Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals

The Future of Disentangling the Heterogeneity of Autism With Neuroimaging Studies

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition. Over the past decade, a considerable number of approaches have been developed to identify potential neuroimaging-based biomarkers of ASD that have uncovered specific neural mechanisms that underlie behaviors associated with ASD. However, the substantial heterogeneity among individuals who are diagnosed with ASD hinders the development of biomarkers. Disentangling the heterogeneity of ASD is pivotal to improving the quality of life for individuals with ASD by facilitating early diagnosis and individualized interventions for those who need support. In this review, we discuss recent advances in neuroimaging that have facilitated the characterization of the heterogeneity of this condition using 3 frameworks: neurosubtyping, dimensional models, and normative models. We also discuss the challenges, possible solutions, and clinical utility of these 3 frameworks. We argue that several factors need to be considered when parsing heterogeneity using neuroimaging, including co-occurring conditions, neurodevelopment, heredity and environment, and multisite and multimodal data. We close with a discussion of future directions for achieving a better understanding of the neural mechanisms that underlie neurodevelopmental heterogeneity and the future of precision medicine in ASD.

Predicting individual autistic symptoms for patients with autism spectrum disorder using interregional morphological connectivity

Intelligent predictive models for autistic symptoms based on neuroimaging datasets were beneficial for the precise intervention of patients with ASD. The goals of this study were twofold: investigating predictive models for autistic symptoms and discovering the brain connectivity patterns for ASD-related behaviors. To achieve these goals, we obtained a cohort of patients with ASD from the ABIDE project. The autistic symptoms were measured using the Autism Diagnostic Observation Schedule (ADOS). The anatomical MRI datasets were preprocessed using the Freesurfer package, resulting in regional morphological features. For each individual, the interregional morphological network was constructed using a novel feature distance-based method. The predictive models for autistic symptoms were built using the support vector regression (SVR) algorithm with feature selection method. The predicted autistic symptoms (i.e., ADOS social score, ADOS behavior) were significantly correlated to the original measures. The most predictive features for ADOS social scores were located in the bilateral fusiform. The most predictive features for ADOS behavior scores were located in the temporal pole and the lingual gyrus. In summary, the autistic symptoms could be predicted using the interregional morphological connectivity and machine learning. The interregional morphological connectivity could be a potential biomarker for autistic symptoms.

Attention-based fusion of multiple graphheat networks for structural to functional brain mapping

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks and graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attention-based fusion of multiple GraphHeat networks (A-GHN) for mapping SC-FC. A-GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 1058 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. On HCP data, we achieve a high Pearson correlation of 0.788 (Desikan-Killiany atlas with 87 regions) and 0.773 (AAL atlas with 86 regions). Furthermore, experiments demonstrate that A-GHN outperforms the existing methods in learning the complex nature of the structure-function relation of the human brain.

Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes

The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry.

Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review

In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.

Predicting the Symptom Severity in Autism Spectrum Disorder Based on EEG Metrics

Autism spectrum disorder (ASD) is associated with the impaired integrating and segregating of related information that is expanded within the large-scale brain network. The varying ASD symptom severities have been explored, relying on their behaviors and related brain activity, but how to effectively predict ASD symptom severity needs further exploration. In this study, we aim to investigate whether the ASD symptom severity could be predicted with electroencephalography (EEG) metrics. Based on a publicly available dataset, the EEG brain networks were constructed, and four types of EEG metrics were calculated. Then, we statistically compared the brain network differences among ASD children with varying severities, i.e., high/low autism diagnostic observation schedule (ADOS) scores, as well as with the typically developing (TD) children. Thereafter, the EEG metrics were utilized to validate whether they could facilitate the prediction of the ASD symptom severity. The results demonstrated that both high- and low-scoring ASD children showed the decreased long-range frontal-occipital connectivity, increased anterior frontal connectivity and altered network properties. Furthermore, we found that the four types of EEG metrics are significantly correlated with the ADOS scores, and their combination can serve as the features to effectively predict the ASD symptom severity. The current findings will expand our knowledge of network dysfunction in ASD children and provide new EEG metrics for predicting the symptom severity.

Ways to Address Perinatal Mast Cell Activation and Focal Brain Inflammation, including Response to SARS-CoV-2, in Autism Spectrum Disorder

The prevalence of autism spectrum disorder (ASD) continues to increase, but no distinct pathogenesis or effective treatment are known yet. The presence of many comorbidities further complicates matters, making a personalized approach necessary. An increasing number of reports indicate that inflammation of the brain leads to neurodegenerative changes, especially during perinatal life, "short-circuiting the electrical system" in the amygdala that is essential for our ability to feel emotions, but also regulates fear. Inflammation of the brain can result from the stimulation of mast cells-found in all tissues including the brain-by neuropeptides, stress, toxins, and viruses such as SARS-CoV-2, leading to the activation of microglia. These resident brain defenders then release even more inflammatory molecules and stop "pruning" nerve connections, disrupting neuronal connectivity, lowering the fear threshold, and derailing the expression of emotions, as seen in ASD. Many epidemiological studies have reported a strong association between ASD and atopic dermatitis (eczema), asthma, and food allergies/intolerance, all of which involve activated mast cells. Mast cells can be triggered by allergens, neuropeptides, stress, and toxins, leading to disruption of the blood-brain barrier (BBB) and activation of microglia. Moreover, many epidemiological studies have reported a strong association between stress and atopic dermatitis (eczema) during gestation, which involves activated mast cells. Both mast cells and microglia can also be activated by SARS-CoV-2 in affected mothers during pregnancy. We showed increased expression of the proinflammatory cytokine IL-18 and its receptor, but decreased expression of the anti-inflammatory cytokine IL-38 and its receptor IL-36R, only in the amygdala of deceased children with ASD. We further showed that the natural flavonoid luteolin is a potent inhibitor of the activation of both mast cells and microglia, but also blocks SARS-CoV-2 binding to its receptor angiotensin-converting enzyme 2 (ACE2). A treatment approach should be tailored to each individual patient and should address hyperactivity/stress, allergies, or food intolerance, with the introduction of natural molecules or drugs to inhibit mast cells and microglia, such as liposomal luteolin.