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Yu Y, Oh Y, Kounios J, Beeman M. Electroencephalography Spectral-power Volatility Predicts Problem-solving Outcomes. J Cogn Neurosci 2024; 36:901-915. [PMID: 38437171 DOI: 10.1162/jocn_a_02136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics.
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Affiliation(s)
- Yuhua Yu
- Northwestern University, Evanston, IL
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2
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Kang JH, Bae JH, Jeon YJ. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering (Basel) 2024; 11:418. [PMID: 38790286 PMCID: PMC11118246 DOI: 10.3390/bioengineering11050418] [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: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
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Affiliation(s)
- Jae-Hwan Kang
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jang-Han Bae
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Young-Ju Jeon
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
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Auer T, Goldthorpe R, Peach R, Hebron H, Violante IR. Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function. Hum Brain Mapp 2024; 45:e26687. [PMID: 38651629 PMCID: PMC11036379 DOI: 10.1002/hbm.26687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/22/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024] Open
Abstract
The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.
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Affiliation(s)
- Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordUK
| | | | | | - Henry Hebron
- School of PsychologyUniversity of SurreyGuildfordUK
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Kim H, Min BK, Lee U, Sim JH, Noh GJ, Lee EK, Choi BM. Electroencephalographic features of elderly patients during anesthesia induction with remimazolam: a sub-study of a randomized controlled trial. Anesthesiology 2024:139687. [PMID: 38207285 DOI: 10.1097/aln.0000000000004904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
BACKGROUND Although remimazolam is used as a general anesthetic in elderly patients due to its hemodynamic stability, the electroencephalogram (EEG) characteristics of remimazolam are not well-known. The purpose of this study was to identify the EEG features of remimazolam-induced unconsciousness in elderly patients and compare them with propofol. METHODS Remimazolam (n=26) or propofol (n=26) were randomly administered for anesthesia induction in surgical patients. The hypnotic agent was blinded only to the patients. During the induction of anesthesia, remimazolam was administered at a rate of 6 mg/kg/h, and propofol was administered at a target effect-site concentration of 3.5 μg/ml. The EEG signals from 8 channels (Fp1,Fp2,Fz,F3,F4,Pz,P3,P4, referenced to A2, using the 10-20 system) were acquired during the induction of anesthesia and in the postoperative care unit. Power spectrum analysis was performed, and directed functional connectivity between frontal and parietal regions was evaluated using normalized symbolic transfer entropy. Functional connectivity in unconscious processes induced by remimazolam or propofol was compared with baseline. To compare each power of frequency over time of the two hypnotic agents, a permutation test with t statistic was conducted. RESULTS Compared to the baseline in the alpha band, the feedback connectivity decreased by an average of 46% and 43%, respectively, after the loss of consciousness induced by remimazolam and propofol (95% CI for the mean difference:-0.073 to -0.044 for remimazolam, P<0.001,-0.068 to -0.042 for propofol,P<0.001). Asymmetry in the feedback and feedforward connectivity in the alpha band was suppressed after the loss of consciousness induced by remimazolam and propofol. There were no significant differences in the power of each frequency over time between the two hypnotic agents (minimum q-value=0.4235). CONCLUSIONS Both regimens showed a greater decrease in feedback connectivity compared to a decrease in feedforward connectivity after loss of consciousness, leading to a disruption of asymmetry between the frontoparietal connectivity.
