1
|
Lemoine É, Neves Briard J, Rioux B, Gharbi O, Podbielski R, Nauche B, Toffa D, Keezer M, Lesage F, Nguyen DK, Bou Assi E. Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review. Comput Struct Biotechnol J 2024; 24:66-86. [PMID: 38204455 PMCID: PMC10776381 DOI: 10.1016/j.csbj.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
Background Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG. Methods We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool. Results We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures. Conclusion The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG. Significance We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.
Collapse
Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, University of Montreal, Canada
- Institute of biomedical engineering, Polytechnique Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Joel Neves Briard
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Bastien Rioux
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Oumayma Gharbi
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | | | - Bénédicte Nauche
- University of Montreal Hospital Center’s Research Center, Canada
| | - Denahin Toffa
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Mark Keezer
- Department of Neurosciences, University of Montreal, Canada
- School of Public Health, University of Montreal, Canada
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
| | - Frédéric Lesage
- Institute of biomedical engineering, Polytechnique Montreal, Canada
| | - Dang K. Nguyen
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Elie Bou Assi
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| |
Collapse
|
2
|
He Y, Liu B, Yang FY, Yang Q, Xu B, Liu L, Chen Y. TAF15 downregulation contributes to the benefits of physical training on dendritic spines and working memory in aged mice. Aging Cell 2024:e14244. [PMID: 38874013 DOI: 10.1111/acel.14244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 05/15/2024] [Accepted: 06/05/2024] [Indexed: 06/15/2024] Open
Abstract
Moderate physical training has been shown to hinder age-related memory decline. While the benefits of physical training on hippocampal memory function are well-documented, little is known about its impact on working memory, which is linked to the prelimbic cortex (PrL), one major subdivision of the prefrontal cortex. Here, we examined the effects of physical training on spatial working memory in a well-established animal model of physical training, starting at 16 months of age and continuing for 5 months (running wheel 1 h/day and 5 days/week). This training strategy improved spatial working memory in aged mice (22-month-old), which was accompanied by an increased spine density and a lower TAF15 expression in the PrL. Specifically, physical training affected both thin and mushroom-type spines on PrL pyramidal cells, and prevented age-related loss of spines on selective segments of apical dendritic branches. Correlation analysis revealed that increased TAF15-expression was detrimental to the dendritic spines. However, physical training downregulated TAF15 expression in the PrL, preserving the dendritic spines on PrL pyramidal cells and improving working memory in trained aged mice. When TAF15 was overexpressed in the PrL via a viral approach, the benefits of physical training on the dendritic spines and working memory were abolished. These data suggest that physical training at a moderate pace might downregulate TAF15 expression in the PrL, which favors the dendritic spines on PrL pyramidal cells, thereby improving spatial working memory.
