, and extending to ATR, CST, and cingulum. ASD+ also showed increase RD than ASD− in the SLF, and the left ILF extending to the left ATR, CST, and cingulum. ASD− did not differ from TDC. (Fig. 2c) The subjects who achieved optimal outcome four years later had less severe brain WM microstructure aberration than those who had persistent diagnosis.
TBSS results among ASD children with optimal outcomes, ASD with persistent symptoms, and controls. TBSS results within the JHU White-Matter Tractography Atlas are reported and labeled accordingly (TFCE FWE corrected, p < 0.05). ANOVA results were symbolled in red. (a) FA metrics: ASD−/ASD+  < TDC: Superior longitudinal fasciculus, Inferior longitudinal fasciculus, Forceps major; Cingulum; Corticospinal tract; Inferior fronto-occipital fasciculus; Anterior thalamic radiation; (b) MD metrics: ASD+  > TDC: Anterior thalamic radiation; Corticospinal tract L; Cingulum; Inferior fronto-occipital fasciculus L; Superior longitudinal fasciculus; ASD− > TDC: Anterior thalamic radiation R; Corticospinal tract R; Cingulum; Forceps major; Inferior fronto-occipital fasciculus R; Inferior longitudinal fasciculus R; Superior longitudinal fasciculus R; ASD+  > ASD−: Anterior thalamic radiation; Corticospinal tract L; Cingulum L; Forceps major; Inferior fronto-occipital fasciculus L; Inferior longitudinal fasciculus L; Superior longitudinal fasciculus; Uncinate fasciculus L; (c) ASD+  > TDC: RD metrics: Inferior longitudinal fasciculus R; Superior longitudinal fasciculus R; ASD+  > ASD−/TDC: Anterior thalamic radiation; Corticospinal tract L; Cingulum L; Inferior fronto-occipital fasciculus L; Inferior longitudinal fasciculus L; Superior longitudinal fasciculus; Uncinate fasciculus L.
Correlation and multiple logistic analysis were used to explore the connections between neuroimaging and symptoms in ASD. The ADI-R develop subscale and the FA had a significant negative association (r = − 0.14, p = 0.03) whereas the MD/RD correlated positively (p < 0.05, see Fig. 3a). Figure 3b shows the findings of multiple logistic analysis. The metrics left in the regression model were depicted. Bilateral SLF, ILF, and extending to ATR, CST, and cingulum FA decreases (ASD+/ASD− < TDC) were associated with ASD with persistent diagnosis (EX(B), 3.76; 95% CI 1.17–8.00; p = 0.007). The right lateral of SLF, ILF, inferior fronto-occipital fasciculus (IFOF), and extending to right ATR, CST, and Cingulum MD decreases (TDC > ASD−) were protective prediction of ASD with persistent diagnosis (EX(B), − 2.82; 95% CI − 6.68 to − 0.17; p = 0.004). The right lateral of SLF and ILF (TDC > ASD+) RD decreases were protective prediction of ASD with persistent symptoms (EX(B), − 10.85; 95% CI − 26.05 to − 1.58; p = 0.002. The association fibers (SLF, ILF, IFOF) and cingulum, particularly in the left lateral, were found to be related with autistic symptoms.
Brain-symptoms data analyses. *p < 0.05; **p < 0.01; ***p < 0.001. (a) Statistically significant correlations between dMRI metrics and ADI-R develop subscale. Dotted lines represent the r value for those correlations. (b) Forest plot of the multivariable regression analysis results.
After matching participants, individual probabilistic tractography of the 27 major WM tracts was successfully obtained. ANOVA with post-hoc Boneferroni correction was used to compare the ASD−, ASD+, and TDC groups. Three damaged fiber bundles were displayed in Fig. 4 in ASD: Superior thalamic radiation _L (STR_L), SLF_L, and Forceps major (Fma).
A 3D reconstruction of the three fiber bundles. The red represented the Superior thalamic radiation _L; The blue fiber represented the Superior longitudinal fasciculus_ L; The green represented the Forceps major.
