A callosal biomarker of behavioral intervention outcomes for autism spectrum disorder? A case-control feasibility study with diffusion tensor imaging.

Tentative results from feasibility analyses are critical for planning future randomized control trials (RCTs) in the emerging field of neural biomarkers of behavioral interventions. The current feasibility study used MRI-derived diffusion imaging data to investigate whether it would be possible to identify neural biomarkers of a behavioral intervention among people diagnosed with autism spectrum disorder (ASD). The corpus callosum has been linked to cognitive processing and callosal abnormalities have been previously found in people diagnosed with ASD. We used a case-control design to evaluate the association between the type of intervention people diagnosed with ASD had previously received and their current white matter integrity in the corpus callosum. Twenty-six children and adolescents with ASD, with and without a history of parent-managed behavioral intervention, underwent an MRI scan with a diffusion data acquisition sequence. We conducted tract-based spatial statistics and a region of interest analysis. The fractional anisotropy values (believed to indicate white matter integrity) in the posterior corpus callosum was significantly different across cases (exposed to parent-managed behavioral intervention) and controls (not exposed to parent-managed behavioral intervention). The effect was modulated by the intensity of the behavioral intervention according to a dose-response relationship. The current feasibility case-control study provides the basis for estimating the statistical power required for future RCTs in this field. In addition, the study demonstrated the effectiveness of purposely-developed motion control protocols and helped to identify regions of interest candidates. Potential clinical applications of diffusion tensor imaging in the evaluation of treatment outcomes in ASD are discussed.

Introduction

Children with autism spectrum disorder (ASD) present with learning delays in social behavior and language, and often engage in stereotypic behavior. In the absence of intensive educational and psychosocial interventions, children with ASD follow a non-remission course often leading to disability and dependence during adulthood. The lifetime cost of an individual with ASD has been estimated at $1.4M in the US and £1.2M in the UK, with these figures almost doubling when there is comorbid intellectual disability [1]. Special education services and parental productivity loss make up most of this cost during childhood, while residential services and individual productivity loss are the main contributors during adulthood.

Unlike other neurodevelopmental disorders, the causal mechanisms of autism are not well understood at the molecular, cellular or system level. In addition, there is some debate regarding the extent to which some on the autism spectrum have a “deficit” per se [2]. Nevertheless, children on the autism spectrum tend to undergo an atypical brain maturation resulting in neuroanatomical and functional differences relative to neurotypical children. Neuroimaging studies have repeatedly reported an atypical connectivity among children with autism consistent with reduced communication between distant brain regions [3, 4]. Studies have also shown an altered functional connectivity when participants engage in a variety of cognitive tasks and also during resting-state analyses (see a review in [5]).

In recent years, the use of diffusion tension imaging (DTI) has allowed novel insights on the macrostructure and microstructure of white matter (WM) in people with autism. This technique examines the WM anisotropic water diffusion as informed by fractional anisotropy (FA) and other diffusivity metrics [6]. Fractional anisotropy, in particular, is a composite value related to axonal density, size, myelination, and fiber organization [7], and provides an indication of structural configuration and brain connectivity.

Studies exploring DTI among people with ASD have shown an altered FA in several WM tracts spanning across different areas of the brain. The most consistent findings have been reported for the corpus callosum, cingulum, uncinate fasciculus, arcuate fasciculus, and the superior and inferior longitudinal fasciculi [3, 4]. Atypical WM architecture of the corpus callosum is among the most consistently reported DTI findings in people with autism [8].

The callosal commissure is the largest interhemispheric WM bundle and it is thought to be involved in social functioning [9], motor skills [9-12], and complex cognitive repertoires [12-14]. The corpus callosum has become a focus of interest for neuroscientific research in autism. Decreases in the volume of the corpus callosum have been reported in several areas including the forceps major (splenium), forceps minor (genu), and corpus callosum body [15]. Interestingly, behavioral similarities have been reported among people with autism and those with callosal agenesis [4, 16]. These include deficits in social skills, problem solving, and abstract reasoning [9, 17].

Neural changes have been documented in a variety of populations due to motor learning and practice [18], physical exercise [19], memory training [20], reading intervention [21], and cognitive therapy [22], to mention a few. Within the ASD population, a seminal study by Pardini et al. [23] reported a relation between WM integrity in the uncinate fasciculus and the duration of cognitive and behavioral treatments.

Comprehensive meta-analyses have shown that both clinic-based and parent-managed intensive behavioral intervention can produce long-term gains in IQ, receptive and productive language, and psychosocial functioning in children with autism [24, 25]. While the outcomes of both parent-managed and clinic-based intensive behavioral intervention for autism are well established [24, 26, 27], there is a dearth of studies evaluating their potential effects on neural plasticity. In particular, few neuroimaging studies have evaluated brain connectivity in relation to comprehensive evidence-based treatments for ASD.

The current feasibility analysis presents a case-control study of parent-managed behavioral intervention (PMBI) for autism. A case-control feasibility study present distinct advantages in this research context. First, it can help to identify potential regions of interest and estimate likely effect size ranges, which are critical for planning future RCTs. Second, feasibility studies can help to determine whether more resource-intensive treatment evaluation designs are warranted [28]. The cases included in the study were comprised of children with autism that had been exposed to PMBI, while controls had received other services. We hypothesized that PMBI can modulate changes in WM integrity informed by FA in individuals with autism. Specific hypothesis regarding the affected WM tracts were not held due to the inconclusive evidence available in the literature, except for the corpus callosum, where we hoped to find differences in the WM microstructure integrity. We first conducted a whole-brain exploratory analysis using track-based spatial statistics (TBSS). This initial approach allowed us to examine whether the brain was globally impacted by ASD treatment exposure status and helped to inform a subsequent region of interest (ROI) analysis. We hypothesized that a history of PMBI may modulate FA in the corpus callosum in children and adolescents with ASD.

