STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews: A Journal of Neuroscience Review Article

High-Definition Electroencephalography: A New Horizon in Neurological Pathology Research

  1. Simone Carozzo Department of medical Science, S. Anna Institute, Via Siris

Abstract

The advent of high-density electroencephalography (HD-EEG) has catalyzed a paradigm shift in the exploration of neurological pathologies. This editorial underscore its transformative potential in elucidating brain dynamics and refining diagnostic approaches for a spectrum of conditions, spanning from epilepsy and dementia to cognitive impairments in preterm infants. Our objective is to optimize the utility of HD-EEG by emphasizing the imperative for methodological homogenization and fostering collaborative endeavors. The remarkable spatial resolution inherent in HD-EEG enables precise mapping of aberrant neural activity, surpassing the capabilities of conventional EEG methodologies. HD-EEG has significant clinical implications, particularly for improving surgical precision in epilepsy patients. It offers deeper insights into cognitive-affecting neurological disorders by capturing brain activity during tasks, extending beyond its diagnostic role. Research discrepancies arise from data acquisition and analysis complexities, highlighting the need for standardized multicentric studies. Integrating composite connectivity indices with machine learning algorithms could enhance HD-EEG's prognostic efficacy, advancing neurological pathology understanding. Case studies illustrate its expanding applications: • Epilepsy: HD-EEG has revolutionized the localization of seizure foci, leading to more effective surgical interventions for patients with medically refractory epilepsy. • Cognitive neuroscience: HD-EEG enables researchers to explore the neural foundations of cognitive functions such as attention, memory, and decision-making more thoroughly. • Dementia: Studies are employing HD-EEG to characterize brain network alterations in dementia, paving the way for earlier diagnosis and potential interventions. • Psychiatric disorders: HD-EEG holds promise for investigating the neurophysiological basis of anxiety in Parkinson's disease and other mental health conditions. • Brain development: HD-EEG illuminates adolescent cortical development via sleep spindle dynamics. Advancements such as the Localize-MI dataset validate source localization methods. Integrating HD-EEG with fMRI and MEG offers comprehensive brain function insights: • Integration of HD-EEG with other imaging modalities (fMRI, MEG) for a comprehensive understanding of brain function. • Creation of wearable HD-EEG technology for prolonged brain activity monitoring in real-world environments. • Utilization of machine learning algorithms to enhance the predictive power of HD-EEG in neurological phenotyping. • Multicentric studies with standardized protocols to overcome data variability challenges and facilitate data sharing. HD-EEG has the potential to revolutionize neurological pathology management by overcoming methodological challenges and fostering collaborative research. Its capability to track sub-second brain dynamics and integrate with computational models promises groundbreaking discoveries in neuroscience.

Keywords

References (21)