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Affiliation(s)
- Hyoungkyu Kim
- Research professor, Ph.D., Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Byoung-Kyong Min
- Professor, Ph.D., Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Uncheol Lee
- Associate professor, Ph.D., Department of Anesthesiology, Center for Consciousness Science, Center for the Study of Complex Systems, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Ji-Hoon Sim
- Assistant professor, M.D., Ph.D., Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Gyu-Jeong Noh
- Professor, M.D., Ph.D., Department of Anesthesiology and Pain Medicine and Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Eun-Kyung Lee
- Professor, Ph.D., Department of Statistics, Ewha Womans University, Seoul, Korea
| | - Byung-Moon Choi
- Professor, M.D., Ph.D., Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Șerban CA, Barborică A, Roceanu AM, Mîndruță IR, Ciurea J, Stancu M, Pâslaru AC, Zăgrean AM, Zăgrean L, Moldovan M. Towards an electroencephalographic measure of awareness based on the reactivity of oscillatory macrostates to hearing a subject's own name. Eur J Neurosci 2024; 59:771-785. [PMID: 37675619 DOI: 10.1111/ejn.16138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 08/09/2023] [Accepted: 08/16/2023] [Indexed: 09/08/2023]
Abstract
We proposed that the brain's electrical activity is composed of a sequence of alternating states with repeating topographic spectral distributions on scalp electroencephalogram (EEG), referred to as oscillatory macrostates. The macrostate showing the largest decrease in the probability of occurrence, measured as a percentage (reactivity), during sensory stimulation was labelled as the default EEG macrostate (DEM). This study aimed to assess the influence of awareness on DEM reactivity (DER). We included 11 middle cerebral artery ischaemic stroke patients with impaired awareness having a median Glasgow Coma Scale (GCS) of 6/15 and a group of 11 matched healthy controls. EEG recordings were carried out during auditory 1 min stimulation epochs repeating either the subject's own name (SON) or the SON in reverse (rSON). The DEM was identified across three SON epochs alternating with three rSON epochs. Compared with the patients, the DEM of controls contained more posterior theta activity reflecting source dipoles that could be mapped in the posterior cingulate cortex. The DER was measured from the 1 min quiet baseline preceding each stimulation epoch. The difference in mean DER between the SON and rSON epochs was measured by the salient EEG reactivity (SER) theoretically ranging from -100% to 100%. The SER was 12.4 ± 2.7% (Mean ± standard error of the mean) in controls and only 1.3 ± 1.9% in the patient group (P < 0.01). The patient SER decreased with the Glasgow Coma Scale. Our data suggest that awareness increases DER to SON as measured by SER.
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Affiliation(s)
- Cosmin-Andrei Șerban
- Physics Department, University of Bucharest, Bucharest, Romania
- Termobit Prod SRL, Bucharest, Romania
- FHC Inc, Bowdoin, Maine, USA
| | - Andrei Barborică
- Physics Department, University of Bucharest, Bucharest, Romania
- Termobit Prod SRL, Bucharest, Romania
- FHC Inc, Bowdoin, Maine, USA
| | | | | | - Jan Ciurea
- Department of Neurosurgery, Bagdasar-Arseni Emergency Hospital, Bucharest, Romania
| | - Mihai Stancu
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Division of Neurobiology, Faculty of Biology, Ludwig Maximilian University, Munich, Germany
| | - Alexandru C Pâslaru
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Ana-Maria Zăgrean
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Leon Zăgrean
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Mihai Moldovan
- Termobit Prod SRL, Bucharest, Romania
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
- Clinical Neurophysiology and Neurology, Rigshospitalet, Copenhagen, Denmark
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Ross D, Wagshul ME, Izzetoglu M, Holtzer R. Cortical thickness moderates intraindividual variability in prefrontal cortex activation patterns of older adults during walking. J Int Neuropsychol Soc 2024; 30:117-127. [PMID: 37366047 PMCID: PMC10751394 DOI: 10.1017/s1355617723000371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
OBJECTIVE Increased intraindividual variability (IIV) in behavioral and cognitive performance is a risk factor for adverse outcomes but research concerning hemodynamic signal IIV is limited. Cortical thinning occurs during aging and is associated with cognitive decline. Dual-task walking (DTW) performance in older adults has been related to cognition and neural integrity. We examined the hypothesis that reduced cortical thickness would be associated with greater increases in IIV in prefrontal cortex oxygenated hemoglobin (HbO2) from single tasks to DTW in healthy older adults while adjusting for behavioral performance. METHOD Participants were 55 healthy community-dwelling older adults (mean age = 74.84, standard deviation (SD) = 4.97). Structural MRI was used to quantify cortical thickness. Functional near-infrared spectroscopy (fNIRS) was used to assess changes in prefrontal cortex HbO2 during walking. HbO2 IIV was operationalized as the SD of HbO2 observations assessed during the first 30 seconds of each task. Linear mixed models were used to examine the moderation effect of cortical thickness throughout the cortex on HbO2 IIV across task conditions. RESULTS Analyses revealed that thinner cortex in several regions was associated with greater increases in HbO2 IIV from the single tasks to DTW (ps < .02). CONCLUSIONS Consistent with neural inefficiency, reduced cortical thickness in the PFC and throughout the cerebral cortex was associated with increases in HbO2 IIV from the single tasks to DTW without behavioral benefit. Reduced cortical thickness and greater IIV of prefrontal cortex HbO2 during DTW may be further investigated as risk factors for developing mobility impairments in aging.
