Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism

Abstract

Autism spectrum disorder (ASD) is a lifelong condition with elusive biological mechanisms. The complexity of factors, including inter-site and developmental differences, hinders the development of a generalizable neuroimaging classifier for ASD. Here, we developed a classifier for ASD using a large-scale, multisite resting-state fMRI dataset of 730 Japanese adults, aiming to capture neural signatures that reflect pathophysiology at the functional network level, neurotransmitters, and clinical symptoms of the autistic brain. Our adult ASD classifier was successfully generalized to adults in the United States, Belgium, and Japan. The classifier further demonstrated its successful transportability to children and adolescents. The classifier contained 141 functional connections (FCs) that were important for discriminating individuals with ASD from typically developing controls. These FCs and their terminal brain regions were associated with difficulties in social interaction and dopamine and serotonin, respectively. Finally, we mapped attention-deficit/hyperactivity disorder (ADHD), schizophrenia (SCZ), and major depressive disorder (MDD) onto the biological axis defined by the ASD classifier. ADHD and SCZ, but not MDD, were located proximate to ASD on the biological dimensions. Our results revealed functional signatures of the ASD brain, grounded in molecular characteristics and clinical symptoms, achieving generalizability and transportability applicable to the evaluation of the biological continuity of related diseases.

Fig. 1. Overview of analytical procedures.

We first constructed a classifier for distinguishing individuals with autism spectrum disorder (ASD) from typically developing controls (TDCs). Glasser’s atlas was used to construct a functional connectome for each individual, and a logistic regression with least absolute shrinkage and selection operator (LASSO) was trained using a tenfold nested cross-validation (CV) with 10 subsamples. The external validity (i.e., generalizability and transportability) of the adult ASD classifier was evaluated using five independent validation datasets from different imaging sites and developmental stages. Then, the potential associations of discriminative features with molecular profiles as well as clinical symptoms were evaluated. Finally, the relationships between ASD and other psychiatric disorders were investigated.

Fig. 2. Classification performance of the ASD classifier in the discovery and five validation datasets.

A The probability of the ASD diagnosis in the discovery dataset. B The probability of the ASD diagnosis in the ABIDE adult validation dataset. C The probability of the ASD diagnosis in the Japanese adult validation dataset. D The probability of ASD diagnosis in the child dataset. E The probability of ASD diagnosis in the adolescent dataset. F The probability of ASD diagnosis in the Healthy Brain Network (HBN) dataset. ABIDE Autism Brain Imaging Data Exchange, AUC area under the curve, ASD autism spectrum disorder, MCC Matthews correlation coefficient, and TDC typically developing control.

Fig. 3. Spatial distribution and network-based characterization of discriminative functional connections for the ASD diagnosis.

A The spatial distribution of discriminative hyper-connections and affected brain regions for the ASD diagnosis. B The spatial distribution of discriminative hypo-connections and affected brain regions for the ASD diagnosis. The node color represents the corresponding resting-state network. C Network-based characterization of hyper-connections. D Network-based characterization of hypo-connections. Edges with red and blue colors indicate that they reach statistical significance (p < 0.05 after Bonferroni correction), whereas those with gray color do not. ASD autism spectrum disorder, DAN dorsal attention network, DMN default mode network, FPN frontoparietal network, TDC typically developing control, and VAN ventral attention network.

Fig. 4. Associations of the clinical symptoms and neurotransmitters with discriminative FCs.

Multivariate statistical methods identified the potential associations of the clinical symptoms and molecular profiles with discriminative FCs. A In the first latent component, 17 out of 141 discriminative FCs contributed to the FC-related composite score. FCs in red colors indicate that as the strength of these FCs increases, the severity of both symptoms decreases (i.e., negative correlation), whereas those in blue colors indicate that as the strength of these FCs increases, the severity of symptoms increases (i.e., positive correlation). B In the second latent component, FCs with red colors indicate that as the strength of these FCs increases, the severity of ADOS-B decreases (i.e., negative correlation), but the severity of ADOS-A increases (i.e., positive correlation). In contrast, FCs with blue colors indicate that as the strength of these FCs increases, the severity of ADOS-B increases (i.e., positive correlation), but the severity of ADOS-A decreases (i.e., negative correlation). C Ranking neurotransmitter importance to discriminative FCs. The pink color indicates statistically significant contributions to discriminative FCs (p < 0.05), whereas the gray color indicates statistically insignificant contributions. ADOS Autism Diagnostic Observations Schedule.

Fig. 5. Distribution of atypical functional connections that are consistent across the datasets.

A Pie-donut chart showing the details of reproducible hypo- and hyper-connections. B The spatial distribution of discriminative hyper-connections consistent across cohorts. C The spatial distribution of discriminative hypo-connections consistent across cohorts. DAN dorsal attention network, DMN default mode network, FPN frontoparietal network, L left, N Negative, P positive, R right, and VAN ventral attention network.

Fig. 6. Relationships between ASD and other psychiatric disorders on the biological axes defined by each classifier.

A Sensitivity of the classifier for autism spectrum disorder (ASD) to attention-deficit/hyperactivity disorder (ADHD), schizophrenia (SCZ), and major depressive disorder (MDD). B Sensitivity of the SCZ classifier to ASD. C Sensitivity of the MDD classifier to ASD.