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1. Decoding information about cognitive health from the brainwaves of sleep

Author

Adra, Noor, Dümmer, Lisa W., Paixao, Luis, Tesh, Ryan A., Sun, Haoqi, Ganglberger, Wolfgang, Westmeijer, Mike, Da Silva Cardoso, Madalena, Kumar, Anagha, Ye, Elissa, Henry, Jonathan, Cash, Sydney S., Kitchener, Erin, Leveroni, Catherine L., Au, Rhoda, Rosand, Jonathan, Salinas, Joel, Lam, Alice D., Thomas, Robert J., and Westover, M. Brandon

Published
2023
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2. 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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4. Eastern Equine Encephalitis Treated With Intravenous Immunoglobulins

Author

Mukerji, Shibani S, Lam, Alice D, and Wilson, Michael R

Subjects
Veterinary Sciences, Agricultural, Veterinary and Food Sciences, Biomedical and Clinical Sciences, Clinical Research, Brain Disorders, Good Health and Well Being, encephalitis, central nervous system infections, central nervous system, viral diseases, movement disorders
Abstract

We report the case of a 68-year-old man from southeastern Massachusetts presenting with encephalitis due to eastern equine encephalitis (EEE) virus. Despite the high morbidity and mortality rate of EEE, the patient made a near complete recovery in the setting of receiving early intravenous immunoglobulins.

Published
2016

8. 33 Associations Between Long-Term Forgetting and Slow Wave Activity in Autosomal-Dominant Alzheimer’s Disease: Findings from the Colombia-Boston (COLBOS) Biomarker Study

Author

Noriega, Daisy T, primary, Baena, Ana, additional, Munera, Diana, additional, Pardilla-Delgado, Enmanuelle, additional, Aduen, Paula, additional, Vila-Castelar, Clara, additional, Langella, Stephanie, additional, Ramirez-Gomez, Liliana, additional, Pluim, Celina, additional, Reiman, Eric M, additional, Lopera, Francisco, additional, Lam, Alice D, additional, and Quiroz, Yakeel T, additional

Published
2023
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9. Smartphone EEG and remote online interpretation for children with epilepsy in the Republic of Guinea: Quality, characteristics, and practice implications

Author

Williams, Jennifer A, Cisse, Fodé Abass, Schaekermann, Mike, Sakadi, Foksouna, Tassiou, Nana Rahamatou, Hotan, Gladia C., Bah, Aissatou Kenda, Hamani, Abdoul Bachir Djibo, Lim, Andrew, Leung, Edward C.W., Fantaneanu, Tadeu A., Milligan, Tracey A., Khatri, Vidita, Hoch, Daniel B., Vyas, Manav V., Lam, Alice D., Cohen, Joseph M., Vogel, Andre C., Law, Edith, and Mateen, Farrah J.

Published
2019
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12. Highlights From the Annual Meeting of the American Epilepsy Society 2022

