Abstract
Although resting state fMRI (RS-fMRI) is increasingly used to generate biomarkers of psychiatric illnesses, analytical choices such as seed size and placement can lead to variable findings. Seed placement especially impacts on RS-fMRI studies of Autism Spectrum Disorder (ASD), because individuals with ASD are known to possess more variable network topographies. Here, we present a novel pipeline for analysing RS-fMRI in ASD using the cingulate cortex as an exemplar anatomical region of interest. Rather than using seeds based on previous literature, or gross morphology, we used a combination of structural information, task-independent (RS-fMRI) and task-dependent functional connectivity (Meta-Analytic Connectivity Modeling) to partition the cingulate cortex into six subregions with unique connectivity fingerprints and diverse behavioural profiles. This parcellation was consistent between groups and highly replicable across individuals (up to 93% detection) suggesting that the organisation of cortico-cingulo connections is highly similar between groups. However, our results showed an age-related increase in connectivity between the anterior middle cingulate cortex and right lateral prefrontal cortex in ASD, whilst this connectivity decreased in controls. There was also a Group × Grey Matter (GM) interaction, showing increased connectivity between the anterior cingulate cortex and the rectal gyrus in concert with increasing rectal gyrus GM in controls. By comparing our approach to previously established methods we revealed that our approach improves network detection in both groups, and that the ability to detect group differences using 4 mm radius spheres varies greatly with seed placement. Using our multi-modal approach we find disrupted cortico-cingulo circuits that, based on task-dependent information, may contribute to ASD deficits in attention and social interaction. Moreover, we highlight how more sensitive approaches to RS-fMRI are crucial for establishing robust and reproducible connectivity-based biomarkers in psychiatric disorders.
Highlights
Spontaneous, or resting state (RS), fluctuations in the blood oxygenation level-dependent (BOLD) signal are increasingly used as a tool to investigate brain connectivity and generate biomarkers of psychiatric disorders (Alaerts et al, 2014; Di Martino et al, 2014; Kelly et al, 2012)
What is the optimal number of cingulate subregions? First, both Autism Spectrum Disorder (ASD) and typically developing (TD) subjects RS data were input as one group into the hierarchical clustering algorithm
ASD and TD individuals were treated as one group, silhouette values were generated for each individual
Summary
Spontaneous, or resting state (RS), fluctuations in the blood oxygenation level-dependent (BOLD) signal are increasingly used as a tool to investigate brain connectivity and generate biomarkers of psychiatric disorders (Alaerts et al, 2014; Di Martino et al, 2014; Kelly et al, 2012). The unconstrained nature of spontaneous fluctuations means that they are susceptible to non-neural factors such as head movement, respiration, and MRI artifacts, leading to a moderate testretest reliability (Shehzad et al, 2009; Thomason et al, 2011; Zuo et al, 2010) and reducing the utility of RS-fMRI as a diagnostic tool. There is some debate about whether these differences in connectivity are due to differences in small head movements (Deen and Pelphrey, 2012; Koldewyn et al, 2014; Tyszka et al, 2014). Balsters et al / NeuroImage: Clinical 11 (2016) 494–507 variability in spatial representations of networks which traditional seed-based approaches are not sensitive enough to detect
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