Atypical measures of diffusion at the gray-white matter boundary in autism spectrum disorder in adulthood

RESEARCH ARTICLE Atypical measures of diffusion at the gray-white matter boundary in autism spectrum disorder in adulthood Anke Bletsch 1 | Tim Schäfer 1 | Caroline Mann 1 | Derek S. Andrews 2 | Eileen Daly 3 | Maria Gudbrandsen 3 | Amber N. V. Ruigrok 4 | Robert Dallyn 3 | Rafael Romero-Garcia 5 | Meng-Chuan Lai 4,6,7 | Michael V. Lombardo 4,8 | Michael C. Craig 3,9 | John Suckling 5 | Edward T. Bullmore 5 | Simon Baron-Cohen 4 | the MRC AIMS Consortium | Declan G. M. Murphy 3 | Flavio Dell'Acqua 3 | Christine Ecker 1,3 1 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany 2 Department of Psychiatry and Behavioral Sciences at the M.I.N.D. Institute, University of California, Davis, California 3 Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK 4 Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK 5 Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK 6 Centre for Addiction and Mental Health and The Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, Canada 7 Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan 8 Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy 9 National Autism Unit, Bethlem Royal Hospital, London, UK Correspondence Anke Bletsch, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Deutschordenstrasse 50, 60528 Frankfurt am Main, Germany. Email: [email protected] Funding information Academic Scholars Award from the Department of Psychiatry, University of Toronto, and the Canadian Institutes of Health Research, Grant/Award Number: PJT-159578; German Research Foundation (DFG) under the Heisenberg Programme, Grant/Award Numbers: EC480/1-1, EC480/2-1; Innovative Medicines Initiative 2 Joint Undertaking, Grant/Award Numbers: 115300, 777394; Medical Research Council UK, Grant/Award Number: G0400061; National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London; Ontario Brain Institute Abstract Autism spectrum disorder (ASD) is a highly complex neurodevelopmental condition that is accompanied by neuroanatomical differences on the macroscopic and microscopic level. Findings from histological, genetic, and more recently in vivo neuroimaging studies converge in suggesting that neuroanatomical abnormalities, specifically around the gray-white matter (GWM) boundary, represent a crucial feature of ASD. However, no research has yet characterized the GWM boundary in ASD based on measures of diffusion. Here, we registered diffusion tensor imaging data to the structural T1-weighted images of 92 adults with ASD and 92 matched neurotypical controls in order to examine between-group differences and group-by-sex interactions in fractional anisotropy and mean diffusivity sampled at the GWM boundary, and at different sampling depths within the superficial white and into the gray matter. As hypothesized, we observed atypical diffusion at and around the GWM boundary in ASD, with between-group differences and group-by-sex interactions depending on tissue class and sampling depth. Furthermore, we identified that altered diffusion at Received: 8 May 2020 Revised: 8 September 2020 Accepted: 30 September 2020 DOI: 10.1002/hbm.25237 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. Hum Brain Mapp. 2021;42:467–484. wileyonlinelibrary.com/journal/hbm 467 10970193, 2021, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hbm.25237 by Universidad De Sevilla, Wiley Online Library on [20/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License via the Province of Ontario Neurodevelopmental Disorders Network, Grant/Award Number: IDS-I 1-02 the GWM boundary partially (i.e., 50%) overlapped with atypical gray-white matter tissue contrast in ASD. Our study thus replicates and extends previous work highlighting the GWM boundary as a crucial target of neuropathology in ASD, and guides future work elucidating etiological mechanisms. KEYWORDS diffusion tensor imaging, multimodal imaging, myelinated and unmyelinated gray matter, superficial white matter 1|INTRODUCTION Autism spectrum disorder (ASD) is a highly complex neurodevelopmental condition characterized by impaired social reciprocity and communication, as well as repetitive behaviors (Wing, 1997). Evidence suggests that ASD is accompanied by differences in brain anatomy and connectivity, on the macroscopic and microscopic level, that include atypicalities at the gray-white matter (GWM) boundary. For example, results from histological (Avino & Hutsler, 2010) and in vivo neuroimaging studies (Andrews et al., 2017; Bezgin, Lewis, & Evans, 2018; Hong, Valk, Di Martino, Milham, & Bernhardt, 2018; Mann et al., 2018) suggest that the GWM boundary is less well defined in ASD. This indistinct boundary is potentially due to an abnormal cell patterning at the GWM transition zone (Avino & Hutsler, 2010), with an excess of interstitial neurons distributed among the cortico-cortical U-fibers of the superficial white matter (Avino & Hutsler, 