Author

Valencia, Ignacio, primary, Alexander, Allyson L., additional, Andrade, Danielle M., additional, Arevalo-Astrada, Miguel, additional, Rubiños, Clio, additional, Auer, Nancy, additional, Bainbridge, Jacquelyn L., additional, Baxendale, Sallie A., additional, Bartolomei, Fabrice, additional, Becker, Danielle A., additional, Berg, Anne T., additional, Bernasconi, Andrea, additional, Bernasconi, Neda, additional, Bernhardt, Boris, additional, Bhatnagar, Shivani, additional, Blümcke, Ingmar, additional, Blumenfeld, Hal, additional, Buchanan, Gordon F., additional, Burdette, David E., additional, Burneo, Jorge G., additional, Busch, Robyn M., additional, Chauvel, Patrick, additional, Chin, Jeannie, additional, Clifford, Lisa, additional, Conner, Kelly R., additional, Cook, Mark J., additional, Conway, Jeannine, additional, Diaz-Arastia, Ramon, additional, Drees, Cornelia, additional, French, Jacqueline A., additional, Ganguly, Taneeta Mindy, additional, Gelfand, Michael A., additional, Glauser, Tracy A., additional, Gleichgerrcht, Ezequiel, additional, Goldman, Alica M., additional, Gonzalez-Martinez, Jorge, additional, Gotman, Jean, additional, Grinspan, Zachary, additional, Guilfoyle, Shanna, additional, Gupta, Gita, additional, Hammer, Michael, additional, Hartman, Adam L., additional, Hentges, Katie, additional, Hogan, R. Edward, additional, Huh, Linda, additional, Hyslop, Ann, additional, Jobst, Barbara, additional, Josephson, Colin B., additional, Kelley, Sarah A., additional, Knupp, Kelly, additional, Koepp, Matthias, additional, Kothare, Sanjeev V., additional, Krook-Magnuson, Esther, additional, Kwasa, Jasmine, additional, La Vega-Talbott, Maite, additional, Lam, Alice D., additional, Lee, Jong Woo, additional, Lowenstein, Daniel H., additional, Maturu, Sarita, additional, Mayor, Luis Carlos, additional, McDonald, Carrie, additional, McKee, Heather R., additional, McKhann, Guy M., additional, Meador, Kimford J., additional, Mefford, Heather C., additional, Michael, Elizabeth H., additional, Mikati, Mohamad A., additional, Millichap, John J., additional, Mitchell, James W., additional, Myers, Leah S., additional, Naritoku, Dean, additional, Neville, Kerri L., additional, Noebels, Jeffrey, additional, O’Brien, Terence J., additional, Oluigbo, Chima O., additional, Patel, Anup D., additional, Pavlova, Milena K., additional, T. Paz, Jeanne, additional, Pennell, Page B., additional, Perry, M. Scott, additional, Perucca, Piero, additional, Pitkänen, Asla, additional, Plueger, Madona, additional, Pugh, Mary Jo, additional, Quigg, Mark, additional, Reddy, Shilpa B., additional, Ryan, Christopher, additional, Reynolds, Tamara S., additional, Sajatovic, Martha, additional, Santana-Gomez, Cesar, additional, Schommer, Linsday, additional, Schuele, Stephan, additional, Shellhaas, Renée A., additional, Shrey, Daniel W., additional, Singh, Rani K., additional, Sperling, Michael R., additional, Suleman, Saher, additional, Templer, Jessica W., additional, Thom, Maria, additional, Trinka, Eugen, additional, Varadkar, Sophia M., additional, Velez-Ruiz, Naymee, additional, Velíšková, Jana, additional, Voskobiynyk, Yuliya, additional, Wagner, Janelle L., additional, Wagnon, Jacy L., additional, Waller, Claire, additional, Waller, Jill, additional, Wang, Zhong Irene, additional, Welborn, Michelle W., additional, and Wirrell, Elaine, additional

Published
2023
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13. 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

19. Dementia detection from brain activity during sleep

Author

Ye, Elissa M, primary, Sun, Haoqi, additional, Krishnamurthy, Parimala V, additional, Adra, Noor, additional, Ganglberger, Wolfgang, additional, Thomas, Robert J, additional, Lam, Alice D, additional, and Westover, M Brandon, additional

Published
2022
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21. Measuring expertise in identifying interictal epileptiform discharges

Author

Harid, Nitish M., Jing, Jin, Hogan, Jacob, Nascimento, Fábio A., Ouyang, An, Zheng, Wei-Long, Ge, Wendong, Zafar, Sahar F., Kim, Jennifer A., Lam, Alice D., Herlopian, Aline, Maus, Douglas, Karakis, Ioannis, Ng, Marcus, Hong, Shenda, Zhu, Yu, Kaplan, Peter W., Cash, Sydney, Shafi, Mouhsin, Martz, Gabriel, Halford, Jonathan J., and Westover, Michael Brandon

Subjects
Epilepsy, Neurology, Humans, Electroencephalography, Neurology (clinical), General Medicine, Article, Time
Abstract

OBJECTIVE. Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills. METHODS. A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader’s skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision. RESULTS. Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were −0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identified that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). SIGNIFICANCE. Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.

Published
2022

25. Silent hippocampal seizures and spikes identified by foramen ovale electrodes in Alzheimer's disease

Author

Lam, Alice D, Deck, Gina, Goldman, Alica, Eskandar, Emad N, Noebels, Jeffrey, and Cole, Andrew J

Subjects
Electrodes -- Usage, Electroencephalography -- Usage, Alzheimer's disease -- Research -- Diagnosis, Magnetic resonance imaging -- Usage, Biological sciences, Health
Abstract

Author(s): Alice D Lam [1]; Gina Deck [1]; Alica Goldman [2]; Emad N Eskandar [3]; Jeffrey Noebels [2]; Andrew J Cole (corresponding author) [1] We directly assessed mesial temporal activity [...]