For further analysis, dMRI metrics were computed for each of the reconstructed tracts by averaging the voxel values along each fiber bundle. ANOVA revealed that the three groups differed in terms of STR L, SLF L, and Fma (see Fig. 5). As to STR_L, ASD− and ASD+ exhibited lower FA than TDC (p < 0.001), but ASD with different prognosis had no significant difference (p > 0.05, Fig. 5a). As AD, the STR L abnormality was shown in ASD+ (p = 0.002), but not in ASD− (p > 0.05, Fig. 5b). ASD+ exhibited a lower RD than ASD− (p < 0.001) and TDC (p = 0.049) (Fig. 5c). It suggested ASD+ was more severe than ASD-. STR_L had no significant correlation with autism symptoms. Figure 5d and e showed that ASD− and ASD+ exhibited reduced FA and RD in SLF_L (p < 0.05). Correlation analysis showed FA was negatively correlated with ADIR develop/ nonverbal communication subscale, and MD, RD were positively correlated with ADIR develop/ nonverbal communication (Fig. 5f–g). The results revealed an association between SLF L fiber bundle impairment and autism symptoms. Regarding Fma tract; ASD+ and ASD− had greater FA than TDC (p < 0.001, Fig. 5h). ASD− exhibited decreased MD than ASD+ and TDC (p < 0.001, Fig. 5i). Further correlation analysis revealed that FA was negatively correlated with ADIR-nonverbal communication subscale, whereas MD was positively correlated (Fig. 5j). ASD− showed the most intact integrity in Fma among the three groups.
Varied fiber tract of ASD via probabilistic tractography. *p < 0.05; **p < 0.01; ***p < 0.001. ASD− ASD with optimal outcome, ASD+ ASD with persistent symptoms, TDC typically developing controls, str_l left superior thalamic radiation, slf_l left superior longitudinal fasciculus, fma forceps major.
Table 2 shows fiber tracts associated with ASD prognoses in four years. The total collinearity amongst dMRI metrics was significant. We separately examine the binary logistic analysis of ASD prognoses with fiber bundles using FA, MD, AD, RD. The STR_L fiber tract was observed to be associated with optimal outcome (FA: EX(B), 6.25; 95% CI 2.50–15.63; p < 0.001; inversely, for MD, AD, RD, EX(B) was 0.10, 0.02,0.17 separately). The STR_L tract integrity was a predictive factor of optimal outcome. Fma MD increasement was a predictive factor of optimal outcome (MD: EX(B), 28.46; 95% CI 4.33–187.10; p < 0.001). It indicated that Fma fiber integrity impairment was a predictive factor of ASD with optimal outcome.
This was the first study to compare ASD with persistent diagnosis versus ASD with optimal outcome in terms of brain white matter abnormalities. The ASD cohort had MRI scans and symptom evaluation during the initial visit, and their symptoms were reassessed four years later. In addition to TBSS, we applied probabilistic tractography to reduce the impact of crossing fibers and construct more precise white matter tracts. The most obvious result of the TBSS and probabilistic tractography analyses was the confirmation of the SLF L white matter abnormalities in ASD, which is consistent with prior studies16,17. Moreover, we identified a correlation between the SLF L anomaly and the severity of ASD in children with ASD. However, no significant difference between ASD− and ASD+ was identified. Statistical analysis revealed no evidence of a connection between SLF and ASD prognosis following a 4-year follow-up period. Another interesting finding is that, the projection fibers such as ATR, CST, also showed abnormalities in ASD by TBSS. Further analysis with probabilistic tractography identified STR L abnormalities between ASD with different prognosis, indicating that ASD with persistent diagnosis showed more severe WM abnormalities than ASD with optimal outcome. Logistic analysis revealed that the integrity of the STR L tract was a predictor factor of optimal outcome.