Methods

Participants

Participants were recruited thorough a news release on a national newspaper, and a mailing campaign through an autism support network. In order to be admitted into the study, participants required (a) a clinical diagnosis conducted by a multidisciplinary team often lead by a pediatrician, child psychiatrist or clinical psychologist using the diagnostic criteria of the 4th or 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM) [29, 30], and (b) no presence of any metallic objects or fragments that would exclude them from participation in an MRI machine. The diagnostic process was often supported by the administration of standardized assessments including the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview–Revised (ADI-R).

A total of 42 participants from the Auckland region and neighboring rural areas in New Zealand expressed interest in the study and met the inclusion criteria. From this pool of participants, 30 individuals were sequentially invited to undergo an MRI scan. One of the subjects could not attend the appointment, another three were removed from the analysis due to excessive head motion. The final sample of 26 individuals (23 males and 3 females, mean age: 13.81 ± 5.04) included 19 subjects diagnosed with ASD, four with Asperger syndrome, and three with pervasive developmental disorder not otherwise specified (PDD-NOS). Six of these participants had comorbid diagnoses of ASD and ADHD, one had comorbid diagnoses of Asperger syndrome and ADHD, and one had comorbid diagnoses of PDD-NOS and ADHD. In addition, 11 participants had a comorbid diagnosis of intellectual disability (Table 1).

Table 1

Participant characteristics.

	PMBI (n = 13)	Other (n = 13)	p value
Sex (males)1	100.0 (13)	69.2 (9)	.033
Age (years)2	12.51±4.65, 6.50–23.08	15.11±5.24, 7.68–23.98	.234
Ethnicity			.793
Caucasian	69.2 (9)	76.9 (10)
Asian	7.7 (1)	15.4 (2)
Maori	15.4 (2)	0.0 (0)
Other	7.7 (1)	7.7 (1)
ASD diagnosis			.344
Autism	76.9 (10)	69.2 (9)
Asperger syndrome	15.4 (2)	15.4 (2)
PDD-NOS	7.7 (1)	15.4 (2)
Selected comorbidity
Intellectual disability	23.1 (3)	61.5 (8)	.052
ADHD	38.5 (5)	23.1 (3)	.193
Autism symptoms
When first diagnosed	13.18 ± 3.52, 6–18	15.09 ± 2.74, 10–18	.171
Currently	6.33 ± 2.77, 1–11	9.50 ± 2.58, 6–14	.008
DSM severity	1.23 ± 0.44, 1–2	1.54 ± 0.52, 1–2	.225
Requires support	76.9 (10)	46.2 (6)
Substantial support	23.1 (3)	53.8 (7)
Severity differential	-0.92 ± 0.86, -2–0	-0.92 ± 0.86, -2–0	.840
Mainstreamness	2.62 ± 0.87, 0–3	2.38 ± 0.96, 0–3	.527
Home-schooled	7.7 (1)	7.7 (1)
Special education school	0.0 (0)	7.7 (1)
Special education classroom	15.4 (2)	23.1 (3)
Mainstream	81.8 (10)	61.5 (8)
Current level of support
Daily special education hours	2.23 ± 1.42, 0–5	2.58 ± 1.17, 1–5	.969
Weekly teacher aid hours	10.00 ± 10.48, 0–30	12.15 ± 13.26, 0–30	.840
Interventions (total)	5.54 ± 1.66, 3–8	3.69 ± 1.48, 2–7	.006
Sensory integration	15.4 (2)	15.4 (2)
Dietary interventions	76.9 (10)	23.1 (3)
Occupational therapy	30.8 (4)	30.8 (4)
CBT	23.1 (3)	15.4 (2)
SLT	46.2 (6)	38.5 (5)
Social worker	15.4 (2)	15.4 (2)
Social support group	23.1 (3)	30.8 (4)
Equine-assisted therapy	15.4 (2)	15.4 (2)
Early intervention (non EIBI)	15.4 (2)	15.4 (2)
Medical	38.5 (5)	15.4 (2)
Other therapies or services	23.1 (3)	53.8 (7)

Notes. 1. % (n); 2. Mean ± SD, range. ANOVAs or non-parametric tests, as appropriate. Critical p value according to Benjamini and Hochberg [31] multiple-comparison correction is .005. Ad hoc autism severity questionnaire included in S1 Appendix. Severity differential was calculated as the difference in DSM-defined severity when first diagnosed and at the time of the study. Mainstreamness defined as the average ordinal level of the New Zealand Ministry of Education Classification (0 = Homeschool/correspondence, 1 = Special education school, 2 = Special education classroom, 3 = Mainstream). ADHD = Attention deficit and hyperactivity disorder; ASD = Autism spectrum disorder; CBT = Cognitive behavioral therapy; DSM = Diagnostic and Statistical Manual of Mental Disorders; EIBI = Early intensive behavioral intervention; PDD-NOS = Pervasive developmental disorder not otherwise specified; PMBI = Parent-managed behavioral intervention; SLT = Speech language therapy.