Published
2017
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28. Responsive neurostimulation for focal motor status epilepticus

Author

Yang, Jimmy C., primary, Harid, Nitish M., additional, Nascimento, Fábio A., additional, Kokkinos, Vasileios, additional, Shaughnessy, Abigail, additional, Lam, Alice D., additional, Westover, M. Brandon, additional, Leslie‐Mazwi, Thabele M., additional, Hochberg, Leigh R., additional, Rosenthal, Eric S., additional, Cole, Andrew J., additional, Richardson, Robert M., additional, and Cash, Sydney S., additional

Published
2021
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34. Circadian and Brain State Modulation of Network Hyperexcitability in Alzheimer’s Disease

Author

Brown, Rosalind, Lam, Alice D., Gonzalez-Sulser, Alfredo, Ying, Andrew, Jones, Mary, Chou, Robert Chang-Chih, Tzioras, Makis, Jordan, Crispin Y., Jedrasiak-Cape, Izabela, Hemonnot, Anne-Laure, Abou Jaoude, Maurice, Cole, Andrew J., Cash, Sydney S., Saito, Takashi, Saido, Takaomi, Ribchester, Richard R., Hashemi, Kevan, and Oren, Iris

Subjects
Male, Confirmation, Amyloid beta-Peptides, Epilepsy, circadian cycle, Mice, Transgenic, Circadian Rhythm, Mice, Inbred C57BL, Disease Models, Animal, Mice, Alzheimer Disease, 3.6, Cortical Excitability, Animals, Disorders of the Nervous System, Female, Electrocorticography, Sleep Stages, Nerve Net, Alzheimer’s disease
Abstract

Network hyperexcitability is a feature of Alzheimer’ disease (AD) as well as numerous transgenic mouse models of AD. While hyperexcitability in AD patients and AD animal models share certain features, the mechanistic overlap remains to be established. We aimed to identify features of network hyperexcitability in AD models that can be related to epileptiform activity signatures in AD patients. We studied network hyperexcitability in mice expressing amyloid precursor protein (APP) with mutations that cause familial AD, and compared a transgenic model that overexpresses human APP (hAPP) (J20), to a knock-in model expressing APP at physiological levels (APPNL/F). We recorded continuous long-term electrocorticogram (ECoG) activity from mice, and studied modulation by circadian cycle, behavioral, and brain state. We report that while J20s exhibit frequent interictal spikes (IISs), APPNL/F mice do not. In J20 mice, IISs were most prevalent during daylight hours and the circadian modulation was associated with sleep. Further analysis of brain state revealed that IIS in J20s are associated with features of rapid eye movement (REM) sleep. We found no evidence of cholinergic changes that may contribute to IIS-circadian coupling in J20s. In contrast to J20s, intracranial recordings capturing IIS in AD patients demonstrated frequent IIS in non-REM (NREM) sleep. The salient differences in sleep-stage coupling of IIS in APP overexpressing mice and AD patients suggests that different mechanisms may underlie network hyperexcitability in mice and humans. We posit that sleep-stage coupling of IIS should be an important consideration in identifying mouse AD models that most closely recapitulate network hyperexcitability in human AD.

Published
2018

38. Circadian and Brain State Modulation of Network Hyperexcitability in Alzheimer’s Disease

Author

Brown, Rosalind, primary, Lam, Alice D., additional, Gonzalez-Sulser, Alfredo, additional, Ying, Andrew, additional, Jones, Mary, additional, Chou, Robert Chang-Chih, additional, Tzioras, Makis, additional, Jordan, Crispin Y., additional, Jedrasiak-Cape, Izabela, additional, Hemonnot, Anne-Laure, additional, Abou Jaoude, Maurice, additional, Cole, Andrew J., additional, Cash, Sydney S., additional, Saito, Takashi, additional, Saido, Takaomi, additional, Ribchester, Richard R., additional, Hashemi, Kevan, additional, and Oren, Iris, additional

