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Coordinated Information Generation and Mental Flexibility: Large-Scale Network Disruption in Children with Autism.

Mišić B, Doesburg SM, Fatima Z, Vidal J, Vakorin VA, Taylor MJ, McIntosh AR - Cereb. Cortex (2014)

Bottom Line: Multivariate partial least-squares analysis revealed 2 distributed networks, operating at fast and slow time scales, that respond completely differently to set shifting in ASD compared with control children, indicating disrupted temporal organization within these networks.When children with ASD engaged these networks, there was no improvement in performance, suggesting that the networks were ineffective in children with ASD.Our data demonstrate that the coordination and temporal organization of large-scale neural assemblies during the performance of cognitive control tasks is disrupted in children with ASD, contributing to executive function deficits in this group.

View Article: PubMed Central - PubMed

Affiliation: Rotman Research Institute, Baycrest Centre, Toronto, Canada Department of Psychology, University of Toronto, Toronto, Canada.

No MeSH data available.


Related in: MedlinePlus

PLS analysis of MSE. Taken together, (A) and (B) represent the dominant latent variable in the data, accounting for the greatest covariance between the study design and neural activity (MSE). (A) The optimal combination (contrast) of groups and conditions, weighted by their contribution to the latent variable. Error bars are estimated by bootstrap resampling. (B) Bootstrap ratios: the optimal combination (spatiotemporal pattern) of sources and time scales, weighted by the reliability of their contribution to the latent variable. For a given source and time scale, a high-valued positive bootstrap ratio means that the contrast in (A) is reliably expressed. A high-valued negative bootstrap ratio means that the opposite contrast is reliably expressed. (C) Statistical maps showing networks of regions that most reliably express the contrast in (A).
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BHU082F5: PLS analysis of MSE. Taken together, (A) and (B) represent the dominant latent variable in the data, accounting for the greatest covariance between the study design and neural activity (MSE). (A) The optimal combination (contrast) of groups and conditions, weighted by their contribution to the latent variable. Error bars are estimated by bootstrap resampling. (B) Bootstrap ratios: the optimal combination (spatiotemporal pattern) of sources and time scales, weighted by the reliability of their contribution to the latent variable. For a given source and time scale, a high-valued positive bootstrap ratio means that the contrast in (A) is reliably expressed. A high-valued negative bootstrap ratio means that the opposite contrast is reliably expressed. (C) Statistical maps showing networks of regions that most reliably express the contrast in (A).

Mentions: Group means for MSE suggested a group by condition interaction (Fig. 3), and this was confirmed by the subsequent PLS analysis (Fig. 5A). Unlike for PSD, the dominant effect for MSE was not a group difference, but rather an interaction between group and condition (P = 0.004, accounting for 47.7% of cross-block covariance) (Fig. 5A). The interaction primarily concerned ID and ED shifts, whereby the 2 groups both showed a difference between ID1/ID3 and ED1/ED3, but did so in opposite ways.Figure 5.


Coordinated Information Generation and Mental Flexibility: Large-Scale Network Disruption in Children with Autism.

Mišić B, Doesburg SM, Fatima Z, Vidal J, Vakorin VA, Taylor MJ, McIntosh AR - Cereb. Cortex (2014)

PLS analysis of MSE. Taken together, (A) and (B) represent the dominant latent variable in the data, accounting for the greatest covariance between the study design and neural activity (MSE). (A) The optimal combination (contrast) of groups and conditions, weighted by their contribution to the latent variable. Error bars are estimated by bootstrap resampling. (B) Bootstrap ratios: the optimal combination (spatiotemporal pattern) of sources and time scales, weighted by the reliability of their contribution to the latent variable. For a given source and time scale, a high-valued positive bootstrap ratio means that the contrast in (A) is reliably expressed. A high-valued negative bootstrap ratio means that the opposite contrast is reliably expressed. (C) Statistical maps showing networks of regions that most reliably express the contrast in (A).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4537433&req=5

BHU082F5: PLS analysis of MSE. Taken together, (A) and (B) represent the dominant latent variable in the data, accounting for the greatest covariance between the study design and neural activity (MSE). (A) The optimal combination (contrast) of groups and conditions, weighted by their contribution to the latent variable. Error bars are estimated by bootstrap resampling. (B) Bootstrap ratios: the optimal combination (spatiotemporal pattern) of sources and time scales, weighted by the reliability of their contribution to the latent variable. For a given source and time scale, a high-valued positive bootstrap ratio means that the contrast in (A) is reliably expressed. A high-valued negative bootstrap ratio means that the opposite contrast is reliably expressed. (C) Statistical maps showing networks of regions that most reliably express the contrast in (A).
Mentions: Group means for MSE suggested a group by condition interaction (Fig. 3), and this was confirmed by the subsequent PLS analysis (Fig. 5A). Unlike for PSD, the dominant effect for MSE was not a group difference, but rather an interaction between group and condition (P = 0.004, accounting for 47.7% of cross-block covariance) (Fig. 5A). The interaction primarily concerned ID and ED shifts, whereby the 2 groups both showed a difference between ID1/ID3 and ED1/ED3, but did so in opposite ways.Figure 5.

Bottom Line: Multivariate partial least-squares analysis revealed 2 distributed networks, operating at fast and slow time scales, that respond completely differently to set shifting in ASD compared with control children, indicating disrupted temporal organization within these networks.When children with ASD engaged these networks, there was no improvement in performance, suggesting that the networks were ineffective in children with ASD.Our data demonstrate that the coordination and temporal organization of large-scale neural assemblies during the performance of cognitive control tasks is disrupted in children with ASD, contributing to executive function deficits in this group.

View Article: PubMed Central - PubMed

Affiliation: Rotman Research Institute, Baycrest Centre, Toronto, Canada Department of Psychology, University of Toronto, Toronto, Canada.

No MeSH data available.


Related in: MedlinePlus