Connectivity in Context: Emphasizing Neurodevelopment in Autism Spectrum Disorder

Commentary Biological Psychiatry Connectivity in Context: Emphasizing Neurodevelopment in Autism Spectrum Disorder James C. McPartland and Shafali S. Jeste Convergent evidence from genetics, neuropathology, and functional neuroimaging implicates aberrant cortical connec- tivity in the pathogenesis of neurodevelopmental disorders, such as autism spectrum disorder (ASD). Chromosomal variants associated with ASD include genes that are critical for synaptic structure and function, leading to a hypothesis that ASD results from a failure to establish necessary cortical connections, particularly in brain regions critical for social cognition and language (1). Although brain connectivity has become a central concept in autism research, its operationa- lization varies widely among studies. Deﬁned broadly, con- nectivity refers to associations between brain components that can range in scale from individual neurons to large brain regions spanning multiple neuroanatomic structures (2). Connectivity may be categorized as structural, functional, or effective. Structural, or anatomic, connectivity refers to physical links between nodes of a network (e.g., white matter tracts measured by diffusion tensor imaging). Functional connectivity reﬂects the statistical dependence of nodes on one another (e.g., temporal covariation in hemodynamic activity in separate regions measured by functional magnetic resonance imaging). Effective connectivity corresponds to the presumed causal inﬂuence of one node on another within prespeciﬁed, theoretically derived networks (e.g., predictive value of electrophysiologic brain activity in one region on activity in disparate regions, analyzed with structural equation modeling) (3). Connectivity patterns in the brain can be represented mathematically as networks, with various mea- sures that quantify the strength and efﬁciency of connections among nodes. Because the brain is a dynamic system, connections at the micro (cellular) and macro (cortical regions) levels change over time through intrinsic and extrinsic inﬂu- ences, with functional and effective connectivity being more temporally sensitive than structural connectivity to these ﬂuctuations. Despite limitations in spatial resolution, electrophysiology (through electroencephalography and magnetoencephalogra- phy) provides an ideal method for the investigation of func- tional connectivity. As a direct measure of postsynaptic brain activity, electrophysiology has several orders of magnitude greater temporal resolution than functional magnetic reso- nance imaging, allowing it to resolve neurophysiologic oscilla- tions and dynamics and to provide information about timing required for analysis of effective connectivity. Kitzbichler et al. (4) apply a sophisticated methodologic approach to address the challenges inherent in functional connectivity research in ASD. To achieve temporal and spatial resolution, they use the structural information provided by magnetic resonance imaging to inform the sources of tempo- rally sensitive magnetoencephalography recordings of brain networks at rest, effectively measuring brain activity with respect to space and time. By generating connectivity matrices for each subject in ﬁve distinct frequency bands and applying tools from mathematical models for network analysis (graph theory), the authors characterize differences in connectivity across cortical regions as a function of diagnostic grouping (ASD vs. typically developing [TD] control subjects) and individual differences (chronologic age, autism symp- toms). To the authors’ credit, and in contrast to much prior work, they present a complex picture of connectivity in the brains of individuals with ASD that is not characterized by simple overconnectivity or underconnectivity. Group differ- ences were evident primarily in high-frequency oscillations, with the ASD group demonstrating networks in the gamma band that were more efﬁcient, integrated, and widely distrib- uted than in the TD group; network density (the average number of connections from each node in the network) in these high-frequency bands was also associated with level of symptoms in core features of ASD. A developmental trend of frontal networks increasing in density with age was observed only in the TD group, suggesting atypical maturation of connectivity networks in individuals with ASD. Although these developmental ﬁndings are constrained by a small sample size and broad age range, Kitzbichler et al. highlight a key issue in the theoretical conceptualization of brain development in individuals with ASD—the role of gen- erically impaired systemic connectivity versus dysfunction in functionally speciﬁc brain systems. Connectivity studies, including the study by Kitzbichler et al., tend to implicate domain-general brain dysfunction in ASD. In these models, the clinical phenotype emerges from limitations in the functional capacity of a disconnected brain. For example, deﬁcient cortical connectivity may preclude the complex neural com- putations required for experiential learning or effective inter- personal interaction, leading to the disruptions in social and communicative behavior that are the hallmarks of ASD (5). In contrast, domain-speciﬁc social accounts of the neuropathol- ogy of ASD implicate dysfunction speciﬁc to brain regions and networks subserving social communication. These accounts posit that the core impairments of ASD result from dysfunction in designated brain systems that evolved to process informa- tion pertaining to conspeciﬁcs (6). These social functions and associated brain regions include face perception (fusiform gyrus), biological motion perception (superior temporal sul- cus), the action-perception system (inferior frontal gyrus), perception of emotional states and emotional experience (amygdala), and social reward (orbitofrontal cortex) (7). Current evidence indicates dysfunction in these functionally speciﬁc brain systems and larger scale networks, raising important questions about developmental primacy. ASD is a SEE CORRESPONDING ARTICLE ON PAGE 794 & 2015 Society of Biological Psychiatry Biological Psychiatry May 1, 2015; 77:772–774 www.sobp.org/journal ISSN: 0006-3223