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. 2010 Dec 15;68(12):1092-9.
doi: 10.1016/j.biopsych.2010.06.027. Epub 2010 Aug 21.

A shift to randomness of brain oscillations in people with autism

Meng-Chuan Lai  1 , Michael V Lombardo, Bhismadev Chakrabarti, Susan A Sadek, Greg Pasco, Sally J Wheelwright, Edward T Bullmore, Simon Baron-Cohen; MRC AIMS Consortium; John Suckling
Collaborators, Affiliations

A shift to randomness of brain oscillations in people with autism

Meng-Chuan Lai et al. Biol Psychiatry. .

Abstract

Background: Resting-state functional magnetic resonance imaging (fMRI) enables investigation of the intrinsic functional organization of the brain. Fractal parameters such as the Hurst exponent, H, describe the complexity of endogenous low-frequency fMRI time series on a continuum from random (H = .5) to ordered (H = 1). Shifts in fractal scaling of physiological time series have been associated with neurological and cardiac conditions.

Methods: Resting-state fMRI time series were recorded in 30 male adults with an autism spectrum condition (ASC) and 33 age- and IQ-matched male volunteers. The Hurst exponent was estimated in the wavelet domain and between-group differences were investigated at global and voxel level and in regions known to be involved in autism.

Results: Complex fractal scaling of fMRI time series was found in both groups but globally there was a significant shift to randomness in the ASC (mean H = .758, SD = .045) compared with neurotypical volunteers (mean H = .788, SD = .047). Between-group differences in H, which was always reduced in the ASC group, were seen in most regions previously reported to be involved in autism, including cortical midline structures, medial temporal structures, lateral temporal and parietal structures, insula, amygdala, basal ganglia, thalamus, and inferior frontal gyrus. Severity of autistic symptoms was negatively correlated with H in retrosplenial and right anterior insular cortex.

Conclusions: Autism is associated with a small but significant shift to randomness of endogenous brain oscillations. Complexity measures may provide physiological indicators for autism as they have done for other medical conditions.

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