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Affiliation(s)
- Daliah Ross
- Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, USA
| | - Mark E. Wagshul
- Department of Radiology, Gruss Magnetic Resonance Research Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Meltem Izzetoglu
- Department of Electrical and Computer Engineering, Villanova University, Villanova, PA, USA
| | - Roee Holtzer
- Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, USA
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
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Ao Y, Yang C, Drewes J, Jiang M, Huang L, Jing X, Northoff G, Wang Y. Spatiotemporal dedifferentiation of the global brain signal topography along the adult lifespan. Hum Brain Mapp 2023; 44:5906-5918. [PMID: 37800366 PMCID: PMC10619384 DOI: 10.1002/hbm.26484] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 08/17/2023] [Accepted: 08/28/2023] [Indexed: 10/07/2023] Open
Abstract
Age-related variations in many regions and/or networks of the human brain have been uncovered using resting-state functional magnetic resonance imaging. However, these findings did not account for the dynamical effect the brain's global activity (global signal [GS]) causes on local characteristics, which is measured by GS topography. To address this gap, we tested GS topography including its correlation with age using a large-scale cross-sectional adult lifespan dataset (n = 492). Both GS topography and its variation with age showed frequency-specific patterns, reflecting the spatiotemporal characteristics of the dynamic change of GS topography with age. A general trend toward dedifferentiation of GS topography with age was observed in both spatial (i.e., less differences of GS between different regions) and temporal (i.e., less differences of GS between different frequencies) dimensions. Further, methodological control analyses suggested that although most age-related dedifferentiation effects remained across different preprocessing strategies, some were triggered by neuro-vascular coupling and physiological noises. Together, these results provide the first evidence for age-related effects on global brain activity and its topographic-dynamic representation in terms of spatiotemporal dedifferentiation.
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Affiliation(s)
- Yujia Ao
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Chengxiao Yang
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Jan Drewes
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Muliang Jiang
- First Affiliated HospitalGuangxi Medical UniversityNanningChina
| | - Lihui Huang
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Xiujuan Jing
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Yifeng Wang
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
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Steinberg SN, King TZ. Within-Individual BOLD Signal Variability and its Implications for Task-Based Cognition: A Systematic Review. Neuropsychol Rev 2023:10.1007/s11065-023-09619-x. [PMID: 37889371 DOI: 10.1007/s11065-023-09619-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 09/08/2023] [Indexed: 10/28/2023]
Abstract
Within-individual blood oxygen level-dependent (BOLD) signal variability, intrinsic moment-to-moment signal fluctuations within a single individual in specific voxels across a given time course, is a relatively new metric recognized in the neuroimaging literature. Within-individual BOLD signal variability has been postulated to provide information beyond that provided by mean-based analysis. Synthesis of the literature using within-individual BOLD signal variability methodology to examine various cognitive domains is needed to understand how intrinsic signal fluctuations contribute to optimal performance. This systematic review summarizes and integrates this literature to assess task-based cognitive performance in healthy groups and few clinical groups. Included papers were published through October 17, 2022. Searches were conducted on PubMed and APA PsycInfo. Studies eligible for inclusion used within-individual BOLD signal variability methodology to examine BOLD signal fluctuations during task-based functional magnetic resonance imaging (fMRI) and/or examined relationships between task-based BOLD signal variability and out-of-scanner behavioral measure performance, were in English, and were empirical research studies. Data from each of the included 19 studies were extracted and study quality was systematically assessed. Results suggest that variability patterns for different cognitive domains across the lifespan (ages 7-85) may depend on task demands, measures, variability quantification method used, and age. As neuroimaging methods explore individual-level contributions to cognition, within-individual BOLD signal variability may be a meaningful metric that can inform understanding of neurocognitive performance. Further research in understudied domains/populations, and with consistent quantification methods/cognitive measures, will help conceptualize how intrinsic BOLD variability impacts cognitive abilities in healthy and clinical groups.
