Unpacking neural mechanisms of selective attention in autism spectrum disorder

The National Institute of Child Health and Human Development awarded Albert Einstein College of Medicine $110,228 on December 10, 2026, under the Child Health and Human Development Extramural Research program (CFDA 93.865) to investigate neural mechanisms of selective attention in autism spectrum disorder.

The recipient will conduct a systematic investigation of selective attention to non-social and social stimuli using high-density electroencephalography with a clinical cohort of 60 adolescents ages 13–17, comparing neural responses between children with autism and age-matched typically developing peers. The research addresses a gap in the literature by examining how social stimuli such as faces influence brain mechanisms of attention in individuals with autism spectrum disorder, utilizing an adapted visuospatial task to test whether neural responses to non-social and social stimuli differ between the two groups.

Albert Einstein College of Medicine, a private tax-exempt educational institution headquartered in the Bronx, New York, serves as the recipient. Performance occurs in Bronx, NY 10461. The period of performance extends from December 10, 2026, through June 30, 2030.

Abstract

& abstract in a world rich with sensory information, selective attention allows US to prioritize certain stimuli while suppressing others. This mechanism is guided by top-down processes, which rely on our goals and expectations of a task to focus on relevant information, even amidst competing distractions. These processes are further influenced by social relevance; from infancy, socially-salient stimuli-like faces-are more likely to capture attention, significantly influencing how we engage with our environment. In autism spectrum disorder (ASD), attentional atypicality is highly prevalent, with an estimated 40-70% of individuals also meeting criteria for attention-deficit/hyperactivity disorder. Moreover, disruption of selective attention likely contributes to hallmark symptoms of autism (e.g. sensory and sociability challenges). While previous studies have attempted to explain altered attention in ASD, findings are often conflicting and rely heavily on behavioral evidence. To date, there is no clear consensus on the neural mechanisms underlying altered attention in ASD. Furthermore, despite the well-established link between altered attention and impaired social functioning central to an autism diagnosis, previous research has largely failed to explore how social stimuli (e.g. faces) influence brain mechanisms of attention in individuals with ASD. To bridge this critical GAP, we propose a systematic investigation of selective attention to non-social and social stimuli within a single clinical cohort (n=60, 13-17 YO), utilizing high-density electroencephalography (EEG) to elucidate and quantify neural mechanisms of attention in individuals with and without autism. In this work, we will adapt a well-characterized visuospatial task to test how neural responses to non-social and social stimuli (aim 1) differ between children with autism and age-matched typically-developing (TD) counterparts. Comparison of neural markers well-known to reflect attention related enhancement (larger visual P1 and N1) and suppression (increased alpha-band power) allows US to quantify attention to visual stimuli for each participant. Presentation of both non-social (houses) and social (faces) stimuli enables US to evaluate the degree to which social salience modulates attention. We theorize individuals with ASD will exhibit reduced neural markers of attention driven sensory gating (alpha- band modulation) and sensory enhancement (enhanced P1/N1S). We expect these differences will be exacerbated in response to social stimuli; individuals with ASD, who often show marked inattention to faces, may lack social-dependent modulation of attention that is evident in TD counterparts. Neural recordings will be complemented with clinical and cognitive assessments (ados-2, SRS-2, CPT-3) to establish links between attentional profiles and ASD symptomatology. Using a cluster-based machine learning approach, we aim to uncover potential ASD subtypes based on neural patterns and clinical symptoms (aim 2), which may offer opportunities to identify personalized intervention strategies.