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Brain Sciences
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Individualized EEG Biomarkers: Search-Related and Applicational Aspects
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Individualized EEG Biomarkers: Search-Related and Applicational Aspects

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neural Engineering, Neuroergonomics and Neurorobotics".

Deadline for manuscript submissions: closed (25 February 2024) | Viewed by 7045

Special Issue Editor

Dr. Inga Griskova-Bulanova

E-Mail Website
Guest Editor
Department of Neurobiology and Biophysics, Vilnius University, LT-10257 Vilnius, Lithuania
Interests: biomarkers; electroencephalography; event-related potentials; state- and trait-related dependence; neuropsychiatric disorders; pharmaco-EEG; neuromodulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

EEG is an affordable and easily implemented technique for use in research and clinical settings. With the increasing availability and use of EEG-based assessments in various neuropsychiatric disorders and psychological conditions, the search for individual sensitive biomarkers has become greatly important. The large sets of EEG data have been subjected to machine learning to build predictive models and facilitate prognostic validity for various brain states and conditions, such as schizophrenia, depression, disorders of consciousness, etc. Furthermore, EEG data are used to develop neuromodulation protocols for diagnostic and treatment purposes. However, their applications in precision and personalised medicine have still not been fully explored, and there are numerous methodological and theoretical challenges that need to be addressed.

This upcoming Special Issue of Brain Sciences aims to highlight the current state of the art in individualised EEG research and showcase the latest findings from the study of different conditions, various angles and disciplines including neurophysiology, neurology, pharmacology, neurogenetics, neuroengineering, psychiatry, and cognitive and emotional neuroscience. In so doing, we aim to raise interest in and draw attention to the variety of easily accessible EEG-based biomarkers and begin discussions on their applicability at the individual level.

We invite contributions describing EEG-informed individualized treatments, studies where individual differences in biomarkers are considered as treatment outcomes, papers focusing on methodological advancements, reports presenting new datasets and analyses of large open EEG datasets. Original research articles, review articles, study protocols, feasibility studies and perspective articles are highly encouraged.

Dr. Inga Griskova-Bulanova
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biomarkers
  • brain disorders
  • brain stimulation
  • pharmacological intervention
  • treatment outcome
  • individual variability
  • big data

Published Papers (6 papers)