Cases were defined as individuals whose direct caregiver had received parent training for the purposes of conducting PMBI. The caregivers of participants exposed to PMBI had received instruction and supervision by a behavioral consultant on educational strategies, whether as a stand-alone intervention or in the context of early-intensive behavioral intervention (EIBI) based on applied behavior analysis. Caregivers had received training in areas including daily living skills, language and communication, leisure and social behavior, and academic abilities. Controls were individuals whose direct caregivers had not received behavioral parent training as defined above. Overall, 13 participants received PMBI, while 13 had received other services. Case and controls were compared in a range of personal and clinical characteristics. These included sex, age, ethnicity, primary diagnosis, ADHD and intellectual disability comorbidities, DSM severity (i.e., level of support required), changes in severity defined as the difference in DSM severity when first diagnosed and at the time of the study, level of mainstream school integration (i.e., home-schooled, special education school, special education classroom, mainstream school), number of daily special education hours received at school, and number of teacher aid hours per week. In addition to PMBI, we documented all current and historical interventions participants had received according to the following categories: sensory integration therapy, dietary interventions (including gluten-free diets), occupational therapy, cognitive-behavioral therapy, speech-language therapy, social worker support, autism social support group (regular attendance to parent groups or other autism-related activities), equine-assisted therapy, early intervention programs (different from EIBI), medical and pharmacological interventions, and other therapies or services. All participant characteristics including treatment history were informed by the primary caregiver by way of a structured interview. Participants did not differ significantly in sociodemographic or treatment history characteristics (there were statistical trends, after multiple-comparison correction, for sex, intellectual disability comorbidity, and total number of interventions received, see Table 1). Interestingly, those in the PMBI group showed a trend toward lower autism symptoms at the time of the study, but not when first diagnosed.

The current study was approved by the University of Auckland’s Human Participants Ethics Committee (Ref: 014993). Parents provided informed consent in writing. All participants also provided assent to the study procedures.

Mock scanner training

All participants underwent a minimum of one and a maximum of two mock scanner sessions to become accustomed to the neuroimaging procedure. During these sessions, participants underwent an abbreviated stillness training procedure developed by Cox et al. [32]. This procedure involved evidence-based behavior modification procedures including prompting, stimulus fading, and contingent social reinforcement as a means to minimize participant’s head and body movement during the mock scanner sessions.

Image acquisition

Imaging data was acquired with a 1.5 Tesla Siemens Avanto scanner (Erlangen, Germany) both for MRI and DTI. T1 MRI images were acquired using a MPRAGE sequence with a voxel resolution of 1x1x1 mm. The voxel size for DTI was 2 x 2 x 2 mm. We used a single shot spin-echo echo planar imaging (EPI) sequence (repetition time = 9746; echo time = 101; field of view = 256; matrix size = 256 × 256) applied in 12 non-collinear directions. Three runs were collected to allow for averaging to occur along each diffusion direction and improve estimations of diffusion indices. Images were acquired with a diffusion weighting of b = 1000 s/mm2, and a reference image with a diffusion weighting of b = 0 s/mm2 was also collected. The total acquisition time was approximately 10 minutes.

DTI processing

White matter microstructure integrity was compared between participants with ASD that had or had not received PMBI. We conducted a whole-brain voxel-based comparison with TBSS. The TBSS allowed us to focus on the major tracts that are broadly believed to be affected in people with ASD (e.g., corpus callosum). Image files were transferred into a Linux work station for processing. DICOM files were converted to NIFTI using MRICRO. Preprocessing was conducted using the FSL FMRIB’s Diffusion Toolbox (FDT) (FSL 5.0; www.fmrib.ox.ac.uk/fsl). Quality assurance involved eddy current induced distortion and head motion correction, as well as removal of non-brain tissue. Given that motion introduces artifacts in DTI metrics, only data with a mean absolute RMS less than 5 mm was included and image quality passing visual inspection (refer to participant attrition above). A binary mask of the brain was generated for each subject from their no-diffusion image. We then used FDTIFIT to fit the diffusion tensor model. We generated FA maps for each participant.

TBSS analysis

As an exploratory analysis, we first began with a whole brain approach using TBSS. To remove likely outliers, TBSS first erodes the FA images of each subject slightly, and zeros end slices. Nonlinear registration to a common space was conducted using the FNIRT tool, which aligns all subjects’ FA images to the 1 x 1 x 1 mm FMRIB58_FA standard-space target, namely, the most representative FA image identified. Each subject’s image is also affine transformed to MNI152 space resulting in a standard-space version of each subject’s FA image. Next, an image detailing the mean FA for all subjects was created and thinned to create a mean FA skeleton, which represents the centers of all tracts common to the group. The FA data for each subject were then projected onto the skeleton. Variance from the FA maps related to the age and sex factors was removed (intracranial volume was not significantly different across groups, t = -0.78, p = 0.44). The resulting images were then subjected to TBSS within FSL. FSL’s randomise function with a threshold-free-cluster-enhancement method was applied for the group-level variable. Final t-stat images are displayed in Fig 1 (a full image collection is available in https://neurovault.org/collections/12006/). While our main interest for the current exploratory analysis was FA, which is a general indicator of WM integrity, we replicated the procedure for mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD).

Fig 1

Red voxels denote greater fractional anisotropy (A), mean diffusivity (B), axial diffusivity (C), and radial diffusivity (D) among individuals exposed to parent-managed behavioral intervention (n = 13) relative to individuals not exposed to parent-managed behavioral intervention (n = 13). Green voxels indicate the mean WM skeleton of all subjects. Red and green voxels are plotted onto longitudinal, sagittal and horizontal standardized anatomical images.

