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10. Sleep functional connectivity, hyperexcitability, and cognition in Alzheimer's disease.

Author

Moguilner, Sebastian G., Berezuk, Courtney, Bender, Alex C., Pellerin, Kyle R., Gomperts, Stephen N., Cash, Sydney S., Sarkis, Rani A., and Lam, Alice D.

Abstract

INTRODUCTION: Sleep disturbances are common in Alzheimer's disease (AD) and may reflect pathologic changes in brain networks. To date, no studies have examined changes in sleep functional connectivity (FC) in AD or their relationship with network hyperexcitability and cognition. METHODS: We assessed electroencephalogram (EEG) sleep FC in 33 healthy controls, 36 individuals with AD without epilepsy, and 14 individuals with AD and epilepsy. RESULTS: AD participants showed increased gamma connectivity in stage 2 sleep (N2), which was associated with longitudinal cognitive decline. Network hyperexcitability in AD was associated with a distinct sleep connectivity signature, characterized by decreased N2 delta connectivity and reversal of several connectivity changes associated with AD. Machine learning algorithms using sleep connectivity features accurately distinguished diagnostic groups and identified "fast cognitive decliners" among study participants who had AD. DISCUSSION: Our findings reveal changes in sleep functional networks associated with cognitive decline in AD and may have implications for disease monitoring and therapeutic development. Highlights: Brain functional connectivity (FC) in Alzheimer's disease is altered during sleep.Sleep FC measures correlate with cognitive decline in AD.Network hyperexcitability in AD has a distinct sleep connectivity signature. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Safety and Efficacy of Natalizumab as Adjunctive Therapy for People With Drug-Resistant Epilepsy: A Phase 2 Study

Author

French, Jacqueline A., Cole, Andrew J., Faught, Edward, Theodore, William H., Vezzani, Annamaria, Liow, Kore, Halford, Jonathan J., Armstrong, Robert, Szaflarski, Jerzy P., Hubbard, Sarah, Patel, Jagdish, Chen, Kun, Feng, Wei, Rizzo, Marco, Elkins, Jacob, Knafler, Gabrielle, Parkerson, Kimberly A., Klein, Pavel, Benbadis, Selim, Tecoma, Evelyn, Balabanov, Antoaneta, Vossler, David, Husain, Aatif, Boro, Alex, Oster, Joel, Gelfand, Michael, Koubeissi, Mohamad, Sirven, Joseph, Harvey, Jay, Kassab, Mounzer, Hogan, Edward, Lee, Ki Hyeong, Beach, Robert, Shin, Hae, Sarkis, Rani, Flitman, Stephen, Honeycutt, William, Ayala, Ricardo, Bautista, Ramon, Berg, Michel, Carran, Melissa, Kudrow, David, Ting, Tricia, and Rafecas, Jose

Published
2021
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16. Initial case series of a novel sensing deep brain stimulation device in drug‐resistant epilepsy and consistent identification of alpha/beta oscillatory activity: A feasibility study

Author

Chua, Melissa M. J., primary, Vissani, Matteo, additional, Liu, David D., additional, Schaper, Frederic L. W. V. J., additional, Warren, Aaron E. L., additional, Caston, Rose, additional, Dworetzky, Barbara A., additional, Bubrick, Ellen J., additional, Sarkis, Rani A., additional, Cosgrove, G. Rees, additional, and Rolston, John D., additional

Published
2023
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20. EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer’s Disease

Author

Azami, Hamed, Moguilner, Sebastian, Penagos, Hector, Sarkis, Rani A., Arnold, Steven E., Gomperts, Stephen N., and Lam, Alice D.

Subjects
Psychiatry and Mental health, Clinical Psychology, General Neuroscience, General Medicine, Geriatrics and Gerontology, Article
Abstract

Background: Alzheimer’s disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales. Objective: To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD. Methods: We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function. Results: SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power. Conclusion: SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.