Children with ASD exhibit lower FA and RD on SLF L, and the FA is adversely connected with nonverbal communication problems associated with ASD. The SLF connects mirror neurons and is thought to be necessary for gestural communication and language development. It was recently included in a five-level anatomical model of social communication46. SLF can be divided into three parts, SLF I-II-III, SLF I/II is involved in the perception of visual space, whereas SLF III is involved in language processing47; both of which are cognitive processes associated with ASD. Previously published dMRI studies indicated that ASD patients have poor SLF fiber integrity17,48. Our cohort study is the first to utilize TBSS and probabilistic tractography to explore the brain WM architecture in ASD with optimal outcome. Regardless of the outcome of the children, ASD displayed a similar SLF abnormality. Our data imply that while abnormalities in SLF L may reflect the degree of communicative difficulties in ASD, they are not connected with four-year prognosis. This may support to the ‘neural normalization’ hypothesis: the neuroimaging of ASD with optimal outcome is similar to normal children34,35. ASD behavioral normalization is a process of neuron remodeling, as opposed to exhibiting different neuroimaging pathways from the outset. The current study emphasized the functional significance of this fiber, albeit further MRI rescans were required to assess the developing trajectory of SLF in ASD.
Concerning STR L, the FA value of ASD+ group was lower than ASD-, indicating a severe STR L fiber bundle abnormality in children with persistent ASD symptoms. The thalamus is engaged in a variety of sensory and cognitive circuits in healthy individuals. The thalamo-cortical circuit is a critical information processing system49 Indirectly, prior research implicating the thalamus in the pathogenesis of autism15,50 supports our findings. Several thalamocortical fibers abnormalities had been found in ASD: the anterior thalamic radiation, major projection from the thalamus to the frontal cortex49,51,52,53; the superior thalamic radiation, projection fiber from the ventral posterior nuclei of the thalamus to the somatosensory area in the post central gyrus of the parietal cortex15,54; and the posterior thalamic radiation, projection fiber from the posterior part of the thalamus to the occipital cortex15. Our cohort analysis revealed that ASD with optimal outcome is associated with distinct neuroimaging abnormalities in STR L, and STR-L integrity is proven to be a predictor of ASD with optimal outcome after four years. Verification of this result will require a larger sample size and a longer period of follow-up.
The splenium of the corpus callosum connects with the occipital lobes to form the Forceps major (Fma). The Fma plays roles in several ASD related cognitions, such as semantic processing55, visuospatial cognition55, and language comprehension56. The results of the present study demonstrate that the ASD− showed higher FA (and lower MD) than ASD+ and TDC. As the FA values of the traced antegrade fibers were extracted to indicate the integrity of the epicenter white matter. It suggested that the ASD with optimal outcome had the most intact WM integrity among the three groups. The compensation theory may explain this phenomenon36. This finding contradicts prior research indicating that ASD had a reduced FA in Fma57. Similarly, previous tractography studies have also reported inconsistent results indicating that individual with ASD did not exhibit WM abnormality in Fma18,58. The inconsistent results may be due to the heterogeneity of ASD. Moreover, Fma integrity impairment (MD increased) predicted ASD with optimal outcome. The finding was not replicated by other dMRI metrics in this study, thus the conclusion must be interpreted cautiously.
The term ’optimal outcome’ referred to individual with ASD history who no longer fit the diagnostic criteria for ASD26,27. Our cohort’s recovery rate is 27%, which is consistent with prior research (1–32%)33,59. We observed a high recovery rate of ASD, maybe because we included all high-functioning autism. Additionally, our criteria for ‘optimal outcome’ are clinical judgement, further objective measures such as social adaptive scales may be adopted in the future. The concept of ‘optimal outcome’ has led to a controversial topic: can ASD be cured? Longitudinal cohort studies of ASD have consistently found that some patients with ASD no longer meet the diagnostic criteria for ASD in adulthood26,27,60,61. Further analyses26,27,62,63 have identified common characteristics in ASD with ‘optimal outcome’: normal intelligence, impaired social cognition, unsatisfied intimate relationships, poor neurocognitive functioning. Studies have also identified some risk factors associated with ASD prognosis, including: intelligence64,65,66, language ability67, symptom severity68, gender69, etc. The effect of symptom severity on prognosis is well established68. In this study, there was no difference in baseline symptom severity between ASD+ and ASD− groups, allowing us to evaluate the effects of neuroimaging features on ASD prognosis. Additional research is required to determine the predictors of ASD prognosis.