Tractography analysis

We conducted a targeted tractography analysis focusing on FA in the corpus callosum. Other tracts that the TBSS analysis showed to be significantly different, for FA and other diffusivity metrics are reported in the S1 Dataset for context. Thereby, the corpus callosum, superior longitudinal fasciculi, cingulum and the uncinated fasciculi were reconstructed. We used the preset parameters of the FreeSurfer Software Suite (v. 6.0.0) for the selected tracts mentioned above. In the generated connectivity map each voxel represents a connectivity value where the higher the number, the greater the probability of the pathway passing through that voxel. Due to the non-specific and widespread distribution of connections across the brain, the reconstruction of the tracts was performed with an FA 0.55 to ensure only the inclusion of major tracts with high probability and to avoid false positives and pathways that may be the result of image noise. The dissection of these tracts was performed according to the procedure described by Catani and Thiebaut de Schotten [33]. The thresholded connectivity maps were binarized and masked into the FA map to derive mean values for each of the focused regions. The same procedure was repeated for MD, AD, and RD metrics.

Statistical comparisons of the tractography outcome measures were performed using the statistical package SPSS Statistics v. 27 (IBM, Armonk, New York, United States). We computed quantile-quantile plots and skewness analyses to verify that the distribution of the dependent variables did not present notable departures from distribution normality and symmetry, respectively. We used the FA of two tracts of interest (forceps major and forceps minor) as dependent variables and exposure status as grouping variable (i.e., exposed to PMBI, not exposed to PMBI). We expected that a history of PMBI would modulate FA in the corpus callosum. We conducted univariate analyses of variance (ANOVA) to compare FA of selected WM tracts across cases and controls (Model 1). Due to sample size restrictions, covariate-specific analyses were not conducted. However, descriptive variables that had shown a statistical trend were selected to be included in a corrected ANOVA as covariates (Model 2). We computed partial eta squared (η) effect sizes for all ANOVAs [34]. In order to provide an indication of causality, we conducted dose-response analysis by exposure intensity. Specifically, individuals with low intensity behavioral intervention were those that had not received PMBI (controls). Individuals with medium intensity PMBI where those exposed to parent training without EIBI. Finally, individuals with high intensity behavioral intervention were those exposed to parent training in addition to (or in the context of) EIBI. The dose-response analysis was conducted by way of a univariate ANOVA with treatment intensity status as predictor variable (low, medium, high) and FA in the corpus callosum as outcome.

Results

The results of the voxelwise TBSS analysis showed significant differences between the WM microstructure of children with autism that had received PMBI when compared to those who did not. Individuals exposed to PMBI revealed multiple regions with increased FA, MD, AD, and RD within WM pathways, which included the corpus callosum, superior longitudinal fasciculus, uncinate fasciculus, and the cingulum. This finding was replicated for other (Fig 1).

The subsequent ROI analysis focused on the corpus callosum (Table 2). We found a significantly different WM diffusion in the posterior region of the corpus callosum among participants exposed to PMBI, F(1,25) = 7.83, p = .011, η = 0.28. This result was specific to the posterior portion of the corpus callosum or forceps major. Specifically, the corpus callosum of children exposed to PMBI did not display differences in FA in the body and most anterior sections of the corpus callosum (forceps minor) relative to the controls, F(1,25) = 0.83, p > .1, η = 0.04. The effect was evident in the corrected model (Model 2), which included age, sex, intracranial volume, intellectual disability comorbidity, and total number of interventions as covariates (Table 2). The dose-response analysis showed a gradual effect of intervention intensity on FA in the forceps major, F(2,25) = 4.47, p = .026, η = 0.32 (see Table 3). The statistical analyses did not establish any WM tracts of interest as significantly different across cases and controls for any of the other DTI metrics (MD, AD, and RD).

Table 2

Fractional anisotropy in the corpus callosum among cases (n = 13) and controls (n = 13).

	M (SD)		F	p	Critical pa	ηp2
	Cases	Controls
Model 1
Forceps major	0.58 (0.06)	0.62 (0.04)	3.63	.069	.025	.13
Forceps minor	0.46 (0.05)	0.49 (0.07)	1.80	.193	.025	.07
Model 2
Forceps major	0.58 (0.06)	0.62 (0.04)	7.83	.011	.025	.28
Forceps minor	0.46 (0.05)	0.49 (0.07)	0.83	.374	.025	.04

Notes. All univariate ANOVAs. a. Critical p according to Benjamini and Hochberg [31] multiple-comparison correction. Model 2 includes age, sex, intracranial volume, intellectual disability comorbidity, and total number of interventions as covariates.

Table 3

Parent training intensity and functional anisotropy in the forceps major.

Intensity	M (SD), n	F	df	p	η p 2
Low	0.62 (0.04), 13	4.47	2	.026	.32
Medium	0.60 (0.05), 5
High	0.57 (0.07), 8

Notes. Univariate ANOVA with total number of treatments, age, sex, intracranial volume, intellectual disability comorbidity, and total number of interventions as covariates. Low intensity = no parent training reported; Medium intensity = parent training without early intensive behavioral intervention; High intensity = parent training in addition to (or in the context of) early intensive behavioral intervention.

Discussion

The present feasibility study expands on previous DTI analyses conducted with children with ASD. The existing literature has largely focused on abnormal tract development that may be specific to those receiving the diagnosis of ASD. However, there is a dearth of studies evaluating the potential impact of varying psychosocial treatment histories on WM microstructure and functioning. Several independent randomized and non-randomized trials support EIBI as an evidence-based remedial approach for the cognitive, verbal and social deficits of children with autism [35] and the intervention is now considered standard practice by a number of authoritative sources (see for example [36]). A meta-analysis by Virues-Ortega [25] showed that PMBI programs produce essentially identical effect sizes relative to clinic-based programs in all outcomes evaluated including IQ, non-verbal IQ, receptive and expressive language, and adaptive behavior.

