Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism

AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions

Autism Spectrum Disorder (ASD) is a neurological condition, with recent statistics from the CDC indicating a rising prevalence of ASD diagnoses among infants and children. This trend emphasizes the critical importance of early detection, as timely diagnosis facilitates early intervention and enhances treatment outcomes. Consequently, there is an increasing urgency for research to develop innovative tools capable of accurately and objectively identifying ASD in its earliest stages. This paper offers a short overview of recent advancements in non-invasive technology for early ASD diagnosis, focusing on an imaging modality, structural MRI technique, which has shown promising results in early ASD diagnosis. This brief review aims to address several key questions: (i) Which imaging radiomics are associated with ASD? (ii) Is the parcellation step of the brain cortex necessary to improve the diagnostic accuracy of ASD? (iii) What databases are available to researchers interested in developing non-invasive technology for ASD? (iv) How can artificial intelligence tools contribute to improving the diagnostic accuracy of ASD? Finally, our review will highlight future trends in ASD diagnostic efforts.

Surface Expansion Regionalization of the Hippocampus in Early Brain Development

The hippocampal formation is implicated in a myriad of crucial functions, particularly centered around memory and emotion, with distinct subdivisions fulfilling specific roles. However, there is no consensus on the spatial organization of these subdivisions, given that the functional connectivity and gene expression-based parcellation along its longitudinal axis differs from the histology-based parcellation along its medial-lateral axis. The dynamic nonuniform surface expansion of the hippocampus during early development reflects the underlying changes of microstructure and functional connectivity, providing important clues on hippocampal subdivisions. Moreover, the thin and convoluted properties bring out the hippocampal maturity largely in the form of expanding surface area. We thus unprecedentedly explore the development-based surface area regionalization and patterns of the hippocampus by leveraging 513 high-quality longitudinal MRI scans during the first two postnatal years. Our findings imply two discrete hippocampal developmental patterns, featuring one pattern of subdivisions along the anterior-posterior axis (head, regions 1 and 5; body, regions 2, 4, 6, and 7; tail, region 3) and the other one along the medial-lateral axis (subiculum, regions 4, 5, and 6; CA fields, regions 1, 2, and 7). Most of the resulting 7 subdivisions exhibit region-specific and nonlinear spatiotemporal surface area expansion patterns with an initial high growth, followed by a transition to low increase. Each subregion displays bilaterally symmetric pattern. The medial portion of the hippocampal head experiences the most rapid surface area expansion. These results provide important references for exploring the fine-grained organization and development of the hippocampus and its intricate cognitions.

Exploring the most discriminative brain structural abnormalities in ASD with multi-stage progressive feature refinement approach

Objective

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by increasing prevalence, diverse impairments, and unclear origins and mechanisms. To gain a better grasp of the origins of ASD, it is essential to identify the most distinctive structural brain abnormalities in individuals with ASD.

Methods

A Multi-Stage Progressive Feature Refinement Approach was employed to identify the most pivotal structural magnetic resonance imaging (MRI) features that distinguish individuals with ASD from typically developing (TD) individuals. The study included 175 individuals with ASD and 69 TD individuals, all aged between 7 and 18 years, matched in terms of age and gender. Both cortical and subcortical features were integrated, with a particular focus on hippocampal subfields.

Results

Out of 317 features, 9 had the most significant impact on distinguishing ASD from TD individuals. These structural features, which include a specific hippocampal subfield, are closely related to the brain areas associated with the reward system.

Conclusion

Structural irregularities in the reward system may play a crucial role in the pathophysiology of ASD, and specific hippocampal subfields may also contribute uniquely, warranting further investigation.

Shedding light on subiculum's role in human brain disorders

Subiculum is a pivotal output component of the hippocampal formation, a structure often overlooked in neuroscientific research. Here, this review aims to explore the role of the subiculum in various brain disorders, shedding light on its significance within the functional-neuroanatomical perspective on neurological diseases. The subiculum's involvement in multiple brain disorders was thoroughly examined. In Alzheimer's disease, subiculum alterations precede cognitive decline, while in epilepsy, the subiculum plays a critical role in seizure initiation. Stress involves the subiculum's impact on the hypothalamic-pituitary-adrenocortical axis. Moreover, the subiculum exhibits structural and functional changes in anxiety, schizophrenia, and Parkinson's disease, contributing to cognitive deficits. Bipolar disorder is linked to subiculum structural abnormalities, while autism spectrum disorder reveals an alteration of inward deformation in the subiculum. Lastly, frontotemporal dementia shows volumetric differences in the subiculum, emphasizing its contribution to the disorder's complexity. Taken together, this review consolidates existing knowledge on the subiculum's role in brain disorders, and may facilitate future research, diagnostic strategies, and therapeutic interventions for various neurological conditions.

Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.