This study contains some limitations. To begin, our sample was limited to individuals with high-functioning ASD to ensure compliance with scanner guidelines. Additionally, we excluded ASD with comorbidities from this investigation, which may limit its generalizability to the broader clinical ASD community. The rigorous inclusion/exclusion criteria were implemented to avoid bias; additional research was required to include a larger sample size with stratified analyses. Second, our original study sample size was moderate, which likely increased our type II error rate. Third, a four-year follow-up is insufficient, as parts of ASD with persistent diagnosis will develop to adapt to social life over time. Larger samples and longer follow-up periods will enable future research on WM microstructure in ASD with optimal outcome. Finally, because we did not rescan the participants at timepoint 2, we cannot assess developing trajectory of neuroimaging anomalies in ASD.
We found that children with ASD showed significant differences in brain WM microstructure in SLF_L and STR_L fibers. STR_L fiber integrity differed between optimal outcome group and persistent diagnosis group, and STR_L was a predictor of behavioral normalization. Our findings may help understand the mechanisms of optimal outcome in ASD and develop therapeutic targets to overcome the new conundrum of heterogeneity impeding ASD pathogenesis research.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5™. Codas 25, 191 (2013).
Google Scholar 
Lord, C., Elsabbagh, M., Baird, G. & Veenstra-Vanderweele, J. Autism spectrum disorder. The Lancet 392, 508–520. https://doi.org/10.1016/s0140-6736(18)31129-2 (2018).
Article  Google Scholar 
Zwaigenbaum, L. et al. Early intervention for children with autism spectrum disorder under 3 years of age: Recommendations for practice and research. Pediatrics 136, S60-81 (2015).
Article  PubMed  Google Scholar 
Rosenberg, R. E. et al. Psychotropic medication use among children with autism spectrum disorders enrolled in a national registry, 2007–2008. J. Autism Dev. Disord. 40, 342–351 (2010).
Article  PubMed  Google Scholar 
Fakhoury, M. Imaging genetics in autism spectrum disorders: Linking genetics and brain imaging in the pursuit of the underlying neurobiological mechanisms. Prog. Neuropsychopharmacol. Biol. Psychiatry 80, 101–114. https://doi.org/10.1016/j.pnpbp.2017.02.026 (2018).
Article  CAS  PubMed  Google Scholar 
Geschwind, D. H. & State, M. W. Gene hunting in autism spectrum disorder: On the path to precision medicine. Lancet Neurol. 14, 1109–1120 (2015).
Article  PubMed  PubMed Central  Google Scholar 
Graziano, P. A., Garic, D. & Dick, A. S. Individual differences in white matter of the uncinate fasciculus and inferior fronto-occipital fasciculus: Possible early biomarkers for callous-unemotional behaviors in young children with disruptive behavior problems. J. Child Psychol. Psychiatry 63(1), 19–33. https://doi.org/10.1111/jcpp.13444 (2021).
Article  PubMed  PubMed Central  Google Scholar 
Li, X. et al. Structural, functional, and molecular imaging of autism spectrum disorder. Neurosci. Bull. 37, 1051–1071. https://doi.org/10.1007/s12264-021-00673-0 (2021).
Article  PubMed  PubMed Central  Google Scholar 
Naigles, L. R. et al. Neural correlates of language variability in preschool-aged boys with autism spectrum disorder. Autism Res. 10, 1107–1119. https://doi.org/10.1002/aur.1756 (2017).
Article  PubMed  PubMed Central  Google Scholar 
Catani, M. et al. Frontal networks in adults with autism spectrum disorder. Brain 139, 616–630. https://doi.org/10.1093/brain/awv351 (2016).
Article  PubMed  PubMed Central  Google Scholar 
Basser, P. J. & Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. B 111, 209–219. https://doi.org/10.1006/jmrb.1996.0086 (1996).
Article  CAS  PubMed  Google Scholar 
Smith, S. M. et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487–1505. https://doi.org/10.1016/j.neuroimage.2006.02.024 (2006).
Article  PubMed  Google Scholar 
Behrens, T. E., Berg, H. J., Jbabdi, S. & Rushworth, M. F. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?. Neuroimage 34, 144–155 (2007).
Article  CAS  PubMed  Google Scholar 
Hau, J. et al. The cingulum and cingulate U-fibers in children and adolescents with autism spectrum disorders. Hum. Brain Mapp. 40, 3153–3164. https://doi.org/10.1002/hbm.24586 (2019).