We have used TBSS analysis and tractographic methods to assess the WM integrity in children with ASD. As an exploratory analysis, we first began with a whole-brain TBSS analysis, which was run for each grouping to ascertain the extent to which the brain of children with autism was influenced by their history of PMBI. The results showed that exposed individuals had higher FA in regions such as the corpus callosum, superior longitudinal fasciculi, left and right cingulum, and uncinated fasciculi. We then conducted a targeted ROI analysis focusing on the corpus callosum identified in the TBSS analysis. The posterior region of the corpus callosum, the forceps major, was found to have significantly lower FA among those exposed to PMBI. The effect was found to follow a dose-response relation when the intensity of PMBI was used as a predictive variable.

Our tentative results are consistent with the view that it may be possible for ontogenic exposures such as PMBI to exert a long-term influence on the neurophysiology of children and adolescents with ASD. Analyses such as the one presented here may help to identify neurophysiological biomarkers of treatment outcomes in future RCTs with larger sample sizes. While the corpus callosum volume has been suggested as a biomarker of autism [37], it has not yet been proposed that a specific region of the corpus callosum may be a candidate marker of exposure to various treatment histories. The search for biomarkers of treatment outcomes in autism remains a largely understudied area. In a notable exception, Bradshaw et al. [38] showed that six months of behavioral intervention (i.e., pivotal response treatment [39]) induced verifiable changes in eye motion toward social stimuli. However, neurophysiological biomarkers are yet to be established.

Interestingly, our results provide additional context to the literature that has identified the corpus callosum as an important brain structure for those with autism. For example, Haar et al. [40] assessed anatomical MRIs of over 1,000 individuals from the Autism Brain Image Data Exchange project. The authors reported that individuals with autism had lower corpus callosum volumes relative to age-matched typically developing peers. The authors had divided the corpus callosum in five segments along the anterior-posterior axis for their analysis. Only the central segment produced a mild albeit statistically significant effect size (d = 0.2). Moreover, when analyzed individually, only two of the 18 participating sites showed the effect. A meta-analysis of DTI studies in autism has also identified the corpus callosum (and the splenium in particular) as the location of significant FA alterations [8].

The corpus callosum has remained a region of interest in autism for some time in the context of brain connectivity and synchronization theories of autism [41]. Interestingly, an earlier meta-analysis by Frazier and Hardam [37] summarizing 10 studies with a pooled sample of 253 individuals with autism had reported relatively large differences in the corpus callosum although the effect disappeared caudally. In this connection, the involvement of the posterior segment of the corpus callosum reported in the current analysis, if substantiated in subsequent studies, may be a unique phenomenon that may not be assimilated simply to a wider involvement of the corpus callosum in people with autism.

It is necessary to clarify the difference in the results of our analytical methods, since TBSS has found that most of the WM of those exposed to PMBI has higher FA values, whereas ROI analyses have only established a difference in FA in the posterior region of the corpus callosum. This can be explained by the different approach of these two methods. Specifically, TBSS and ROI analyses test different aspects of the WM pathways. By focusing only on the voxels of each path that are present in every subject, and not the entire tract of each individual, TBSS can control for the anatomical differences between subjects. On the other hand, ROI analyses test FA across all voxels within the tract for each individual, thereby being more vulnerable to WM covariates as age or comorbid disorders. Thus, it is possible that the age range and existing comorbidities may have had an impact on our ROI analysis (see for example [42]). We have mitigated this concern to the extent possible by conducting a thorough comparison of numerous descriptive variables and adding critical covariates to our analytical models.

Since the present study was not an RCT but a feasibility case-control study with a relatively small sample, our results should be considered in light of some limitations. First, the lack of a control group should lead to caution in the interpretation of these results. Participants that were exposed or not exposed to PMBI were comparable in a range of critical characteristics. Future longitudinal RCTs should prospectively compare a non-PMBI control group with a PMBI intervention group before the intervention and at various time points over the course, and, potentially, after the intervention.

The age of participants may be an important cofound. While total brain volume remains relatively constant after age five, internal remodeling occurs within the brain. For example, Mills et al. [43] examined a large longitudinal sample (ages 8–30 years) finding that cerebral white mater increases gradually from childhood until mid-to-late adolescence. While cases and controls did not differ significantly in age or intracranial volume, it would be beneficial in future studies to shorten the age range of participants to minimize any age-mediated volume variability.

The current sample of participants reflects the sex distribution of the autistic population. Therefore, cases and controls were not sex-matched. Subgroup analysis by sex and other critical characteristics including pre-intervention functioning, treatment duration, and treatment success will require larger samples in order to highlight WM microstructural differences in subgroup analyses.

From a methodological standpoint, TBSS attempts to overcome the shortcomings of voxel-based morphometry and ROI analyses. However, it remains a concern that TBSS does not account for head motion within the scan. While the mock scanner procedure and data pre-processing minimize the effects of head movement, these could cause false FA values to be reported. It is important to indicate that the current data were collected with only 12 motion probing gradients. While six directions have been theorized to be sufficient for diffusion-weighted analyses focusing on FA differences, current diffusion study protocols usually employ 30 directions or more. Therefore, increasing the number of motion probing gradients would improve the resolution of the scans, but it is unlikely that our results would have been significantly skewed because of a lack of scanning directions. Additionally, our protocol consisted of three runs, allowing us to average across each gradient direction and improve our ability to estimate diffusion indices.

