Journal Publications

Frohlich, J., Reiter, L. T., Saravanapandian, V., DiStefano, C., Huberty, S., Hyde, C., ... & Jeste, S.S. (2019). Mechanisms underlying the EEG biomarker in Dup15q syndrome. Molecular Autism, 10(1), 29.

Frohlich, J., Miller, M., Bird, L. M., Garces, P., Purtell, H., Hoener, … & Hipp, J.F. (2019). Electrophysiological phenotype in Angelman syndrome differs between genotypes. Biological Psychiatry 85(9) 752-759. https://doi.org/10.1016/j.biopsych.2019.01.008 [PDF]

Li, Q., Şentürk, D., Sugar, C. A., Jeste, S., DiStefano, C., Frohlich, J., & Telesca, D. (2018). Inferring Brain Signals Synchronicity from a Sample of EEG Readings. Journal of the American Statistical Association, (just-accepted), 1-33.

Frohlich, J., Senturk, D., Saravanapandian, V., Golshani, P., Reiter, L. T., Sankar, R., … & Jeste, S. S. (2016). A Quantitative Electrophysiological Biomarker of Duplication 15q11. 2-q13. 1 Syndrome. PLoS One11(12), e0167179. [PDF]

Mohammad-Rezazadeh, I., Frohlich, J., Loo, S. K., & Jeste, S. S. (2016). Brain Connectivity in Autism Spectrum Disorder. Current Opinion in Neurology29(2), 137–147. http://doi.org/10.1097/WCO.0000000000000301

Jeste, S. S., Frohlich, J., & Loo, S. K. (2015). Electrophysiological biomarkers of diagnosis and outcome in neurodevelopmental disorders. Current Opinion in Neurology. 28 (2), 110-116 http://doi.org/10.1097/WCO.0000000000000181

Frohlich, J., Irimia, A., & Jeste, S. S. (2015). Trajectory of frequency stability in typical development. Brain Imaging and Behavior9(1), 5–18. http://doi.org/10.1007/s11682-014-9339-3

Frohlich, J., & van Horn, J. D. (2014). Reviewing the ketamine model for schizophrenia. Journal of Psychopharmacology (Oxford, England)28(4), 287–302. http://doi.org/10.1177/0269881113512909


Book Chapters

Coben, R., Mohammad-Rezazadeh, I., Frohlich, J., Jurgiel, J., & Michelini, G. (2017). Imaging brain connectivity in autism spectrum disorder. In Casanova, M.F., El-Baz, A., Suri, J.S. (Ed.)  Autism Imaging and Devices. Pages 245-286. CRC Press.

Frohlich, J, Van Horn, J.D. (2016) Chapter 60: Ketamine and the Dissociatives: Comparisons with Schizophrenia. In Preedy VR (Ed.) Neuropathology of Drug Addictions and Substance Misuse, Volume 2: Stimulants, Cub and Dissociative Drugs, Hallucinogens, Steroids, Inhalants and International Aspects. Part III: Dissociative Drugs, Section C: Structural and Functional Aspects. Pages 649-660.  Academic Press.


Selected figures

Figure 1, Frohlich et al 2019, Molecular Autism . Dup15q syndrome versus TD.  a  Spectral profiles of children with Dup15q syndrome (red) and TD children (blue). PSDs are averaged across channels and participants; colored highlights represent 95% confidence intervals. Power is significantly higher in Dup15q syndrome at 20.2–28.5 Hz ( p  < 0.05 corrected).  b  Dup15q syndrome topographic scalp power (mean across participants at  f  = 23.1 Hz).  c  TD topographic scalp power (mean across participants at  f  = 23.1 Hz).  d  Dup15q syndrome versus TD power difference effect sizes (Cohen’s  d ) at  f  = 23.1 Hz. Mean effect size across channels,  d  = 1.06 (min,  d  = 0.339; max,  d  = 1.98)

