Pre- and Post-therapy Assessment of Clinical Outcomes and White Matter Integrity in Autism Spectrum Disorder: Pilot Study

Sharing Our World: Impact of Group Motor Skill Learning on Joint Attention in Children with Autism Spectrum Disorder

Impaired joint attention is a common feature of autism spectrum disorder (ASD), affecting social interaction and communication. We explored if group basketball learning could enhance joint attention in autistic children, and how this relates to brain changes, particularly white matter development integrity. Forty-nine autistic children, aged 4-12 years, were recruited from special education centers. The experimental group underwent a 12-week basketball motor skill learning, while the control group received standard care. Eye-tracking and brain scans were conducted. The 12-week basketball motor skill learning improved joint attention in the experimental group, evidenced by better eye tracking metrics and enhanced white matter integrity. Moreover, reduced time to first fixation correlated positively with decreased mean diffusivity of the left superior corona radiata and left superior fronto-occipital fasciculus in the experimental group. Basketball-based motor skill intervention effectively improved joint attention in autistic children. Improved white matter fiber integrity related to sensory perception, spatial and early attention function may underlie this effect. These findings highlight the potential of group motor skill learning within clinical rehabilitation for treating ASD.

White matter integrity assessment in spinocerebellar ataxia type 2 (SCA2) patients

AIM

To assess the burden of white matter (WM) damage in the cerebrum and cerebellum of spinocerebellar ataxia type 2 (SCA2) patients in an attempt to identify key regions affected by the neurodegenerative processes using diffusion tensor imaging (DTI).

MATERIALS AND METHODS

Nine SCA2 patients and 16 age-matched healthy controls were examined twice (SCA2 patients 3.6 ± 0.7 years and controls 3.3 ± 1.0 years apart) on the same 1.5 T scanner by acquiring T1-weighted and diffusion-weighted (b-value = 1,000 s/mm2) images. Using tract-based spatial statistics, DTI analysis on fractional anisotropy (FA), mean diffusivity (MD), axial (AD)/radial (RD) diffusivity was performed.

RESULTS

At baseline magnetic resonance imaging (MRI), FA was significantly decreased in SCA2 patients in the corticospinal tracts, inferior and superior cerebellar peduncles, middle cerebellar peduncles, cerebral peduncles, right superior and posterior corona radiata. RD was only significantly increased in SCA2 patients in the middle cerebellar peduncles. No significant AD and MD changes were observed. Tract-based spatial statistics (TBSS) analysis between SCA2 patients at baseline and at follow-up showed no significant changes in any of the DTI metrics.

CONCLUSIONS

DTI is a sensitive tool for following the progression of WM neurodegeneration and severity assessment in patients with SCA2. These findings add to a better understanding of the neurological underpinnings of the symptoms experienced by SCA2 patients.

The Effects of Hyperbaric Oxygen Treatment on Verbal Scores in Children With Autism Spectrum Disorder: A Retrospective Trial

Introduction Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects millions worldwide. Suggested pathophysiology includes cerebral hypoperfusion, inflammation, mitochondrial and immune dysregulation, and oxidative stress. Debate exists concerning the benefit of hyperbaric oxygen therapy (HBOT) in treating ASD and its impacts on verbal behavior. The present study directly assesses the impacts of HBOT treatments on verbal behavior using a novel and unique manner.  Materials and methods A two-group quasi-experimental trial using a pretest and a posttest was designed to retrospectively assess (n = 65) any association between HBOT and change in verbal scores in children (n = 65) with ASD. All children completed two verbal tests six months apart, either the Verbal Behavior Milestones Assessment and Placement Program (VBMAPP) or the Assessment of Basic Language and Learning Skills (ABLLS), based on their developmental age. The control cohort received applied behavior analysis (ABA) without HBOT. The experimental cohort received ABA and a minimum of 40 HBOT treatments, breathing 100% oxygen at 2.0 atmosphere absolute (ATA) for 60 minutes. Results Sixty-five children were included, of which 32 received HBOT (mean (M) = 5.1, standard deviation (SD) = 2.93), with an age range of two to 17 years. More than 63% of the subjects had an autism severity level of three. The 23 children administered VBMAPP who received HBOT showed substantial mean differences with high effect sizes (ESs) (-0.743 to -1.65) and a total score (TS) ES equal to -1.23 as measured by Cohen's d. There was a statistically significant improvement (p < 0.05) in all VBMAPP milestone domains and TS. TS change from baseline versus those in the non-HBOT (Control-ABA) group (n=12) was 46.41 ± 20.14 vs 14.42 ± 6.99; p < 0.0001, ES = -1.23. The 30 children administered the ABLLS showed substantial mean difference (TS) change from baseline 268.89 ± 182.05 vs 190.81 ± 135.26 and exhibited small to medium (-.114 to -.773) ESs with a TS ES = -0.487. Due to the high within-group variability (low statistical power) within the ABLLS cohort, there was a non-significant mean difference between the control (ABA) and experimental (ABA + HBO2) groups' difference scores (p > 0.2024), despite the medium (TS) ES. Conclusions The child cohorts administered the VBMAPP and the ABLLS demonstrated substantial improvements between the non-HBOT (control-ABA) and HBOT (experimental-ABA + HBO2) groups as measured by the significant mean differences and small to large ESs. Simply put, the children in the experimental cohort acquired more verbal skills than their counterparts in the control group.

