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Ciliento R, Gjini K, Dabbs K, Hermann B, Riedner B, Jones S, Fatima S, Johnson S, Bendlin B, Lam AD, Boly M, Struck AF. Prevalence and localization of nocturnal epileptiform discharges in mild cognitive impairment. Brain Commun 2023; 5:fcad302. [PMID: 37965047 PMCID: PMC10642616 DOI: 10.1093/braincomms/fcad302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/18/2023] [Accepted: 11/06/2023] [Indexed: 11/16/2023] Open
Abstract
Recent evidence shows that identifying and treating epileptiform abnormalities in patients with Alzheimer's disease could represent a potential avenue to improve clinical outcome. Specifically, animal and human studies have revealed that in the early phase of Alzheimer's disease, there is an increased risk of seizures. It has also been demonstrated that the administration of anti-seizure medications can slow the functional progression of the disease only in patients with EEG signs of cortical hyperexcitability. In addition, although it is not known at what disease stage hyperexcitability emerges, there remains no consensus regarding the imaging and diagnostic methods best able to detect interictal events to further distinguish different phenotypes of Alzheimer's disease. In this exploratory work, we studied 13 subjects with amnestic mild cognitive impairment and 20 healthy controls using overnight high-density EEG with 256 channels. All participants also underwent MRI and neuropsychological assessment. Electronic source reconstruction was also used to better select and localize spikes. We found spikes in six of 13 (46%) amnestic mild cognitive impairment compared with two of 20 (10%) healthy control participants (P = 0.035), representing a spike prevalence similar to that detected in previous studies of patients with early-stage Alzheimer's disease. The interictal events were low-amplitude temporal spikes more prevalent during non-rapid eye movement sleep. No statistically significant differences were found in cognitive performance between amnestic mild cognitive impairment patients with and without spikes, but a trend in immediate and delayed memory was observed. Moreover, no imaging findings of cortical and subcortical atrophy were found between amnestic mild cognitive impairment participants with and without epileptiform spikes. In summary, our exploratory study shows that patients with amnestic mild cognitive impairment reveal EEG signs of hyperexcitability early in the disease course, while no other significant differences in neuropsychological or imaging features were observed among the subgroups. If confirmed with longitudinal data, these exploratory findings could represent one of the first signatures of a preclinical epileptiform phenotype of amnestic mild cognitive impairment and its progression.
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Affiliation(s)
- Rosario Ciliento
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Klevest Gjini
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Kevin Dabbs
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Brady Riedner
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
| | - Stephanie Jones
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
| | - Safoora Fatima
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Sterling Johnson
- Department of Medicine, University of Wisconsin, Madison, WI 53705, USA
| | - Barbara Bendlin
- Department of Medicine, University of Wisconsin, Madison, WI 53705, USA
| | - Alice D Lam
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02115, USA
| | - Melanie Boly
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
- Department of Neurology, William S. Middleton Veterans Administration Hospital, Madison, WI 53705, USA
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Abreu M, Carmo AS, Peralta AR, Sá F, Plácido da Silva H, Bentes C, Fred AL. PreEpiSeizures: description and outcomes of physiological data acquisition using wearable devices during video-EEG monitoring in people with epilepsy. Front Physiol 2023; 14:1248899. [PMID: 37881691 PMCID: PMC10597694 DOI: 10.3389/fphys.2023.1248899] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/04/2023] [Indexed: 10/27/2023] Open
Abstract
The PreEpiSeizures project was created to better understand epilepsy and seizures through wearable technologies. The motivation was to capture physiological information related to epileptic seizures, besides Electroencephalography (EEG) during video-EEG monitorings. If other physiological signals have reliable information of epileptic seizures, unobtrusive wearable technology could be used to monitor epilepsy in daily life. The development of wearable solutions for epilepsy is limited by the nonexistence of datasets which could validate these solutions. Three different form factors were developed and deployed, and the signal quality was assessed for all acquired biosignals. The wearable data acquisition was performed during the video-EEG of patients with epilepsy. The results achieved so far include 59 patients from 2 hospitals totaling 2,721 h of wearable data and 348 seizures. Besides the wearable data, the Electrocardiogram of the hospital is also useable, totalling 5,838 h of hospital data. The quality ECG signals collected with the proposed wearable is equated with the hospital system, and all other biosignals also achieved state-of-the-art quality. During the data acquisition, 18 challenges were identified, and are presented alongside their possible solutions. Though this is an ongoing work, there were many lessons learned which could help to predict possible problems in wearable data collections and also contribute to the epilepsy community with new physiological information. This work contributes with original wearable data and results relevant to epilepsy research, and discusses relevant challenges that impact wearable health monitoring.
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Affiliation(s)
- Mariana Abreu
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Ana Sofia Carmo
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Ana Rita Peralta
- Lab EEG-Sono, Centro Hospitalar Universitário Lisboa Norte, Hospital de Santa Maria, Lisboa, Portugal
| | - Francisca Sá
- Departamento Neurologia, Centro Hospitalar Lisboa Ocidental, Hospital Egas Moniz, Lisboa, Portugal
| | - Hugo Plácido da Silva
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Lisbon Unit for Learning and Intelligent Systems (LUMLIS), A Unit of the European Laboratory for Learning and Intelligent Systems (ELLIS), Lisboa, Portugal
| | - Carla Bentes
- Lab EEG-Sono, Centro Hospitalar Universitário Lisboa Norte, Hospital de Santa Maria, Lisboa, Portugal
| | - Ana Luísa Fred
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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Espinoso A, Andrzejak RG. Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients. Phys Rev E 2022; 105:034212. [PMID: 35428047 DOI: 10.1103/physreve.105.034212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity.
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Affiliation(s)
- Anaïs Espinoso
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain and Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Carrer Baldiri Reixac 10-12, 08028 Barcelona, Catalonia, Spain
| | - Ralph G Andrzejak
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
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