Timothy Kim, Adam Naples, Max Rolison & James McPartland • The resting EEG paradigm is a well-suited neuroscience tool for individuals with developmental disabilities and infants because it is inexpensive, noninvasive, and does not demand an overt response (Coben, 2009). • Differences in resting EEG spectral power have successfully discriminated children with ASD from controls and correlate with clinical characteristics (Wang et al., 2013). • Resting EEG activity may differentiate high- and normal-risk infants (Bosl et al., 2011). • Alpha asymmetry is associated with mood reactivity and cognitive functioning (Gotlib, 1998). • Atypical patterns of alpha asymmetry have been observed in school-age children with autism (Stroganova et al., 2007). • Atypical trajectories of alpha asymmetry have been observed in high-risk infants (Gabbard-Durnam et al., 2007), which demonstrates that alpha asymmetry is a promising potential ASD endophenotype. • Previous resting EEG studies suggest a U-shaped profile of electrophysiological power alterations in ASD, with excessive power in low-frequency, such as theta, and high- frequency power (Wang et al., 2013). Current Study • The experiment measured and compared electrophysiological brain activity in infants at high-risk for ASD with activity in infants at normal-risk over the first two years of life using the resting EEG paradigm. • High-risk infants (HR): infants with an older sibling diagnosed with ASD • Normal-risk infants (NR): infants with no first-degree relatives with ASD • We evaluated the hypotheses that, relative to NR infants, HR infants would display: • Differing patterns of alpha asymmetry. • Differentiated resting EEG activity in theta spectral power. McPartland Lab Yale Child Study Center, New Haven, CT PARTICIPANTS & METHODS CONCLUSIONS & FUTURE DIRECTIONS RESULTS REFERENCES Bosl, W., Tierney, A., Tager-Flusberg, H., & Nelson, C. (2011). EEG complexity as a biomarker for autism spectrum disorder risk. BMC medicine, 9(1), 1. Coben, R. (2009). The importance of electroencephalogram assessment for autistic disorders. Biofeedback, 37(2), 71-80. Gabard-Durnam, L., Tierney, A. L., Vogel-Farley, V., Tager-Flusberg, H., & Nelson, C. A. (2015). Alpha asymmetry in infants at risk for autism spectrum disorders. Journal of autism and developmental disorders, 45(2), 473-480. Gotlib, I. H. (1998). EEG alpha asymmetry, depression, and cognitive functioning. Cognition & Emotion, 12(3), 449-478. Stroganova, T. A., Nygren, G., Tsetlin, M. M., Posikera, I. N., Gillberg, C., Elam, M., & Orekhova, E. V. (2007). Abnormal EEG lateralization in boys with autism. Clinical Neurophysiology, 118(8), 1842-1854. Tau, G. Z., & Peterson, B. S. (2010). Normal development of brain circuits. Neuropsychopharmacology, 35(1), 147-168. Wang, J., Barstein, J., Ethridge, L. E., Mosconi, M. W., Takarae, Y., & Sweeney, J. A. (2013). Resting state EEG abnormalities in autism spectrum disorders. Journal of neurodevelopmental disorders, 5(1), 1. Figure 2: Spectral power maps for HR and NR infants at ≤ 12 and > 12 month time points. + • In infants ≤ 12 months, there was not a significant difference in the number of bad trials between males (M = 59.93, SD = 23.140) and females (M = 52.62, SD = 23.905; p = .882) as well as NR (M = 56.36, SD = 20.190) and HR (M = 56.97, SD = 24.985; p = .218) (Fig. 5). • In infants > 12 months, there was not a significant difference in the number of bad trials between males (M = 56.21, SD = 20.602) and females (M = 52.75, SD = 28.429; p = .136) as well as NR (M = 54.43, SD = 21.110) and HR (M = 56.07, SD = 26.470; p = .194). • Indicates that spectral power results were not due to the number of trials excluded. • In infants ≤ 12 months, there was a significant difference in alpha asymmetry between HR than NR infants (p = .023). However, in infants > 12 months, there was no significant difference (p = .508). • Alpha symmetry was greater in the younger cohort of infants. While the effect was not significant in the older cohort, the