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Research

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18 pages, 428 KiB  
Article
Movement Termination of Slow-Wave Sleep—A Potential Biomarker?
by Yvonne Höller, Stefanía Guðrún Eyjólfsdóttir, Matej Rusiňák, Lárus Steinþór Guðmundsson and Eugen Trinka
Brain Sci. 2024, 14(5), 493; https://doi.org/10.3390/brainsci14050493 - 13 May 2024
Viewed by 399
Abstract
The duration of slow-wave sleep (SWS) is related to the reported sleep quality and to the important variables of mental and physical health. The internal cues to end an episode of SWS are poorly understood. One such internal cue is the initiation of [...] Read more.
The duration of slow-wave sleep (SWS) is related to the reported sleep quality and to the important variables of mental and physical health. The internal cues to end an episode of SWS are poorly understood. One such internal cue is the initiation of a body movement, which is detectable as electromyographic (EMG) activity in sleep-electroencephalography (EEG). In the present study, we characterized the termination of SWS episodes by movement to explore its potential as a biomarker. To this end, we characterized the relation between the occurrence of SWS termination by movement and individual characteristics (age, sex), SWS duration and spectral content, chronotype, depression, medication, overnight memory performance, and, as a potential neurological application, epilepsy. We analyzed 94 full-night EEG-EMG recordings (75/94 had confirmed epilepsy) in the video-EEG monitoring unit of the EpiCARE Centre Salzburg, Austria. Segments of SWS were counted and rated for their termination by movement or not through the visual inspection of continuous EEG and EMG recordings. Multiple linear regression was used to predict the number of SWS episodes that ended with movement by depression, chronotype, type of epilepsy (focal, generalized, no epilepsy, unclear), medication, gender, total duration of SWS, occurrence of seizures during the night, occurrence of tonic-clonic seizures during the night, and SWS frequency spectra. Furthermore, we assessed whether SWS movement termination was related to overnight memory retention. According to multiple linear regression, patients with overall longer SWS experienced more SWS episodes that ended with movement (t = 5.64; p = 0.001). No other variable was related to the proportion of SWS that ended with movement, including no epilepsy-related variable. A small sample (n = 4) of patients taking Sertraline experienced no SWS that ended with movement, which was significant compared to all other patients (t = 8.00; p < 0.001) and to n = 35 patients who did not take any medication (t = 4.22; p < 0.001). While this result was based on a small subsample and must be interpreted with caution, it warrants replication in a larger sample with and without seizures to further elucidate the role of the movement termination of SWS and its potential to serve as a biomarker for sleep continuity and for medication effects on sleep. Full article
(This article belongs to the Special Issue Individualized EEG Biomarkers: Search-Related and Applicational Aspects)
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19 pages, 1529 KiB  
Article
Evaluation of the Relation between Ictal EEG Features and XAI Explanations
by Sergio E. Sánchez-Hernández, Sulema Torres-Ramos, Israel Román-Godínez and Ricardo A. Salido-Ruiz
Brain Sci. 2024, 14(4), 306; https://doi.org/10.3390/brainsci14040306 - 25 Mar 2024
Viewed by 838
Abstract
Epilepsy is a neurological disease with one of the highest rates of incidence worldwide. Although EEG is a crucial tool for its diagnosis, the manual detection of epileptic seizures is time consuming. Automated methods are needed to streamline this process; although there are [...] Read more.
Epilepsy is a neurological disease with one of the highest rates of incidence worldwide. Although EEG is a crucial tool for its diagnosis, the manual detection of epileptic seizures is time consuming. Automated methods are needed to streamline this process; although there are already several works that have achieved this, the process by which it is executed remains a black box that prevents understanding of the ways in which machine learning algorithms make their decisions. A state-of-the-art deep learning model for seizure detection and three EEG databases were chosen for this study. The developed models were trained and evaluated under different conditions (i.e., three distinct levels of overlap among the chosen EEG data windows). The classifiers with the best performance were selected, then Shapley Additive Explanations (SHAPs) and Local Interpretable Model-Agnostic Explanations (LIMEs) were employed to estimate the importance value of each EEG channel and the Spearman’s rank correlation coefficient was computed between the EEG features of epileptic signals and the importance values. The results show that the database and training conditions may affect a classifier’s performance. The most significant accuracy rates were 0.84, 0.73, and 0.64 for the CHB-MIT, Siena, and TUSZ EEG datasets, respectively. In addition, most EEG features displayed negligible or low correlation with the importance values. Finally, it was concluded that a correlation between the EEG features and the importance values (generated by SHAP and LIME) may have been absent even for the high-performance models. Full article
(This article belongs to the Special Issue Individualized EEG Biomarkers: Search-Related and Applicational Aspects)
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14 pages, 1514 KiB  
Article
Stereotactic Electroencephalogram Recordings in Temporal Lobectomy Patients Demonstrates the Predictive Value of Interictal Cross-Frequency Correlations: A Retrospective Study
by Anish Vinay Sathe, Mahdi Alizadeh, Emily Johannan, Christian Raimondo, Michael Sperling, Ashwini Sharan and Michael Kogan
Brain Sci. 2024, 14(3), 212; https://doi.org/10.3390/brainsci14030212 - 26 Feb 2024
Viewed by 789
Abstract
Background: Positive correlations between low- and high-frequency spectra from stereotactic electroencephalogram (SEEG) recordings have been implicated in pathological brain activity interictally and have been used for ictal detection in both focal and network models. Objective: We evaluated SEEG signals in patients who ultimately [...] Read more.
Background: Positive correlations between low- and high-frequency spectra from stereotactic electroencephalogram (SEEG) recordings have been implicated in pathological brain activity interictally and have been used for ictal detection in both focal and network models. Objective: We evaluated SEEG signals in patients who ultimately underwent temporal lobectomy to evaluate their utility in seizure localization and prediction of seizure freedom post-resection. Methods: We retrospectively analyzed cross-frequency correlations between beta and high gamma (HG) interictal SEEG signals from 22 patients. We compared signals based on temporal versus extra-temporal locations, seizure-free (SF) versus non-seizure-free (NSF) outcomes, and mesial (M) versus mesial temporal-plus (M+) onset. Results: Positive cross-correlations were increased in temporal areas. NSF patients showed a higher proportion of positive electrodes in temporal areas. SF patients had a greater proportion of significant channels in mesial versus lateral temporal areas. HG/Beta correlations in mesial versus lateral temporal areas predicted seizure freedom better than ictal SEEG seizure onset localization to M or M+ locations. Conclusions: We present preliminary data that local HG/Beta correlations may predict epilepsy focus and surgical outcome and may have utility as adjunct methods to conventional SEEG analysis. Further studies are needed to determine strategies for prospective studies and clinical use. Full article
(This article belongs to the Special Issue Individualized EEG Biomarkers: Search-Related and Applicational Aspects)
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21 pages, 2867 KiB  
Article
Emotional Bias among Individuals at Risk for Seasonal Affective Disorder—An EEG Study during Remission in Summer
by Dagný Theódórsdóttir and Yvonne Höller
Brain Sci. 2024, 14(1), 2; https://doi.org/10.3390/brainsci14010002 - 20 Dec 2023
Viewed by 1366
Abstract
Emotional bias in attention and memory is well researched in depression. Patients with depression prioritize processing of negative information over positive input. While there is evidence that emotional bias exists in seasonal affective disorder (SAD) during winter, it is unclear whether such altered [...] Read more.
Emotional bias in attention and memory is well researched in depression. Patients with depression prioritize processing of negative information over positive input. While there is evidence that emotional bias exists in seasonal affective disorder (SAD) during winter, it is unclear whether such altered cognition exists also during summer. Moreover, it is unclear whether such bias affects attention, memory, or both. In this study, we investigated 110 individuals in summer, 34 of whom reported suffering from low mood during winter, according to the seasonal pattern assessment questionnaire. While the electroencephalogram was recorded, participants learned 60 emotional pictures and subsequently were asked to recognize them in an old/new task. There were no clear group differences in behavioral measures, and no brain response differences in frontal alpha power during learning. During recognition, at 100–300 ms post stimulus individuals with higher seasonality scores exhibited larger alpha power in response to negative as compared to neutral stimuli, while individuals with low seasonality scores exhibited larger alpha power in response to positive as compared to neutral stimuli. While we cannot draw conclusions whether this is an effect of attention or memory, the finding suggests that early cognitive processes are altered already during summer in individuals with increased likelihood to experience SAD during winter. Our data provide evidence for an all-year-round cognitive vulnerability in this population. Full article
(This article belongs to the Special Issue Individualized EEG Biomarkers: Search-Related and Applicational Aspects)
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Review

