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Reduced predictable information in brain signals in autism spectrum disorder.

Gómez C, Lizier JT, Schaum M, Wollstadt P, Grützner C, Uhlhaas P, Freitag CM, Schlitt S, Bölte S, Hornero R, Wibral M - Front Neuroinform (2014)

Bottom Line: Recent results suggest altered brain dynamics as a potential cause of symptoms in ASD.Our results showed a decrease of AIS values in the hippocampus of ASD patients in comparison with controls, meaning that brain signals in ASD were either less predictable, reduced in their dynamic richness or both.The observed changes in AIS are compatible with Bayesian theories of reduced use or precision of priors in ASD.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering Group, E. T. S. Ingenieros de Telecomunicación, University of Valladolid Valladolid, Spain.

ABSTRACT
Autism spectrum disorder (ASD) is a common developmental disorder characterized by communication difficulties and impaired social interaction. Recent results suggest altered brain dynamics as a potential cause of symptoms in ASD. Here, we aim to describe potential information-processing consequences of these alterations by measuring active information storage (AIS)-a key quantity in the theory of distributed computation in biological networks. AIS is defined as the mutual information between the past state of a process and its next measurement. It measures the amount of stored information that is used for computation of the next time step of a process. AIS is high for rich but predictable dynamics. We recorded magnetoencephalography (MEG) signals in 10 ASD patients and 14 matched control subjects in a visual task. After a beamformer source analysis, 12 task-relevant sources were obtained. For these sources, stationary baseline activity was analyzed using AIS. Our results showed a decrease of AIS values in the hippocampus of ASD patients in comparison with controls, meaning that brain signals in ASD were either less predictable, reduced in their dynamic richness or both. Our study suggests the usefulness of AIS to detect an abnormal type of dynamics in ASD. The observed changes in AIS are compatible with Bayesian theories of reduced use or precision of priors in ASD.

No MeSH data available.


Related in: MedlinePlus

Source time courses, power spectra and the correlation of autocorrelation decay constant, and AIS for the hippocampal source. (A) Exemplary dipole moment time course of a single trial (baseline) for the hippocampal source. (B) Source spectral power for the hippocampal source, separately averaged for the healthy controls (HC, blue), and the ASD patients (ASD, red). Note that the spectrum was cut at 10 Hz as this was the lowest frequency included in the AIS analysis. (C) Correlation plot between the autocorrelation decay time (ACT) and the AIS. Data are shown separately for healthy controls (HC, blue), and the ASD patients (ASD, red). See Table 1 for details on correlation coefficients.
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Figure 4: Source time courses, power spectra and the correlation of autocorrelation decay constant, and AIS for the hippocampal source. (A) Exemplary dipole moment time course of a single trial (baseline) for the hippocampal source. (B) Source spectral power for the hippocampal source, separately averaged for the healthy controls (HC, blue), and the ASD patients (ASD, red). Note that the spectrum was cut at 10 Hz as this was the lowest frequency included in the AIS analysis. (C) Correlation plot between the autocorrelation decay time (ACT) and the AIS. Data are shown separately for healthy controls (HC, blue), and the ASD patients (ASD, red). See Table 1 for details on correlation coefficients.

Mentions: Similarly, ACT showed a negative Pearson correlation with the AIS (p < 0.034) (Figure 4).


Reduced predictable information in brain signals in autism spectrum disorder.

Gómez C, Lizier JT, Schaum M, Wollstadt P, Grützner C, Uhlhaas P, Freitag CM, Schlitt S, Bölte S, Hornero R, Wibral M - Front Neuroinform (2014)

Source time courses, power spectra and the correlation of autocorrelation decay constant, and AIS for the hippocampal source. (A) Exemplary dipole moment time course of a single trial (baseline) for the hippocampal source. (B) Source spectral power for the hippocampal source, separately averaged for the healthy controls (HC, blue), and the ASD patients (ASD, red). Note that the spectrum was cut at 10 Hz as this was the lowest frequency included in the AIS analysis. (C) Correlation plot between the autocorrelation decay time (ACT) and the AIS. Data are shown separately for healthy controls (HC, blue), and the ASD patients (ASD, red). See Table 1 for details on correlation coefficients.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3924322&req=5

Figure 4: Source time courses, power spectra and the correlation of autocorrelation decay constant, and AIS for the hippocampal source. (A) Exemplary dipole moment time course of a single trial (baseline) for the hippocampal source. (B) Source spectral power for the hippocampal source, separately averaged for the healthy controls (HC, blue), and the ASD patients (ASD, red). Note that the spectrum was cut at 10 Hz as this was the lowest frequency included in the AIS analysis. (C) Correlation plot between the autocorrelation decay time (ACT) and the AIS. Data are shown separately for healthy controls (HC, blue), and the ASD patients (ASD, red). See Table 1 for details on correlation coefficients.
Mentions: Similarly, ACT showed a negative Pearson correlation with the AIS (p < 0.034) (Figure 4).

Bottom Line: Recent results suggest altered brain dynamics as a potential cause of symptoms in ASD.Our results showed a decrease of AIS values in the hippocampus of ASD patients in comparison with controls, meaning that brain signals in ASD were either less predictable, reduced in their dynamic richness or both.The observed changes in AIS are compatible with Bayesian theories of reduced use or precision of priors in ASD.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering Group, E. T. S. Ingenieros de Telecomunicación, University of Valladolid Valladolid, Spain.

ABSTRACT
Autism spectrum disorder (ASD) is a common developmental disorder characterized by communication difficulties and impaired social interaction. Recent results suggest altered brain dynamics as a potential cause of symptoms in ASD. Here, we aim to describe potential information-processing consequences of these alterations by measuring active information storage (AIS)-a key quantity in the theory of distributed computation in biological networks. AIS is defined as the mutual information between the past state of a process and its next measurement. It measures the amount of stored information that is used for computation of the next time step of a process. AIS is high for rich but predictable dynamics. We recorded magnetoencephalography (MEG) signals in 10 ASD patients and 14 matched control subjects in a visual task. After a beamformer source analysis, 12 task-relevant sources were obtained. For these sources, stationary baseline activity was analyzed using AIS. Our results showed a decrease of AIS values in the hippocampus of ASD patients in comparison with controls, meaning that brain signals in ASD were either less predictable, reduced in their dynamic richness or both. Our study suggests the usefulness of AIS to detect an abnormal type of dynamics in ASD. The observed changes in AIS are compatible with Bayesian theories of reduced use or precision of priors in ASD.

No MeSH data available.


Related in: MedlinePlus