2010; Bailey et al., 1998; Casanova, Buxhoeveden, Switala, & Roy, 2002; Hutsler, Love, & Zhang, 2007; Simms, Kemper, Timbie, Bauman, & Blatt, 2009; Wegiel et al., 2010), which may result from deviations in fetal neuronal proliferation or decreased developmental apoptosis (Avino & Hutsler, 2010; Chun & Shatz, 1989; for a review, see McFadden & Minshew, 2013). Moreover, abnormalities at and around the GWM boundary have been inferred from a reduced graywhitemattertissuecontrast(GWC)inASD,whichmaybedrivenby increased T1-weighted signal intensity within the gray matter (Andrews et al., 2017). However, it is currently unknown whether this increase in signal intensity arises from alterations in myelination and/or differences in the underlying gray matter cytoarchitecture. Here, we examined the GWM boundary and adjacent white and gray matter in ASD based on measures of diffusion. Diffusion metrics that utilize tensor-based models (DTI), such as fractional anisotropy (FA) and mean diffusivity (MD), are particularly sensitive to white matter neuroarchitecture and changes thereof (Beaulieu, 2002; Jbabdi, Sotiropoulos, Haber, Van Essen, & Behrens, 2015; Jones, Knösche, & Turner, 2013). To date, DTI studies in ASD have predominantly focused on examining large-scale brain anatomical connectivity based on fiber tracts within the deep white matter (for reviews, see Aoki, Abe, Nippashi, & Yamasue, 2013; Catani et al., 2016; Travers et al., 2012). Few studies have examined the superficial white matter in ASD, which is located directly beneath the GWM boundary and predominantly consists of short-range association fibers. Nevertheless, those which are available observed decreased FA in the frontal lobe and increased MD in frontal, temporal, and parietal lobes (Shukla, Keehn, Smylie, & Müller, 2011; Sundaram et al., 2008). Consistent with these results, a recent study highlighted the potentially important role of aberrant white matter neuroarchitecture in the superficial white matter in ASD by linking it to atypical functional connectivity and symptom severity (Hong, Hyung, Paquola, & Bernhardt, 2019). Deviations within the superficial white matter have also been reported by postmortem studies; these reported differences in both the density and thickness of myelinated axons underneath the anterior cingulate and the lateral prefrontal cortices in ASD (Zikopoulos, Liu, et al., 2018; Zikopoulos & Barbas, 2010; Zikopoulos, García-Cabezas, & Barbas, 2018). Thus, there is increasing evidence to support the suggestion that measures of diffusion within the superficial white matter are significantly altered in ASD (in addition to abnormalities within the deeper white matter). However, it is unknown if atypical diffusion within the superficial white matter in ASD corresponds to aberrant diffusion at the adjacent GWM boundary, and/or within the cortical gray matter. The interpretation of diffusion metrics sampled within the cortical gray matter is challenging due to its highly complex composition in terms of both myeloand cytoarchitecture (for a review, see Roebroeck, Miller, & Aggarwal, 2019). For example, based on its myelin content, cortical gray matter can be further subdivided into myelinated and unmyelinated parts, with axons in the deeper layers being predominantly myelinated, and axons in the outer (i.e., more superficial) cortical layers being only lightly—or not at all—myelinated (Geyer, Weiss, Reimann, Lohmann, & Turner, 2011; Nave & Werner, 2014; Rowley et al., 2015; Sprooten et al., 2019). Intracortical myelin maturation has further been shown to extend into adulthood (Grydeland, Walhovd, Tamnes, Westlye, & Fjell, 2013; Rowley et al., 2017; Shafee, Buckner, & Fischl, 2015) and the strongest developmental effect of age-related increases was observed around the internal layer of projection neurons (Whitaker et al., 2016). A number of recent neuroimaging studies have therefore applied DTI to examine the laminar structure of the cortical gray matter in (postmortem tissue of) healthy humans ex vivo (Aggarwal, Nauen, Troncoso, & Mori, 2015; Bastiani et al., 2016; Dell'Acqua, Bodi, Slater, Catani, & Modo, 2013; Kleinnijenhuis et al., 2013; Leuze et al., 2014) and in vivo (Kleinnijenhuis et al., 2015; McNab et al., 2013). In ASD, however, studies utilizing measures of diffusion to examine the 468 BLETSCH ET AL. 10970193, 2021, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hbm.25237 by Universidad De Sevilla, Wiley Online Library on [20/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License cortical gray matter are scarce and only included small samples of children and adolescents with ASD as compared to typically developing (TD) controls. These studies reported widespread reductions in FA of the gray matter in young children with ASD (Pichiecchio et al., 2016), decreases in MD in frontal and insular regions and increases in MD in occipital regions of the gray matter in older children with ASD (Li, Xue, Ellmore, Frye, & Wong, 