Collapse
Affiliation(s)
- Yun He
- Department of Anatomy, School of Medicine, Yangtze University, Jingzhou, China
| | - Benju Liu
- Department of Anatomy, School of Medicine, Yangtze University, Jingzhou, China
| | - Fu-Yuan Yang
- Health Science Center, Yangtze University, Jingzhou, China
| | - Qun Yang
- Department of Medical Imaging, School of Medicine, Yangtze University, Jingzhou, China
| | - Benke Xu
- Department of Anatomy, School of Medicine, Yangtze University, Jingzhou, China
| | - Lian Liu
- Department of Pharmacology, School of Medicine, Yangtze University, Jingzhou, China
| | - Yuncai Chen
- Department of Anatomy, School of Medicine, Yangtze University, Jingzhou, China
| |
Collapse
|
3
|
Meziane HB, Jabès A, Klencklen G, Banta Lavenex P, Lavenex P. EEG markers of successful allocentric spatial working memory maintenance in humans. Eur J Neurosci 2024. [PMID: 38863237 DOI: 10.1111/ejn.16446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 06/02/2024] [Accepted: 06/04/2024] [Indexed: 06/13/2024]
Abstract
Several brain regions in the frontal, occipital and medial temporal lobes are known to contribute to spatial information processing. In contrast, the oscillatory patterns contributing to allocentric spatial working memory maintenance are poorly understood, especially in humans. Here, we tested twenty-three 21- to 32-year-old and twenty-two 64- to 76-year-old healthy right-handed adults in a real-world, spatial working memory task and recorded electroencephalographic (EEG) activity during the maintenance period. We established criteria for designating recall trials as perfect (no errors) or failed (errors and random search) and identified 8 young and 13 older adults who had at least 1 perfect and 1 failed trial amongst 10 recall trials. Individual alpha frequency-based analyses were used to identify oscillatory patterns during the maintenance period of perfect and failed trials. Spectral scalp topographies showed that individual theta frequency band relative power was stronger in perfect than in failed trials in the frontal midline and posterior regions. Similarly, gamma band (30-40 Hz) relative power was stronger in perfect than in failed trials over the right motor cortex. Exact low-resolution brain electromagnetic tomography in the frequency domain identified greater theta power in perfect than in failed trials in the secondary visual area (BA19) and greater gamma power in perfect than in failed trials in the right supplementary motor area. The findings of this exploratory study suggest that theta oscillations in the occipital lobe and gamma oscillations in the secondary motor cortex (BA6) play a particular role in successful allocentric spatial working memory maintenance.
Collapse
Affiliation(s)
- Hadj Boumediene Meziane
- Faculty of Psychology, Swiss Distance University Institute, Brig, Switzerland
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| | - Adeline Jabès
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| | - Giuliana Klencklen
- Faculty of Psychology, Swiss Distance University Institute, Brig, Switzerland
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| | - Pamela Banta Lavenex
- Faculty of Psychology, Swiss Distance University Institute, Brig, Switzerland
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| | - Pierre Lavenex
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| |
Collapse
|
4
|
Earl EH, Goyal M, Mishra S, Kannan B, Mishra A, Chowdhury N, Mishra P. EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning. Clin Neurophysiol 2024; 164:130-137. [PMID: 38870669 DOI: 10.1016/j.clinph.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 04/02/2024] [Accepted: 05/22/2024] [Indexed: 06/15/2024]
Abstract
OBJECTIVE Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered EEG based Functional connectivity (FC) with Emotional stimuli in major depressive disorder (MDD) in addition to resting state FC may help in improving the diagnostic accuracy of machine learning classification models. We explored the potential of EEG-based FC during resting state and emotional processing, for diagnosing MDD using machine learning approach. METHODS EEG was recorded during resting state and while watching emotionally contagious happy and sad videos in 24 drug-naïve MDD patients and 25 healthy controls. FC was quantified using the Phase Lag Index. Three Random Forest classifier models were constructed to classify MDD patients and healthy controls, Model-I incorporating FC features from the resting state and Model-II and Model-III incorporating FC features while watching happy and sad videos respectively. RESULTS Important features distinguishing MDD and healthy controls were from all frequency bands and represent functional connectivity between fronto-temporal, fronto-parietal and fronto occipital regions. The cross-validation accuracies for Model-I, Model-II and Model-III were 92.3%, 94.9% and 89.7% and test accuracies were 60%, 80% and 70% respectively. Incorporating emotionally contagious videos improved the classification accuracies. CONCLUSION Findings support EEG FC patterns during resting state and emotional processing along with machine learning can be used to diagnose MDD. Future research should focus on replicating and validating these results. SIGNIFICANCE EEG FC pattern combined with machine learning may be used for assisting in diagnosing MDD.