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38 pages, 982 KiB  
Review
EEG in Down Syndrome—A Review and Insights into Potential Neural Mechanisms
by James Chmiel, Filip Rybakowski and Jerzy Leszek
Brain Sci. 2024, 14(2), 136; https://doi.org/10.3390/brainsci14020136 - 27 Jan 2024
Viewed by 1409
Abstract
Introduction: Down syndrome (DS) stands out as one of the most prevalent genetic disorders, imposing a significant burden on both society and the healthcare system. Scientists are making efforts to understand the neural mechanisms behind the pathophysiology of this disorder. Among the [...] Read more.
Introduction: Down syndrome (DS) stands out as one of the most prevalent genetic disorders, imposing a significant burden on both society and the healthcare system. Scientists are making efforts to understand the neural mechanisms behind the pathophysiology of this disorder. Among the valuable methods for studying these mechanisms is electroencephalography (EEG), a non-invasive technique that measures the brain’s electrical activity, characterised by its excellent temporal resolution. This review aims to consolidate studies examining EEG usage in individuals with DS. The objective was to identify shared elements of disrupted EEG activity and, crucially, to elucidate the neural mechanisms underpinning these deviations. Searches were conducted on Pubmed/Medline, Research Gate, and Cochrane databases. Results: The literature search yielded 17 relevant articles. Despite the significant time span, small sample size, and overall heterogeneity of the included studies, three common features of aberrant EEG activity in people with DS were found. Potential mechanisms for this altered activity were delineated. Conclusions: The studies included in this review show altered EEG activity in people with DS compared to the control group. To bolster these current findings, future investigations with larger sample sizes are imperative. Full article
(This article belongs to the Special Issue Individualized EEG Biomarkers: Search-Related and Applicational Aspects)
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20 pages, 401 KiB  
Review
Individual/Peak Gamma Frequency: What Do We Know?
by Aurimas Mockevičius, Kristina Šveistytė and Inga Griškova-Bulanova
Brain Sci. 2023, 13(5), 792; https://doi.org/10.3390/brainsci13050792 - 12 May 2023
Cited by 1 | Viewed by 1574
Abstract
In recent years, the concept of individualized measures of electroencephalographic (EEG) activity has emerged. Gamma-band activity plays an important role in many sensory and cognitive processes. Thus, peak frequency in the gamma range has received considerable attention. However, peak or individual gamma frequency [...] Read more.
In recent years, the concept of individualized measures of electroencephalographic (EEG) activity has emerged. Gamma-band activity plays an important role in many sensory and cognitive processes. Thus, peak frequency in the gamma range has received considerable attention. However, peak or individual gamma frequency (IGF) is rarely used as a primary measure of interest; consequently, little is known about its nature and functional significance. With this review, we attempt to comprehensively overview available information on the functional properties of peak gamma frequency, addressing its relationship with certain processes and/or modulation by various factors. Here, we show that IGFs seem to be related to various endogenous and exogenous factors. Broad functional aspects that are related to IGF might point to the differences in underlying mechanisms. Therefore, research utilizing different types of stimulation for IGF estimation and covering several functional aspects in the same population is required. Moreover, IGFs span a wide range of frequencies (30–100 Hz). This could be partly due to the variability of methods used to extract the measures of IGF. In order to overcome this issue, further studies aiming at the optimization of IGF extraction would be greatly beneficial. Full article
(This article belongs to the Special Issue Individualized EEG Biomarkers: Search-Related and Applicational Aspects)
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