Disentangling neural heterogeneity in autism

Two main theories of neural atypicality have been postulated in autism. One theory proposes that autism can be explained as the result of atypical patterns of hypo and hyper functional connectivity (FC) within and between brain areas. A complementary theory suggests that atypical functional communication in autism could result from an altered ratio between excitatory and inhibitory input (E:I imbalance). These theories have been previously explored as they might apply to all individuals with a behavioral diagnosis. However, given the multiscale heterogeneity characterizing autism, different subsets of individuals with autism may display different patterns of functional connectivity atypicalities and E:I imbalance. This thesis sets out to explore how neural atypicalities in connectivity and E:I imbalance might be differentially expressed in subsets of the autistic population. To this end, two empirical investigations were conducted. First, the connectivity hypothesis was explored by investigating whether behaviorally-defined subtypes were associated with different patterns of FC atypicalities. Behaviorally-defined subtypes were obtained by stratifying autistic individuals based on their relative balance between social communication (SC) and restricted and repetitive behaviors (RRBs) core symptom domains. This approach yielded three behaviorally-based subtypes: SC>RRB, SCRRB displayed hypoconnectivity between somatomotor and perisylvian circuitry, while subtypes SC=RRB showed hypoconnectivity between somatomotor and visual association areas and hyperconnectivity between medial motor and anterior salience networks. Finally, these subtype-specific FC alterations were shown to be enriched for partially distinct genetic mechanisms, some of which related to excitatory-inhibitory neurons and astrocytes. In a second study, the EI imbalance hypothesis was explored by investigating whether autism subtypes could be identified based on an E:I-sensitive metric computed from electroencephalographic (EEG) data. Specifically, the Hurst exponent (H) – a metric that has been shown to be affected by changes in excitatory input – was computed on EEG time-series data, obtained in two resting state conditions of eyes open and closed. H-based clustering revealed two E:I-based neurosubtypes across conditions with opposing patterns of E:I imbalance compared to neurotypical controls. Autism neurosubtype 1 showed on-average higher H values, while neurosubtype 2 displayed on-average lower H. These opposing E:I balance patterns were present globally across the brain, with the limited exception of an orthogonal larger decrease in H in non-frontal electrodes in neurosubtype 2. Finally, investigation at the behavioral level identified distinct multivariate brain-behavior relationships between age, intelligence, autistic traits and H. Taken together, these empirical findings demonstrate that the two major theories of neural atypicality in autism – FC alteration and E:I imbalance – do not apply equally to all individuals with a behavioral diagnosis. Rather, different subtypes of autism exist that display contrasting patterns of neural atypicality compared to typically-developing individuals. These contrasting patterns might be driven by differentially altered primary or compensatory E:I mechanisms shaping distinct atypical cortical organizations within the subtypes. Interestingly, the relationship between specific neural atypicalities and variability at the behavioral and genetic level is, however, subtle across the subtypes. This limited multiscale association could suggest that heterogeneity in autism might be due to the presence, within the larger population, of subtype-specific mosaic-like patterns of atypicalities at the behavioral and biological level. Further research is required to thoroughly characterize how these levels map onto one another within the subtypes and determine the pathophysiological mechanisms driving their development.