Structural MRI in autism: Findings and future directions

Task-based functional neuroimaging in infants: a systematic review

Background

Infancy is characterized by rapid neurological transformations leading to consolidation of lifelong function capabilities. Studying the infant brain is crucial for understanding how these mechanisms develop during this sensitive period. We review the neuroimaging modalities used with infants in stimulus-induced activity paradigms specifically, for the unique opportunity the latter provide for assessment of brain function.

Methods

Conducted a systematic review of literature published between 1977-2021, via a comprehensive search of four major databases. Standardized appraisal tools and inclusion/exclusion criteria were set according to the PRISMA guidelines.

Results

Two-hundred and thirteen papers met the criteria of the review process. The results show clear evidence of overall cumulative growth in the number of infant functional neuroimaging studies, with electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to be the most utilized and fastest growing modalities with behaving infants. However, there is a high level of exclusion rates associated with technical limitations, leading to limited motor control studies (about 6 % ) in this population.

Conclusion

Although the use of functional neuroimaging modalities with infants increases, there are impediments to effective adoption of existing technologies with this population. Developing new imaging modalities and experimental designs to monitor brain activity in awake and behaving infants is vital.

Neuroanatomical correlates of individual differences in the object choice task in chimpanzees (Pan troglodytes)

Declarative and imperative joint attention or joint engagement are important milestones in human infant development. These have been shown to be a significant predictor of later language development and are impaired in some individuals with, or at risk for, a diagnosis of autism spectrum disorder. Comparatively, while chimpanzees and other great apes have been reported to engage in imperative joint attention, evidence of declarative joint attention remains unclear based on existing studies. Some have suggested that differences in methods of assessing joint attention may have an influence on performance in nonhuman primates. Here, we report data on a measure of receptive joint attention (object choice task) in a sample of captive chimpanzees. Chimpanzees, as a group, performed significantly better than chance. By contrast, when considering individual performance, there was no significant difference in the number of those who passed and those who failed. Using quantitative genetic analyses, we found that performance on the object choice task was not significantly heritable nor were there any significant effects of sex, rearing history, or colony. Lastly, we found significant differences in gray matter covariation, between those who passed or failed the task. Those who passed contributed more to gray matter covariation in several brain regions within the social brain network, consistent with hypotheses regarding the importance of these regions in human and nonhuman primate social cognition.

The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study

Background

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain.

Objective

This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics.

Methods

We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature.

Results

On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation.

Conclusions

Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.

Alterations in Local Connectivity and Their Developmental Trajectories in Autism Spectrum Disorder: Does Being Female Matter?

Autism spectrum disorder (ASD) is diagnosed more often in males with a ratio of 1:4 females/males. This bias is even stronger in neuroimaging studies. There is a growing evidence suggesting that local connectivity and its developmental trajectory is altered in ASD. Here, we aim to investigate how local connectivity and its age-related trajectories vary with ASD in both males and females. We used resting-state fMRI data from the ABIDE I and II repository: males (n = 102) and females (n = 92) with ASD, and typically developing males (n = 104) and females (n = 92) aged between 6 and 26. Local connectivity was quantified as regional homogeneity. We found increases in local connectivity in participants with ASD in the somatomotor and limbic networks and decreased local connectivity within the default mode network. These alterations were more pronounced in females with ASD. In addition, the association between local connectivity and ASD symptoms was more robust in females. Females with ASD had the most distinct developmental trajectories of local connectivity compared with other groups. Overall, our findings of more pronounced local connectivity alterations in females with ASD could indicate a greater etiological load for an ASD diagnosis in this group congruent with the female protective effect hypothesis.

"Autism-plus" spectrum disorders: intersection with psychosis and the schizophrenia spectrum

Patients are often encountered clinically who have autism spectrum disorders (ASD) and also have symptoms suggestive of a comorbid psychotic disorder. A careful assessment for the presence of comorbid disorders is important. However, the core deficits seen in ASD, in social reciprocity, communication, and restricted behaviors and interests, can be mistaken for psychosis. Also, there is a subset of patients who present with a complex neurodevelopmental disorder with impairments that cross diagnostic categories. This article reviews the connections between ASD and psychosis, and highlights the key points to consider in patients who present with these "autism-plus" disorders.

Resting state EEG abnormalities in autism spectrum disorders

Autism spectrum disorders (ASD) are a group of complex and heterogeneous developmental disorders involving multiple neural system dysfunctions. In an effort to understand neurophysiological substrates, identify etiopathophysiologically distinct subgroups of patients, and track outcomes of novel treatments with translational biomarkers, EEG (electroencephalography) studies offer a promising research strategy in ASD. Resting-state EEG studies of ASD suggest a U-shaped profile of electrophysiological power alterations, with excessive power in low-frequency and high-frequency bands, abnormal functional connectivity, and enhanced power in the left hemisphere of the brain. In this review, we provide a summary of recent findings, discuss limitations in available research that may contribute to inconsistencies in the literature, and offer suggestions for future research in this area for advancing the understanding of ASD.

Initiation of joint attention is associated with morphometric variation in the anterior cingulate cortex of chimpanzees (Pan troglodytes)

In developing human children, joint attention (JA) is an important preverbal skill fundamental to the development of language. Poor JA skills have been described as a behavioral risk factor for some neurodevelopmental disorders, such as autism spectrum disorder. It has been hypothesized that the anterior cingulate cortex (ACC) plays an important role in the development of JA in human children. Here, we tested whether the morphometry and lateralization of the ACC differed between chimpanzees that were classified as either consistently or inconsistently engaging in JA with a human experimenter. Results showed that chimpanzees that performed poorly on the JA task had larger gray matter (GM) volumes in the ACC compared to apes that performed well on the task. In addition, both population-level asymmetries and sex differences in the volume of GM were found within the ACC. Specifically, females had relatively larger GM volumes in two of the three subregions of the ACC compared to males, and significant leftward asymmetries were found for two of the subregions whereas a rightward bias was observed in the third. Based on these findings, we suggest that the ACC plays an important role in mediating JA, not just in humans, but also chimpanzees. We further suggest that the differences found between groups may reflect inherent differences in the amount of white matter within the ACC, thereby suggesting reduced connectivity between the ACC and other cortical regions in chimpanzees with poor JA skills.

Autism spectrum disorders and childhood-onset schizophrenia: clinical and biological contributions to a relation revisited

OBJECTIVE

To highlight emerging evidence for clinical and biological links between autism/pervasive developmental disorder (PDD) and schizophrenia, with particular attention to childhood-onset schizophrenia (COS).

METHOD

Clinical, demographic, and brain developmental data from the National Institute of Mental Health (and other) COS studies and selected family, imaging, and genetic data from studies of autism, PDD, and schizophrenia were reviewed.

RESULTS

In the two large studies that have examined this systematically, COS is preceded by and comorbid with PDD in 30% to 50% of cases. Epidemiological and family studies find association between the disorders. Both disorders have evidence of accelerated trajectories of anatomic brain development at ages near disorder onset. A growing number of risk genes and/or rare small chromosomal variants (microdeletions or duplications) are shared by schizophrenia and autism.

CONCLUSIONS

Biological risk does not closely follow DSM phenotypes, and core neurobiological processes are likely common for subsets of these two heterogeneous clinical groups. Long-term prospective follow-up of autistic populations and greater diagnostic distinction between schizophrenia spectrum and autism spectrum disorders in adult relatives are needed.