Exposure to Bisphenol F and Bisphenol S during development induces autism-like endophenotypes in adult Drosophila melanogaster

Bisphenol F (BPF) and Bisphenol S (BPS) are being widely used by the industry with the claim of "safer substances", even with the scarcity of toxicological studies. Given the etiological gap of autism spectrum disorder (ASD), the environment may be a causal factor, so we investigated whether exposure to BPF and BPS during the developmental period can induce ASD-like modeling in adult flies. Drosophila melanogaster flies were exposed during development (embryonic and larval period) to concentrations of 0.25, 0.5, and 1 mM of BPF and BPS, separately inserted into the food. When they transformed into pupae were transferred to a standard diet, ensuring that the flies (adult stage) did not have contact with bisphenols. Thus, after hatching, consolidated behavioral tests were carried out for studies with ASD-type models in flies. It was observed that 1 mM BPF and BPS caused hyperactivity (evidenced by open-field test, negative geotaxis, increased aggressiveness and reproduction of repetitive behaviors). The flies belonging to the 1 mM groups of BPF and BPS also showed reduced cognitive capacity, elucidated by the learning behavior through aversive stimulus. Within the population dynamics that flies exposed to 1 mM BPF and 0.5 and 1 mM BPS showed a change in social interaction, remaining more distant from each other. Exposure to 1 mM BPF, 0.5 and 1 mM BPS increased brain size and reduced Shank immunoreactivity of adult flies. These findings complement each other and show that exposure to BPF and BPS during the development period can elucidate a model with endophenotypes similar to ASD in adult flies. Furthermore, when analyzing comparatively, BPS demonstrated a greater potential for damage when compared to BPF. Therefore, in general these data sets contradict the idea that these substances can be used freely.

Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.

Relational memory weakness in autism despite the use of a controlled encoding task

Introduction

Recent work challenged past findings that documented relational memory impairments in autism. Previous studies often relied solely on explicit behavioral responses to assess relational memory integrity, but successful performance on behavioral tasks may rely on other cognitive abilities (e.g., executive functioning) that are impacted in some autistic individuals. Eye-tracking tasks do not require explicit behavioral responses, and, further, eye movements provide an indirect measure of memory. The current study examined whether memory-specific viewing patterns toward scenes differ between autistic and non-autistic individuals.

Methods

Using a long-term memory paradigm that equated for complexity between item and relational memory tasks, participants studied a series of scenes. Following the initial study phase, scenes were re-presented, accompanied by an orienting question that directed participants to attend to either features of an item (i.e., in the item condition) or spatial relationships between items (i.e., in the relational condition) that might be subsequently modified during test. At test, participants viewed scenes that were unchanged (i.e., repeated from study), scenes that underwent an "item" modification (an exemplar switch) or a "relational" modification (a location switch), and scenes that had not been presented before. Eye movements were recorded throughout.

Results

During study, there were no significant group differences in viewing directed to regions of scenes that might be manipulated at test, suggesting comparable processing of scene details during encoding. However, there was a group difference in explicit recognition accuracy for scenes that underwent a relational change. Marginal group differences in the expression of memory-based viewing effects during test for relational scenes were consistent with this behavioral outcome, particularly when analyses were limited to scenes recognized correctly with high confidence. Group differences were also evident in correlational analyses that examined the association between study phase viewing and recognition accuracy and between performance on the Picture Sequence Memory Test and recognition accuracy.

Discussion

Together, our findings suggest differences in the integrity of relational memory representations and/or in the relationships between subcomponents of memory in autism.

Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges

Deep learning has achieved remarkable progress, particularly in neuroimaging analysis. Deep learning applications have also been extended from adult to pediatric medical images, and thus, this paper aims to present a systematic review of this recent research. We first introduce the commonly used deep learning methods and architectures in neuroimaging, such as convolutional neural networks, auto-encoders, and generative adversarial networks. A non-exhaustive list of commonly used publicly available pediatric neuroimaging datasets and repositories are included, followed by a categorical review of recent works in pediatric MRI-based deep learning studies in the past five years. These works are categorized into recognizing neurodevelopmental disorders, identifying brain and tissue structures, estimating brain age/maturity, predicting neurodevelopment outcomes, and optimizing MRI brain imaging and analysis. Finally, we also discuss the recent achievements and challenges on these applications of deep learning to pediatric neuroimaging.

Molecular Abnormalities in BTBR Mice and Their Relevance to Schizophrenia and Autism Spectrum Disorders: An Overview of Transcriptomic and Proteomic Studies

Animal models of psychopathologies are of exceptional interest for neurobiologists because these models allow us to clarify molecular mechanisms underlying the pathologies. One such model is the inbred BTBR strain of mice, which is characterized by behavioral, neuroanatomical, and physiological hallmarks of schizophrenia (SCZ) and autism spectrum disorders (ASDs). Despite the active use of BTBR mice as a model object, the understanding of the molecular features of this strain that cause the observed behavioral phenotype remains insufficient. Here, we analyzed recently published data from independent transcriptomic and proteomic studies on hippocampal and corticostriatal samples from BTBR mice to search for the most consistent aberrations in gene or protein expression. Next, we compared reproducible molecular signatures of BTBR mice with data on postmortem samples from ASD and SCZ patients. Taken together, these data helped us to elucidate brain-region-specific molecular abnormalities in BTBR mice as well as their relevance to the anomalies seen in ASDs or SCZ in humans.