pattern of results was in the same direction. • In infants ≤ 12 months, there were significant interactions in hemisphere*risk for theta (p = .022) but in infants > 12 months, there were significant interactions in hemisphere*sex*risk for theta (p = .015). • For infants ≤ 12 m, NR infants demonstrated left-lateralized theta asymmetry and HR infants demonstrated right-lateralized theta asymmetry. Figure 5: Sum of bad trials for participants grouped by age and risk. No significant differences in sums. • EEG recorded continuously at 500 Hz using 128-channel Hydrocel Geodesic Sensor Nets. • Infants seated on parent’s lap, watched bubbles being blown for 2 minutes. • Using Netstation 4.5.4 software, EEG data were filtered, segmented into 120 overlapping 1s epochs, processed through artifact detection, and hand-edited for artifacts. • Processed and cleaned data were averaged from lateral electrodes across both hemispheres (Fig. 1). An Analysis of Resting EEG Data in Infants at High-Risk for Developing Autism Spectrum Disorder (ASD) ≤ 12 months > 12 months BACKGROUND Figure 1: Resting EEG Electrode Chart. Data were averaged across 4 lateral electrodes for both right (91, 95, 96, 100) and left hemispheres (57, 58, 59, 64 ). • Different patterns of alpha asymmetry were observed in the two risk groups • HR infants exhibited trends towards right lateralization across age groups. • May indicate differences in emotional reactivity, which is part of the clinical phenotype of ASD and may allow for earlier detection of ASD. • The larger effect of alpha asymmetry in the younger age group may indicate a relationship between early neural pruning and alpha asymmetry differences through either excessive or insufficient neural pruning that leads to hyper- connective and hypo-connective neural circuits. • Excessive low-frequency theta power in HR infants correspond to findings that link excessive theta levels to ASD. Future Directions • Compare EEG results in HR infants who develop ASD versus HR infants who do not develop ASD. • Investigate relationships among EEG and the behavioral phenotype. • Examine EEG power differences in other frequency bands (gamma and beta). • Examine the continuous relationship between age and brain activity. • Explore alternative analytic approaches to resting EEG data, such as coherence and multiscale entropy. Figure 3: Alpha (6-8 Hz) asymmetry boxplots based on risk for infants ≤12 m (left) and >12 m (right). Significant difference seen in infants ≤12 m ( p = .023) and no significant difference in infants >12 m ( p = .508). ACKNOWLEDGEMENTS Acknowledgements: Autism Science Foundation Undergraduate Summer Research Fellowship (Kim), Autism Speaks Translational Postdoctoral Fellowship (Naples), Simons Foundation 94924 (Klin, Pelphrey, McPartland), NIMH K23MH086785, NIMH R01MH100173, NIMH R01MH100173-02S1, NARSAD Atherton Young Investigator Award (McPartland), P01 HD003008, Project 1 (Chawarska), R01MH087554 (Chawarska), CTSA UL1 RR024139 (Shic). Figure 4: Theta (3-5 Hz) power levels for infants ≤12 m (left) and infants >12 m (right). 30.00 40.00 50.00 60.00 70.00 ≤12m NR ≤12 HR >12m NR >12m HR # of Segments with Artifact by Age and Risk Status 0 5 10 15 20 25 30 35 40 45 50 frequency 10 -1 10 0 10 1 10 2 power (log 10) Left Hemisphere Power High Risk Normal Risk 0 5 10 15 20 25 30 35 40 45 50 frequency 10 -1 10 0 10 1 10 2 power (log 10) Right Hemisphere Power High Risk Normal Risk NR HR Male Female Male Female ≤12 m 6 7 16 8 >12 m 7 10 11 2 0 5 10 15 20 25 30 35 40 45 50 frequency 10 -1 10 0 10 1 10 2 power (log 10) Left Hemisphere Power High Risk Normal Risk (Hz) 0 5 10 15 20 25 30 35 40 45 50 frequency 10 -1 10 0 10 1 10 2 power (log 10) Right Hemisphere Power High Risk Normal Risk (Hz) (Hz) (Hz) −0.8 −0.4 0.0 0.4 NR HR Risk Alpha spectral power levels (R-L) 0.000 2.000 4.000 6.000 8.000 10.000 12.000 14.000 L R L R L R L R NR HR NR HR F M Theta spectral power levels 0.000 2.000 4.000 6.000 8.000 10.000 12.000 14.000 L R L R NR HR Theta spectral power