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Affiliation(s)
- Stephanie N Steinberg
- Department of Psychology, Georgia State University, Urban Life Building, 11th Floor, 140 Decatur St, Atlanta, GA, 30303, USA
| | - Tricia Z King
- Department of Psychology, Georgia State University, Urban Life Building, 11th Floor, 140 Decatur St, Atlanta, GA, 30303, USA.
- Neuroscience Institute, Georgia State University, Atlanta, GA, 30302, USA.
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Lewandowska M, Tołpa K, Rogala J, Piotrowski T, Dreszer J. Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender-related differences. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2023; 19:18. [PMID: 37798774 PMCID: PMC10552392 DOI: 10.1186/s12993-023-00218-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 09/18/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND The study aimed to determine how the resting-state EEG (rsEEG) complexity changes both over time and space (channels). The complexity of rsEEG and its sex/gender differences were examined using the multivariate Multiscale Entropy (mMSE) in 95 healthy adults. Following the probability maps (Giacometti et al. in J Neurosci Methods 229:84-96, 2014), channel sets have been identified that correspond to the functional networks. For each channel set the area under curve (AUC), which represents the total complexity, MaxSlope-the maximum complexity change of the EEG signal at thefine scales (1:4 timescales), and AvgEnt-to the average entropy level at coarse-grained scales (9:12 timescales), respectively, were extracted. To check dynamic changes between the entropy level at the fine and coarse-grained scales, the difference in mMSE between the #9 and #4 timescale (DiffEnt) was also calculated. RESULTS We found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlope and DiffEnt but lower AvgEnt in all channel sets. CONCLUSIONS Novel results of the present study are: (1) an identification of the mMSE features that capture entropy at the fine and coarse timescales in the channel sets corresponding to the main resting-state networks; (2) the sex/gender differences in these features.
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Affiliation(s)
- Monika Lewandowska
- Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100, Torun, Poland
| | - Krzysztof Tołpa
- Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100, Torun, Poland
| | - Jacek Rogala
- Faculty of Physics, University of Warsaw, Pasteur 5 Street, 02-093, Warsaw, Poland
| | - Tomasz Piotrowski
- Institute of Engineering and Technology, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Torun, Grudziądzka 5 Street, 87-100, Torun, Poland
| | - Joanna Dreszer
- Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100, Torun, Poland.
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Gordillo D, Ramos da Cruz J, Moreno D, Garobbio S, Herzog MH. Do we really measure what we think we are measuring? iScience 2023; 26:106017. [PMID: 36844457 PMCID: PMC9947309 DOI: 10.1016/j.isci.2023.106017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/18/2022] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Tests used in the empirical sciences are often (implicitly) assumed to be representative of a given research question in the sense that similar tests should lead to similar results. Here, we show that this assumption is not always valid. We illustrate our argument with the example of resting-state electroencephalogram (EEG). We used multiple analysis methods, contrary to typical EEG studies where one analysis method is used. We found, first, that many EEG features correlated significantly with cognitive tasks. However, these EEG features correlated weakly with each other. Similarly, in a second analysis, we found that many EEG features were significantly different in older compared to younger participants. When we compared these EEG features pairwise, we did not find strong correlations. In addition, EEG features predicted cognitive tasks poorly as shown by cross-validated regression analysis. We discuss several explanations of these results.
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Affiliation(s)
- Dario Gordillo
- Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
- Corresponding author
| | - Janir Ramos da Cruz
- Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
- Institute for Systems and Robotics – Lisboa (LARSyS), Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
- Wyss Center for Bio and Neuroengineering, CH-1202 Geneva, Switzerland
| | - Dana Moreno
- Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Simona Garobbio
- Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Michael H. Herzog
- Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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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.
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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.
