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Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism.

Matlis S, Boric K, Chu CJ, Kramer MA - BMC Neurol (2015)

Bottom Line: The interpretation of prior studies in this area has been limited by mixed results and the lack of validation procedures.Three biomarkers of ASD were identified in the first patient group: (1) reduced posterior/anterior power ratio in the alpha frequency range (8-14 Hz), which we label the "peak alpha ratio", (2) reduced global density in functional networks, and (3) a reduction in the mean connectivity strength of a subset of functional network edges.These results corroborate the general finding that ASD subjects have decreased alpha power gradients and network connectivities compared to control subjects.

View Article: PubMed Central - PubMed

Affiliation: Graduate Program in Neuroscience, Boston University, 677 Beacon st., Boston, MA, 02215, USA. smatlis@bu.edu.

ABSTRACT

Background: Autism spectrum disorders (ASD) are increasingly prevalent and have a significant impact on the lives of patients and their families. Currently, the diagnosis is determined by clinical judgment and no definitive physiological biomarker for ASD exists. Quantitative biomarkers obtainable from clinical neuroimaging data - such as the scalp electroencephalogram (EEG) - would provide an important aid to clinicians in the diagnosis of ASD. The interpretation of prior studies in this area has been limited by mixed results and the lack of validation procedures. Here we use retrospective clinical data from a well-characterized population of children with ASD to evaluate the rhythms and coupling patterns present in the EEG to develop and validate an electrophysiological biomarker of ASD.

Methods: EEG data were acquired from a population of ASD (n = 27) and control (n = 55) children 4-8 years old. Data were divided into training (n = 13 ASD, n = 24 control) and validation (n = 14 ASD, n = 31 control) groups. Evaluation of spectral and functional network properties in the first group of patients motivated three biomarkers that were computed in the second group of age-matched patients for validation.

Results: Three biomarkers of ASD were identified in the first patient group: (1) reduced posterior/anterior power ratio in the alpha frequency range (8-14 Hz), which we label the "peak alpha ratio", (2) reduced global density in functional networks, and (3) a reduction in the mean connectivity strength of a subset of functional network edges. Of these three biomarkers, the first and third were validated in a second group of patients. Using the two validated biomarkers, we were able to classify ASD subjects with 83 % sensitivity and 68 % specificity in a post-hoc analysis.

Conclusions: This study demonstrates that clinical EEG can provide quantitative biomarkers to assist diagnosis of autism. These results corroborate the general finding that ASD subjects have decreased alpha power gradients and network connectivities compared to control subjects. In addition, this study demonstrates the necessity of using statistical techniques to validate EEG biomarkers identified using exploratory methods.

No MeSH data available.


Related in: MedlinePlus

Construction of power ratio and functional networks from multivariate scalp EEG recordings. ai Example EEG data from re-referenced 18 channels (broadband, 0.5 - 50 Hz) according to the bipolar “double banana” montage. Filtered and unfiltered data are divided into 2 s epochs. aii From unfiltered data power spectra are calculated for each channel using the multitaper method. aiii The ratio of power spectra are obtained from the power spectra of the posterior four derivations (T5-O1, P3-O1, P4-O2, T6-O2) divided by the anterior four derivations (Fp1-F7, Fp1-F3, Fp2-F4, Fp2-F8). Shown here is the mean posterior/frontal power spectra ratio to illustrate the properties of the peak alpha-ratio statistic. b For each channel pair filtered data (0.5 - 50 Hz) from 2 s epochs are used to calculate the cross-correlation. Two example traces for Fp2-F8 and T4-T6 show a correlation here with maximal coupling at a time lag of −50 ms. The significance of the maximum absolute value of the cross-correlation (blue circle) is determined using an analytic procedure (see Methods). c Example binary coupling networks derived from four 2-s epochs. Significant electrode coupling is represented in blue and indicated with an edge. These networks are averaged, resulting in a weighted coupling network for each subject. These are then compared against bootstrapped edge weight distributions in (d). d To create bootstrapped edge weight distributions, surrogate networks mirroring the original datasets are created by randomly sampling functional networks with replacement from all epochs of all subjects of both groups. Original ASD and control edge weights are compared to the surrogate edge weight distributions, and edges most significantly outside the distribution (p < 1/100,000) are selected to make a mask of highly significant edges. This mask is used to select the edges with the greatest difference between the ASD and control groups
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Fig1: Construction of power ratio and functional networks from multivariate scalp EEG recordings. ai Example EEG data from re-referenced 18 channels (broadband, 0.5 - 50 Hz) according to the bipolar “double banana” montage. Filtered and unfiltered data are divided into 2 s epochs. aii From unfiltered data power spectra are calculated for each channel using the multitaper method. aiii The ratio of power spectra are obtained from the power spectra of the posterior four derivations (T5-O1, P3-O1, P4-O2, T6-O2) divided by the anterior four derivations (Fp1-F7, Fp1-F3, Fp2-F4, Fp2-F8). Shown here is the mean posterior/frontal power spectra ratio to illustrate the properties of the peak alpha-ratio statistic. b For each channel pair filtered data (0.5 - 50 Hz) from 2 s epochs are used to calculate the cross-correlation. Two example traces for Fp2-F8 and T4-T6 show a correlation here with maximal coupling at a time lag of −50 ms. The significance of the maximum absolute value of the cross-correlation (blue circle) is determined using an analytic procedure (see Methods). c Example binary coupling networks derived from four 2-s epochs. Significant electrode coupling is represented in blue and indicated with an edge. These networks are averaged, resulting in a weighted coupling network for each subject. These are then compared against bootstrapped edge weight distributions in (d). d To create bootstrapped edge weight distributions, surrogate networks mirroring the original datasets are created by randomly sampling functional networks with replacement from all epochs of all subjects of both groups. Original ASD and control edge weights are compared to the surrogate edge weight distributions, and edges most significantly outside the distribution (p < 1/100,000) are selected to make a mask of highly significant edges. This mask is used to select the edges with the greatest difference between the ASD and control groups