  1. Klamer S, Rona S, Elshahabi A, Lerche H, Braun C, Honegger J, et al. Multimodal effective connectivity analysis reveals seizure focus and propagation in musicogenic epilepsy. NeuroImage. 2015;113:70-77. doi:10.1016/j.neuroimage.2015.03.027
  2. Li Y, Fogarty A, Razavi B, Ardestani PM, Falco-Walter J, Werbaneth K, et al. Impact of high-density EEG in presurgical evaluation for refractory epilepsy patients. Clinical Neurology and Neurosurgery. 2022;219:107336. doi:10.1016/j.clineuro.2022.107336
  3. Mammone N, De Salvo S, Ieracitano C, Marino S, Cartella E, Bramanti A, et al. Compressibility of High-Density EEG Signals in Stroke Patients. Sensors. 2018;18(12):4107. doi:10.3390/s18124107
  4. Kuo CC, Tucker DM, Luu P, Jenson K, Tsai JJ, Ojemann JG, et al. EEG source imaging of epileptic activity at seizure onset. Epilepsy Research. 2018;146:160-171. doi:10.1016/j.eplepsyres.2018.07.006
  5. Buril J, Burilova P, Pokorna A, Balaz M. Use of high-density EEG in patients with Parkinson's disease treated with deep brain stimulation. Biomedical Papers. 2020;164(4):366-370. doi:10.5507/bp.2020.042
  6. Mikulan E, Russo S, Parmigiani S, Sarasso S, Zauli FM, Rubino A, et al. Simultaneous human intracerebral stimulation and HD-EEG, ground-truth for source localization methods. Scientific Data. 2020;7(1). doi:10.1038/s41597-020-0467-x
  7. Duma GM, Danieli A, Mattar MG, Baggio M, Vettorel A, Bonanni P, et al. Resting state network dynamic reconfiguration and neuropsychological functioning in temporal lobe epilepsy: An HD-EEG investigation. Cortex. 2022;157:1-13. doi:10.1016/j.cortex.2022.08.010
  8. Fiedler P, Fonseca C, Supriyanto E, Zanow F, Haueisen J. A high‐density 256‐channel cap for dry electroencephalography. Human Brain Mapping. 2021;43(4):1295-1308. doi:10.1002/hbm.25721
  9. Klamer S, Elshahabi A, Lerche H, Braun C, Erb M, Scheffler K, et al. Differences Between MEG and High-Density EEG Source Localizations Using a Distributed Source Model in Comparison to fMRI. Brain Topography. 2014;28(1):87-94. doi:10.1007/s10548-014-0405-3
  10. Del Popolo Cristaldi F, Mento G, Buodo G, Sarlo M. Emotion regulation strategies differentially modulate neural activity across affective prediction stages: An HD-EEG investigation. Frontiers in Behavioral Neuroscience. 2022;16. doi:10.3389/fnbeh.2022.947063
  11. Bocskai G, Pótári A, Gombos F, Kovács I. The adolescent pattern of sleep spindle development revealed by HD‐EEG. Journal of Sleep Research. 2022;32(2). doi:10.1111/jsr.13618
  12. Morabito FC, Ieracitano C, Mammone N. An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing. Clinical EEG and Neuroscience. 2021;54(1):51-60. doi:10.1177/15500594211063662
  13. Polverino P, Ajčević M, Catalan M, Mazzon G, Bertolotti C, Manganotti P. Brain oscillatory patterns in mild cognitive impairment due to Alzheimer’s and Parkinson’s disease: An exploratory high-density EEG study. Clinical Neurophysiology. 2022;138:1-8. doi:10.1016/j.clinph.2022.01.136
  14. Mento G, Toffoli L, Della Longa L, Farroni T, Del Popolo Cristaldi F, Duma GM. Adaptive Cognitive Control in Prematurely Born Children: An HD-EEG Investigation. Brain Sciences. 2022;12(8):1074. doi:10.3390/brainsci12081074
  15. Delval A, Girard B, Lacan L, Chaton L, Flamein F, Storme L, Derambure P, Tich SN, Lamblin MD, Betrouni N. Neurophysiological recordings improve the accuracy of the evaluation of the outcome in perinatal hypoxic ischemic encephalopathy. European Journal of Paediatric Neurology. 2022 Jan 1;36:51-6.
  16. Chehimy K, Halabi R, Diab MO, Hassan M, Mheich A. Comparing Healthy Subjects and Alzheimer’s Disease Patients using Brain Network Similarity: a Preliminary Study. In2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME) 2021 Oct 7 (pp. 189-192).
  17. Prado P, Birba A, Cruzat J, Santamaría-García H, Parra M, Moguilner S, Tagliazucchi E, Ibáñez A. Dementia ConnEEGtome: towards multicentric harmonization of EEG connectivity in neurodegeneration. International Journal of Psychophysiology. 2022 Feb 1;172:24-38.
  18. Bhatia S, Ham AT, Kutluay E. High-density (HD) scalp EEG findings in “benign” childhood epilepsy with centrotemporal spikes (BCECTS). Clinical EEG and Neuroscience. 2024 Mar;55(2):248-51.
  19. Tabbal S, El Aroussi B, Bouchard M, Marchand G, Haddad S. Development and Validation of a Method for the Simultaneous Quantification of 21 Microbial Volatile Organic Compounds in Ambient and Exhaled Air by Thermal Desorption and Gas Chromatography–Mass Spectrometry. 2022 Sep 5;13(9):1432.
  20. Stoyell SM, Baxter BS, McLaren J, Kwon H, Chinappen DM, Ostrowski L, Zhu L, Grieco JA, Kramer MA, Morgan AK, Emerton BC. Diazepam induced sleep spindle increase correlates with cognitive recovery in a child with epileptic encephalopathy. BMC neurology. 2021 Dec;21:1-1.
  21. Holler M, Huber R, Knoll F. Coupled regularization with multiple data discrepancies. Inverse problems. 2018 Aug 1;34(8):084003.
Support