levels −0.4 0.0 0.4 NR HR Risk Alpha spectral power levels (R-L) θ θ θ θ α α α α • Spectral power was estimated using a Multitaper Fast Fourier Transform. • Theta (θ; 3-5 Hz) spectral power levels for the left and right hemispheres and alpha (α; 6-8 Hz) asymmetry, or the difference in alpha power levels between hemispheres, were examined (Fig. 2-4). • Participants grouped into two cohorts: infants ≤ 12 months and infants > 12 months. • Alpha asymmetry and theta power were analyzed using repeated measures analysis of variance (ANOVA) and paired samples t-tests. • Within-subject factors: • Hemisphere (Left/Right) • Between-subject factors: • Risk (HR/NR) • Sex (Male/Female) • Age (≤12m/>12m) ≤ 12 months > 12 months • For infants > 12 m, theta power was greater in HR infants. Across groups, theta power was greater in the left hemisphere. • In HR infants, theta power was greater in females than in males. Theta power was not significantly different or in the opposite direction in males.
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Timothy Kim, Adam Naples, Max Rolison & James McPartland
• The resting EEG paradigm is a well-suited neuroscience tool for individuals with developmental disabilities and infants because it is inexpensive, noninvasive, and does not demand an overt response (Coben, 2009).
• Differences in resting EEG spectral power have successfully discriminated children with ASD from controls and correlate with clinical characteristics (Wang et al., 2013).
• Resting EEG activity may differentiate high- and normal-risk infants (Bosl et al., 2011).• Alpha asymmetry is associated with mood reactivity and cognitive functioning (Gotlib,
1998).• Atypical patterns of alpha asymmetry have been observed in school-age children with
autism (Stroganova et al., 2007).• Atypical trajectories of alpha asymmetry have been observed in high-risk infants
(Gabbard-Durnam et al., 2007), which demonstrates that alpha asymmetry is a promising potential ASD endophenotype.
• Previous resting EEG studies suggest a U-shaped profile of electrophysiological power alterations in ASD, with excessive power in low-frequency, such as theta, and high-frequency power (Wang et al., 2013).
Current Study
• The experiment measured and compared electrophysiological brain activity in infants at high-risk for ASD with activity in infants at normal-risk over the first two years of life using the resting EEG paradigm.
• High-risk infants (HR): infants with an older sibling diagnosed with ASD• Normal-risk infants (NR): infants with no first-degree relatives with ASD
• We evaluated the hypotheses that, relative to NR infants, HR infants would display: • Differing patterns of alpha asymmetry.• Differentiated resting EEG activity in theta spectral power.
McPartland LabYale Child Study Center, New Haven, CT
PARTICIPANTS & METHODS
CONCLUSIONS & FUTURE DIRECTIONS
RESULTS
REFERENCES
Bosl, W., Tierney, A., Tager-Flusberg, H., & Nelson, C. (2011). EEG complexity as a biomarker for autism spectrum disorder risk. BMC medicine, 9(1), 1.Coben, R. (2009). The importance of electroencephalogram assessment for autistic disorders. Biofeedback, 37(2), 71-80.Gabard-Durnam, L., Tierney, A. L., Vogel-Farley, V., Tager-Flusberg, H., & Nelson, C. A. (2015). Alpha asymmetry in infants at risk for autism spectrum disorders. Journal of autism and developmental disorders, 45(2), 473-480.Gotlib, I. H. (1998). EEG alpha asymmetry, depression, and cognitive functioning. Cognition & Emotion, 12(3), 449-478.Stroganova, T. A., Nygren, G., Tsetlin, M. M., Posikera, I. N., Gillberg, C., Elam, M., & Orekhova, E. V. (2007). Abnormal EEG lateralization in boys with autism. Clinical Neurophysiology, 118(8), 1842-1854.Tau, G. Z., & Peterson, B. S. (2010). Normal development of brain circuits. Neuropsychopharmacology, 35(1), 147-168.Wang, J., Barstein, J., Ethridge, L. E., Mosconi, M. W., Takarae, Y., & Sweeney, J. A. (2013). Resting state EEG abnormalities in autism spectrum disorders. Journal of neurodevelopmental disorders, 5(1), 1.