Mentions: To characterize the power spectra for each patient we computed a summary statistic – the “peak alpha-ratio” – as follows (Fig. 1). First, we computed the power spectrum of each signal for each epoch of the dataset, and then averaged the power spectra across all epochs. Second, we computed the ratio of this average power between four pairs of posterior to anterior signals (Far Left: T5-O1/Fp1-F7; Medial Left: P3-O1/Fp1-F3; Medial Right: P4-O2/Fp2-F4; Far Right: T6-O2/Fp2-F8). Third, we determined the maximum value of the ratio within the alpha frequency range (8–14 Hz) for each of the four channel pairs. These four maximum back/front ratios were then averaged to produce the summary statistic, mean “peak alpha-ratio”, for each patient. We choose to compute the spectral ratio for three reasons. First, the posterior to anterior alpha gradient is one of the most widely observed EEG features in healthy controls and thus is an intuitive feature to evaluate in a disease population [57]. In addition, this metric has been previously correlated with behavioral inhibition and sociability [58, 59]. Second, as described in Results, changes in power (not the ratio) between the ASD and control subjects at all electrode deviations reveal no significant differences. Third, we choose to compute the frontal/posterior ratio to normalize the spectral results of each individual subject. This choice of normalization protects against artifacts that impact the overall amplitude of voltage activity for each subject (e.g., a subject with thicker hair may be expected to have reduced electrode conductance and an overall reduction in EEG amplitude), and we expect this normalization to make the results more robust to changes in clinical settings and routine (e.g., to changes in electrode recording equipment).


Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism.

Matlis S, Boric K, Chu CJ, Kramer MA - BMC Neurol (2015)

Construction of power ratio and functional networks from multivariate scalp EEG recordings. ai Example EEG data from re-referenced 18 channels (broadband, 0.5 - 50 Hz) according to the bipolar “double banana” montage. Filtered and unfiltered data are divided into 2 s epochs. aii From unfiltered data power spectra are calculated for each channel using the multitaper method. aiii The ratio of power spectra are obtained from the power spectra of the posterior four derivations (T5-O1, P3-O1, P4-O2, T6-O2) divided by the anterior four derivations (Fp1-F7, Fp1-F3, Fp2-F4, Fp2-F8). Shown here is the mean posterior/frontal power spectra ratio to illustrate the properties of the peak alpha-ratio statistic. b For each channel pair filtered data (0.5 - 50 Hz) from 2 s epochs are used to calculate the cross-correlation. Two example traces for Fp2-F8 and T4-T6 show a correlation here with maximal coupling at a time lag of −50 ms. The significance of the maximum absolute value of the cross-correlation (blue circle) is determined using an analytic procedure (see Methods). c Example binary coupling networks derived from four 2-s epochs. Significant electrode coupling is represented in blue and indicated with an edge. These networks are averaged, resulting in a weighted coupling network for each subject. These are then compared against bootstrapped edge weight distributions in (d). d To create bootstrapped edge weight distributions, surrogate networks mirroring the original datasets are created by randomly sampling functional networks with replacement from all epochs of all subjects of both groups. Original ASD and control edge weights are compared to the surrogate edge weight distributions, and edges most significantly outside the distribution (p < 1/100,000) are selected to make a mask of highly significant edges. This mask is used to select the edges with the greatest difference between the ASD and control groups
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4482270&req=5