The current study aimed to explore relationships between common interventions of ASD and WM integrity. In spite of our tentative findings, it is important to highlight that FA is known to reflect a variety of WM changes. For example, despite being commonly associated with decreased tract integrity, increased FA could also reflect an increase in neurons, an increase in myelin, or increased inflammation. In order to better characterize what biological mechanism is underpinning the changes we are observing in these brains, future extensions of this work would need to consider mean diffusivity, axial diffusivity, and radial diffusivity in greater detail that it had been possible with the current dataset.

Finally, the results of our study could be strengthened by the application of DTI techniques at the beginning of the therapeutic process to better characterize the causality link between DTI metrics and therapy. Likewise, the case-control design did not allow for greater homogeneity among those exposed and not exposed to PMBI in terms of their treatment histories. The presence of baseline DTI data and pre-specified treatment integrity criteria could help to verify if there is a link between pre-treatment mean FA values and treatment effectiveness.

A potential extension of the current feasibility study would involve to replicate the proposed design, including the dose-response analysis, within a large neuroimaging repository. Unfortunately, existing databases, including the Autism Brain Imaging Data Exchange (ABIDE I and ABIDE II) [44], do not include treatment data. The inclusion of treatment outcome data would be a positive addition to these collections, maybe following international guidelines for ASD treatment outcomes (see, for example, ICHOM Connect [45]). Larger samples sizes with narrower age ranges and longitudinal analyses with matched controls or randomized group assignment are desirable methodological standards for future research in this area. To our knowledge, relations between treatment efficacy and DTI measures have not yet been reported in the ASD population, making this an extremely important avenue for future research. In addition, subgroup analyses by age can help to determine whether early intensive interventions could have a long-lasting impact on brain development later in childhood and into the adolescence and adult age.

Conclusions

The current feasibility study used MRI-derived diffusion imaging data (TBSS and seed-based tractography) to investigate whether there was a relationship between the intervention received by individuals diagnosed with ASD and their current brain connectivity. In particular, we report differences in the WM integrity of the posterior corpus callosum in those exposed to PMBI. This preliminary finding was substantiated by a PMBI intensity dose-response analysis. The corpus callosum is the largest interhemispheric WM bundle and has been previously associated with functional and structural abnormalities in people diagnosed with autism, being an important target area for future analyses. The preliminary results are consistent with disorder-specific alterations of the WM microstructure in people with ASD and is the first to apply neuroimaging techniques to determine whether there is a relationship between intervention history and current brain connectivity. The study also demonstrated that a purposely-developed behavioral protocol for motion control can be used effectively to obtain usable neuroimaging with minimal experimental mortality. Therefore, the present case-control feasibility study provides the basis for more resource-intensive treatment evaluations including RCTs, and longitudinal RCTs in particular, to be conducted in the future. The current line of work will help to explore clinical applications of DTI to measure treatment efficacy in ASD and other neurobehavioral disorders. Progress in the emerging field of neural biomarkers of behavioral interventions may be critical to enhance our understanding of the neural processes mobilized by intensive interventions and to identify early biomarkers of treatment outcomes.

Ad hoc autism severity questionnaire.

(PDF)

Click here for additional data file.

Full dataset and variable dictionary.

(XLSX)

Click here for additional data file.

20 Oct 2021

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Niels Bergsland

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please note that according to our submission guidelines (http://journals.plos.org/plosone/s/submission-guidelines), outmoded terms and potentially stigmatizing labels should be changed to more current, acceptable terminology. To this effect,  “Caucasian” should be changed to “white” or “of [Western] European descent” (as appropriate)

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: - The provided data repo link (neurovault.org/collections/ZEPLXWYX , dated +2years ago) does NOT contain the input data for the 30 subjects (nor the 26 used)..only final images.

- It was said that 30 subject were recruited, one left study, another 4 removed, and 26 remaining! (in this way you started with 31 subjects, or ended with 25 as the case)...in another place, "26 of the 29 subjects passed quality assurance, discarding 3 individuals" implying only 3 had artifacts, and original were 29. Both sentences needs to be altered to be more clear. following, only the age of males were stated ..why?

- Again, the 26 subjects contained 17 asd, 4Asperger, 4 otherwise (totaling 25?!). It's also useful to identify which seven has ADHD (all ASD? asperger? which mix?) Especially as they are identified as a separate raw on table1 (raising total count to 32?)

- It would be beneficial applying the same procedure on a larger publicly available datasets if possible, maybe a subset of Autism Brain Image Data Exchange or others, to confirm generalizability of findings and mitigate the effects of using a small sample size (n<30) .

- In defining PMBI cases: how long does parent actively conducted those learnt training on their kids? Since this matter directly affect the hypothesis of brain - changes, as some may only conducted those strategies for negligible amount of times. Moreover, what are other services include? and why it's hypothesized it has no effect on the ROI of study, in a way similar to PMBI? Finding an actual distinction can be a result of many other obvious cofounding variables.

- "PMBI can lead to measurable volume changes in known regions of interest for autism": DTI shouldn't reveal volume changes, but micro white matter architecture and integrity.

- Why only FA was investigated? Not also other common measures as mean diffusivity/ axial diffusivity/ ... ?

- The use of references in the discussion doesn't help a lot. For example, Results were said to be consistent with literature, and an example [48] was given although it was anatomical, not WM connectivity (as well as 50).

- Line 143: end of sentence missing (are what?)

Reviewer #2: Virues-Otega et al. evaluated white matter integrity between subjects with autism spectrum disorder that had or had not received PMBI. As results, increased FA was demonstrated in the forceps major of subjects with autism spectrum disorder that had received PMBI. Overall, the manuscript is well-written, and the findings appear robust. However, I do have some concerns.

- The major drawback of this study is the small sample size. Furthermore, longitudinal data is preferable to evaluate the effect of treatment/behavioral intervention.