Collapse
Affiliation(s)
- Estelle Havilla Earl
- Department of Physiology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Manish Goyal
- Department of Physiology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Shree Mishra
- Department of Psychiatry, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Balakrishnan Kannan
- Department of Physiology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Anushree Mishra
- Department of Psychiatry, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Nilotpal Chowdhury
- Department of Pathology, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
| | - Priyadarshini Mishra
- Department of Physiology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India.
| |
Collapse
|
5
|
Nam S, Yoo S, Park SK, Kim Y, Kim JT. Relationship between preinduction electroencephalogram patterns and propofol sensitivity in adult patients. J Clin Monit Comput 2024:10.1007/s10877-024-01149-y. [PMID: 38561555 DOI: 10.1007/s10877-024-01149-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/05/2024] [Indexed: 04/04/2024]
Abstract
PURPOSE To determine the precise induction dose, an objective assessment of individual propofol sensitivity is necessary. This study aimed to investigate whether preinduction electroencephalogram (EEG) data are useful in determining the optimal propofol dose for the induction of general anesthesia in healthy adult patients. METHODS Seventy healthy adult patients underwent total intravenous anesthesia (TIVA), and the effect-site target concentration of propofol was observed to measure each individual's propofol requirements for loss of responsiveness. We analyzed preinduction EEG data to assess its relationship with propofol requirements and conducted multiple regression analyses considering various patient-related factors. RESULTS Patients with higher relative delta power (ρ = 0.47, p < 0.01) and higher absolute delta power (ρ = 0.34, p = 0.01) required a greater amount of propofol for anesthesia induction. In contrast, patients with higher relative beta power (ρ = -0.33, p < 0.01) required less propofol to achieve unresponsiveness. Multiple regression analysis revealed an independent association between relative delta power and propofol requirements. CONCLUSION Preinduction EEG, particularly relative delta power, is associated with propofol requirements during the induction of general anesthesia. The utilization of preinduction EEG data may improve the precision of induction dose selection for individuals.
Collapse
Affiliation(s)
- Seungpyo Nam
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seokha Yoo
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sun-Kyung Park
- Department of Anesthesiology and Pain Medicine and Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Youngwon Kim
- Department of Anesthesiology and Pain Medicine and Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jin-Tae Kim
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
| |
Collapse
|
6
|
Ryalino C, Sahinovic MM, Drost G, Absalom AR. Intraoperative monitoring of the central and peripheral nervous systems: a narrative review. Br J Anaesth 2024; 132:285-299. [PMID: 38114354 DOI: 10.1016/j.bja.2023.11.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 12/21/2023] Open
Abstract
The central and peripheral nervous systems are the primary target organs during anaesthesia. At the time of the inception of the British Journal of Anaesthesia, monitoring of the central nervous system comprised clinical observation, which provided only limited information. During the 100 yr since then, and particularly in the past few decades, significant progress has been made, providing anaesthetists with tools to obtain real-time assessments of cerebral neurophysiology during surgical procedures. In this narrative review article, we discuss the rationale and uses of electroencephalography, evoked potentials, near-infrared spectroscopy, and transcranial Doppler ultrasonography for intraoperative monitoring of the central and peripheral nervous systems.
Collapse
Affiliation(s)
- Christopher Ryalino
- Department of Anaesthesiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Marko M Sahinovic
- Department of Anaesthesiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Gea Drost
- Department of Neurology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands; Department of Neurosurgery, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Anthony R Absalom
- Department of Anaesthesiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands.
