Limits...
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

Sample mean networks for the ASD and control subjects exhibit variability, and the mean group networks exhibit qualitatively similar patterns. a Example networks from 5 ASD subjects (top row, blue) and 5 control subjects (bottom row, red) are shown to demonstrate how individual subjects varied in their mean network edge weights. While some edges were consistently more represented (as in the frontal area, for example), individual subjects did not exhibit identical network weight patterns across the group. b Mean group networks for ASD (top row, blue) and control (bottom row, red) appear to have superficially similar patterns of edge weights
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: Sample mean networks for the ASD and control subjects exhibit variability, and the mean group networks exhibit qualitatively similar patterns. a Example networks from 5 ASD subjects (top row, blue) and 5 control subjects (bottom row, red) are shown to demonstrate how individual subjects varied in their mean network edge weights. While some edges were consistently more represented (as in the frontal area, for example), individual subjects did not exhibit identical network weight patterns across the group. b Mean group networks for ASD (top row, blue) and control (bottom row, red) appear to have superficially similar patterns of edge weights

Mentions: We expect high variability in the functional networks inferred from each 2 s epoch, as the brain responds to evolving internal and external demands. To establish more stable functional network representations, we computed the average functional network of each patient. In practice, the average functional network is the mean of all functional networks inferred across time for a patient. The average functional network is a weighted network, in which the edge weight indicates the proportion of times that edge appears in all epochs for a patient. For example, an edge weight of 0 indicates that two pairs of sensors (i.e., derivations) are never correlated across all 2 s epochs, while an edge weight of 1 indicates two pairs of sensors that remain correlated in each 2 s epoch. We have recently shown that average functional networks computed for more than 100 s of data constitute stable network templates or “cores” [64, 66]. These template networks computed for the ASD and control subjects reveal heterogeneous network structures within each group, rather than a common difference visually distinguishing each ASD subject from each control subject (Fig. 4a). We then computed the mean of these template networks across subjects within each group, resulting in the mean ASD template network, and the mean control template network (Fig. 4b). The mean ASD and control template networks displayed grossly similar structures, with slight differences in specific edge weights difficult to discern from visual inspection alone.Fig. 4


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

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

Sample mean networks for the ASD and control subjects exhibit variability, and the mean group networks exhibit qualitatively similar patterns. a Example networks from 5 ASD subjects (top row, blue) and 5 control subjects (bottom row, red) are shown to demonstrate how individual subjects varied in their mean network edge weights. While some edges were consistently more represented (as in the frontal area, for example), individual subjects did not exhibit identical network weight patterns across the group. b Mean group networks for ASD (top row, blue) and control (bottom row, red) appear to have superficially similar patterns of edge weights
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: Sample mean networks for the ASD and control subjects exhibit variability, and the mean group networks exhibit qualitatively similar patterns. a Example networks from 5 ASD subjects (top row, blue) and 5 control subjects (bottom row, red) are shown to demonstrate how individual subjects varied in their mean network edge weights. While some edges were consistently more represented (as in the frontal area, for example), individual subjects did not exhibit identical network weight patterns across the group. b Mean group networks for ASD (top row, blue) and control (bottom row, red) appear to have superficially similar patterns of edge weights
Mentions: We expect high variability in the functional networks inferred from each 2 s epoch, as the brain responds to evolving internal and external demands. To establish more stable functional network representations, we computed the average functional network of each patient. In practice, the average functional network is the mean of all functional networks inferred across time for a patient. The average functional network is a weighted network, in which the edge weight indicates the proportion of times that edge appears in all epochs for a patient. For example, an edge weight of 0 indicates that two pairs of sensors (i.e., derivations) are never correlated across all 2 s epochs, while an edge weight of 1 indicates two pairs of sensors that remain correlated in each 2 s epoch. We have recently shown that average functional networks computed for more than 100 s of data constitute stable network templates or “cores” [64, 66]. These template networks computed for the ASD and control subjects reveal heterogeneous network structures within each group, rather than a common difference visually distinguishing each ASD subject from each control subject (Fig. 4a). We then computed the mean of these template networks across subjects within each group, resulting in the mean ASD template network, and the mean control template network (Fig. 4b). The mean ASD and control template networks displayed grossly similar structures, with slight differences in specific edge weights difficult to discern from visual inspection alone.Fig. 4

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