2013), and widespread MD increases of the gray matter in adolescents with ASD (Groen, Buitelaar, van der Gaag, & Zwiers, 2011). In adult individuals with ASD, however, to our best knowledge, cortical gray matter has not yet been assessed based on measures of diffusion. Thus, the objectives of our study were to (a) characterize the GWM boundary based on measures of diffusion in individuals with ASD relative to TD controls, and (b) identify the tissue and depthdependency of diffusion alterations in ASD within the adjacent superficial white and gray matter. Moreover, we aimed to (c) establish whether diffusion alterations in ASD are modulated by sex (i.e., groupby-sex interactions), and (d) identify how variability in measures of diffusion at the GWM boundary relates to interindividual differences in GWC in ASD. Based on previous research, we expected individuals with ASD to have significant differences in measures of diffusion at and around the GWM boundary compared to TD controls. Furthermore, we predicted at least a partial spatial overlap of between-group differences in diffusion properties and in GWC at the GWM transition zone. 2|MATERIALS AND METHODS 2.1 |Participants Overall, 92 right-handed adults with ASD (53 males and 39 females) and 92 age, sex, and IQ matched right-handed TD controls (51 males and 41 females) aged 18–52 years were assessed at the Institute of Psychiatry, Psychology, and Neuroscience (IoPPN), London, and the Autism Research Centre, Cambridge. Approximately equal ratios of ASD to TD individuals, as well as males to females, were recruited within sites (see Table 1). Females and males were recruited in approximately equal numbers across groups, which also allowed us to examine sex-differences in the brain in ASD. ASD diagnosis was made by a consultant psychiatrist using ICD-10 research diagnostic criteria and confirmed using the Autism Diagnostic Interview-Revised (ADI-R; Lord, Rutter, & Le Couteur, 1994). The ADI-R rather than the Autism Diagnostic Observation Schedule (ADOS; Lord et al., 2000) was employed as inclusion criteria to ensure that all participants with ASD met the criteria for childhood autism. ADI-R interviews were completed for 88 individuals with ASD (53 males and 35 females), out of which 73 individuals (47 males and 26 females) reached algorithm cut-offs for autism in all three domains (social, communication, and restricted/stereotyped behavior). Although failure to reach cut-off in one domain was permitted (i.e., by one point in the social or communication domain, or alternatively by two points in the repetitive TABLE 1 Participant demographics and global brain measures ASD (n= 92) TD controls (n= 92) Test statistic χ 2 /tp Age (years) 26.71 ± 7.20 (18–48) 28.38 ± 6.73 (18–52) −1.62 .11 Sex (male/female) 53/39 51/41 0.02 .88 Site (London/Cambridge) 42/50 47/45 0.35 .56 Sex by site Male (London/Cambridge) 29/24 30/21 0.05 .82 Female (London/Cambridge) 13/26 17/24 0.27 .60 WASI (FSIQ) 113.48 ± 12.89 (83–136) 116.40 ± 10.39 (93–137) −1.69 .09 ADI-R Social Interaction 17 ± 5 (9–28) ——— ADI-R Communication 13 ± 4 (7–24) ——— ADI-R Repetitive Behavior 5±2(1–10) ——— ADOS 8 ± 5 (0–18) ——— Total brain volume (l) 1.25 ± 0.14 (1.01–1.72) 1.24 ± 0.12 (0.95–1.48) 0.81 .42 Total cortical volume (l) 0.78 ± 0.08 (0.62–1.06) 0.76 ± 0.07 (0.61–0.90) 1.21 .23 Total gray matter volume (l) 0.60 ± 0.07 (0.48–0.83) 0.60 ± 0.05 (0.47–0.71) 1.29 .20 Total white matter volume (l) 0.47 ± 0.06 (0.35–0.66) 0.47 ± 0.06 (0.34–0.59) 0.23 .82 Total surface area (m 2 ) 0.19 ± 0.02 (0.15–0.26) 0.19 ± 0.02 (0.15–0.23) 0.87 .38 Mean cortical thickness (mm) 2.67 ± 0.09 (2.46–2.89) 2.66 ± 0.08 (2.45–2.88) 0.51 .61 Note: Data expressed as mean ± SD (range). Abbreviations: ADI-R, Autism Diagnostic Interview-Revised; ADOS, Autism Diagnostic Observation Schedule; ASD: autism spectrum disorder; TD: typically developing; FSIQ: full-scale intelligence quotient; WASI, Wechsler Abbreviated Scale of Intelligence. BLETSCH ET AL.469 10970193, 2021, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hbm.25237 by Universidad De Sevilla, Wiley Online Library on [20/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License behavior domain) and applied to the remaining 15 participants (6 males and 9 females). We were unable to complete ADI-R for four females with ASD as their parents/caregivers were not available. However, all of them reached algorithm cutoffs for “autism spectrum”on the ADOS module 4 (social and communication total score) diagnostic algorithm. In all other participants, ADOS scores were used to measure current symptoms and not as an inclusion criterion. Exclusion criteria included a history of major psychiatric disorder (e.g., psychosis), head injury, genetic disorder associated with autism (e.g., fragile-X syndrome, tuberous sclerosis), or any other medical condition affecting brain function (e.g., epilepsy). Participants taking antipsychotic medication, mood stabilizers, or