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. 2016 Sep 15;11(9):e0163133.
doi: 10.1371/journal.pone.0163133. eCollection 2016.

The Contribution of Increased Gamma Band Connectivity to Visual Non-Verbal Reasoning in Autistic Children: A MEG Study

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Free PMC article

The Contribution of Increased Gamma Band Connectivity to Visual Non-Verbal Reasoning in Autistic Children: A MEG Study

Natsumi Takesaki et al. PLoS One. .
Free PMC article

Abstract

Some individuals with autism spectrum (AS) perform better on visual reasoning tasks than would be predicted by their general cognitive performance. In individuals with AS, mechanisms in the brain's visual area that underlie visual processing play a more prominent role in visual reasoning tasks than they do in normal individuals. In addition, increased connectivity with the visual area is thought to be one of the neural bases of autistic visual cognitive abilities. However, the contribution of such brain connectivity to visual cognitive abilities is not well understood, particularly in children. In this study, we investigated how functional connectivity between the visual areas and higher-order regions, which is reflected by alpha, beta and gamma band oscillations, contributes to the performance of visual reasoning tasks in typically developing (TD) (n = 18) children and AS children (n = 18). Brain activity was measured using a custom child-sized magneto-encephalograph. Imaginary coherence analysis was used as a proxy to estimate the functional connectivity between the occipital and other areas of the brain. Stronger connectivity from the occipital area, as evidenced by higher imaginary coherence in the gamma band, was associated with higher performance in the AS children only. We observed no significant correlation between the alpha or beta bands imaginary coherence and performance in the both groups. Alpha and beta bands reflect top-down pathways, while gamma band oscillations reflect a bottom-up influence. Therefore, our results suggest that visual reasoning in AS children is at least partially based on an enhanced reliance on visual perception and increased bottom-up connectivity from the visual areas.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The custom child-sized MEG system and the sensor level connectivity analysis.
(a) In the custom child-sized MEG system, the MEG sensors are as close to the whole head as possible for optimal recording in young children. During MEG recording, the children lay supine on the bed and viewed video programs projected onto a screen. (b) We calculated ImCoh values between the seed sensor (white circle) and the other 146 sensors (red lines).
Fig 2
Fig 2. Seed sensors for high signals from visual cortices and low noise from muscular activity.
(a) A representative example of the magnetic responses to the visual stimuli obtained from one subject (8-year-old boy). MEG waveforms (147 channels) are overlaid at the corrected baseline. (b) Isocontour maps of the magnetic field after pattern reversal visual stimuli. The magnetic field strength is indicated by color, varying from blue (flux-in) to red (flux-out)) at the response peak 75 and 175 msec after pattern reversal. Two seed sensors are indicated by gray circles and the other 146 sensors are indicated by dots. In two seed sensors, signals from visual cortices (i.e., visually evoked response) were optimally recorded. (c) Contamination of muscular activity in resting state brain activity. Topography of gamma band (30–58 Hz) power during rest with (right) and without (left) visually confirmed muscle activity. Sensors located in the ventral area (right) are vulnerable to muscular noise. (d) Vulnerable areas to muscular noise are close to the muscles of head and neck (e.g. temporal muscles). Two seed sensors indicated by gray circles are located outside of these vulnerable areas to muscular noise.
Fig 3
Fig 3. The performance of TD and AS children on a mental rotation task.
No significant difference was found for the number of correct answers (a) and the efficiency index (d). AS children responded more quickly than TD children for all responses including incorrect answers (b) and for correct responses excluding incorrect answers (c). The error bars represent one standard deviation.
Fig 4
Fig 4. Correlation between performance of a mental rotation task and ImCoh.
In TD children (upper row), there were no significant correlations for any sensor pair (i.e., alpha < 0.00034). In AS children (lower row), there was a significant positive correlation for one sensor pair (red lines); right occipital–left frontal pair (r = 0.756, P = 0.0001). ImCoh: imaginary coherence.
Fig 5
Fig 5. Correlation between performance on Matrix Analogies and ImCoh.
In TD children (upper row), there were no significant correlations for any sensor pair (i.e., alpha < 0.00034). In AS children (lower row), there was a significant positive correlation for one sensor pair (red lines); right occipital–left frontal pair (r = 0.792, P < 0.0001). ImCoh: imaginary coherence.

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References

    1. Mottron L. Changing perceptions: The power of autism. Nature. 2011;479:33–35. 10.1038/479033a - DOI - PubMed
    1. Dawson M, Soulieres I, Gernsbacher MA, Mottron L. The level and nature of autistic intelligence. Psychol Sci. 2007;18:657–662. - PMC - PubMed
    1. Charman T, Jones CR, Pickles A, Simonoff E, Baird G, Happe F. Defining the cognitive phenotype of autism. Brain Res. 2011;1380:10–21. 10.1016/j.brainres.2010.10.075 - DOI - PubMed
    1. Nader AM, Courchesne V, Dawson M, Soulieres I. Does WISC-IV Underestimate the Intelligence of Autistic Children? J Autism Dev Disord. 2014. - PubMed
    1. Simard I, Luck D, Mottron L, Zeffiro TA, Soulieres I. Autistic fluid intelligence: Increased reliance on visual functional connectivity with diminished modulation of coupling by task difficulty. NeuroImage Clinical. 2015;9:467–478. 10.1016/j.nicl.2015.09.007 - DOI - PMC - PubMed

Grant support

Yoshio Minabe was supported by the Centre of Innovation Programme from the Japan Science and Technology Agency, JST. Mitsuru Kikuchi was supported by Grants-in-Aid for Scientific Research (B) (Research Number 26293262). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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