Published
2023

22. Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease

Author

Olah, Marta, Menon, Vilas, Habib, Naomi, Taga, Mariko F., Ma, Yiyi, Yung, Christina J., Cimpean, Maria, Khairallah, Anthony, Coronas-Samano, Guillermo, Sankowski, Roman, Grün, Dominic, Kroshilina, Alexandra A., Dionne, Danielle, Sarkis, Rani A., Cosgrove, Garth R., Helgager, Jeffrey, Golden, Jeffrey A., Pennell, Page B., Prinz, Marco, Vonsattel, Jean Paul G., Teich, Andrew F., Schneider, Julie A., Bennett, David A., Regev, Aviv, Elyaman, Wassim, Bradshaw, Elizabeth M., and De Jager, Philip L.

Published
2020
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26. Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation

Author

Jing, Jin, primary, Ge, Wendong, additional, Hong, Shenda, additional, Fernandes, Marta Bento, additional, Lin, Zhen, additional, Yang, Chaoqi, additional, An, Sungtae, additional, Struck, Aaron F., additional, Herlopian, Aline, additional, Karakis, Ioannis, additional, Halford, Jonathan J., additional, Ng, Marcus C., additional, Johnson, Emily L., additional, Appavu, Brian L., additional, Sarkis, Rani A., additional, Osman, Gamaleldin, additional, Kaplan, Peter W., additional, Dhakar, Monica B., additional, Arcot Jayagopal, Lakshman, additional, Sheikh, Zubeda, additional, Taraschenko, Olga, additional, Schmitt, Sarah, additional, Haider, Hiba A., additional, Kim, Jennifer A., additional, Swisher, Christa B., additional, Gaspard, Nicolas, additional, Cervenka, Mackenzie C., additional, Rodriguez Ruiz, Andres A., additional, Lee, Jong Woo, additional, Tabaeizadeh, Mohammad, additional, Gilmore, Emily J., additional, Nordstrom, Kristy, additional, Yoo, Ji Yeoun, additional, Holmes, Manisha G., additional, Herman, Susan T., additional, Williams, Jennifer A., additional, Pathmanathan, Jay, additional, Nascimento, Fábio A., additional, Fan, Ziwei, additional, Nasiri, Samaneh, additional, Shafi, Mouhsin M., additional, Cash, Sydney S., additional, Hoch, Daniel B., additional, Cole, Andrew J., additional, Rosenthal, Eric S., additional, Zafar, Sahar F., additional, Sun, Jimeng, additional, and Westover, M. Brandon, additional

Published
2023
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37. Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs

Author

Jing, Jin, primary, Ge, Wendong, additional, Struck, Aaron F., additional, Fernandes, Marta Bento, additional, Hong, Shenda, additional, An, Sungtae, additional, Fatima, Safoora, additional, Herlopian, Aline, additional, Karakis, Ioannis, additional, Halford, Jonathan J., additional, Ng, Marcus C., additional, Johnson, Emily L., additional, Appavu, Brian L., additional, Sarkis, Rani A., additional, Osman, Gamaleldin, additional, Kaplan, Peter W., additional, Dhakar, Monica B., additional, Jayagopal, Lakshman Arcot, additional, Sheikh, Zubeda, additional, Taraschenko, Olga, additional, Schmitt, Sarah, additional, Haider, Hiba A., additional, Kim, Jennifer A., additional, Swisher, Christa B., additional, Gaspard, Nicolas, additional, Cervenka, Mackenzie C., additional, Rodriguez Ruiz, Andres A., additional, Lee, Jong Woo, additional, Tabaeizadeh, Mohammad, additional, Gilmore, Emily J., additional, Nordstrom, Kristy, additional, Yoo, Ji Yeoun, additional, Holmes, Manisha G., additional, Herman, Susan T., additional, Williams, Jennifer A., additional, Pathmanathan, Jay, additional, Nascimento, Fábio A., additional, Fan, Ziwei, additional, Nasiri, Samaneh, additional, Shafi, Mouhsin M., additional, Cash, Sydney S., additional, Hoch, Daniel B., additional, Cole, Andrew J., additional, Rosenthal, Eric S., additional, Zafar, Sahar F., additional, Sun, Jimeng, additional, and Westover, M. Brandon, additional

Published
2022
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41. Update in Progress: Cognitive Phenotypes in Temporal Lobe Epilepsy.