benzodiazepines were also excluded. A small number of included participants with ASD took antidepressant medication (n= 7), out of which n= 5 participants also had a comorbid clinical diagnosis of depression. In addition, n=4 other ASD participants had a comorbid diagnosis of depression but did not take any medication. In all participants, the Beck Depression Inventory (BDI; Beck & Steer, 1987) was administered to assess severity of depressive symptoms. In addition, to provide a comprehensive clinical characterization of the assessed sample, several clinical questionnaires were administered in all participants focusing on core autistic symptoms and traits (i.e., Autism Spectrum Quotient [AQ]; Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001), including empathy (i.e., Empathy Quotient; Baron-Cohen & Wheelwright, 2004) and systemizing abilities (i.e., Systemizing Quotient; Baron-Cohen, Richler, Bisarya, Gurunathan, & Wheelwright, 2003), as well as assessing common comorbidities, that is, anxiety (i.e., Beck Anxiety Inventory [BAI]; Beck & Steer, 1993) and obsessive and compulsive behaviors (i.e., Obsessive–Compulsive InventoryRevised; Abramowitz & Deacon, 2006; Huppert et al., 2007). Information on these clinical characteristics of the sample is provided in the Supplementary Material (see Supplementary Table S1). Overall intellectual ability was assessed using the Wechsler Abbreviated Scale of Intelligence (Wechsler, 1999) and all included participants have a full-scale IQ (FSIQ) >70 (see Table 1). Informed written consent was given by all participants in accordance with ethics approval by the National Research Ethics Committee, Suffolk, UK (Ref: 04/Q0102/26). 2.2 |MRI data acquisition All participants were scanned with contemporary magnetic resonance imaging (MRI) scanners operating at 3-T and fitted with an eightchannel receive-only head coil (GE Signa System, General-Electric) at the IoPPN, King's College, London, and Addenbrooke's Hospital, Cambridge. To ensure standardization of structural MRI scans across scanner platforms, a specialized acquisition protocol using quantitative imaging (driven equilibrium single-pulse estimation of T1) was utilized. This protocol has previously been validated and extensively described elsewhere (Deoni et al., 2008; Ecker et al., 2012), resulting in highresolution structural T1-weighted inversion-recovery images, with 1x1x1mm resolution, a 256 ×256 ×176 matrix, TR = 1,800 ms, TI = 850 ms, FA = 20, and FOV = 25.6 cm. DTI data were acquired using a spin-echo echo-planar imaging double refocused sequence providing whole head coverage with isotropic image resolution (2.4 ×2.4 ×2.4 mm; 32 diffusion-weighted volumes with different noncollinear diffusion directions with b-factor 1,300 s/mm 2 , and six nondiffusion-weighted volumes; 60 slices without slice gap; TE = 104.5 ms; TR = 20 R-R intervals; 128 ×128 acquisition matrix; FOV = 30.7 ×30.7 cm). The acquisition was gated to the cardiac cycle using a digital pulse oximeter placed on participants' forefinger. 2.3 |Cortical reconstruction using FreeSurfer We employed an automated analytical pipeline using FreeSurfer v6.0.0 software (http://surfer.nmr.mgh.harvard.edu/) to identify the GWM boundary by deriving models of the cortical surface for each T1-weighted image. These well-validated and fully automated procedures have been extensively described elsewhere (Dale, Fischl, & Sereno, 1999; Fischl & Dale, 2000; Fischl, Sereno, & Dale, 1999; Jovicich et al., 2006; Ségonne et al., 2004). In brief, a single-filled white-matter volume was generated for each hemisphere after intensity normalization, extra-cerebral tissue was cropped, and image segmentation performed using a connected components algorithm. A triangular tessellated surface was then generated for each whitematter volume. Deformation of this tessellated white matter surface resulted in a cortical mesh for the surfaces that defines the boundary between gray and white matter (i.e., white matter surface), and gray matter and cerebrospinal fluid (i.e., pial surface). This surface deformation is the result of the minimization of an energy functional that utilizes intensity gradients in order to place these surfaces where the greatest shift in intensity defines the transition between tissue classes (Dale et al., 1999). The use of intensity gradients across tissue classes assures that boundary placement is not reliant solely on absolute signal intensity and allows for subvoxel resolution in the placement of these boundary surfaces (Dale et al., 1999; Dale & Sereno, 1993; Fischl & Dale, 2000). These automated methods have previously been validated against histological analyses and have shown a high degree of accuracy in placing the GWM boundary (Rosas et al., 2002). The resulting surface models were visually inspected by three raters for reconstruction errors. Scans with segmentation errors were alternately manually edited by two of the raters and re-assessed by all three, while scans of insufficient quality, mostly due to motion artifacts, were excluded from the analysis. For further details on quality assessments and the number of excluded scans, see Supplementary Methods 1. 