Figure 1, Frohlich et al 2019, Molecular Autism. Dup15q syndrome versus TD. a Spectral profiles of children with Dup15q syndrome (red) and TD children (blue). PSDs are averaged across channels and participants; colored highlights represent 95% confidence intervals. Power is significantly higher in Dup15q syndrome at 20.2–28.5 Hz (p < 0.05 corrected). b Dup15q syndrome topographic scalp power (mean across participants at f = 23.1 Hz). c TD topographic scalp power (mean across participants at f = 23.1 Hz). d Dup15q syndrome versus TD power difference effect sizes (Cohen’s d) at f = 23.1 Hz. Mean effect size across channels, d = 1.06 (min, d = 0.339; max, d = 1.98)

Figure 4, Frohlich et al. 2019 Molecular Autism.  EEG signature of pharmacological GABAA receptor modulation in healthy adult participants. Healthy adult participants ( n  = 12) were challenged orally with a GABAA PAM (5 mg midazolam).  a  Average power change in all channels 1 h following drug administration referenced to baseline (absolute power averaged across participants). Most channels displayed an increase in power in the beta band.  b  Channel-averaged power change. The colored highlight represents the 95% confidence interval. The average power change appears to largely plateau between the peak power change (16.1 Hz, red vertical line) and the Dup15q syndrome peak frequency (23.1 Hz, black vertical line).  c  Scalp topography of the −log10( p  value) multiplied by the sign of the  t -statistic from a two-tailed  t  test at 23.1 Hz. Three central channels (Fz, Cz, and Pz, indicated with a star symbol) survive an FDR correction for multiple channels ( p  value threshold = 3 × 10−3).  d  Power change averaged across central channels. We visualized the average power change for those channels that survived the FDR correction at 23.1 Hz. The colored highlight represents the 95% confidence interval. The power change peaks at 23.0 Hz (red vertical line), very close to the Dup15q syndrome peak frequency (23.1 Hz, black vertical line; Cf. Fig.  1 a).

Figure 4, Frohlich et al. 2019 Molecular Autism. EEG signature of pharmacological GABAA receptor modulation in healthy adult participants. Healthy adult participants (n = 12) were challenged orally with a GABAA PAM (5 mg midazolam). a Average power change in all channels 1 h following drug administration referenced to baseline (absolute power averaged across participants). Most channels displayed an increase in power in the beta band. b Channel-averaged power change. The colored highlight represents the 95% confidence interval. The average power change appears to largely plateau between the peak power change (16.1 Hz, red vertical line) and the Dup15q syndrome peak frequency (23.1 Hz, black vertical line). c Scalp topography of the −log10(p value) multiplied by the sign of the t-statistic from a two-tailed t test at 23.1 Hz. Three central channels (Fz, Cz, and Pz, indicated with a star symbol) survive an FDR correction for multiple channels (p value threshold = 3 × 10−3). d Power change averaged across central channels. We visualized the average power change for those channels that survived the FDR correction at 23.1 Hz. The colored highlight represents the 95% confidence interval. The power change peaks at 23.0 Hz (red vertical line), very close to the Dup15q syndrome peak frequency (23.1 Hz, black vertical line; Cf. Fig. 1a).

Figure 5, Frohlich et al., 2019 Biological Psychiatry . Spectral power differs between  Angelman syndrome  (AS)  genotypes .  (A)  Grand average power  spectral density  derived from the linear mixed model with age set to the mean log2 age of 4.7 years (average across all visits and electrodes). Deletion AS (Del) is shown in green, nondeletion AS (Non-Del) is shown in blue. The colored bands show 95% confidence intervals. The black bar indicates frequency ranges with significant group differences (corrected for multiple testing across frequencies). The gray lines indicate the specific hypotheses tested in the delta and beta bands (see  Figures 3  and  4 ).  (B)  Difference in spectral power between deletion AS and nondeletion AS. The colored bands show 95% confidence intervals.  (C)  Developmental trajectory of theta power (5.3 Hz) derived from the linear mixed model (average across all electrodes). Longitudinal visits are connected by solid lines.  (D)  Correlation between theta peak frequency and age. Longitudinal visits are connected by solid lines.  (E–G)  Scalp topography of power change in decibels,  effect size  (Cohen’s  d ), and  p  values for  t  tests between deletion AS and nondeletion AS derived from the linear mixed model for 5.3 Hz and the mean log2 age of 4.7 years.