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12
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Ferrari-Díaz M, Bravo-Chávez RI, Silva-Pereyra J, Fernández T, García-Peña C, Rodríguez-Camacho M. Verbal intelligence and leisure activities are associated with cognitive performance and resting-state electroencephalogram. Front Aging Neurosci 2022; 14:921518. [PMID: 36268192 PMCID: PMC9577299 DOI: 10.3389/fnagi.2022.921518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
Cognitive reserve (CR) is the adaptability of cognitive processes that helps to explain differences in the susceptibility of cognitive or daily functions to resist the onslaught of brain-related injury or the normal aging process. The underlying brain mechanisms of CR studied through electroencephalogram (EEG) are scarcely reported. To our knowledge, few studies have considered a combination of exclusively dynamic proxy measures of CR. We evaluated the association of CR with cognition and resting-state EEG in older adults using three of the most frequently used dynamic proxy measures of CR: verbal intelligence, leisure activities, and physical activities. Multiple linear regression analyses with the CR proxies as independent variables and cognitive performance and the absolute power (AP) on six resting-state EEG components (beta, alpha1, alpha2, gamma, theta, and delta) as outcomes were performed. Eighty-eight healthy older adults aged 60–77 (58 female) were selected from previous study data. Verbal intelligence was a significant positive predictor of perceptual organization, working memory, processing speed, executive functions, and central delta power. Leisure activities were a significant positive predictor of posterior alpha2 power. The dynamic proxy variables of CR are differently associated with cognitive performance and resting-state EEG. Implementing leisure activities and tasks to increase vocabulary may promote better cognitive performance through compensation or neural efficiency mechanisms.
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Affiliation(s)
- Martina Ferrari-Díaz
- Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla, Mexico
| | - Ricardo Iván Bravo-Chávez
- Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla, Mexico
| | - Juan Silva-Pereyra
- Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla, Mexico
- *Correspondence: Juan Silva-Pereyra,
| | - Thalía Fernández
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Mexico
| | - Carmen García-Peña
- Departamento de Investigación, Instituto Nacional de Geriatría, Ciudad de México, Mexico
| | - Mario Rodríguez-Camacho
- Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla, Mexico
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13
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Mann‐Krzisnik D, Mitsis GD. Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition. Hum Brain Mapp 2022; 43:4045-4073. [PMID: 35567768 PMCID: PMC9374895 DOI: 10.1002/hbm.25902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 11/11/2022] Open
Abstract
The relation between electrophysiology and BOLD-fMRI requires further elucidation. One approach for studying this relation is to find time-frequency features from electrophysiology that explain the variance of BOLD time-series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD-fMRI data. We propose a framework for extracting the spatial distribution of these time-frequency features while also estimating more flexible, region-specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide insight into the relation between electrophysiology and BOLD-fMRI and can be used to construct estimates of BOLD time-series. We demonstrated the performance of this method on simulated data while also examining the effects of simulated measurement noise and physiological confounds. Afterwards, we validated our method on publicly available task-based and resting-state EEG-fMRI data. We adjusted our method to accommodate the multisubject nature of these datasets, enabling the investigation of inter-subject variability with regards to EEG-to-BOLD neurovascular coupling mechanisms. We thus also demonstrate how EEG features for modelling the BOLD signal differ across subjects.
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Affiliation(s)
- Dylan Mann‐Krzisnik
- Graduate Program in Biological and Biomedical EngineeringMcGill UniversityMontréalQuebecCanada
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14
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Shou G, Yuan H, Cha YH, Sweeney JA, Ding L. Age-related changes of whole-brain dynamics in spontaneous neuronal coactivations. Sci Rep 2022; 12:12140. [PMID: 35840643 PMCID: PMC9287374 DOI: 10.1038/s41598-022-16125-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/05/2022] [Indexed: 01/04/2023] Open
Abstract
Human brains experience whole-brain anatomic and functional changes throughout the lifespan. Age-related whole-brain network changes have been studied with functional magnetic resonance imaging (fMRI) to determine their low-frequency spatial and temporal characteristics. However, little is known about age-related changes in whole-brain fast dynamics at the scale of neuronal events. The present study investigated age-related whole-brain dynamics in resting-state electroencephalography (EEG) signals from 73 healthy participants from 6 to 65 years old via characterizing transient neuronal coactivations at a resolution of tens of milliseconds. These uncovered transient patterns suggest fluctuating brain states at different energy levels of global activations. Our results indicate that with increasing age, shorter lifetimes and more occurrences were observed in the brain states that show the global high activations and more consecutive visits to the global highest-activation brain state. There were also reduced transitional steps during consecutive visits to the global lowest-activation brain state. These age-related effects suggest reduced stability and increased fluctuations when visiting high-energy brain states and with a bias toward staying low-energy brain states. These age-related whole-brain dynamics changes are further supported by changes observed in classic alpha and beta power, suggesting its promising applications in examining the effect of normal healthy brain aging, brain development, and brain disease.