2.4 |Diffusion tensor MRI processing and coregistration with the structural MRI data Diffusion data were first denoised and Gibb's ringing corrected using in-house software (based on Kellner, Dhital, Kiselev, & Reisert, 2016; Veraart et al., 2016). Data outlier regeneration (SD = 4) and slice-tovolume correction (SD = 6) was performed using eddy from the FSL 470 BLETSCH ET AL. 10970193, 2021, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hbm.25237 by Universidad De Sevilla, Wiley Online Library on [20/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License software toolbox (Andersson & Sotiropoulos, 2016). To maintain compatibility with previous studies (Catani et al., 2016), eddy current and motion correction, as well as robust tensor estimation (Tax, Jeurissen, Vos, Viergever, & Leemans, 2014) was performed using ExploreDTI (Leemans, Jeurissen, Sijbers, & Jones, 2009). To extract vertex-wise parameter estimates for FA and MD sampled along the GWM boundary and at different sampling depths, we first coregistered each individual's diffusion data to the respective structural T1-weighted image using the command bbregister in Freesurfer v6.0.0 (https://surfer.nmr. mgh.harvard.edu/fswiki/bbregister) (Greve & Fischl, 2009). Here, the FA mask in diffusion space was registered to the same-subject anatomical image. The output of this transformation is a “register.dat”file which comprises the rigid-body transformation matrix from the diffusion to the structural space. Finally, individual's diffusion data in structural space were registered to a common template, that is, fsaverage in Freesurfer, to allow for the calculation of between-group differences. 2.5 |Computation of diffusion measures at different sampling depths and of GWC Diffusion measures, FA and MD, were sampled at each vertex along the GWM boundary (white matter surface), as well as at different percentile fractions of the total orthogonal distance projected from the white matter to pial surfaces (i.e., projection fractions) of 30% and 60% cortical thickness (CT) from the GWM boundary to the pial surface. These sampling depths within the gray matter were selected to capture diffusion at a deeper, and thus more heavily myelinated part of the gray matter (30% CT; Rowley et al., 2015; Sprooten et al., 2019), and at a more superficial, presumably lightly or not at all myelinated part of the gray matter (60% CT; Rowley et al., 2015; Sprooten et al., 2019). Further, within the white matter diffusion measures were sampled at absolute distances of −1and−2 mm below the GWM boundary. These sampling depths were chosen to target the short-range U-fibers (at −1 mm) and to also capture the terminations of the long-range fibers, which run between −1.5 and −2.5 mm below the GWM boundary (at −2 mm; Schüz & Braitenberg, 2002). Based on previous work by our group (Andrews et al., 2017) and as adapted from Salat et al. (2009), the GWC was computed from the T1-weighted images as the ratio between the gray matter signal intensity (GMI), sampled at a projection fraction of 30% CT into the cortical ribbon starting from the GWM boundary, in relation to the signal intensity of the white matter (WMI), sampled at an absolute distance of −1 mm into the white matter starting from the GWM boundary, at each cerebral vertex (i), GWC i = 100 ×(WMI i −GMI i )/ 0.5 ×(WMI i + GMI i ). Thus, a decreased GWC corresponds to a lower contrast between the intensities of gray matter at 30% CT and white matter at −1 mm and hence indicates a “blurring”of the GWM transition zone. To improve the ability to detect population changes, each parameter, that is, FA, MD, and GWC, was subsequently smoothed using a 15 mm full width at half maximum Gaussian kernel prior to statistical analyses. 2.6 |Statistical analyses Statistical analyses were conducted using the SurfStat toolbox (http://www.math.mcgill.ca/keith/surfstat/) for MATLAB (R2017b; MathWorks). Vertex-wise statistical analyses of FA and MD measures sampled at the different sampling depths (i.e., −2 and −1 mm into the WM, at the GWM boundary, as well as 30 and 60% CT into the gray matter) as well as measures of GWC (Y) were estimated by the regression of a general linear model (GLM) with (a) diagnostic group, sex, and acquisition site as categorical fixed-effects factors; (b) a group-bysex interaction term; and (c) a linear and a quadratic age term, and FSIQ as continuous covariates: Y i =β 0 +β 1 Group + β 2 Sex + β 3 Group ×Sex +ß 4 Site + ß 5 Age + ß 6 Age 2 +ß 7 FSIQ + ε i , where ε i is the residual error at vertex i. Between-group differences were estimated from the corresponding coefficient β 1 , normalized by the corresponding standard error. We further examined group-by-sex interaction effects (coefficient β 3 ) across