Impaired white matter integrity in infants and young children with autism spectrum disorder: What evidence does diffusion tensor imaging provide?

BACKGROUND

Abnormal functional connections are associated with impaired white matter tract integrity in the brain. Diffusion tensor imaging (DTI) is a promising method for evaluating white matter integrity in infants and young children. This work aims to shed light on the location and nature of the decrease in white matter integrity.

METHODS

Here, the results of 19 studies have been presented that investigated white matter integrity in infants and young children (6 months to 12 years) with autism using diffusion tensor imaging.

RESULTS

In most of the reviewed studies, an increase in Fractional Anisotropy (FA) and a decrease in Radial Diffusivity (RD) were reported in Corpus Callosum (CC), Uncinate Fasciculus (UF), Cingulum (Cg), Inferior Longitudinal Fasciculus (ILF), and Superior Longitudinal Fasciculus (SLF), and in the Inferior Fronto-Occipital Fasciculus (IFOF) tract, a decrease in FA and an increase in RD were reported.

CONCLUSION

In the reviewed articles, except for one study, the diffusion indices were different compared to the control group.

EEG Correlates of Cognitive Functions in a Child with ASD and White Matter Signal Abnormalities: A Case Report with Two-and-a-Half-Year Follow-Up

This research aimed to examine the EEG correlates of different stimuli processing instances in a child with ASD and white matter signal abnormalities and to investigate their relationship to the results of behavioral tests. The prospective case study reports two and a half years of follow-up data from a child aged 38 to 66 months. Cognitive, speech-language, sensory, and EEG correlates of auditory-verbal and auditory-visual-verbal information processing were recorded during five test periods, and their mutual interrelation was analyzed. EEG findings revealed no functional theta frequency range redistribution in the frontal regions favoring the left hemisphere during speech processing. The results pointed to a positive linear trend in the relative theta frequency range and a negative linear trend in the relative alpha frequency range when listening to and watching the cartoon. There was a statistically significant correlation between EEG signals and behavioral test results. Based on the obtained results, it may be concluded that EEG signals and their association with the results of behavioral tests should be evaluated with certain restraints considering the characteristics of the stimuli during EEG recording.

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.

A Tutorial for Implementing Matrix Training in Practice

Matrix training consists of arranging targets for instruction to promote fine-grained stimulus control resulting in the establishment of skills without direct training. Recent reviews of the matrix training literature (Curiel et al., 2020a, b.; Kemmerer et al., 2021) highlighted the efficacy and efficiency of the approach with learners with and without disabilities. These reviews noted substantial variations in procedures across studies, suggesting the approach may be flexibly deployed across content areas and teaching procedures. This outcome is positive for practitioners as they may customize matrix training to meet the unique needs of their clients. However, it also necessitates decision making to sort through the variations in the literature. This tutorial was developed to help practitioners weigh various considerations when using matrix training. Tools and resources are provided to illustrate and accelerate adoption into practice settings.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40617-022-00733-5.

Atypical structural connectivity of language networks in autism spectrum disorder: A meta-analysis of diffusion tensor imaging studies

Patients with autism spectrum disorder (ASD) often show pervasive and complex language impairments that are closely associated with aberrant structural connectivity of language networks. However, the characteristics of white matter connectivity in ASD have remained inconclusive in previous diffusion tensor imaging (DTI) studies. The current meta‐analysis aimed to comprehensively elucidate the abnormality in language‐related white matter connectivity in individuals with ASD. We searched PubMed, Web of Science, Scopus, and Medline databases to identify relevant studies. The standardized mean difference was calculated to measure the pooled difference in DTI metrics in each tract between the ASD and typically developing (TD) groups. The moderating effects of age, sex, language ability, and symptom severity were investigated using subgroup and meta‐regression analysis. Thirty‐three DTI studies involving 831 individuals with ASD and 836 TD controls were included in the meta‐analysis. ASD subjects showed significantly lower fractional anisotropy or higher mean diffusivity across language‐associated tracts than TD controls. These abnormalities tended to be more prominent in the left language networks than in the right. In addition, children with ASD exhibit more pronounced and pervasive disturbances in white matter connectivity than adults. These results support the under‐connectivity hypothesis and demonstrate the widespread abnormal microstructure of language‐related tracts in patients with ASD. Otherwise, white matter abnormalities in the autistic brain could vary depending on the developmental stage and hemisphere.

