Mapping the landscape of brain functional dynamics in autism spectrum disorder

Autism spectrum disorder (ASD) is an increasingly prevalent neurodevelopmental condition, comprising a wide range of
phenotypes and heterogeneous conditions, such as autism and Asperger's syndrome. ASD patients have to face various
functioning challenges across lifespan, such as difficulties in social cognition, language skills, executive functions, and motor
abilities [1]-[3]. Their deviations in behaviours and brain functions from typically developed (TD) individuals are underpinned by
the atypical development of neural structures and brain activity. For instance, increased caudate volume was found to be
correlated with complex repetitive motor behaviours [4]. Atypical brain activity has been widely observed in brain regions
associated with theory of mind, social cognition, and executive functions [5]-[7]. Therefore, it is crucial to understand the
neurobiological underpinnings of ASD both to increase awareness and inform options to give more tailored interventions.
Recently, it has been posited that ASD is a disorder of brain connectivity leading to altered information processing [5].
Regarding structural brain connectivity, it has been implied that altered white-matter connectivity in ASD is associated with
function impairment [8], [9]. Meanwhile, there is ample evidence of widespread functional dysconnectivity in ASD [10], [11]. As
an example, underconnectivity was found between nucleus accumbens (NAcc), which forms the neurobiological basis of social
functions, and other brain regions, such as thalamus, and anterior cingulate cortex [12]. However, despite the large and fast growing number of FC studies, the results regarding altered FC, plausibly plagued by heterogeneity in traits of ASD, remains

inconsistent [13]. In addition, most studies assessing FC in ASD have been conducted based on the premise that FC remained
static. Static FC analysis fails to capture the time-resolved transitions of functional connectivity between networks or regions
and the 'on average' results from previous studies may mask important differences in dynamics.

Dynamic FC (dFC) approaches, which have gained increasing attention recently, focus on the transient changes of functional
connectivity during an RS- or task-based fMRI. Joint electroencephalographic (EEG) and fMRI studies have suggested a solid
biological origin for dFC [14], [15], and recent reports have suggested dynamic functional connectivity as a good measurement
to explore the neurobiological basis of ASD. Metrics characterising dFC, such as dwelling times and temporal variability, have
been implied to be significantly associated with function impairment in ASD [16] [17]. Studies investigating dFC can also
validate atypical brain connectivity discovered with static FC. For instance, Fu et al. found increased dFNC between
Hypothalamus/Subthalamus and Sensory Regions which extended previous findings of hyperconnectivity between thalamus
and sensory cortex based on static FC analysis [18].

By clustering dynamic FC into a discrete set of states, researchers have attempted to identify brain disorders characterised by
their atypical dynamic patterns. Hyatt and their colleagues have identified four dFC states with ASD showing different dwelling
times compared with TD [19]. They also found significant associations between dFC measures and social cognitive ability scores
in ASD. Furthermore, with dFC, machine learning has been implemented to diagnose ASD. A study based on the Autism Brain
Imaging Data Exchange (ABIDE) database reached 83% accuracy to classify ASD using central moment features extracted from
multilevel dFC by support vector machines (SVM) classifiers [20]. As an emerging area, evidence for dFC abnormalities in ASD
remain limited with heterogeneity in methodology. Further research should be conducted with carefully selected analytic
approaches and null models.