neuroanatomical features. All included continuous covariates were mean centered across groups to improve interpretability of the coefficients. The utilized general linear model was derived after we initially assessed the goodness-of-fit of different model versions using nested model comparisons in a vertex-wise fashion (see Supplementary Figures S1–S3). To determine if the addition of a new model term (e.g., group-by-sex interaction or the BDI total score) leads to a significantly improved goodness-of-fit, we employed a step-up model selection procedure comparing the reduced model to the more complex model, and performed an F-test for nested model comparisons at each vertex. This approach allows to identify the most parsimonious model at each vertex, that is, the most simple plausible model explaining the variability in the examined parameter of interest (i.e., FA, MD, or GWC) with the smallest set of predictors. All nested model comparisons were conducted in the total sample, that is, all individuals with ASD and controls. Here, the inclusion of the group-by-sex interaction term yielded an improved goodness-of-fit, whereas the additional inclusion of the BDI was not significant (see Supplementary Figures S1–S3). The group-by-sex interaction term was therefore included as a model term in all subsequent analyses. As previous findings by our group indicate the developmental trajectory of GWC to incorporate linear as well as nonlinear effects of age in ASD (Mann et al., 2018), we further included a linear as well as a quadratic age term in our GLM to control for those and possible age-related between-group differences in FA and MD. The developmental trajectory of FA and MD is also expected to differ between groups. In neurotypical development, the myelination process follows a quadratic trajectory over the lifespan (Bartzokis, 2004a, 2004b; Bartzokis et al., 2001, 2003; Sowell et al., 2003; see also Rowley et al., 2017 for intracortical myelin and Ouyang, Kang, Detre, Roberts, & Huang, 2017 for the superficial white matter). To control for the presence of comorbid depressive symptoms, we repeated the analyses following the exclusion of individuals with antidepressant medication intake and/or a comorbid diagnosis of BLETSCH ET AL.471 10970193, 2021, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hbm.25237 by Universidad De Sevilla, Wiley Online Library on [20/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License depression. In addition, to test the robustness of our findings, we further repeated the analyses when covarying for depressive symptoms, as assessed by BDI. Moreover, to control for variability in total gray and white matter volumes, we examined the main effect of group when covarying for the effect of total brain measures. Finally, we repeated the analyses when stratifying our sample by sex. In all surface-based analyses, corrections for multiple comparisons across the whole brain were performed using random field theorybased cluster analysis for nonisotropic images using a cluster-based significance threshold of p< .05 (two-tailed; Worsley, Andermann, Koulis, MacDonald, & Evans, 1999). To determine the functional relevance of the observed findings, we further calculated Pearson correlation coefficients between scores in clinical questionnaires assessing ASD-related clinical symptomatology and common comorbidities (i.e., anxiety, depression) with the mean FA, MD, and GWC extracted from clusters obtained in the main effect of group analyses. To identify the extent to which the spatially distributed patterns of significant between-group differences of the different parameters, GWC, as well as FA and MD sampled at the GWM boundary, were either unique to one measure or overlapping, we further conducted pair-wise χ 2 tests, to test the null hypotheses that differences in either two parameters (GWC and FA, GWC and MD, as well as FA and MD) are equally distributed. Moreover, to examine if the respective observed degree of overlap supports the assumption of two spatially (in)dependent patterns, we utilized a simulation strategy generating N= 5,000 random difference maps, that incorporated random t-values that were thresholded at p< 0.05 for the respective measures (i.e., GWC, FA, and/or MD). The degree of overlap, estimated as number of vertices with alterations in the respective two measures, was subsequently calculated in each simulation. This provides a probability estimation of obtaining the observed percentage of overlap based on randomly varying difference patterns. 3|RESULTS 3.1 |Participant demographics and global brain measures There were no significant differences between individuals with ASD and TD controls in age (t(181.12) = 1.62, p= .11), FSIQ (t[174.12) = 1.69, p= .09), or total brain measures, such as total brain volume (t(178.58) = −0.81, p=.42),totalsurfacearea(t(177.42) = −0.87, p=.38), and mean CT (t(181.06) = −0.51, p= .61; see Table 1). For a detailed clinical characterization of the sample, see Supplementary Table S1. 