Article  PubMed  PubMed Central  Google Scholar 
Nair, A., Treiber, J. M., Shukla, D. K., Shih, P. & Müller, R. A. Impaired thalamocortical connectivity in autism spectrum disorder: A study of functional and anatomical connectivity. Brain 136, 1942–1955. https://doi.org/10.1093/brain/awt079 (2013).
Article  PubMed  PubMed Central  Google Scholar 
Lo, Y. C., Chen, Y. J., Hsu, Y. C., Tseng, W. I. & Gau, S. S. Reduced tract integrity of the model for social communication is a neural substrate of social communication deficits in autism spectrum disorder. J. Child Psychol. Psychiatry 58, 576–585. https://doi.org/10.1111/jcpp.12641 (2017).
Article  PubMed  Google Scholar 
Poustka, L. et al. Fronto-temporal disconnectivity and symptom severity in children with autism spectrum disorder. World J Biol Psychiatry 13, 269–280 (2012).
Article  PubMed  Google Scholar 
Boets, B. et al. Alterations in the inferior longitudinal fasciculus in autism and associations with visual processing: A diffusion-weighted MRI study. Mol. Autism 9, 10. https://doi.org/10.1186/s13229-018-0188-6 (2018).
Article  PubMed  PubMed Central  Google Scholar 
Koldewyn, K. et al. Differences in the right inferior longitudinal fasciculus butno general disruption of white matter tracts in children with autismspectrum disorder. Proc. Natl. Acad. Sci. USA. 111, 1981–1986 (2014).
Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 
Verly, M. et al. Structural and functional underconnectivity as a negative predictor for language in autism. Hum. Brain Mapp. 35, 3602–3615 (2014).
Article  PubMed  Google Scholar 
Basser, P. J. & Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. 213, 560–570 (2011).
Article  ADS  CAS  PubMed  Google Scholar 
Sbardella, E., Tona, F., Petsas, N. & Pantano, P. DTI measurements in multiple sclerosis: Evaluation of brain damage and clinical implications. Mult. Scler. Int. 671–730, 2013. https://doi.org/10.1155/2013/671730 (2013).
Article  Google Scholar 
Hoppenbrouwers, M., Vandermosten, M. & Boets, B. Autism as a disconnection syndrome: A qualitative and quantitative review of diffusion tensor imaging studies. Res. Autism Spect. Disord. 8, 387–412 (2014).
Article  Google Scholar 
Ameis, S. H. & Catani, M. Altered white matter connectivity as a neural substrate for social impairment in autism spectrum disorder. Cortex 62, 158–181. https://doi.org/10.1016/j.cortex.2014.10.014 (2015).
Article  PubMed  Google Scholar 
Di, X., Azeez, A., Li, X., Haque, E. & Biswal, B. B. Disrupted focal white matter integrity in autism spectrum disorder: A voxel-based meta-analysis of diffusion tensor imaging studies. Prog. Neuropsychopharmacol. Biol. Psychiatry 82, 242–248. https://doi.org/10.1016/j.pnpbp.2017.11.007 (2018).
Article  PubMed  Google Scholar 
Fein, D. et al. Optimal outcome in individuals with a history of autism. J. Child Psychol. Psychiatry 54, 195–205. https://doi.org/10.1111/jcpp.12037 (2013).
Article  PubMed  PubMed Central  Google Scholar 
Orinstein, A. J. et al. Social function and communication in optimal outcome children and adolescents with an autism history on structured test measures. J. Autism Dev. Disord. 45, 2443–2463 (2015).
Article  PubMed  PubMed Central  Google Scholar 
Fountain, C. et al. Six developmental trajectories characterize children with autism. Pediatrics 129, e1112-1120 (2012).
Article  PubMed  PubMed Central  Google Scholar 
Guthrie, W., Swineford, L. B., Nottke, C. & Wetherby, A. M. Early diagnosis of autism spectrum disorder: Stability and change in clinical diagnosis and symptom presentation. J. Child Psychol. Psychiatry 54, 582–590 (2013).