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Affiliation(s)
- Guofa Shou
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, USA
| | - Han Yuan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, USA.,Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, USA
| | - Yoon-Hee Cha
- Department of Neurology, University of Minnesota, Minneapolis, MN, USA
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, USA
| | - Lei Ding
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, USA. .,Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, USA. .,University of Oklahoma, 173 Felgar St., Gallogly Hall, Room 101, Norman, OK, 73019, USA.
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15
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Zhao R, Su Q, Song Y, Yang Q, Wang S, Zhang J, Qin W, Yu C, Liang M. Brain-activation-based individual identification reveals individually unique activation patterns elicited by pain and touch. Neuroimage 2022; 260:119436. [PMID: 35788043 DOI: 10.1016/j.neuroimage.2022.119436] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 11/15/2022] Open
Abstract
Pain is subjective and perceived differently in different people. However, individual differences in pain-elicited brain activations are largely overlooked and often discarded as noises. Here, we used a brain-activation-based individual identification procedure to investigate the uniqueness of the activation patterns within the whole brain or brain regions elicited by nociceptive (laser) and tactile (electrical) stimuli in each of 62 healthy participants. Specifically, brain activation patterns were used as "fingerprints" to identify each individual participant within and across sensory modalities, and individual identification accuracy was calculated to measure each individual's identifiability. We found that individual participants could be successfully identified using their brain activation patterns elicited by nociceptive stimuli, tactile stimuli, or even across modalities. However, different participants had different identifiability; importantly, the within-pain, but not within-touch or cross-modality, individual identifiability obtained from three brain regions (i.e., the left superior frontal gyrus, the middle temporal gyrus and the insular gyrus) were inversely correlated with the scores of Pain Vigilance and Awareness Questionnaire (i.e., how a person is alerted to pain) across participants. These results suggest that each individual has a unique pattern of brain responses to nociceptive stimuli which contains both modality-nonspecific and pain-specific information and may be associated with pain-related behaviors shaped by his/her own personal experiences and highlight the importance of a transition from group-level to individual-level characterization of brain activity in neuroimaging studies.
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Affiliation(s)
- Rui Zhao
- School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, 300070, China; Department of Orthopedics Surgery, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qian Su
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin 300060, China
| | - YingChao Song
- School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, 300070, China
| | - QingQing Yang
- School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, 300070, China; Department of Radiology, Zhejiang University School of Medicine First Affiliated Hospital, Zhejiang 310009, China
| | - Sijia Wang
- School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, 300070, China
| | - Juan Zhang
- Department of Prosthodontics, School and Hospital of Stomatology, Tianjin Medical University, Tianjin, 300070, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Chunshui Yu
- School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, 300070, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, 300070, China.
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16
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The modulatory effect of adaptive task-switching training on resting-state neural network dynamics in younger and older adults. Sci Rep 2022; 12:9541. [PMID: 35680953 PMCID: PMC9184743 DOI: 10.1038/s41598-022-13708-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/26/2022] [Indexed: 11/08/2022] Open
Abstract
With increasing life expectancy and active aging, it becomes crucial to investigate methods which could compensate for generally detected cognitive aging processes. A promising candidate is adaptive cognitive training, during which task difficulty is adjusted to the participants' performance level to enhance the training and potential transfer effects. Measuring intrinsic brain activity is suitable for detecting possible distributed training-effects since resting-state dynamics are linked to the brain's functional flexibility and the effectiveness of different cognitive processes. Therefore, we investigated if adaptive task-switching training could modulate resting-state neural dynamics in younger (18-25 years) and older (60-75 years) adults (79 people altogether). We examined spectral power density on resting-state EEG data for measuring oscillatory activity, and multiscale entropy for detecting intrinsic neural complexity. Decreased coarse timescale entropy and lower frequency band power as well as increased fine timescale entropy and higher frequency band power revealed a shift from more global to local information processing with aging before training. However, cognitive training modulated these age-group differences, as coarse timescale entropy and lower frequency band power increased from pre- to post-training in the old-training group. Overall, our results suggest that cognitive training can modulate neural dynamics even when measured outside of the trained task.