The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results

Autism spectrum disorder (ASD) is a combination of developmental anomalies that causes social and behavioral impairments, affecting around 2% of US children. Common symptoms include difficulties in communications, interactions, and behavioral disabilities. The onset of symptoms can start in early childhood, yet repeated visits to a pediatric specialist are needed before reaching a diagnosis. Still, this diagnosis is usually subjective, and scores can vary from one specialist to another. Previous literature suggests differences in brain development, environmental, and/or genetic factors play a role in developing autism, yet scientists still do not know exactly the pathology of this disorder. Currently, the gold standard diagnosis of ASD is a set of diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS) or Autism Diagnostic Interview-Revised (ADI-R) report. These gold standard diagnostic instruments are an intensive, lengthy, and subjective process that involves a set of behavioral and communications tests and clinical history information conducted by a team of qualified clinicians. Emerging advancements in neuroimaging and machine learning techniques can provide a fast and objective alternative to conventional repetitive observational assessments. This paper provides a thorough study of implementing feature engineering tools to find discriminant insights from brain imaging of white matter connectivity and using a machine learning framework for an accurate classification of autistic individuals. This work highlights important findings of impacted brain areas that contribute to an autism diagnosis and presents promising accuracy results. We verified our proposed framework on a large publicly available DTI dataset of 225 subjects from the Autism Brain Imaging Data Exchange-II (ABIDE-II) initiative, achieving a high global balanced accuracy over the 5 sites of up to 99% with 5-fold cross validation. The data used was slightly unbalanced, including 125 autistic subjects and 100 typically developed (TD) ones. The achieved balanced accuracy of the proposed technique is the highest in the literature, which elucidates the importance of feature engineering steps involved in extracting useful knowledge and the promising potentials of adopting neuroimaging for the diagnosis of autism.

Growing Up Fast: Managing Autism Spectrum Disorder and Precocious Puberty

CASE

John is a 4-year-old boy with autism spectrum disorder (ASD) and developmental delay who presented with concerns about increasing aggressive behavior at a follow-up visit with his developmental-behavioral pediatrician. Diagnosis of ASD was made via Diagnostic and Statistical Manual of Mental Disorders, 5th version criteria at initial evaluation at 34 months. Medical history at that time was pertinent for rapid linear growth since the age of 1 and recent pubic hair growth and penile enlargement. Family history was significant for early puberty in a maternal uncle and 4 distant maternal relatives. Standardized testing included administration of the Childhood Autism Rating Scale 2-Standard, which was consistent with severe symptoms of ASD, and the Mullen Scales of Early Learning, which indicated moderate delay in fine motor skills and expressive language and severe delay in receptive language and visual receptive skills.At initial assessment, John's parents also reported a pattern of aggressive behavior, which included frequent hitting of other children at childcare, consistently forceful play with peers and family members, and nightly tantrums with hitting and throwing at bedtime. Triggers of aggressive behavior included other children taking his toys, transition away from preferred activities, and being told "no."John was concurrently evaluated by a pediatric endocrinologist at 34 months. At that assessment, his height Z-score was +2.5, and he had Tanner 2 pubic hair, Tanner 3 genitalia, and 6 cc testicular volumes. Radiograph of the hand revealed a bone age of 6 years (+7.8 S.D.). Laboratory studies revealed a markedly elevated testosterone level and low gonadotropin (luteinizing hormone [LH] and follicle-stimulating hormone) levels and a normal dehydroepiandrosterone sulfate, suggestive of peripheral precocious puberty. Targeted genetic testing with sequencing of the LHCGR gene revealed a heterozygous D578G mutation resulting in the rare condition Familial Male-Limited Precocious Puberty (FMPP), characterized by constitutive activation of the LH receptor. FMPP, also referred to as testotoxicosis, was attributed as the cause of John's peripheral precocious puberty.By the age of 4, John's height Z-score was +3.1, his genitalia larger, and his bone age 10 years (+10.3 S.D.). His parents elected to start off-label therapy with bicalutamide (a nonsteroidal antiandrogen) and anastrazole (an aromatase inhibitor), recommended by the endocrinologist. Unexpectedly, as John's hyperandrogenism was treated, John's family reported intensified aggression toward other children and adults, especially at school, in addition to multiple daily instances of biting when upset. What is your next step in John's treatment of his challenging behavior?

REFERENCE

1. Shenker A, Laue L, Kosugi S, et al. A constitutively activating mutation of the luteinizing hormone receptor in familial male precocious puberty. Nature. 1993;365:652-654.

Deviation from normative brain development is associated with symptom severity in autism spectrum disorder

Background

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity.

Methods

The developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6-25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity.

Results

Machine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = - 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity.

Limitations

This work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable.

Conclusions

Findings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.