Published
2018
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39. Validation of a smartphone-based EEG among people with epilepsy: A prospective study

Author

Mckenzie, Erica D., Lim, Andrew S P, Leung, Edward C W, Cole, Andrew J., Lam, Alice D., Eloyan, Ani, Nirola, Damber K., Tshering, Lhab, Thibert, Ronald, Garcia, Rodrigo Zepeda, Bui, Esther, Deki, Sonam, Lee, Liesly, Clark, Sarah J., Cohen, Joseph M., Mantia, Jo, Brizzi, Kate T., Sorets, Tali R., Wahlster, Sarah, Borzello, Mia, Stopczynski, Arkadiusz, Cash, Sydney S., Mateen, Farrah J., Mckenzie, Erica D., Lim, Andrew S P, Leung, Edward C W, Cole, Andrew J., Lam, Alice D., Eloyan, Ani, Nirola, Damber K., Tshering, Lhab, Thibert, Ronald, Garcia, Rodrigo Zepeda, Bui, Esther, Deki, Sonam, Lee, Liesly, Clark, Sarah J., Cohen, Joseph M., Mantia, Jo, Brizzi, Kate T., Sorets, Tali R., Wahlster, Sarah, Borzello, Mia, Stopczynski, Arkadiusz, Cash, Sydney S., and Mateen, Farrah J.

Abstract

Our objective was to assess the ability of a smartphone-based electroencephalography (EEG) application, the Smartphone Brain Scanner-2 (SBS2), to detect epileptiform abnormalities compared to standard clinical EEG. The SBS2 system consists of an Android tablet wirelessly connected to a 14-electrode EasyCap headset (cost ∼ 300 USD). SBS2 and standard EEG were performed in people with suspected epilepsy in Bhutan (2014-2015), and recordings were interpreted by neurologists. Among 205 participants (54% female, median age 24 years), epileptiform discharges were detected on 14% of SBS2 and 25% of standard EEGs. The SBS2 had 39.2% sensitivity (95% confidence interval (CI) 25.8%, 53.9%) and 94.8% specificity (95% CI 90.0%, 97.7%) for epileptiform discharges with positive and negative predictive values of 0.71 (95% CI 0.51, 0.87) and 0.82 (95% CI 0.76, 0.89) respectively. 31% of focal and 82% of generalized abnormalities were identified on SBS2 recordings. Cohen's kappa (κ) for the SBS2 EEG and standard EEG for the epileptiform versus non-epileptiform outcome was κ = 0.40 (95% CI 0.25, 0.55). No safety or tolerability concerns were reported. Despite limitations in sensitivity, the SBS2 may become a viable supportive test for the capture of epileptiform abnormalities, and extend EEG access to new, especially resource-limited, populations at a reduced cost.

Published
2017

40. Validation of a smartphone-based EEG among people with epilepsy: A prospective study

Author

McKenzie, Erica D., primary, Lim, Andrew S. P., additional, Leung, Edward C. W., additional, Cole, Andrew J., additional, Lam, Alice D., additional, Eloyan, Ani, additional, Nirola, Damber K., additional, Tshering, Lhab, additional, Thibert, Ronald, additional, Garcia, Rodrigo Zepeda, additional, Bui, Esther, additional, Deki, Sonam, additional, Lee, Liesly, additional, Clark, Sarah J., additional, Cohen, Joseph M., additional, Mantia, Jo, additional, Brizzi, Kate T., additional, Sorets, Tali R., additional, Wahlster, Sarah, additional, Borzello, Mia, additional, Stopczynski, Arkadiusz, additional, Cash, Sydney S., additional, and Mateen, Farrah J., additional

Published
2017
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44. Medication prescribing and patient-reported outcome measures in people with epilepsy in Bhutan

Author

McKenzie, Erica D., primary, Nirola, Damber K., additional, Deki, Sonam, additional, Tshering, Lhab, additional, Patenaude, Bryan, additional, Clark, Sarah J., additional, Cash, Sydney S., additional, Thibert, Ronald, additional, Zepeda, Rodrigo, additional, Leung, Edward C.W., additional, Lam, Alice D., additional, Lim, Andrew S.P., additional, Mantia, Jo, additional, Cohen, Joseph, additional, Cole, Andrew J., additional, and Mateen, Farrah J., additional

Published
2016
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