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17
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Wang H, Burles F, Subramaniapillai S, Pasvanis S, Rajah MN, Protzner AB. Sex differences in the relationship between age, performance, and BOLD signal variability during spatial context memory processing. Neurobiol Aging 2022; 118:77-87. [DOI: 10.1016/j.neurobiolaging.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 06/17/2022] [Accepted: 06/21/2022] [Indexed: 10/17/2022]
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18
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Lutkenhoff ES, Nigri A, Rossi Sebastiano D, Sattin D, Visani E, Rosazza C, D'Incerti L, Bruzzone MG, Franceschetti S, Leonardi M, Ferraro S, Monti MM. EEG Power spectra and subcortical pathology in chronic disorders of consciousness. Psychol Med 2022; 52:1491-1500. [PMID: 32962777 DOI: 10.1017/s003329172000330x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Despite a growing understanding of disorders of consciousness following severe brain injury, the association between long-term impairment of consciousness, spontaneous brain oscillations, and underlying subcortical damage, and the ability of such information to aid patient diagnosis, remains incomplete. METHODS Cross-sectional observational sample of 116 patients with a disorder of consciousness secondary to brain injury, collected prospectively at a tertiary center between 2011 and 2013. Multimodal analyses relating clinical measures of impairment, electroencephalographic measures of spontaneous brain activity, and magnetic resonance imaging data of subcortical atrophy were conducted in 2018. RESULTS In the final analyzed sample of 61 patients, systematic associations were found between electroencephalographic power spectra and subcortical damage. Specifically, the ratio of beta-to-delta relative power was negatively associated with greater atrophy in regions of the bilateral thalamus and globus pallidus (both left > right) previously shown to be preferentially atrophied in chronic disorders of consciousness. Power spectrum total density was also negatively associated with widespread atrophy in regions of the left globus pallidus, right caudate, and in the brainstem. Furthermore, we showed that the combination of demographics, encephalographic, and imaging data in an analytic framework can be employed to aid behavioral diagnosis. CONCLUSIONS These results ground, for the first time, electroencephalographic presentation detected with routine clinical techniques in the underlying brain pathology of disorders of consciousness and demonstrate how multimodal combination of clinical, electroencephalographic, and imaging data can be employed in potentially mitigating the high rates of misdiagnosis typical of this patient cohort.
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Affiliation(s)
- Evan S Lutkenhoff
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Davide Rossi Sebastiano
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Davide Sattin
- Neurology, Public Health, Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Elisa Visani
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Cristina Rosazza
- Scientific Direction, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Ludovico D'Incerti
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Silvana Franceschetti
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Matilde Leonardi
- Neurology, Public Health, Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Stefania Ferraro
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
- School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China: On the behalf of the Coma Research Center, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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19
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Jauny G, Eustache F, Hinault TT. M/EEG Dynamics Underlying Reserve, Resilience, and Maintenance in Aging: A Review. Front Psychol 2022; 13:861973. [PMID: 35693495 PMCID: PMC9174693 DOI: 10.3389/fpsyg.2022.861973] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/14/2022] [Indexed: 12/27/2022] Open
Abstract
Cognitive reserve and resilience refer to the set of processes allowing the preservation of cognitive performance in the presence of structural and functional brain changes. Investigations of these concepts have provided unique insights into the heterogeneity of cognitive and brain changes associated with aging. Previous work mainly relied on methods benefiting from a high spatial precision but a low temporal resolution, and thus the temporal brain dynamics underlying these concepts remains poorly known. Moreover, while spontaneous fluctuations of neural activity have long been considered as noise, recent work highlights its critical contribution to brain functions. In this study, we synthesized the current state of knowledge from magnetoencephalography (MEG) and electroencephalography (EEG) studies that investigated the contribution of maintenance of neural synchrony, and variability of brain dynamics, to cognitive changes associated with healthy aging and the progression of neurodegenerative disease (such as Alzheimer's disease). The reviewed findings highlight that compensations could be associated with increased synchrony of higher (>10 Hz) frequency bands. Maintenance of young-like synchrony patterns was also observed in healthy older individuals. Both maintenance and compensation appear to be highly related to preserved structural integrity (brain reserve). However, increased synchrony was also found to be deleterious in some cases and reflects neurodegenerative processes. These results provide major elements on the stability or variability of functional networks as well as maintenance of neural synchrony over time, and their association with individual cognitive changes with aging. These findings could provide new and interesting considerations about cognitive reserve, maintenance, and resilience of brain functions and cognition.
