While diagnosing autism spectrum disorder (ASD) based on an objective test is desired, the current diagnostic practice involves observation-based criteria. This study is a systematic review and meta-analysis of studies that aim to diagnose ASD using magnetic resonance imaging (MRI). The main objective is to describe the state of the art of diagnosing ASD using MRI in terms of performance metrics and interpretation. Furthermore, subgroups, including different MRI modalities and statistical heterogeneity, are analyzed. Studies that dichotomously diagnose individuals with ASD and healthy controls by analyses progressing from magnetic resonance imaging obtained in a resting state were systematically selected by two independent reviewers. Studies were sought on Web of Science and PubMed, which were last accessed on February 24, 2023. The included studies were assessed on quality and risk of bias using the revised Quality Assessment of Diagnostic Accuracy Studies tool. A bivariate random-effects model was used for syntheses. One hundred and thirty-four studies were included comprising 159 eligible experiments. Despite the overlap in the studied samples, an estimated 4982 unique participants consisting of 2439 individuals with ASD and 2543 healthy controls were included. The pooled summary estimates of diagnostic performance are 76.0% sensitivity (95% CI 74.1–77.8), 75.7% specificity (95% CI 74.0–77.4), and an area under curve of 0.823, but uncertainty in the study assessments limits confidence. The main limitations are heterogeneity and uncertainty about the generalization of diagnostic performance. Therefore, comparisons between subgroups were considered inappropriate. Despite the current limitations, methods progressing from MRI approach the diagnostic performance needed for clinical practice. The state of the art has obstacles but shows potential for future clinical application.

Functional magnetic resonance imaging (fMRI) has become instrumental in researching the functioning of the brain. One application of fMRI is investigating the brains of people with autism spectrum disorder (ASD). The Autism Brain Imaging Data Exchange (ABIDE) facilitates this research through its extensive data-sharing initiative. While ABIDE offers raw data and data preprocessed with various atlases, independent component analysis (ICA) for dimensionality reduction remains underutilized. ICA is a data-driven way to reduce dimensionality without prior assumptions on delineations. Additionally, ICA separates the noise from the signal, and the signal components correspond well to functional brain networks called resting-state networks (RSNs). Currently, no large, readily available dataset preprocessed with ICA exists. Here, we address this gap by presenting ABIDE’s data preprocessed to extract ICA-based resting-state networks, which are publicly available. These RSNs unveil neural activation clusters without atlas constraints, offering a perspective on ASD analyses that complements the predominantly atlas-based literature. This contribution provides a resource for further research into ASD, benchmarking between methodologies, and the development of new analytical approaches.

Functional magnetic resonance imaging (fMRI) has become instrumental in researching brain function. One application of fMRI is investigating potential neural features that distinguish people with autism spectrum disorder (ASD) from healthy controls. The Autism Brain Imaging Data Exchange (ABIDE) facilitates this research through its extensive data-sharing initiative. While ABIDE offers data preprocessed with various atlases, independent component analysis (ICA) for dimensionality reduction remains underutilized. We address this gap by presenting ICA-based resting-state networks (RSNs) from preprocessed scans from ABIDE, now publicly available: https://github.com/SjirSchielen/groupICAonABIDE. These RSNs unveil neural activation clusters without atlas constraints, offering a perspective on ASD analyses that complements the predominantly atlas-based literature. This contribution provides a valuable resource for further research into ASD, potentially aiding in developing new analytical approaches.

Autism spectrum disorder is a pervasive developmental disorder with heterogeneous symptomatology. Currently, subjective evaluations of behavioral symptoms determine diagnosis and treatment, as the neurological interactions that underpin autism remain largely unknown. This study investigates the relationship between effective connectivity (EC) among resting-state networks and autism symptomatology in a large dataset. EC is estimated with spectral dynamic causal modeling for functional magnetic resonance imaging and then related to Autism Diagnostic Observation Schedule scores through parametric empirical Bayes and statistical correlation analyses. Furthermore, EC is used innovatively in machine learning for individual-level prediction. Group-level analyses reveal multiple connections associated with symptom domains, including the newly identified effective connection from the lateral visual network to the posterior default mode network, which is consistently negatively correlated with scores for restricted and repetitive behaviors. Despite the identified group-level associations between EC and symptom domains, EC shows limited generalizability in symptom prediction and heterogeneity in feature importance at the individual level. These findings suggest the presence of subgroups within the spectrum, where informative connectivity patterns vary between individuals.