Introduction
It is estimated that 80% of
individuals with ASD present with motor atypicalities (Green et al.,
2009; Weimer et al., 2001), including delayed walking, clumsy gait, and
disturbances in reach-to-grasp movements (Fournier et al., 2010;
Ghaziuddin and Butler, 1998; Miyahara et al., 1997; Teitelbaum et al.,
1998). While there is substantial evidence for atypical motor behavior
in autism and movement atypicalities are commonly reported clinically,
less is known about the underlying neural substrates. What is more, it
is unknown whether these atypicalities are a product of movement
planning, sensorimotor integration, alterations in basic motor
circuitry, or whether these differences emerge due to dysfunction in
downstream processes, such as movement execution or the coordination of
movement sequencing.
The majority of work surrounding ASD and the neurophysiology of motor
movements focuses on the high-level construct of movement imitation and
visual-motor integration, reflecting a tendency in the ASD literature to
explore constructs which directly link to impairments in social
communication (Bernier et al., 2007; Deschrijver et al., 2017; Ewen et
al., 2016; Lidstone and Mostofsky, 2021; Oberman et al., 2005; Sowden et
al., 2016; Spengler et al., 2010; Williams et al., 2004; Williams et
al., 2001). While existing research provides clear evidence of
differences in imitative behavior and neural processes engaged during
both the initiation and execution of imitative movements, the role of
basic sensorimotor dysfunction in these impaired processes is not well
understood. The paucity of data
surrounding the neural development of sensorimotor planning in a
neurotypical population indicates that a thorough characterization of
the ‘normative’ trajectory is needed in order to assess whether children
with ASD exhibit significant deviation from this trajectory.
The functional dynamics of
spontaneous, voluntary motor movements (such as self-paced finger
flexion) are well-characterized in healthy adults using EEG. A common
method for studying movement related cortical potentials (MRCP) involves
time-locking EEG data to the execution of a motor movement and examining
the preceding signal. The sensitivity of EEG on the timescale of
milliseconds is a major advantage of this methodology over functional
magnetic resonance imaging (fMRI) techniques, which resolve biological
signals along a much longer timescale. Using EEG, classical
event-related potential (ERP) components of the MRCP can be observed at
the scalp. The earliest of these components, known as the early
berescheftspotential (BP) or readiness potential (RP), begins 1-3
seconds prior to movement onset and is typically seen as a slow
negativity over the central-parietal scalp generated by supplementary
motor regions (Deecke, 2007; Ikeda et al., 1992; Kornhuber and Deecke,
1965). The late BP component, beginning 300-500 ms prior to a recorded
movement, is visible over the central scalp contralateral to the
effector and is caused by a rapid increase in motor excitability thought
to be driven primarily by premotor and motor areas (Shibasaki et al.,
1980; Shibasaki and Hallett, 2006). Motor excitability peaks at movement
onset in a component known as the Motor Potential (MP), generated in
primary motor cortices (Böcker et al., 1994). The MP is immediately
followed by a rapid positivity known as the reafferent potential (RAP),
reflecting sensory-motor integration. The RAP may be generated by the
primary somatosensory cortices and peaks between 90 – 130 ms after
movement onset (Bötzel et al., 1997; Bötzel et al., 1993). Modulation of
movement-related EEG activity has been shown as a function of the
complexity and meaning of the intended action, task instructions, and
the physical state of the individual performing the action (Bozzacchi et
al., 2012; Di Russo et al., 2017; Freude and Ullsperger, 1987; Simonetta
et al., 1991). Although not yet explored, it is likely that MRCP
components during cued movements may vary as a function of reaction time
within an individual; that is, whether presentation of a cue leads to a
prompt motor response or a slower response.
However, when motor movements are cued by external stimuli, data
indicate that MRCP morphology may differ from the classical models
derived from tasks where participants execute spontaneous, self-paced
movements (Pereira et al., 2018). For example, the self-initiated BP
component has been shown to have an anterior topographic distribution
relative to the BP elicited from a cued movement in response to sensory
stimuli (Jenkins et al., 2000). Data from fMRI studies suggest that
while both spontaneous and auditory-cued motor movements showed strong
activation in both motor and premotor areas, auditory-cued movements
induced additional bilateral activation in primary auditory cortices,
and activation in the basal ganglia was identified for self-initiated
movements only (Cunnington et al., 2002). Although sensory processing is
a necessary prerequisite for the generation of cued movements, this
presents additional complexity to data interpretation. Despite the
overlap of sensory and motor preparatory processes in cued
motor-response tasks, it is clear that time-locking to a specific task
event (either the stimulus or the response) exaggerates some ERP
components and reduces the visibility of others (Takeda et al., 2008).
It should be noted that both stimulus-locked and response-locked ERP
analyses suffer from the issue of temporal smearing of some
components—in the case of stimulus-locked analyses, the temporal
smearing of motor components, and for motor-locked analyses, the
temporal smearing of components related to the sensory stimulus. As
such, care should be taken when interpreting data from tasks involving
both the processing of a sensory stimulus and the execution of a motor
response.
In the current work, we tested the integrity of basic sensorimotor
processing in autism in a large sample of participants, in the context
of a simple reaction time task. Using response-locked averaging of EEG
data, we investigated the neural processes preceding a cued movement
response in a large sample of children with ASD (n=84) and without ASD
(n=84) with similar age, sex, and IQ. Participants completed an
audio-visual speeded reaction time task and were instructed to respond
quickly with a button press using the right hand in response to a
visual, auditory, or audiovisual bi-sensory cue (Brandwein et al., 2015;
Brandwein et al., 2013; Brandwein et al., 2011; Crosse et al., 2022;
Molholm et al., 2002). The current work represents a re-analysis of an
existing dataset, focusing for the first time on motor-related
potentials to assess functional dynamics of sensorimotor processing in
Autism. The density of data, both in terms of trials per participant
(972 ± 58 trials) and number of participants within group, allowed us to
additionally consider how these responses differed with respect to age,
speed of response, and sensory cue type, both in Autism and typical
development.
Methods
This study involves a secondary analysis of a large dataset collected
over a period of 13 years (Brandwein et al., 2015; Brandwein et al.,
2013; Crosse et al., 2022). Here, we examined the development of
sensorimotor processing and how these processes may differ in Autism.
The current work used both a priori and exploratory analytic approaches.
A priori analyses were focused on well-defined ERP componentry that have
been extensively studied and characterized in adults. Exploratory
analysis was undertaken to leverage the richness of the current dataset
with regard to the dense electrode array, high temporal resolution, and
paucity of existing normative data, the full data matrix (all channels
and time-points) were examined across a timewindow of 650 ms, beginning
350 ms prior to the button-press response and ending 300 ms
post-response.