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.