Autism spectrum disorder (ASD) exhibits pronounced heterogeneity across genetic, neurobiological, and clinical phenotypic levels, posing substantial challenges for mechanistic elucidation and clinical translation. This review synthesizes advances in data-driven approaches to parsing ASD heterogeneity and centers the discussion on three complementary strata: neural, behavioral, and transdiagnostic subtypes. At the neuroimaging level, studies leveraging features such as functional connectivity and brain structure have consistently identified two core neurosubtypes characterized by increased and decreased neural activity, respectively. These neurosubtypes differ in time-varying dynamics, spatial architecture, and network hierarchy, and they are closely associated with specific symptom dimensions and cognitive functions. At the behavioral level, data-driven methods delineate phenotypes along axes of severity and functional impairment, and further reveal their links to neural circuits. Transdiagnostic investigations indicate that ASD and frequently co-occurring disorders share neurobiological substrates and cognitive endophenotypes. Collectively, these findings argue against a simple one-to-one correspondence between behavioral and neural subtypes; instead, the evidence is more consistent with multi-to-one, one-to-many, or many-to-many mappings that converge on the overall functional impairment. Notwithstanding this progress, major challenges remain, including sample heterogeneity, methodological inconsistency, and the integration of categorical and dimensional models. Future research should prioritize large samples, multi-site collaboration, longitudinal designs, and transdiagnostic frameworks, coupled with reverse validation via intervention response, to build robust evidence for mechanism-informed individualized assessment and intervention in ASD.
Xingke Wang, Zhou Zhang, Shuang Li et al.· Translational Psychiatry· 0 citations
BACKGROUND
Social deficit in autism spectrum disorder (ASD) varies substantially across individuals, yet the neural mechanisms underlying this variability remain poorly understood. Resting state electrophysiological measures may under-engage social information processing and may be less sensitive to ASD-related neural differences. Here we combined EEG with eye tracking during a low demand viewing paradigm to probe neural dynamics and to identify data-driven neurodynamic modes associated with variability in social orienting.
METHODS
We recruited 88 autistic and 71 typically developing (TD) participants for eyes-open resting-state EEG. A subset of these participants, including 58 autistic and 61 TD participants, additionally completed a Social vs. Geometric paradigm with simultaneous EEG and eye tracking. Alpha-band resting-state and task-state EEG were segmented into five microstate (MS) classes (A-E). We compared MS temporal and complexity features between conditions and used support vector machine classification to test whether resting-state or task-state MS features better differentiated ASD from TD participants. For the more discriminative condition, MS-based alpha activity was further characterized by amplitude and phase-locking value (PLV). Participant-level MS-based PLV features were then used for k-means clustering, and moderation models examined whether PLV shaped the association between autistic traits and social orienting.
RESULTS
Task-state MS features differentiated ASD from TD more accurately than resting-state features. Group differences were primarily expressed in MS-based alpha PLV across the five MS classes, whereas alpha amplitude showed no significant group differences. Clustering identified two PLV-based synchronization modes that were present in both ASD and TD participants. Within ASD, these modes differed in social orienting, and MS A PLV moderated the association between autistic traits and social scene preference ratio.
LIMITATIONS
Given the cross-sectional design, tracing the developmental trajectories of these distinct neurodynamic modes will require future multi-center, longitudinal tracking.
CONCLUSIONS
These findings suggest that social orienting variability within ASD is associated with heterogeneous neurodynamic modes that become most visible under naturalistic social input and are more strongly associated with phase synchronization.
Xingke Wang, Sheng Yang, Heli Lu et al.· Molecular Autism· 0 citations