Article  PubMed  PubMed Central  Google Scholar 
Louwerse, A. et al. ASD symptom severity in adolescence of individuals diagnosed with PDD-NOS in childhood: Stability and the relation with psychiatric comorbidity and societal participation. J. Autism Dev. Disord. 45, 3908–3918. https://doi.org/10.1007/s10803-015-2595-2 (2015).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Rondeau, E. et al. Is pervasive developmental disorder not otherwise specified less stable than autistic disorder? A meta-analysis. J. Autism Dev. Disord. 41, 1267 (2011).
Article  PubMed  Google Scholar 
Woolfenden, S., Sarkozy, V., Ridley, G. & Williams, K. A systematic review of the diagnostic stability of autism spectrum disorder. Res. Autism Spect. Disord. 6, 345–354 (2012).
Article  Google Scholar 
Helt, M. et al. Can children with autism recover? If so, how?. Neuropsychol. Rev. 18, 339–366 (2008).
Article  PubMed  Google Scholar 
Dawson, G., Jones, E., Merkle, K., Ve Nema, K. & Webb, S. J. Early behavioral intervention is associated with normalized brain activity in young children with autism. J. Am. Acad. Child Adolescent Psychiatry 51, 1150–1159 (2013).
Article  Google Scholar 
Port, R. G. et al. Maturation of auditory neural processes in autism spectrum disorder: A longitudinal MEG study. NeuroImage. Clinical 11, 566–577. https://doi.org/10.1016/j.nicl.2016.03.021 (2016).
Article  PubMed  PubMed Central  Google Scholar 
Eigsti, I. M. et al. Language comprehension and brain function in individuals with an optimal outcome from autism. NeuroImage. Clinical 10, 182–191. https://doi.org/10.1016/j.nicl.2015.11.014 (2016).
Article  PubMed  Google Scholar 
Marz, K. D. et al. Accelerated head and body growth in infants later diagnosed with autism spectrum disorders: A comparative study of optimal outcome children. J. Child. Neurol. 24, 833–845. https://doi.org/10.1177/088307380833134 (2009).
Article  Google Scholar 
Travers, B. G. et al. Longitudinal processing speed impairments in males with autism and the effects of white matter microstructure. Neuropsychologia 53, 137–145. https://doi.org/10.1016/j.neuropsychologia.2013.11.008 (2014).
Article  PubMed  Google Scholar 
Dougherty, C. C., Evans, D. W., Katuwal, G. J. & Michael, A. M. Asymmetry of fusiform structure in autism spectrum disorder: Trajectory and association with symptom severity. Mol. Autism 7, 28. https://doi.org/10.1186/s13229-016-0089-5 (2016).
Article  PubMed  PubMed Central  Google Scholar 
Lord, C., Rutter, M. & Couteur, A. L. Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J. Autism Dev. Disord. 24, 659–685. https://doi.org/10.1007/BF02172145 (1994).
Article  CAS  PubMed  Google Scholar 
Lord, C. et al. The autism diagnostic observation schedule-generic: A Standard measure of social and communication deficits associated with the spectrum of autism. J. Autism Dev. Disord. 30, 205–223 (2000).
Article  CAS  PubMed  Google Scholar 
Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208–S219 (2004).
Article  PubMed  Google Scholar 
Wakana, S., Jiang, H., Nagae-Poetscher, L. M., Zijl, P. V. & Mori, S. Fiber tract-based atlas of human white matter anatomy. Radiology 230, 77 (2004).
Article  PubMed  Google Scholar 
Wakana, S. et al. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 36, 630–644 (2007).
Article  PubMed  Google Scholar 
de Groot, M. et al. Improving alignment in tract-based spatial statistics: Evaluation and optimization of image registration. Neuroimage 76, 400–411. https://doi.org/10.1016/j.neuroimage.2013.03.015 (2013).
Article  PubMed  Google Scholar 
Catani, M. & Bambini, V. A model for social communication and language evolution and development (SCALED). Curr. Opin. Neurobiol. 28, 165–171 (2014).
Article  CAS  PubMed  Google Scholar 
Parlatini, V. et al. Functional segregation and integration within fronto-parietal networks. Neuroimage 146, 367–375. https://doi.org/10.1016/j.neuroimage.2016.08.031 (2017).
