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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 PSD. 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 (PSD). (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 frequencies, weighted by the reliability of their contribution to the latent variable. For a given source and frequency, a high-valued positive bootstrap ratio means that the contrast in (A) is reliably expressed (i.e., greater power for the Control group). A high-valued negative bootstrap ratio means that the opposite contrast is reliably expressed (i.e., greater power for the ASD group). (C) The number of sources with positive/negative bootstrap ratios exceeding ±2. “Peak” frequencies (4, 10, 20, 27, and 40 Hz) are marked by gray lines. (D) Statistical maps showing networks of regions that most reliably express the contrast in (A), shown for each of the peak frequencies in (C).
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BHU082F4: PLS analysis of PSD. 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 (PSD). (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 frequencies, weighted by the reliability of their contribution to the latent variable. For a given source and frequency, a high-valued positive bootstrap ratio means that the contrast in (A) is reliably expressed (i.e., greater power for the Control group). A high-valued negative bootstrap ratio means that the opposite contrast is reliably expressed (i.e., greater power for the ASD group). (C) The number of sources with positive/negative bootstrap ratios exceeding ±2. “Peak” frequencies (4, 10, 20, 27, and 40 Hz) are marked by gray lines. (D) Statistical maps showing networks of regions that most reliably express the contrast in (A), shown for each of the peak frequencies in (C).

Mentions: In PLS, this is achieved by computing the covariance matrix between the 2 sets of variables and decomposing this matrix into mutually orthogonal “latent variables” using singular value decomposition (SVD; Eckart and Young 1936). Each latent variable represents a particular relation between the study design on one hand and neural activity on the other. Specifically, each latent variable is expressed as a vector of design saliences (e.g., Fig. 4A) and a vector of source saliences (e.g., Fig. 4B), as well as a scalar singular value (s). The elements of the design salience vectors are interpreted as a contrast between groups and/or conditions, while the source saliences represent a weighted pattern of sources and frequencies/scales that maximally express that contrast. The singular value reflects the proportion of covariance between the design variables (groups and conditions) and neuromagnetic variables (sources and frequencies/scales) that is accounted for by each latent variable. This allows effect size (ηk) for the kth latent variable to be estimated as the ratio of the square of the singular value associated with that particular latent variable to the sum of all squared singular values derived from the decomposition.(1)ηk=sk2∑isi2.Figure 4.


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 PSD. 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 (PSD). (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 frequencies, weighted by the reliability of their contribution to the latent variable. For a given source and frequency, a high-valued positive bootstrap ratio means that the contrast in (A) is reliably expressed (i.e., greater power for the Control group). A high-valued negative bootstrap ratio means that the opposite contrast is reliably expressed (i.e., greater power for the ASD group). (C) The number of sources with positive/negative bootstrap ratios exceeding ±2. “Peak” frequencies (4, 10, 20, 27, and 40 Hz) are marked by gray lines. (D) Statistical maps showing networks of regions that most reliably express the contrast in (A), shown for each of the peak frequencies in (C).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

BHU082F4: PLS analysis of PSD. 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 (PSD). (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 frequencies, weighted by the reliability of their contribution to the latent variable. For a given source and frequency, a high-valued positive bootstrap ratio means that the contrast in (A) is reliably expressed (i.e., greater power for the Control group). A high-valued negative bootstrap ratio means that the opposite contrast is reliably expressed (i.e., greater power for the ASD group). (C) The number of sources with positive/negative bootstrap ratios exceeding ±2. “Peak” frequencies (4, 10, 20, 27, and 40 Hz) are marked by gray lines. (D) Statistical maps showing networks of regions that most reliably express the contrast in (A), shown for each of the peak frequencies in (C).
Mentions: In PLS, this is achieved by computing the covariance matrix between the 2 sets of variables and decomposing this matrix into mutually orthogonal “latent variables” using singular value decomposition (SVD; Eckart and Young 1936). Each latent variable represents a particular relation between the study design on one hand and neural activity on the other. Specifically, each latent variable is expressed as a vector of design saliences (e.g., Fig. 4A) and a vector of source saliences (e.g., Fig. 4B), as well as a scalar singular value (s). The elements of the design salience vectors are interpreted as a contrast between groups and/or conditions, while the source saliences represent a weighted pattern of sources and frequencies/scales that maximally express that contrast. The singular value reflects the proportion of covariance between the design variables (groups and conditions) and neuromagnetic variables (sources and frequencies/scales) that is accounted for by each latent variable. This allows effect size (ηk) for the kth latent variable to be estimated as the ratio of the square of the singular value associated with that particular latent variable to the sum of all squared singular values derived from the decomposition.(1)ηk=sk2∑isi2.Figure 4.

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