3.2 |Between-group differences in FA at the different sampling depths Individuals with ASD relative to TD controls predominantly displayed significant reductions in FA, which were most extensive at the GWM boundary, and gradually decreased in spatial extent with increasing sampling distance from the GWM boundary (see Figure 1 and Supplementary Table S2). More specifically, significantly reduced FA in ASD was observed across sampling depths in the right prefrontal cortex (Brodmann area [BA] 10/45–46), as well as the left fusiform and inferior temporal gyrus (BA 20/37). Within the superficial white matter, reduced FA was further observed in the right temporal lobe (BA 20–22) and in the left lateral orbitofrontal cortex (BA 10/45/47). At the GWM boundary and within the gray matter, reduced FA was further observed in right frontoparietal (BA 1–4/6/39–40) and left occipitotemporal regions (BA 19/37). In contrast, significant increases in FA in ASD were observed across sampling depths in a large cluster centered on the right medial orbitofrontal cortex and superior frontal gyrus (BA 9–11/32). At 60% CT exclusively, FA was significantly increased in two small clusters located on the right precentral and postcentral gyrus (BA 1–4), as well as in the left paracentral lobule (BA 4). The results pattern remained largely unchanged upon exclusion of individuals with antidepressant medication intake and/or a comorbid diagnosis of depression (see Supplementary Figure S4), as well as when covarying for BDI total score or total gray and white matter volumes, respectively (see Supplementary Figures S5a and S6). The results for the analysis conducted with males and females separately are presentedinSupplementaryFiguresS7 and S8. 3.3 |Between-group differences in MD at the different sampling depths At the GWM boundary and within the superficial white matter, individuals with ASD mostly had significantly increased measures of MD relative to TD controls. These increases were predominantly observed in bilateral frontal lobes (BA 10–11/45), including the orbitofrontal cortex and pars triangularis, as well as in the left fusiform and inferior temporal gyrus (BA 20/37; see Figure 2 and Supplementary Table S3). In contrast, we observed widespread MD reductions in ASD within the cortical gray matter. Those were most extensive at 60% CT and gradually increased in spatial extent with increasing CT projection fraction. More specifically, individuals with ASD had significantly decreased MD in the bilateral temporal lobes (BA 20–22), preand postcentral gyrus (BA 1–4), as well as in a large cluster in the right medial orbitofrontal cortex and superior frontal gyrus (BA 9–11/32). At 60% CT only, decreases in MD were also observed in a large cluster centering on the left paracentral lobule (BA 4/6–7/24/31). Neither the exclusion of individuals with antidepressant medication intake and/or a comorbid diagnosis of depression (see Supplementary Figure S9), nor the inclusion of the BDI total score or total gray and white matter volumes as covariate/s resulted in a significant change of the resulting patterns of betweengroup differences (see Supplementary Figures S5b and S10). The results stratified by sex are presented in Supplementary Figures S11 and S12. 472 BLETSCH ET AL. 10970193, 2021, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hbm.25237 by Universidad De Sevilla, Wiley Online Library on [20/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3.4 |Between-group differences in GWC As reported previously (Andrews et al., 2017), the GWC was significantly reduced in individuals with ASD in several clusters across the cortex (see Figure 3a and Supplementary Table S4). These reductions were observed bilaterally in the fusiform gyrus (BA 37), the postcentral gyrus (BA 1–3), the inferior and superior parietal cortex (BA 7/40), the occipital cortex (BA 18–19), the medial orbitofrontal cortex (BA 10–11), and the cingulate cortex (BA 24/30/32–33). Overall, the right hemisphere was more affected than the left, with reductions in GWC in ASD expanding to the precentral gyrus and paracentral lobule (BA 4), the inferior, middle, and superior temporal gyri and temporal pole (BA 20–21/38/41), the posterior cingulate cortex (BA 23/31), as well as the ventrolateral and dorsolateral prefrontal cortex (BA 9/44–46). There were no clusters where individuals with ASD showed a significant increase in GWC as compared to TD controls. The results pattern FIGURE 1 Main effect of group for fractional anisotropy (FA). Regions of increased and decreased FA in individuals with autism spectrum disorder (ASD) compared to typically developing (TD) controls at the gray-white matter (GWM) boundary (0%), at different cortical thickness (CT) projection fractions within the gray matter (i.e., 30 and 60% CT, sampled from the GWM boundary into the thickness of the cortical ribbon), and within the superficial white matter (sampled at absolute distances of −1 and −2 mm below the GWM boundary). Displayed are the unthresholded (left panel) and thresholded (right panel) t-maps, where increased FA estimates in ASD are marked in yellow to red (left panel), respectively, red to yellow (right panel), and decreased FA estimates in ASD are marked in cyan to purple (left panel), respectively, blue to cyan (right panel; random field theory [RFT]-based cluster corrected p< .05, two-tailed) BLETSCH ET AL.473 10970193, 2021, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hbm.25237 by Universidad De Sevilla, Wiley Online Library on [20/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License remained largely unchanged when individuals with antidepressant medication intake and/or a comorbid diagnosis of depression where excluded from the analysis (see Supplementary Figure S13), as well as when BDI total score or total gray and white matter volumes were included as covariate/s (see Supplementary Figures S5c and S14). For the results stratified by sex, see Supplementary Figure S15. We did not, however, observe any significant group-by-sex interaction effects for measures of GWC (see Figure 3b). 3.5 |Group-by-sex interaction effects in FA at the different sampling depths In addition to the main effect of group, we observed significant groupby-sex interactions for measures of FA (see Figure 4 and Supplementary Table S5). These were most pronounced within the superficial white matter and increased in spatial extent with increasing sampling distance away from the GWM boundary. Significant group-by-sex FIGURE 2 Main effect of group for mean diffusivity (MD). Regions of increased and decreased MD in individuals with autism spectrum disorder (ASD) compared to typically developing (TD) controls at the gray-white matter (GWM) boundary (0%), at different cortical thickness (CT) projection fractions within the gray matter (i.e., 30 and 60% CT, sampled from the GWM boundary into the thickness of the cortical ribbon), and within the superficial white matter (sampled at absolute distances of −1 and −2 mm below the GWM boundary). Displayed are the unthresholded (left panel) and thresholded (right panel) t-maps, where increased MD estimates in ASD are marked in yellow to red (left panel), respectively, red to yellow (right panel), and decreased MD estimates in ASD are marked in cyan to purple (left panel), respectively, blue to cyan (right panel; random field theory [RFT]-based cluster corrected p< .05, two-tailed) 474 BLETSCH ET AL. 10970193, 2021, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hbm.25237 by Universidad De Sevilla, Wiley Online Library on [20/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License interactions within the superficial white matter were located in right paracentral and cingulate regions (BA 4/23–24/30–31), as well as in the right orbitofrontal and ventrolateral prefrontal cortex (BA 10/44–45). At −2 mm within the white matter, significant group-by-sex interaction effects were further observed in the left ventrolateral and dorsolateral prefrontal cortex (BA 44–46), the bilateral supplementary motor area (SMA; BA 6), the bilateral precentral and postcentral gyri (BA 1–4), the left insula (BA 13), the left middle and superior temporal gyrus (BA 21–22), the right superior frontal gyrus (BA 8), as well as the right inferior parietal cortex (BA 39). (a) (b) (c) (d) FIGURE 3 Main effect of group and group-by-sex interaction effects for the graywhite matter tissue contrast (GWC), and the overlap between the patterns of between-group differences in GWC and diffusion measures. (a) Regions of significantly reduced GWC in individuals with autism spectrum disorder (ASD) compared to typically developing (TD) controls sampled as ratio between the gray matter signal intensity sampled 30% into the thickness of the cortical ribbon from the gray-white matter (GWM) boundary and white matter signal intensity sampled −1mm below the GWM boundary (random field theory [RFT]- based cluster corrected p< .05, two-tailed). (b) Regions with a significant group-by-sex interaction effect for GWC. Displayed are the unthresholded (left panel) and thresholded (right panel) t-maps (RFT-based cluster corrected p< .05, twotailed). 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Acta Neuropathologica,136(5), 759–778. https:// doi.org/10.1007/s00401-018-1904-1 SUPPORTING INFORMATION Additional supporting information may be found online in the Supporting Information section at the end of this article. How to cite this article: Bletsch A, Schäfer T, Mann C, et al. Atypical measures of diffusion at the gray-white matter boundary in autism spectrum disorder in adulthood. Hum Brain Mapp. 2021;42:467–484. https://doi.org/10.1002/hbm. 25237 484 BLETSCH ET AL. 10970193, 2021, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hbm.25237 by Universidad De Sevilla, Wiley Online Library on [20/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License