Research Ideas and Outcomes : Project Report
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Corresponding author: Richard AI Bethlehem (rb643@medschl.cam.ac.uk)
Received: 24 Apr 2017 | Published: 02 May 2017
© 2017 Richard Bethlehem, Marcel Falkiewicz, Jan Freyberg, Owen Parsons, Seyedeh-Rezvan Farahibozorg, Charlotte Pretzsch, Bjoern Soergel, Daniel Margulies
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Bethlehem R, Falkiewicz M, Freyberg J, Parsons O, Farahibozorg S, Pretzsch C, Soergel B, Margulies D (2017) Gradients of cortical hierarchy in Autism. Research Ideas and Outcomes 3: e13391. https://doi.org/10.3897/rio.3.e13391
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Autism is a developmental condition associated with altered functional connectivity. We propose to re-frame the functional connectivity alterations in terms of gradients that capture the functional hierarchy of cortical processing from sensory to default-mode network regions. We hypothesized that this hierarchy will be altered in ASD. To test that, we compared the scale of gradients in people with autism and healthy controls. The present results do not support our hypothesis. There are two alternative implications: either the processing hierarchies are preserved in autism or the scale of the gradients does not capture them. In the future we will attempt to settle which alternative is more likely.
gradients, autism, functional connectivity, resting-state fMRI, ABIDE, cortical hierarchy
Autism Spectrum Disorder (ASD) is characterised by local and global disruptions of functional connectivity (FC) (
Connectivity gradients (
Hence, we set out to recreate the gradients from the orginal paper by
To make the project practically feasible in the course of Brainhack, we used pre-processed male adult data (age range: 18-55) from the ABIDE dataset (
Our primary outcome measure was the scale of the gradient as estimated by the linear fit to the sorted gradient values. Individual fits were visually inspected to quantitatively assess the fit; examples can be found on the above mentioned GitHub website. As a secondary measure we computed a goodness of fit ratio for the gradient values inside and outside of brain masks obtained from NeuroSynth that accompanied the keywords listed in figure 4 of
Gradient slopes show no distinguishable difference between the autism and neurotypical groups (Fig.
Linear fit of gradient slopes for the top 10 gradients for each group (1 == neurotypical individuals; 2 == individuals with autism).
Goodness of fit for principal gradient. Ranked according to the median goodness of fit for both groups.
In order to improve feasibility, the present study used one specific pre-processed parcellation template. This greatly improved processing time at the cost of reduced spatial specificity. The orginal paper by
Autism is well known for its heterogeneity and it is possible that, by averaging over the entire cohort, potential subtle sub-group effects are lost. Future analysis will look into combining topographical gradient information with phenotypic information provided with ABIDE.
This work was completed during Brainhack Global - Cambridge 2017.