Article  PubMed  Google Scholar 
Barnea-Goraly, N. & Hardan, A. Advances in clinical neuroimaging: Implications for autism spectrum disorders. Expert Opin. Med. Diagn. 5, 475–482 (2011).
Article  PubMed  Google Scholar 
Martínez, K. et al. Atypical age-dependency of executive function and white matter microstructure in children and adolescents with autism spectrum disorders. Eur. Child Adolesc. Psychiatry 26, 1361–1376. https://doi.org/10.1007/s00787-017-0990-2 (2017).
Article  PubMed  Google Scholar 
Hardan, A. Y. et al. An MRI and proton spectroscopy study of the thalamus in children with autism. Psychiatry Res. 163, 97–105 (2008).
Article  PubMed  PubMed Central  Google Scholar 
Cheung, C. et al. White matter fractional anisotrophy differences and correlates of diagnostic symptoms in autism. J. Child Psychol. Psychiatry 50, 1102–1112 (2010).
Article  Google Scholar 
Tan, G. C., Doke, T. F., Ashburner, J., Wood, N. W. & Frackowiak, R. S. Normal variation in fronto-occipital circuitry and cerebellar structure with an autism-associated polymorphism of CNTNAP2. Neuroimage 53, 1030–1042. https://doi.org/10.1016/j.neuroimage.2010.02.018 (2010).
Article  CAS  PubMed  Google Scholar 
Haigh, S. M., Keller, T. A., Minshew, N. J. & Eack, S. M. Reduced white matter integrity and deficits in neuropsychological functioning in adults with autism spectrum disorder. Autism Res, 13, 702–714. https://doi.org/10.1002/aur.2271 (2020).
Article  PubMed  Google Scholar 
Cheon, K. A. et al. Involvement of the anterior thalamic radiation in boys with high functioning autism spectrum disorders: A diffusion tensor imaging study. Brain Res. 1417, 77–86. https://doi.org/10.1016/j.brainres.2011.08.020 (2011).
Article  ADS  CAS  PubMed  Google Scholar 
Fryer, S. L. et al. Microstructural integrity of the corpus callosum linked with neuropsychological performance in adolescents. Brain Cogn. 67, 225–233. https://doi.org/10.1016/j.bandc.2008.01.009 (2008).
Article  PubMed  PubMed Central  Google Scholar 
Kato, Y. et al. White matter tract-cognitive relationships in children with high-functioning autism spectrum disorder. Psychiatry Investig. 16, 220–233. https://doi.org/10.30773/pi.2019.01.16 (2019).
Article  PubMed  PubMed Central  Google Scholar 
Shukla, D. K., Keehn, B. & Müller, R. A. Tract-specific analyses of diffusion tensor imaging show widespread white matter compromise in autism spectrum disorder. J. Child Psychol. Psychiatry 52, 286–295. https://doi.org/10.1111/j.1469-7610.2010.02342.x (2011).
Article  PubMed  Google Scholar 
Solders, S. K., Carper, R. A. & Müller, R. A. White matter compromise in autism? Differentiating motion confounds from true differences in diffusion tensor imaging. Autism Res. 10, 1606–1620. https://doi.org/10.1002/aur.1807 (2017).
Article  PubMed  PubMed Central  Google Scholar 
Anderson, D. K., Liang, J. W. & Lord, C. Predicting young adult outcome among more and less cognitively able individuals with autism spectrum disorders. J. Child Psychol. Psychiatry 55, 485–494. https://doi.org/10.1111/jcpp.12178 (2014).
Article  PubMed  Google Scholar 
Helles, A., Gillberg, I. C., Gillberg, C. & Billstedt, E. Asperger syndrome in males over two decades: Quality of life in relation to diagnostic stability and psychiatric comorbidity. Autism 21, 458–469. https://doi.org/10.1177/1362361316650090 (2017).
Article  PubMed  Google Scholar 
Gillberg, I. C., Helles, A., Billstedt, E. & Gillberg, C. Boys with asperger syndrome grow up: Psychiatric and neurodevelopmental disorders 20 years after initial diagnosis. J. Autism Dev. Disord. 46, 74–82. https://doi.org/10.1007/s10803-015-2544-0 (2016).