- Why the authors only evaluated FA despite its unspecificity? AD and RD are assumed to represent axon and myelin integrity, respectively, I suggest including MD, AD, and RD in the analysis.

- Were there any significant differences in intelligence and brain volumes between groups?

- Multiple comparisons correction should be applied in the ROI analysis.

Minor comments:

- This study included not only subjects with autism but also subjects with Asperger syndrome and pervasive developmental disorder; please change the title “autism” —> “autism spectrum disorder.”

- Please use the consistent term “autism spectrum disorder” throughout the manuscript.

- The introduction of this manuscript is too long (4.5 pages) and unfocused.

- Please define all abbreviations on their first use in text, such as DSM, ADOS, and ADIS.

- Page 8, line 176. “Overall, 11 participants received PMBI, while 14 had received other services,” please describe “other services” more precisely and explain will or will not this service affect the results.

- Please provide the p-values in table 1.

- Page 7, line 166: “The final sample of 26 individuals included 17 subjects with ASD, four with Asperger syndrome, and four with pervasive developmental disorder…” Did the total number of participants calculated correctly?

- Please state the duration of PMBI.

- Please describe the statistical analysis of TBSS. Did the authors include age, gender, and intracranial volume as covariates?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Christina Andica

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Submitted filename: PONE-D-21-30729.docx

Click here for additional data file.

17 Dec 2021

IMPORTANT. Submitted also as part of the submission package.

PONE-D-21-30729

Title: A Callosal Biomarker of Behavioral Intervention Outcomes for Autism Spectrum Disorder? A Case-Control Feasibility Study with Diffusion Tensor Imaging

Dear Dr. Bergsland,

Thank you for the opportunity of having our work reviewed at PLOS One. We appreciate your comments and those from the reviewer panel. We believe that these comments have helped to improve the quality of our manuscript very significantly. We are excited to submit a revised manuscript of our study. Below I present a detailed response to all comments. The enclose manuscript has been revised accordingly (edited text in blue fonts). We have made a sincere effort to respond to all concerns as thoroughly as possible (which has required a fresh re-analysis of our dataset). I take the opportunity to thank you for your support throughout the editorial process.

Sincerely,

Javier Virues-Ortega,

On behalf of the authors

Editor comments (different from the reviewers’)

1. TBSS is missing a reference to the original Smith et al. paper.

This has been corrected.

2. Please also use the term "sex" rather than "gender", the former which is the biological meaning whereas the latter is the sociological construct, which is not relevant in the context of your manuscript.

Done as suggested.

3. No T1 image is described in the imaging protocol.

Please, refer to the additions to the Image Acquisition section on p. 11.

4. Finally, please carefully the manuscript for typos (e.g., "Read and green" rather than "Red and green", "mod-to-late" rather than "mid-to-late")

Done as suggested.

Reviewer #1:

1. The provided data repo link (neurovault.org/collections/ZEPLXWYX , dated +2years ago) does NOT contain the input data for the 30 subjects (nor the 26 used)..only final images.

We have updated the Neurovault file (see link below) to include all datasets (n = 26). All input data will be shared through Figshare upon the manuscript acceptance.

https://neurovault.org/collections/12006/

2. It was said that 30 subject were recruited, one left study, another 4 removed, and 26 remaining! (in this way you started with 31 subjects, or ended with 25 as the case)...in another place, "26 of the 29 subjects passed quality assurance, discarding 3 individuals" implying only 3 had artifacts, and original were 29. Both sentences needs to be altered to be more clear. following, only the age of males were stated ..why? Again, the 26 subjects contained 17 asd, 4Asperger, 4 otherwise (totaling 25?!).

We have reprocessed our data and included one additional dataset. We now provide a more complete narrative of the attrition process (see p. 8). To avoid confusion, we refer to the participant selection process in the Participants section only.

3. It's also useful to identify which seven has ADHD (all ASD? asperger? which mix?) Especially as they are identified as a separate raw on table1 (raising total count to 32?)

This information has been added to p. 8.

4. It would be beneficial applying the same procedure on a larger publicly available datasets if possible, maybe a subset of Autism Brain Image Data Exchange or others, to confirm generalizability of findings and mitigate the effects of using a small sample size (n<30).

Existing databases (including the ABIDE I and ABIDE II) do not include treatment data. We have indicated in the discussion that a positive addition to these systems may be to include treatment outcome data, maybe following international outcomes standards such as the ICHOM autism coreset.

5. In defining PMBI cases: how long does parent actively conducted those learnt training on their kids? Since this matter directly affect the hypothesis of brain - changes, as some may only conducted those strategies for negligible amount of times. Moreover, what are other services include? and why it's hypothesized it has no effect on the ROI of study, in a way similar to PMBI? Finding an actual distinction can be a result of many other obvious cofounding variables.

Participants received training from qualified therapists for a significant period of time (at least one month). However, training length would not accurately portray the potential impact of the intervention, as parents would have showed different levels of adherence to parent training strategies even if the parent training intervention were comparable in terms of the length of training received by parents. In order to better address these concerns we implemented the following mitigating actions: (1) we now provide a detailed treatment history for exposed individuals and controls (see current Table 1), (2) we classified as exposed individuals those receiving parent training in the context of other treatments (this affected two participants now reclassified as cases that receiving early intensive behavioral intervention, which routinely includes parent training), (3) we replicated the tractography analysis using the total number of distinct interventions received as a covariate, and (4) we conducted a post hoc dose-response analysis using parent training intensity as predictor and FA as outcome. The three levels of parent training intensity are defined as follows.

