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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

MSE analysis. Sample entropy is calculated by counting the number of sequences of (m + 1) data points will be similar to each other given that they were similar for the first m points. This reflects the unpredictability of the time series and the information that is generated by the underlying system. (A) An example of the sample entropy algorithm (m = 1), for 2 different starting points. (B) A multiscale representation of the signal is achieved by downsampling the original time series to progressively coarser time scales. (C) Two example signals and (D) their MSE curves.
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BHU082F2: MSE analysis. Sample entropy is calculated by counting the number of sequences of (m + 1) data points will be similar to each other given that they were similar for the first m points. This reflects the unpredictability of the time series and the information that is generated by the underlying system. (A) An example of the sample entropy algorithm (m = 1), for 2 different starting points. (B) A multiscale representation of the signal is achieved by downsampling the original time series to progressively coarser time scales. (C) Two example signals and (D) their MSE curves.

Mentions: In MSE analysis (Costa et al. 2002, 2005), each single-trial time series is downsampled to multiple temporal scales and sample entropy (SE) (Richman and Moorman 2000) is calculated for each scale. For a given temporal scale τ, the corresponding time series is derived by averaging data points in nonoverlapping windows of length τ from the original time series (τ = 1 corresponds to the original time series) (Fig. 2B). Time scales can be converted to seconds by dividing scale τ by the sampling rate (625 Hz). The SE algorithm calculates the conditional probability that any 2 sequences of (m+1) data points will be similar to each other given that they were similar for the first m points, which reflects the degree of regularity in a given time series (see example in Fig. 2A). The SE metric is the negative of the natural logarithm of this quantity, so higher values of SE are assigned to less regular and more variable time series (Fig. 2C,D). In the present study, pattern length was set to m = 2 and the similarity criterion to r = 0.5. The pattern length (also known as the embedding dimension) was judged to be optimal following the method proposed by Small and Tse (2004). The similarity criterion (also known as the tolerance) was chosen following the procedure described by Richman and Moorman (2000). MSE was calculated for each of the 529 sources and averaged across trials. To convert time scale into milliseconds, we divided the time scale by the sampling rate (625 Hz).Figure 2.


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)

MSE analysis. Sample entropy is calculated by counting the number of sequences of (m + 1) data points will be similar to each other given that they were similar for the first m points. This reflects the unpredictability of the time series and the information that is generated by the underlying system. (A) An example of the sample entropy algorithm (m = 1), for 2 different starting points. (B) A multiscale representation of the signal is achieved by downsampling the original time series to progressively coarser time scales. (C) Two example signals and (D) their MSE curves.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

BHU082F2: MSE analysis. Sample entropy is calculated by counting the number of sequences of (m + 1) data points will be similar to each other given that they were similar for the first m points. This reflects the unpredictability of the time series and the information that is generated by the underlying system. (A) An example of the sample entropy algorithm (m = 1), for 2 different starting points. (B) A multiscale representation of the signal is achieved by downsampling the original time series to progressively coarser time scales. (C) Two example signals and (D) their MSE curves.
Mentions: In MSE analysis (Costa et al. 2002, 2005), each single-trial time series is downsampled to multiple temporal scales and sample entropy (SE) (Richman and Moorman 2000) is calculated for each scale. For a given temporal scale τ, the corresponding time series is derived by averaging data points in nonoverlapping windows of length τ from the original time series (τ = 1 corresponds to the original time series) (Fig. 2B). Time scales can be converted to seconds by dividing scale τ by the sampling rate (625 Hz). The SE algorithm calculates the conditional probability that any 2 sequences of (m+1) data points will be similar to each other given that they were similar for the first m points, which reflects the degree of regularity in a given time series (see example in Fig. 2A). The SE metric is the negative of the natural logarithm of this quantity, so higher values of SE are assigned to less regular and more variable time series (Fig. 2C,D). In the present study, pattern length was set to m = 2 and the similarity criterion to r = 0.5. The pattern length (also known as the embedding dimension) was judged to be optimal following the method proposed by Small and Tse (2004). The similarity criterion (also known as the tolerance) was chosen following the procedure described by Richman and Moorman (2000). MSE was calculated for each of the 529 sources and averaged across trials. To convert time scale into milliseconds, we divided the time scale by the sampling rate (625 Hz).Figure 2.

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