Article  PubMed  Google Scholar 
Suh, J. et al. Ratings of broader autism phenotype and personality traits in optimal outcomes from autism spectrum disorder. J. Autism Dev. Disord. 46, 3505–3518. https://doi.org/10.1007/s10803-016-2868-4 (2016).
Article  PubMed  PubMed Central  Google Scholar 
Orinstein, A. et al. Psychiatric symptoms in youth with a history of autism and optimal outcome. J. Autism Dev. Disord. 45, 3703–3714. https://doi.org/10.1007/s10803-015-2520-8 (2015).
Article  PubMed  PubMed Central  Google Scholar 
Woodman, A. C., Smith, L. E., Greenberg, J. S. & Mailick, M. R. Change in autism symptoms and maladaptive behaviors in adolescence and adulthood: The role of positive family processes. J. Autism Dev. Disord. 45, 111–126. https://doi.org/10.1007/s10803-014-2199-2 (2015).
Article  PubMed  PubMed Central  Google Scholar 
Woodman, A. C., Smith, L. E., Greenberg, J. S. & Mailick, M. R. Contextual factors predict patterns of change in functioning over 10 years among adolescents and adults with autism spectrum disorders. J. Autism Dev. Disord. 46, 176–189. https://doi.org/10.1007/s10803-015-2561-z (2016).
Article  PubMed  PubMed Central  Google Scholar 
Lord, C., Bishop, S. & Anderson, D. Developmental trajectories as autism phenotypes. Am. J. Med. Genet. C 169, 198–208. https://doi.org/10.1002/ajmg.c.31440 (2015).
Article  Google Scholar 
Bal, V. H., Kim, S. H., Cheong, D. & Lord, C. Daily living skills in individuals with autism spectrum disorder from 2 to 21 years of age. Autism 19, 774–784. https://doi.org/10.1177/1362361315575840 (2015).
Article  PubMed  Google Scholar 
Magiati, I., Tay, X. W. & Howlin, P. Cognitive, language, social and behavioural outcomes in adults with autism spectrum disorders: A systematic review of longitudinal follow-up studies in adulthood. Clin. Psychol. Rev. 34, 73–86. https://doi.org/10.1016/j.cpr.2013.11.002 (2014).
Article  PubMed  Google Scholar 
Taylor, J. L., Henninger, N. A. & Mailick, M. R. Longitudinal patterns of employment and postsecondary education for adults with autism and average-range IQ. Autism 19, 785–793. https://doi.org/10.1177/1362361315585643 (2015).
Article  PubMed  PubMed Central  Google Scholar 
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We would like to thank the research staff for their dedication to this project. We are also grateful to all the families for generously donating their time so that this study could be possible.
This work was supported by the National Key Research& Development Program of China (NO. 2016YFC1306104). It provided the fund for the investigation. This work was supported by the National Natural Science Foundation of China (NO. 81371495). It provided the fund for the investigation. This work was also supported by the Sichuan Province Science and Technology Support Program (NO. 2020YFH0049). It provided the fund for editing and the subject’s allowance.
These authors contributed equally: Manxue Zhang and Xiao Hu.
Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
Manxue Zhang, Xiao Hu, Jian Jiao, Danfeng Yuan, Sixun Li, Tingting Luo, Meiwen Wang, Mingjing Situ, Xueli Sun & Yi Huang
The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
Manxue Zhang
West China Second Hospital of Sichuan University, Chengdu, China
Xiao Hu
Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
Yi Huang
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Concept and design: M.Z., X.S., Y.H. Acquisition, analysis, or interpretation of data: M.Z., J.J., D.Y., S.L., T.L., M.W., Y.H. Drafting of the manuscript: M.Z. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: M.Z., X.H. Obtained funding: Y.H. Administrative, technical, or material support: X.H., Y.H. Supervision: X.H., M.S., Y.H.
Correspondence to Xueli Sun or Yi Huang.
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Zhang, M., Hu, X., Jiao, J. et al. Brain white matter microstructure abnormalities in children with optimal outcome from autism: a four-year follow-up study. Sci Rep 12, 20151 (2022). https://doi.org/10.1038/s41598-022-21085-8
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