From Brain Networks to Behavior: Using Functional Connectivity to Understand Autism Spectrum Disorder

Image Citation: [1]

By Shreya Karthik ‘29

Edited by Thomas Wang ‘26 

It is estimated that 1 in every 88 children experiences some form of Autism Spectrum Disorder (ASD), with a prevalence of about 1% globally [2, 3, 4]. ASD is a group of neurodevelopmental disorders broadly characterized by deficits in social behavior, a pattern of restrictive and repetitive behaviors [5, 6, 7], and difficulties processing sensory stimuli [8, 9, 10]. 

The impact of an ASD diagnosis is substantial in educational, medical, and social domains of daily life, which can reduce adaptability to daily life. Longitudinal studies report that 50% of individuals with ASD have poor or very poor long-term outcomes [11]. Earlier intervention can alter these numbers for a more positive life outlook; however, for this, an earlier and more accurate diagnosis must be facilitated. Currently, the gold standards for the diagnosis of ASD–the Autism Diagnostic Observation Schedule 2 and the Autism Diagnostic Interview-Revised–only work well for children at least one year old, and their predictive validity decreases when the examiner is not the primary diagnostician [9]. Such limitations delay the ability to diagnose and help children with ASD early in life, in turn reducing the effectiveness of behavioral treatments and long-term life outcomes [9]. Instead, focusing on the comparatively less explored underlying neurophysiological mechanisms can identify biomarkers that enable a greater understanding of the causes of ASD, also facilitating earlier and more precise diagnosis and intervention [9, 12]. 

For future studies measuring neural connectivity to determine the neural circuitry of ASD, resting-state functional magnetic resonance imaging (rs-fMRI) is an ideal tool. rs-fMRI can be obtained in 5-6 minutes without a task, which is ideal for individuals with ASD with a wide range of cognitive abilities [9]. It reveals the functional connectivity (FC) of brain regions, providing insight into the large-scale organization of the typical and atypical brain for comparison, which is ideal since recent studies have reached a universal agreement that ASD is associated with alterations in brain FC [13, 14].

Figure 1: Sagittal cut and lateral view of the brain depicting regions of the social brain(purple) and regions with lowered connectivity to the social brain (green).

Typically, studies have found that youth with ASD show hypoconnectivity [14].  More specifically, a systematic review and meta-analysis on task-based fMRI studies and a separate study found that the most reliable finding was a disturbance to the function of social brain regions in those with ASD [15, 9]. The social brain includes regions activated during social tasks to facilitate cognition in the following facets: evaluating others’ mental states (i.e., feelings, disposition, and actions), and social communication [16]; processes such as the theory of mind, empathy, and recognizing others’ facial expressions. While individual studies may report different activation loci during these processes, some regions that are consistently activated across studies involving the social brain include the medial prefrontal cortex, temporal parietal junction, posterior superior temporal sulcus, inferior frontal gyrus, amygdala, and anterior cingulate cortex among others to be a critical part of the social brain [17,16, 18, 19, 20, 21, 5, 22, 23, 24, 25, 26]. 

Despite this considerable body of knowledge, an fMRI-based biomarker of ASD has yet to be identified. There are most likely three reasons for these discrepancies (1) resting state studies only each target a few regions at a time; (2) most studies do not account for the heterogeneity of the severity of ASD; and (3) research on youth with ASD is lacking in comparison to adults [9, 12]. With each study only examining a few regions at a time, it has provided a limited understanding of the full picture. Additionally, as most studies focus on group differences using a traditional case-control approach, providing an understanding of only an average ASD patient, this lends to a failure to address the full heterogeneity seen on the spectrum despite the fact that individual brain differences would be very helpful in widening understanding of clinically relevant information and understand the common neurological base alongside clinically heterogeneous symptoms [15]. 

To address the aforementioned gaps, future studies should examine younger populations, conducting a whole-brain FC analysis using rs-fmri data with two objectives: (1) identify deficits in FC in those with ASD with a traditional case-control analysis and (2) compare the deficits in ASD FC to scores on behavioral measures of social communication to investigate whether the FC irregularities can implicate the severity of social behavior deficits in individuals with ASD. 

The results of such research hold tremendous value as they could give insight into not only the conserved and common neurological disruptions of ASD – aid in the identification of biomarkers of ASD– but also what disruptions give way to the diversity of symptom severity seen on the spectrum– biomarkers of heterogeneity of ASD. Such an implication would allow for identified regions to be used as an objective diagnostic method for ASD, especially considering that the analysis of resting state fMRI has no subjectivity involved, like typically used behavioral assessments do. In the future, these results can be augmented upon for the identification of ASD at earlier ages, and also measure the severity of symptoms to create individualized treatment plans that appreciate the heterogeneity of the spectrum and lead to better life stability outcomes. Ultimately, ASD is not just a neurodevelopmental disorder; it is a change in lifestyle and interaction that must be understood from an individual perspective, taking into consideration the diversity of symptoms on the neural and social level.

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