Author

Sarkis, Rani A.

Subjects
*TEMPORAL lobe epilepsy, *EPILEPSY, *HIPPOCAMPAL sclerosis, *MISSING data (Statistics), *PHENOTYPES, *PHENOTYPIC plasticity
Abstract

Moving Towards a Taxonomy of Cognitive Impairments in Epilepsy: Application of Latent Profile Analysis to 1178 Patients With Temporal Lobe Epilepsy Reyes A, Hermann BP, Busch RM, Drane DL, Barr WB, Hamberger MJ, Roesch SC, McDonald CR. Brain Commun. 2022;4(6):fcac289. doi:10.1093/braincomms/fcac289 In efforts to understand the cognitive heterogeneity within and across epilepsy syndromes, cognitive phenotyping has been proposed as a new taxonomy aimed at developing a harmonized approach to cognitive classification in epilepsy. Data- and clinically driven approaches have been previously used with variability in the phenotypes derived across studies. In our study, we utilize latent profile analysis to test several models of phenotypes in a large multicentre sample of patients with temporal lobe epilepsy and evaluate their demographic and clinical profiles. For the first time, we examine the added value of replacing missing data and examine factors that may be contributing to missingness. A sample of 1178 participants met the inclusion criteria for the study, which included a diagnosis of temporal lobe epilepsy and the availability of comprehensive neuropsychological data. Models with two to five classes were examined using latent profile analysis and the optimal model was selected based on fit indices, posterior probabilities and proportion of sample sizes. The models were also examined with imputed data to investigate the impact of missing data on model selection. Based on the fit indices, posterior probability and distinctiveness of the latent classes, a three-class solution was the optimal solution. This three-class solution comprised a group of patients with multidomain impairments, a group with impairments predominantly in language and a group with no impairments. Overall, the multidomain group demonstrated a worse clinical profile and comprised a greater proportion of patients with mesial temporal sclerosis, a longer disease duration and a higher number of anti-seizure medications. The four-class and five-class solutions demonstrated the lowest probabilities of a group membership. Analyses with imputed data demonstrated that the four-class solution was the optimal solution; however, there was a weak agreement between the missing and imputed data sets for the four-Class solutions (κ = 0.288, P < 0.001). This study represents the first to use latent profile analysis to test and compare multiple models of cognitive phenotypes in temporal lobe epilepsy and to determine the impact of missing data on model fit. We found that the three-phenotype model was the most meaningful based on several fit indices and produced phenotypes with unique demographic and clinical profiles. Our findings demonstrate that latent profile analysis is a rigorous method to identify phenotypes in large, heterogeneous epilepsy samples. Furthermore, this study highlights the importance of examining the impact of missing data in phenotyping methods. Our latent profile analysis-derived phenotypes can inform future studies aimed at identifying cognitive phenotypes in other neurological disorders. [ABSTRACT FROM AUTHOR]

Published
2023
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42. Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in Electroencephalograms.

Author

Jing, Jin, Ge, Wendong, Struck, Aaron F., Fernandes, Marta Bento, Hong, Shenda, An, Sungtae, Fatima, Safoora, Herlopian, Aline, Karakis, Ioannis, Halford, Jonathan J., Ng, Marcus C., Johnson, Emily L., Appavu, Brian L., Sarkis, Rani A., Osman, Gamaleldin, Kaplan, Peter W., Dhakar, Monica B., Jayagopal, Lakshman Arcot, Sheikh, Zubeda, and Taraschenko, Olga

Published
2023
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