| |
Collapse
|
7
|
Thornberry C, Caffrey M, Commins S. Theta oscillatory power decreases in humans are associated with spatial learning in a virtual water maze task. Eur J Neurosci 2023; 58:4341-4356. [PMID: 37957526 DOI: 10.1111/ejn.16185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 10/20/2023] [Indexed: 11/15/2023]
Abstract
Theta oscillations (4-8 Hz) in humans play a role in navigation processes, including spatial encoding, retrieval and sensorimotor integration. Increased theta power at frontal and parietal midline regions is known to contribute to successful navigation. However, the dynamics of cortical theta and its role in spatial learning are not fully understood. This study aimed to investigate theta oscillations via electroencephalogram (EEG) during spatial learning in a virtual water maze. Participants were separated into a learning group (n = 25) who learned the location of a hidden goal across 12 trials, or a time-matched non-learning group (n = 25) who were required to simply navigate the same arena, but without a goal. We compared all trials, at two phases of learning, the trial start and the goal approach. We also compared the first six trials with the last six trials within-groups. The learning group showed reduced low-frequency theta power at the frontal and parietal midline during the start phase and largely reduced theta combined with a short increase at both midlines during the goal-approach phase. These patterns were not found in the non-learning group, who instead displayed extensive increases in low-frequency oscillations at both regions during the trial start and at the parietal midline during goal approach. Our results support the theory that theta plays a crucial role in spatial encoding during exploration, as opposed to sensorimotor integration. We suggest our findings provide evidence for a link between learning and a reduction of theta oscillations in humans.
Collapse
Affiliation(s)
- Conor Thornberry
- Department of Psychology, Maynooth University, Maynooth, Ireland
| | - Michelle Caffrey
- Department of Psychology, Maynooth University, Maynooth, Ireland
| | - Sean Commins
- Department of Psychology, Maynooth University, Maynooth, Ireland
| |
Collapse
|
8
|
Lemoine É, Toffa D, Pelletier-Mc Duff G, Xu AQ, Jemel M, Tessier JD, Lesage F, Nguyen DK, Bou Assi E. Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography. Sci Rep 2023; 13:12650. [PMID: 37542101 PMCID: PMC10403587 DOI: 10.1038/s41598-023-39799-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/31/2023] [Indexed: 08/06/2023] Open
Abstract
Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55-0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.
Collapse
Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Denahin Toffa
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Geneviève Pelletier-Mc Duff
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - An Qi Xu
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Mezen Jemel
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Jean-Daniel Tessier
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Frédéric Lesage
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Qc, Canada
- Centre de Recherche de l'institut de Cardiologie de Montréal, Montréal, Qc, Canada
| | - Dang K Nguyen
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Elie Bou Assi
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada.
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada.
| |
Collapse
|
9
|
Tinga AM, Menger NS, de Back TT, Louwerse MM. Age Differences in Learning-Related Neurophysiological Changes. J PSYCHOPHYSIOL 2023. [DOI: 10.1027/0269-8803/a000317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Abstract. Research in young adults has demonstrated that neurophysiological measures are able to provide insight into learning processes. However, to date, it remains unclear whether neurophysiological changes during learning in older adults are comparable to those in younger adults. The current study addressed this issue by exploring age differences in changes over time in a range of neurophysiological outcome measures collected during visuomotor sequence learning. Specifically, measures of electroencephalography (EEG), skin conductance, heart rate, heart rate variability, respiration rate, and eye-related measures, in addition to behavioral performance measures, were collected in younger ( Mage = 27.24 years) and older adults ( Mage = 58.06 years) during learning. Behavioral responses became more accurate over time in both age groups during visuomotor sequence learning. Yet, older adults needed more time in each trial to enhance the precision of their movement. Changes in EEG during learning demonstrated a stronger increase in theta power in older compared to younger adults and a decrease in gamma power in older adults while increasing slightly in younger adults. No such differences between the two age groups were found on other neurophysiological outcome measures, suggesting changes in brain activity during learning to be more sensitive to age differences than changes in peripheral physiology. Additionally, differences in which neurophysiological outcomes were associated with behavioral performance on the learning task were found between younger and older adults. This indicates that the neurophysiological underpinnings of learning may differ between younger and older adults. Therefore, the current findings highlight the importance of taking age into account when aiming to gain insight into behavioral performance through neurophysiology during learning.