Figure 5, Frohlich et al., 2019 Biological Psychiatry. Spectral power differs between Angelman syndrome (AS) genotypes. (A) Grand average power spectral density derived from the linear mixed model with age set to the mean log2 age of 4.7 years (average across all visits and electrodes). Deletion AS (Del) is shown in green, nondeletion AS (Non-Del) is shown in blue. The colored bands show 95% confidence intervals. The black bar indicates frequency ranges with significant group differences (corrected for multiple testing across frequencies). The gray lines indicate the specific hypotheses tested in the delta and beta bands (see Figures 3 and 4). (B) Difference in spectral power between deletion AS and nondeletion AS. The colored bands show 95% confidence intervals. (C) Developmental trajectory of theta power (5.3 Hz) derived from the linear mixed model (average across all electrodes). Longitudinal visits are connected by solid lines. (D) Correlation between theta peak frequency and age. Longitudinal visits are connected by solid lines. (E–G) Scalp topography of power change in decibels, effect size (Cohen’s d), and p values for t tests between deletion AS and nondeletion AS derived from the linear mixed model for 5.3 Hz and the mean log2 age of 4.7 years.

Figure 1, Frohlich et al., 2016 PLoS One . Qualitative analysis of spontaneous electroencephalogram (EEG) recordings from participants with Dup15q syndrome revealed overt beta frequency oscillations apparent upon visual inspection.  (A)  EEG recordings from 9 scalp regions of interest (ROIs) were analyzed: left frontal (orange), left central (yellow), left posterior (green), mid-frontal (aquamarine), mid-central (cyan), mid-posterior (blue), right frontal (purple), right central (pink), right posterior (red).  (B)  3-dimenisonal head model showing ROI electrode locations.  (C)  3 s of broadband EEG recordings from a representative 29-month-old TD child from 36 channels across 9 ROIs.  (D)  Same duration of EEG recorded from a 27-month-old child with nonsyndromic ASD.  (E)  EEG from a representative Dup15q syndrome participant (age 28 months) reveals spontaneous beta oscillations (SBOs) in virtually all channels and all ROIs. The overt quality of SBOs likely allows for their easy detection in clinical EEG recordings. By contrast,  (F)  a 43-month-old participant with both Dup15q syndrome and epilepsy does not show nearly such distinct SBOs. It is possible that beta activity is reduced in children with both Dup15q syndrome and epilepsy.

Figure 1, Frohlich et al., 2016 PLoS One. Qualitative analysis of spontaneous electroencephalogram (EEG) recordings from participants with Dup15q syndrome revealed overt beta frequency oscillations apparent upon visual inspection. (A) EEG recordings from 9 scalp regions of interest (ROIs) were analyzed: left frontal (orange), left central (yellow), left posterior (green), mid-frontal (aquamarine), mid-central (cyan), mid-posterior (blue), right frontal (purple), right central (pink), right posterior (red). (B) 3-dimenisonal head model showing ROI electrode locations. (C) 3 s of broadband EEG recordings from a representative 29-month-old TD child from 36 channels across 9 ROIs. (D) Same duration of EEG recorded from a 27-month-old child with nonsyndromic ASD. (E) EEG from a representative Dup15q syndrome participant (age 28 months) reveals spontaneous beta oscillations (SBOs) in virtually all channels and all ROIs. The overt quality of SBOs likely allows for their easy detection in clinical EEG recordings. By contrast, (F) a 43-month-old participant with both Dup15q syndrome and epilepsy does not show nearly such distinct SBOs. It is possible that beta activity is reduced in children with both Dup15q syndrome and epilepsy.