Figure 2: Spectral power maps for HR and NR infants at ≤ 12 and > 12 month time points.
+
• In infants ≤ 12 months, there was not a significant difference in the number of bad trials between males (M = 59.93, SD = 23.140) and females (M = 52.62, SD = 23.905; p = .882) as well as NR (M = 56.36, SD = 20.190) and HR (M = 56.97, SD = 24.985; p = .218) (Fig. 5).
• In infants > 12 months, there was not a significant difference in the number of bad trials between males (M = 56.21, SD = 20.602) and females (M = 52.75, SD = 28.429; p = .136) as well as NR (M = 54.43, SD = 21.110) and HR (M = 56.07, SD = 26.470; p = .194). • Indicates that spectral power results were not due to the number of trials excluded.
• In infants ≤ 12 months, there was a significant difference in alpha asymmetry between HR than NR infants (p = .023). However, in infants > 12 months, there was no significant difference (p = .508). • Alpha symmetry was greater in the younger cohort of infants. While the effect was not
significant in the older cohort, the pattern of results was in the same direction.• In infants ≤ 12 months, there were significant interactions in hemisphere*risk for theta (p =
.022) but in infants > 12 months, there were significant interactions in hemisphere*sex*risk for theta (p = .015).• For infants ≤ 12 m, NR infants demonstrated left-lateralized theta asymmetry and HR
infants demonstrated right-lateralized theta asymmetry.
Figure 5: Sum of
bad trials for
participants
grouped by age
and risk. No significant differences in sums.
• EEG recorded continuously at 500 Hz using 128-channel Hydrocel Geodesic Sensor Nets.
• Infants seated on parent’s lap, watched bubbles being blown for 2 minutes.
• Using Netstation 4.5.4 software, EEG data were filtered, segmented into 120 overlapping 1s epochs, processed through artifact detection, and hand-edited for artifacts.
• Processed and cleaned data were averaged from lateral electrodes across both hemispheres (Fig. 1).
An Analysis of Resting EEG Data in Infants at High-Risk for Developing Autism Spectrum Disorder (ASD)
≤ 12 months
> 12 months
BACKGROUND
Figure 1: Resting EEG
Electrode Chart. Data were averaged across 4 lateral electrodes for both right (91, 95, 96, 100) and left
hemispheres (57, 58, 59, 64 ).
• Different patterns of alpha asymmetry were observed in the two risk groups• HR infants exhibited trends towards right lateralization across age groups.• May indicate differences in emotional reactivity, which is part of the clinical
phenotype of ASD and may allow for earlier detection of ASD.• The larger effect of alpha asymmetry in the younger age group may indicate a
relationship between early neural pruning and alpha asymmetry differences through either excessive or insufficient neural pruning that leads to hyper-connective and hypo-connective neural circuits.
• Excessive low-frequency theta power in HR infants correspond to findings that link excessive theta levels to ASD.
Future Directions
• Compare EEG results in HR infants who develop ASD versus HR infants who do not develop ASD.
• Investigate relationships among EEG and the behavioral phenotype.• Examine EEG power differences in other frequency bands (gamma and beta).• Examine the continuous relationship between age and brain activity.• Explore alternative analytic approaches to resting EEG data, such as coherence and
multiscale entropy.
Figure 3: Alpha (6-8 Hz) asymmetry boxplots based on risk for infants ≤12 m (left) and >12 m (right). Significant
difference seen in infants ≤12 m (p = .023) and no significant difference in infants >12 m (p = .508).
