Abstract
Autism Spectrum Disorder (ASD) encompasses a
range of neurodevelopmental conditions characterized
by social challenges, repetitive behaviors, and communication difficulties. While diagnosis traditionally relies
on behavioral observations, new biomedical approaches,
such as the Research Domain Criteria (RDoC), aim to
identify biomarkers that integrate genetic, neural, and
behavioral factors. Notable biomarkers include genetic
variants, molecular alterations such as abnormal neurotransmitter levels, and markers associated with immune dysfunction. Brain organoids have also enabled
the investigation of specific neural mechanisms. In
neuroimaging, techniques such as functional magnetic
resonance imaging (fMRI) and functional near-infrared
spectroscopy (fNIRS) have identified atypical connectivity patterns in infants at high risk for ASD. Similarly,
measures like electroencephalography (EEG) and eye
tracking have revealed differences in visual attention
and brain activity, while physiological indicators such as electrodermal activity (EDA) and heart rate variability (HRV) reflect sensory and autonomic dysfunctions.
The use of digital biomarkers is rapidly growing, with
devices like tablets and virtual reality capturing data on
children¿s interactions. Analyzed using artificial intelligence, these data show promise for improving early
ASD detection, though further validation is needed.
Integrating traditional and digital approaches is essential for advancing diagnosis and intervention strategies.
