Functional Connectome-Based Predictive Modeling in Autism

Abstract

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

Keywords: Clinical translation; Development; Fingerprinting; Individual differences; Machine learning; Resting-state fMRI.

Figure 1.

Predictive modeling applications in autism. (A) Prediction-based approaches can serve two needs in autism research: they can help to disentangle the complex brain-based features giving rise to autism symptomatology (biological insight) or be used to potentially inform decisions related to providing care for individuals with autism (clinical utility). Because brain-based insights and clinically useful models are interdependent, their discussion is interwoven throughout the manuscript. (B) Three frameworks for prediction-based modeling using functional connectivity data that we discuss in this review: case-control classification, dimensional prediction, and subtyping. Dim., dimension.

Figure 2.

Windows of intervention in autism. The schematic illustrates the clinical utility of correctly identifying a hypothetical individual with autism and then acting on that information to provide appropriate support services. The dark line indicates the individual with autism and the impact of their symptoms (broadly conceived, on the y-axis) over time if no support services are accessed. If autism is diagnosed early (in childhood and adolescence), resources can be allocated to the individual and their caregivers (pink and blue dotted lines, respectively). If correct diagnosis and interventions are delayed, resources can still be leveraged later in life, although they might be less efficacious. The green shading indicates the utility of correct diagnosis and allocation of resources; the darker the green color, the more responsive individuals might be to support services. We stress that this is a hypothetical example; symptoms might not increase from childhood to adolescence, and individuals with late diagnoses might not necessarily have more significant symptoms overall. Indeed, trajectories of symptoms vary across individuals and can vary at different points in the lifespan.

Figure 3.

Case-control prediction is possible using measures of infant brain functional connectivity. (A) Classifying 24-month-olds using 6-month-old imaging data. Classification accuracy was 96.6%. (B) Post hoc visualization of functional connections and their relationship to different phenotypic scales. A red line indicates a connection that shows more negative connectivity in the autism group, whereas a blue line indicates more positive connectivity. ASD, autism spectrum disorder; CSBS, Communication and Symbolic Behavior Scales; MSEL, Mullen Scales of Early Learning; RBS-R, Repetitive Behaviors Scale-Revised. Adapted with permission from (48).

Figure 4.

Dimensional prediction of autism symptoms. (A) Models predictive of autism symptoms are built on training data and then validated on left-out testing data within the same dataset. Predicted symptom scores from this process are shown on the y-axis; observed symptom scores are shown on the x-axis. (B) Post hoc visualization of predictive functional features (data are summarized at the node level and are shaded according to degree). (C) Application of the predictive model derived from autism symptoms to an external dataset to predict attention-deficit/hyperactivity disorder (ADHD) symptoms in young children. ADOS, Autism Diagnostic Observation Schedule; BA, Brodmann area; ROI, region of interest [as defined in (33)]; SRS, Social Responsiveness Scale. Adapted with permission from (27).

Figure 5.

Subtyping connectomes in autism. (A) Easson et al. (109) identified two subtypes. Each is composed of individuals with and without autism. These subtypes exhibit differences in functional connectivity patterns; an average matrix for each subtype is shown. (B) A multivariate brain-behavior analysis (partial least squares regression) reveals that subtypes exhibit unique brain-behavior relationships among a set of key behavioral measures in autism. ADOS, Autism Diagnostic Observation Schedule; CN, cerebellar network; Comm., communication; CON, cingulo-opercular network; DMN, default mode network; FPN, frontoparietal network; ON, occipital network; RRB, restricted repetitive behaviors; SA, social affect; SMN, sensorimotor network; SRS, Social Responsiveness Scale. Adapted with permission from (109).