Introduction
Schizophrenia and Autism Spectrum Disorders (ASD) are common heterogenous conditions that have historically been considered distinct. Recently, cognitive and behavioural overlap between them has been suggested, following observations that they share similarities in psychosocial and pathophysiological features and phenotypes that may be signatures of underlying comorbidity (Chisholm, Lin, Abu-Akel & Wood, 2015). Clinically-motivated research has focused on practical rigid diagnostic clarification with limited consideration of variation within specific symptoms (Ford, Apputhurai, Meyer, & Crewther, 2018). Improving the mechanistic understanding of variable cognition and behaviour, and the underlying genetic and neural correlates is a crucial step towards better characterisation, which could ultimately enhance diagnostic and treatment practices (De Giorgi et al., 2019; Klopper et al., 2017). We pursue a mechanistic approach here by probing phenotype variability within a healthy population.
Schizotypy refers to a set of personality traits mirroring symptoms of schizophrenia at a subclinical level, characterised by dimensions (positive, negative and disorganised) that run on a continuum from healthy to psychosis (Nelson, Seal, Pantelis, & Phillips, 2013; Raine, 1991). In a large healthy sample (N=1678), Ford et al. (2018) used latent profile analysis to identify eight clusters encompassing dimensions of schizotypy and ASD. Subgroups involving psychosocial difficulties represented a shared social autism-negative schizotypy domain (Abu-Akel et al., 2017), an autism-schizotypy subgroup reflected a non-specific overlap of ASD and schizotypal traits, while psychosis and a moderate aspect of schizotypy appeared to be independent. Further research identified consistent overlapping and diametrically opposed facets of phenotypes. With a sample of N=640, Nenadić et al. (2021) used Principal Components Analysis (PCA) to study multiple psychometric measures of schizotypy and ASD in a German and separate Swiss/French population. They identified loss of function and communication deficits as phenotypes of ASD traits showing convergence with negative and disorganized features of schizotypy. However, attention to detail in ASD was diametrically opposed to positive schizotypal trait dimensions.
Poor Smooth Pursuit Eye Movements (SPEM) are an established endophenotype of schizophrenia and schizotypy. Deficits in motion processing and target prediction in schizophrenia contribute to SPEM abnormalities (Barnes, 2008) with findings replicated in schizotypy (Koychev et al., 2016). Often thought of as two separate systems, SPEM and saccadic movements form part of a single sensorimotor repertoire, served by continuous estimates of future eye positions and velocity-related error signals (Goettker & Gegenfurtner, 2021). Along the pathway driving the oculomotor response, there are dynamic interactions between input, predictions and errors enabling tracking whilst maintaining perceptual stability (Goettker, Braun, & Gegenfurtner, 2019). We previously showed that motion tracking under gravity may present a means of studying individual differences in incorporating predicted gravity into cognitive processing (Meso, De Vai, Mahabeer & Hills, 2020). Predictions of future positions of moving objects exploit gravity and participants’ eye movements are guided by prediction to different extents (Jörges & López-Moliner, 2019). Observers are generally capable of distinguishing different settings of gravitational acceleration of parabolic trajectories with poor precision (Jörges, Hagenfeld, & López-Moliner, 2018). ASD has also been shown to drive deficits in ocular motor function with a recent review identifying saccade accuracy, inhibitory control and impaired tracking as common issues (Johnson, Lum, Rinehard & Fielding, 2016). However, the initiation of eye movements and disengaging from targets did not seem impaired in ASD groups.
The functioning of the ocular motor system is underpinned by a network of brain areas within cortex and beyond (Masson and Perrinet, 2012). The cerebellum is integral to problem-solving in spatial orientation due to its role in vestibular processing and representations of gravity needed for behaviour rely on it (MacNeilage & Glasauer, 2018). Cerebellar lesion patients exhibited deficits in perception of gravitational direction in a perceived tilt task (Dakin, Peters, Giunti & Day, 2018). Activity in the Frontal Eye Fields (FEF), an area that serves target prediction with an internal representation, has been found to be reduced during SPEM in schizophrenic patients (Faiola et al., 2020). Corresponding reductions have not been observed in schizotypy (Meyhöfer et al., 2015). Furthermore, using functional Magnetic Resonance Imaging and connectivity analyses, patients with recent-onset psychosis and low-schizotypy controls were indistinguishable based on brain activity during SPEM performance. However, using machine-learning, participant group classifications were made using a right FEF seed region, based on its connectivity within subcortical and cortical structures, frontal cortex, cerebellum and hypothalamus (Schröder et al., 2022). As interconnected cortical areas MT (Middle Temporal) and MST (Medial Superior Temporal) process visual motion signals producing commands for smooth pursuit, FEF may regulate this output with real-time gain control (Ono & Mustari, 2012; Ono, 2015). These findings, which highlight a potential predictive role for FEF within a wider network, suggest that individual variation in SPEM ability is subtler than previously thought and demands a fine-grained exploration of prediction, representation and information integration mechanisms.
In a recent study, high schizotypy participants had worse performance during predictive pursuit than a control low schizotypy group, but SPEM in a sinusoidal tracking task showed no differences (Faiola, Meyhöfer & Ettinger, 2020). In the prediction task, participants tracked a stimulus that moved at a constant velocity and was pseudorandomly blanked in half the trials, with instructions given to continue eye movements during the blank. These findings imply the prediction deficit is separate from general SPEM performance even along a sinusoidal path, consistent with findings of Meso et al., (2020), who manipulated prediction using two gravity conditions. The current work will address three questions. First, can we conceptually replicate the previous work on tracking under gravity (Meso et al., 2020) with additional controls and the inclusion of an ASD inventory? Second, can we unveil differences in tracking performance dynamics and organise them in terms of anticipation (pre-sensory response), early open-loop and later closed-loop responses? Finally, can we characterise the multidimensional relationship between schizotypy and ASD subtraits, and link these to eye-tracking measures for a meaningful interpretation of clusters? We take a combined experimental and theoretical approach and answer each one of these questions in turn.