Collapse
Affiliation(s)
- Angelica M. Tinga
- Department of Cognitive Science & Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
| | - Nick S. Menger
- Department of Cognitive Science & Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
| | - Tycho T. de Back
- Department of Cognitive Science & Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
| | - Max M. Louwerse
- Department of Cognitive Science & Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
| |
Collapse
|
10
|
Pappalettera C, Cacciotti A, Nucci L, Miraglia F, Rossini PM, Vecchio F. Approximate entropy analysis across electroencephalographic rhythmic frequency bands during physiological aging of human brain. GeroScience 2022; 45:1131-1145. [PMID: 36538178 PMCID: PMC9886767 DOI: 10.1007/s11357-022-00710-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/03/2022] [Indexed: 12/24/2022] Open
Abstract
Aging is the inevitable biological process that results in a progressive structural and functional decline associated with alterations in the resting/task-related brain activity, morphology, plasticity, and functionality. In the present study, we analyzed the effects of physiological aging on the human brain through entropy measures of electroencephalographic (EEG) signals. One hundred sixty-one participants were recruited and divided according to their age into young (n = 72) and elderly (n = 89) groups. Approximate entropy (ApEn) values were calculated in each participant for each EEG recording channel and both for the total EEG spectrum and for each of the main EEG frequency rhythms: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-11 Hz), alpha 2 (11-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-45 Hz), to identify eventual statistical differences between young and elderly. To demonstrate that the ApEn represents the age-related brain changes, the computed ApEn values were used as features in an age-related classification of subjects (young vs elderly), through linear, quadratic, and cubic support vector machine (SVM). Topographic maps of the statistical results showed statistically significant difference between the ApEn values of the two groups found in the total spectrum and in delta, theta, beta 2, and gamma. The classifiers (linear, quadratic, and cubic SVMs) revealed high levels of accuracy (respectively 93.20 ± 0.37, 93.16 ± 0.30, 90.62 ± 0.62) and area under the curve (respectively 0.95, 0.94, 0.93). ApEn seems to be a powerful, very sensitive-specific measure for the study of cognitive decline and global cortical alteration/degeneration in the elderly EEG activity.
Collapse
Affiliation(s)
- Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy ,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Alessia Cacciotti
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy ,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy ,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy. .,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
| |
Collapse
|
11
|
Hyperconnectivity matters in early-onset Alzheimer's disease: a resting-state EEG connectivity study. Neurophysiol Clin 2022; 52:459-471. [DOI: 10.1016/j.neucli.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 10/17/2022] [Accepted: 10/21/2022] [Indexed: 11/11/2022] Open
|
12
|
Bugos JA, Bidelman GM, Moreno S, Shen D, Lu J, Alain C. Music and Visual Art Training Increase Auditory-Evoked Theta Oscillations in Older Adults. Brain Sci 2022; 12:brainsci12101300. [PMID: 36291234 PMCID: PMC9599228 DOI: 10.3390/brainsci12101300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/18/2022] [Accepted: 09/20/2022] [Indexed: 11/30/2022] Open
Abstract
Music training was shown to induce changes in auditory processing in older adults. However, most findings stem from correlational studies and fewer examine long-term sustainable benefits. Moreover, research shows small and variable changes in auditory event-related potential (ERP) amplitudes and/or latencies in older adults. Conventional time domain analysis methods, however, are susceptible to latency jitter in evoked responses and may miss important information of brain processing. Here, we used time-frequency analyses to examine training-related changes in auditory-evoked oscillatory activity in healthy older adults (N = 50) assigned to a music training (n = 16), visual art training (n = 17), or a no-treatment control (n = 17) group. All three groups were presented with oddball auditory paradigms with synthesized piano tones or vowels during the acquisition of high-density EEG. Neurophysiological measures were collected at three-time points: pre-training, post-training, and at a three-month follow-up. Training programs were administered for 12-weeks. Increased theta power was found pre and post- training for the music (p = 0.010) and visual art group (p = 0.010) as compared to controls (p = 0.776) and maintained at the three-month follow-up. Results showed training-related plasticity on auditory processing in aging adults. Neuroplastic changes were maintained three months post-training, suggesting music and visual art programs yield lasting benefits that might facilitate encoding, retention, and memory retrieval.