ACKNOWLEDGEMENTS
Acknowledgements: Autism Science Foundation Undergraduate Summer Research Fellowship (Kim), Autism Speaks Translational Postdoctoral Fellowship (Naples), Simons Foundation 94924 (Klin, Pelphrey, McPartland), NIMH K23MH086785, NIMH R01MH100173, NIMH R01MH100173-02S1, NARSAD Atherton Young Investigator Award (McPartland), P01 HD003008, Project 1 (Chawarska), R01MH087554 (Chawarska), CTSA UL1 RR024139 (Shic).
Figure 4: Theta (3-5 Hz) power levels for infants ≤12 m (left) and infants >12 m (right).
30.00
40.00
50.00
60.00
70.00
≤12m NR ≤12 HR >12m NR >12m HR
# of Segments with Artifact by Age and Risk Status
0 5 10 15 20 25 30 35 40 45 50frequency
10-1
100
101
102
pow
er (l
og 1
0)
Left Hemisphere Power
High RiskNormal Risk
0 5 10 15 20 25 30 35 40 45 50frequency
10-1
100
101
102
pow
er (l
og 1
0)
Right Hemisphere Power
High RiskNormal Risk
NR HR
Male Female Male Female
≤12 m 6 7 16 8
>12 m 7 10 11 2
0 5 10 15 20 25 30 35 40 45 50frequency
10-1
100
101
102
pow
er (l
og 1
0)
Left Hemisphere Power
High RiskNormal Risk
(Hz)0 5 10 15 20 25 30 35 40 45 50
frequency
10-1
100
101
102
pow
er (l
og 1
0)
Right Hemisphere Power
High RiskNormal Risk
(Hz)
(Hz) (Hz)
−0.8
−0.4
0.0
0.4
NR HRRisk
lnRLH
ALLH
ARL
## Df Sum Sq Mean Sq F value Pr(>F)## Risk 1 0.317 0.3167 1.556 0.219## Sex 1 0.049 0.0494 0.243 0.625## Risk:Sex 1 0.012 0.0125 0.061 0.806## Residuals 45 9.162 0.2036
## Warning in read.spss("Final Resting Data Group 2 Input.sav", to.data.frame## = TRUE): Final Resting Data Group 2 Input.sav: Unrecognized record type 7,## subtype 18 encountered in system file
## Warning in read.spss("Final Resting Data Group 2 Input.sav", to.data.frame## = TRUE): Final Resting Data Group 2 Input.sav: Unrecognized record type 7,## subtype 24 encountered in system file
5
Alph
a sp
ectra
l pow
er le
vels
(R-L
)
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
L R L R L R L R
NR HR NR HR
F M
Thet
a sp
ectra
l pow
er le
vels
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
L R L R
NR HR
Thet
a sp
ectra
l pow
er le
vels
Now for group 2
−0.4
0.0
0.4
NR HRRisk
lnRHA
#### Welch Two Sample t-test#### data: lnRHA by Risk## t = -0.53227, df = 32.005, p-value = 0.5982## alternative hypothesis: true difference in means is not equal to 0## 95 percent confidence interval:## -0.2530934 0.1482244## sample estimates:## mean in group NR mean in group HR## -0.06430608 -0.01187156
6
Alph
a sp
ectra
l pow
er le
vels
(R-L
)
θ
θ
θ
θ
α α
α α
• Spectral power was estimated using a Multitaper Fast Fourier Transform.
• Theta (θ; 3-5 Hz) spectral power levels for the left and right hemispheres and alpha (α; 6-8 Hz) asymmetry, or the difference in alpha power levels between hemispheres, were examined (Fig. 2-4).
• Participants grouped into two cohorts: infants ≤ 12 months and infants > 12 months.
• Alpha asymmetry and theta power were analyzed using repeated measures analysis of variance (ANOVA) and paired samples t-tests.
• Within-subject factors:• Hemisphere (Left/Right)
• Between-subject factors:• Risk (HR/NR)• Sex (Male/Female)• Age (≤12m/>12m)
≤ 12 months > 12 months
• For infants > 12 m, theta power was greater in HR infants. Across groups, theta power was greater in the left hemisphere.
• In HR infants, theta power was greater in females than in males. Theta power was not significantly different or in the opposite direction in males.
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