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Review
. 2011 Oct;21(10):2233-43.
doi: 10.1093/cercor/bhq296. Epub 2011 Mar 4.

Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders

Affiliations
Review

Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders

Ralph-Axel Müller et al. Cereb Cortex. 2011 Oct.

Abstract

Growing consensus suggests that autism spectrum disorders (ASD) are associated with atypical brain networks, thus shifting the focus to the study of connectivity. Many functional connectivity studies have reported underconnectivity in ASD, but results in others have been divergent. We conducted a survey of 32 functional connectivity magnetic resonance imaging studies of ASD for numerous methodological variables to distinguish studies supporting general underconnectivity (GU) from those not consistent with this hypothesis (NGU). Distinguishing patterns were apparent for several data analysis choices. The study types differed significantly with respect to low-pass filtering, task regression, and whole-brain field of view. GU studies were more likely to examine task-driven time series in regions of interest, without the use of low-pass filtering. Conversely, NGU studies mostly applied task regression (for removal of activation effects) and low-pass filtering, testing for correlations across the whole brain. Results thus suggest that underconnectivity findings may be contingent on specific methodological choices. Whereas underconnectivity reflects reduced efficiency of within-network communication in ASD, diffusely increased functional connectivity can be attributed to impaired experience-driven mechanisms (e.g., synaptic pruning). Both GU and NGU findings reflect important aspects of network dysfunction associated with sociocommunicative, cognitive, and sensorimotor impairments in ASD.

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Figures

Figure 1.
Figure 1.
(A) Fraction of studies by type for each selected methodological choice. (B) Prediction bias, calculated as the difference between study types (fraction GU − fraction NGU). (C) Prediction differential, calculated as the difference between Yes and No options for each methodological choice. (D) Radar plots for two types of studies showing differences in methodological fingerprints. *Excluding resting studies. **Significant difference between study types. For further details, see text.

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