Collapse
Affiliation(s)
- Jennifer A. Bugos
- School of Music, University of South Florida, Tampa, FL 33620, USA
- Correspondence: ; Tel.: +1-352-339-4076
| | - Gavin M. Bidelman
- Department of Speech, Language, and Hearing Sciences, Indiana University, Bloomington, IN 47408, USA
| | - Sylvain Moreno
- School of Interactive Arts and Technology, Simon Fraser University, Burnaby, BC V3T OA3, Canada
- Circle Innovation, Burnaby, BC V3T OA3, Canada
| | - Dawei Shen
- Rotman Research Institute, Toronto, ON M6A 2E1, Canada
| | - Jing Lu
- MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic and Science Technology of China, Chengdu 611731, China
| | - Claude Alain
- Rotman Research Institute, Toronto, ON M6A 2E1, Canada
- Department of Psychology, University of Toronto, Toronto, ON M5S 3G3, Canada
| |
Collapse
|
13
|
Wang J, Liu Q, Tian F, Zhou S, Parra MA, Wang H, Yu X. Disrupted Spatiotemporal Complexity of Resting-State Electroencephalogram Dynamics Is Associated With Adaptive and Maladaptive Rumination in Major Depressive Disorder. Front Neurosci 2022; 16:829755. [PMID: 35615274 PMCID: PMC9125314 DOI: 10.3389/fnins.2022.829755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/28/2022] [Indexed: 01/10/2023] Open
Abstract
Patients with major depressive disorder (MDD) exhibit abnormal rumination, including both adaptive and maladaptive forms. However, the neural substrates of rumination in depression remain poorly understood. We hypothesize that divergent spatiotemporal complexity of brain oscillations would be associated with the levels of rumination in MDD. We employed the multi-scale entropy (MSE), power and phase-amplitude coupling (PAC) to estimate the complexity of rhythmic dynamics from the eye-closed high-density electroencephalographic (EEG) data in treatment-naive patients with MDD (n = 24) and healthy controls (n = 22). The depressive, brooding, and reflective subscales of the Ruminative Response Scale were assessed. MDD patients showed higher MSE in timescales finer than 5 (cluster P = 0.038) and gamma power (cluster P = 0.034), as well as lower PAC values between alpha/low beta and gamma bands (cluster P = 0.002- 0.021). Higher reflective rumination in MDD was region-specifically associated with the more localized EEG dynamics, including the greater MSE in scales finer than 8 (cluster P = 0.008), power in gamma (cluster P = 0.018) and PAC in low beta-gamma (cluster P = 0.042), as well as weaker alpha-gamma PAC (cluster P = 0.016- 0.029). Besides, the depressive and brooding rumination in MDD showed the lack of correlations with global long-range EEG variables. Our findings support the disturbed neural communications and point to the spatial reorganization of brain networks in a timescale-dependent migration toward local during adaptive and maladaptive rumination in MDD. These findings may provide potential implications on probing and modulating dynamic neuronal fluctuations during the rumination in depression.
Collapse
Affiliation(s)
- Jing Wang
- Peking University Sixth Hospital (Institute of Mental Health), National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Qi Liu
- Peking University Sixth Hospital (Institute of Mental Health), National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Feng Tian
- Peking University Sixth Hospital (Institute of Mental Health), National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health, Peking University, Beijing, China
- Department of Psychiatry, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Shuzhe Zhou
- Peking University Sixth Hospital (Institute of Mental Health), National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Mario Alfredo Parra
- School of Psychological Sciences and Health, Department of Psychology, University of Strathclyde, Glasgow, United Kingdom
| | - Huali Wang
- Peking University Sixth Hospital (Institute of Mental Health), National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Xin Yu
- Peking University Sixth Hospital (Institute of Mental Health), National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| |
Collapse
|