Fig1: Construction of power ratio and functional networks from multivariate scalp EEG recordings. ai Example EEG data from re-referenced 18 channels (broadband, 0.5 - 50 Hz) according to the bipolar “double banana” montage. Filtered and unfiltered data are divided into 2 s epochs. aii From unfiltered data power spectra are calculated for each channel using the multitaper method. aiii The ratio of power spectra are obtained from the power spectra of the posterior four derivations (T5-O1, P3-O1, P4-O2, T6-O2) divided by the anterior four derivations (Fp1-F7, Fp1-F3, Fp2-F4, Fp2-F8). Shown here is the mean posterior/frontal power spectra ratio to illustrate the properties of the peak alpha-ratio statistic. b For each channel pair filtered data (0.5 - 50 Hz) from 2 s epochs are used to calculate the cross-correlation. Two example traces for Fp2-F8 and T4-T6 show a correlation here with maximal coupling at a time lag of −50 ms. The significance of the maximum absolute value of the cross-correlation (blue circle) is determined using an analytic procedure (see Methods). c Example binary coupling networks derived from four 2-s epochs. Significant electrode coupling is represented in blue and indicated with an edge. These networks are averaged, resulting in a weighted coupling network for each subject. These are then compared against bootstrapped edge weight distributions in (d). d To create bootstrapped edge weight distributions, surrogate networks mirroring the original datasets are created by randomly sampling functional networks with replacement from all epochs of all subjects of both groups. Original ASD and control edge weights are compared to the surrogate edge weight distributions, and edges most significantly outside the distribution (p < 1/100,000) are selected to make a mask of highly significant edges. This mask is used to select the edges with the greatest difference between the ASD and control groups
Mentions: To characterize the power spectra for each patient we computed a summary statistic – the “peak alpha-ratio” – as follows (Fig. 1). First, we computed the power spectrum of each signal for each epoch of the dataset, and then averaged the power spectra across all epochs. Second, we computed the ratio of this average power between four pairs of posterior to anterior signals (Far Left: T5-O1/Fp1-F7; Medial Left: P3-O1/Fp1-F3; Medial Right: P4-O2/Fp2-F4; Far Right: T6-O2/Fp2-F8). Third, we determined the maximum value of the ratio within the alpha frequency range (8–14 Hz) for each of the four channel pairs. These four maximum back/front ratios were then averaged to produce the summary statistic, mean “peak alpha-ratio”, for each patient. We choose to compute the spectral ratio for three reasons. First, the posterior to anterior alpha gradient is one of the most widely observed EEG features in healthy controls and thus is an intuitive feature to evaluate in a disease population [57]. In addition, this metric has been previously correlated with behavioral inhibition and sociability [58, 59]. Second, as described in Results, changes in power (not the ratio) between the ASD and control subjects at all electrode deviations reveal no significant differences. Third, we choose to compute the frontal/posterior ratio to normalize the spectral results of each individual subject. This choice of normalization protects against artifacts that impact the overall amplitude of voltage activity for each subject (e.g., a subject with thicker hair may be expected to have reduced electrode conductance and an overall reduction in EEG amplitude), and we expect this normalization to make the results more robust to changes in clinical settings and routine (e.g., to changes in electrode recording equipment).

Bottom Line: The interpretation of prior studies in this area has been limited by mixed results and the lack of validation procedures.Three biomarkers of ASD were identified in the first patient group: (1) reduced posterior/anterior power ratio in the alpha frequency range (8-14 Hz), which we label the "peak alpha ratio", (2) reduced global density in functional networks, and (3) a reduction in the mean connectivity strength of a subset of functional network edges.These results corroborate the general finding that ASD subjects have decreased alpha power gradients and network connectivities compared to control subjects.

View Article: PubMed Central - PubMed

Affiliation: Graduate Program in Neuroscience, Boston University, 677 Beacon st., Boston, MA, 02215, USA. smatlis@bu.edu.

ABSTRACT

Background: Autism spectrum disorders (ASD) are increasingly prevalent and have a significant impact on the lives of patients and their families. Currently, the diagnosis is determined by clinical judgment and no definitive physiological biomarker for ASD exists. Quantitative biomarkers obtainable from clinical neuroimaging data - such as the scalp electroencephalogram (EEG) - would provide an important aid to clinicians in the diagnosis of ASD. The interpretation of prior studies in this area has been limited by mixed results and the lack of validation procedures. Here we use retrospective clinical data from a well-characterized population of children with ASD to evaluate the rhythms and coupling patterns present in the EEG to develop and validate an electrophysiological biomarker of ASD.

Methods: EEG data were acquired from a population of ASD (n = 27) and control (n = 55) children 4-8 years old. Data were divided into training (n = 13 ASD, n = 24 control) and validation (n = 14 ASD, n = 31 control) groups. Evaluation of spectral and functional network properties in the first group of patients motivated three biomarkers that were computed in the second group of age-matched patients for validation.

Results: Three biomarkers of ASD were identified in the first patient group: (1) reduced posterior/anterior power ratio in the alpha frequency range (8-14 Hz), which we label the "peak alpha ratio", (2) reduced global density in functional networks, and (3) a reduction in the mean connectivity strength of a subset of functional network edges. Of these three biomarkers, the first and third were validated in a second group of patients. Using the two validated biomarkers, we were able to classify ASD subjects with 83 % sensitivity and 68 % specificity in a post-hoc analysis.

Conclusions: This study demonstrates that clinical EEG can provide quantitative biomarkers to assist diagnosis of autism. These results corroborate the general finding that ASD subjects have decreased alpha power gradients and network connectivities compared to control subjects. In addition, this study demonstrates the necessity of using statistical techniques to validate EEG biomarkers identified using exploratory methods.

No MeSH data available.


Related in: MedlinePlus