1 Not receiving parent training (i.e., controls)

2 Receiving parent training not in the context of early intensive behavioral intervention

3 Receiving parent training in the context of early intensive behavioral intervention

Please, refer to the manuscript for the changes described above.

6. "PMBI can lead to measurable volume changes in known regions of interest for autism": DTI shouldn't reveal volume changes, but micro white matter architecture and integrity.

We have corrected this sentence as suggested.

7. Why only FA was investigated? Not also other common measures as mean diffusivity/ axial diffusivity/ ... ?

We agree with reviewers that FA alone cannot provide a complete picture of tract integrity, and appreciate their dialogue around this issue. Given this project intended to be a proof of concept pilot for future interventional work in ASD, we chose to include the most common DTI metric to describe "tract integrity." To address concerns raised by reviewers we now report axial diffusivity, radial diffusivity, and mean diffusivity for both TBSS and ROI analysis. However, and according to our initial hypothesis, we have kept a targeted ROI analysis focused on FA. However, we now briefly report on the other metrics as well. The following comments has been added to the discussion.

"The current study aimed to explore relationships between common interventions of ASD and white matter integrity. In spite of our tentative findings, it is important to highlight that FA is known to reflect a variety of biological changes within white matter. For example, despite being commonly associated with decreased tract integrity, increased FA could also reflect an increase in neurons, an increase in myelin, or increased inflammation. In order to better characterize what biological mechanism is underpinning the changes we are observing in these brains, future extensions of this work would need to consider mean diffusivity, axial diffusivity, and radial diffusivity in greater detail that it had been possible with the current dataset."

8. The use of references in the discussion doesn't help a lot. For example, Results were said to be consistent with literature, and an example [48] was given although it was anatomical, not WM connectivity (as well as 50).

The paragraph referred by the reviewer has been modified to incorporate a meta-analysis of DTI in autism (p. 15):

9. Line 143: end of sentence missing (are what?)

Omitted word has been re-added

Reviewer #2:

Virues-Ortega et al. evaluated white matter integrity between subjects with autism spectrum disorder that had or had not received PMBI. As results, increased FA was demonstrated in the forceps major of subjects with autism spectrum disorder that had received PMBI. Overall, the manuscript is well-written, and the findings appear robust. However, I do have some concerns.

1. The major drawback of this study is the small sample size. Furthermore, longitudinal data is preferable to evaluate the effect of treatment/behavioral intervention.

While the sample size was sufficient for the goals of a feasibility case-control study, and is supported by a post hoc achieved power analysis (beta = 0.94). This has been acknowledged as a key avenue for future research in the discussion (p. 17).

2. Why the authors only evaluated FA despite its unspecificity? AD and RD are assumed to represent axon and myelin integrity, respectively, I suggest including MD, AD, and RD in the analysis.

Please, refer to the response to comment #7 (Reviewer 1).

3. Were there any significant differences in intelligence and brain volumes between groups?

IQ data was not available. We have added data from an ad hoc autism symptom scale with separate scores for current symptoms and symptoms when first diagnosed. There were no significant differences in autism symptoms at the time of diagnosis (see Table 1). We have also added information on comorbid intellectual disability.

4. Multiple comparisons correction should be applied in the ROI analysis.

We have added the critical p according to Bejamini & Hochberg (1995) multiple-comparison correction. We have revised the ROI to make it more targeted to the corpus callosum in line with our original hypothesis.

5. Minor comments:

- This study included not only subjects with autism but also subjects with Asperger syndrome and pervasive developmental disorder; please change the title “autism” —> “autism spectrum disorder.” Please use the consistent term “autism spectrum disorder” throughout the manuscript.

Done as suggested.

- The introduction of this manuscript is too long (4.5 pages) and unfocused.

We have reduced the length of the introduction to 3 pages. We have made a serious attempt to make the introduction focused and succinct.

- Please define all abbreviations on their first use in text, such as DSM, ADOS, and ADIS.

Done as suggested.

- Page 8, line 176. “Overall, 11 participants received PMBI, while 14 had received other services,” please describe “other services” more precisely and explain will or will not this service affect the results.

Please, refer to the answer to Reviewer #1 (Comment 5) for an in-depth response to this concern. The current Table 1 includes full details of other interventions (see also edits in p. 9).

- Please provide the p-values in table 1.

Done as suggested.

- Page 7, line 166: “The final sample of 26 individuals included 17 subjects with ASD, four with Asperger syndrome, and four with pervasive developmental disorder…” Did the total number of participants calculated correctly?

Please, refer to the answer to Reviewer #1 (Comment 2).

- Please state the duration of PMBI.

Please, refer to the answer to Reviewer #1 (Comment 2).

- Please describe the statistical analysis of TBSS. Did the authors include age, gender, and intracranial volume as covariates?

We included age and gender as covariates. Intracranial volume was not statistically different across groups and was not included as a regressor (see p. 13).

30 Dec 2021

A Callosal Biomarker of Behavioral Intervention Outcomes for Autism Spectrum Disorder? A Case-Control Feasibility Study with Diffusion Tensor Imaging

PONE-D-21-30729R1

Dear Dr. Virues-Ortega,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Niels Bergsland

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The authors have satisfactorily responded to all my comments and suggestion. This manuscript is now acceptable for publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: Yes: Christina Andica

19 Jan 2022

PONE-D-21-30729R1

A Callosal Biomarker of Behavioral Intervention Outcomes for Autism Spectrum Disorder? A Case-Control Feasibility Study with Diffusion Tensor Imaging

Dear Dr. Virues-Ortega:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Niels Bergsland

Academic Editor

PLOS ONE