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20
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Ribeiro M, Castelo-Branco M. Slow fluctuations in ongoing brain activity decrease in amplitude with ageing yet their impact on task-related evoked responses is dissociable from behavior. eLife 2022; 11:e75722. [PMID: 35608164 PMCID: PMC9129875 DOI: 10.7554/elife.75722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 05/12/2022] [Indexed: 11/21/2022] Open
Abstract
In humans, ageing is characterized by decreased brain signal variability and increased behavioral variability. To understand how reduced brain variability segregates with increased behavioral variability, we investigated the association between reaction time variability, evoked brain responses and ongoing brain signal dynamics, in young (N=36) and older adults (N=39). We studied the electroencephalogram (EEG) and pupil size fluctuations to characterize the cortical and arousal responses elicited by a cued go/no-go task. Evoked responses were strongly modulated by slow (<2 Hz) fluctuations of the ongoing signals, which presented reduced power in the older participants. Although variability of the evoked responses was lower in the older participants, once we adjusted for the effect of the ongoing signal fluctuations, evoked responses were equally variable in both groups. Moreover, the modulation of the evoked responses caused by the ongoing signal fluctuations had no impact on reaction time, thereby explaining why although ongoing brain signal variability is decreased in older individuals, behavioral variability is not. Finally, we showed that adjusting for the effect of the ongoing signal was critical to unmask the link between neural responses and behavior as well as the link between task-related evoked EEG and pupil responses.
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Affiliation(s)
- Maria Ribeiro
- CIBIT-ICNAS, University of CoimbraCoimbraPortugal
- Faculty of Medicine, University of CoimbraCoimbraPortugal
| | - Miguel Castelo-Branco
- CIBIT-ICNAS, University of CoimbraCoimbraPortugal
- Faculty of Medicine, University of CoimbraCoimbraPortugal
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21
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Vaghari D, Kabir E, Henson RN. Late combination shows that MEG adds to MRI in classifying MCI versus controls. Neuroimage 2022; 252:119054. [PMID: 35247546 PMCID: PMC8987738 DOI: 10.1016/j.neuroimage.2022.119054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/20/2022] [Accepted: 03/01/2022] [Indexed: 12/12/2022] Open
Abstract
Early detection of Alzheimer's disease (AD) is essential for developing effective treatments. Neuroimaging techniques like Magnetic Resonance Imaging (MRI) have the potential to detect brain changes before symptoms emerge. Structural MRI can detect atrophy related to AD, but it is possible that functional changes are observed even earlier. We therefore examined the potential of Magnetoencephalography (MEG) to detect differences in functional brain activity in people with Mild Cognitive Impairment (MCI) - a state at risk of early AD. We introduce a framework for multimodal combination to ask whether MEG data from a resting-state provides complementary information beyond structural MRI data in the classification of MCI versus controls. More specifically, we used multi-kernel learning of support vector machines to classify 163 MCI cases versus 144 healthy elderly controls from the BioFIND dataset. When using the covariance of planar gradiometer data in the low Gamma range (30-48 Hz), we found that adding a MEG kernel improved classification accuracy above kernels that captured several potential confounds (e.g., age, education, time-of-day, head motion). However, accuracy using MEG alone (68%) was worse than MRI alone (71%). When simply concatenating (normalized) features from MEG and MRI into one kernel (Early combination), there was no advantage of combining MEG with MRI versus MRI alone. When combining kernels of modality-specific features (Intermediate combination), there was an improvement in multimodal classification to 74%. The biggest multimodal improvement however occurred when we combined kernels from the predictions of modality-specific classifiers (Late combination), which achieved 77% accuracy (a reliable improvement in terms of permutation testing). We also explored other MEG features, such as the variance versus covariance of magnetometer versus planar gradiometer data within each of 6 frequency bands (delta, theta, alpha, beta, low gamma, or high gamma), and found that they generally provided complementary information for classification above MRI. We conclude that MEG can improve on the MRI-based classification of MCI.
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Affiliation(s)
- Delshad Vaghari
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ehsanollah Kabir
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK.
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22
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Zhong XZ, Chen JJ. Resting-state functional magnetic resonance imaging signal variations in aging: The role of neural activity. Hum Brain Mapp 2022; 43:2880-2897. [PMID: 35293656 PMCID: PMC9120570 DOI: 10.1002/hbm.25823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 01/20/2022] [Accepted: 02/23/2022] [Indexed: 11/23/2022] Open
Abstract
Resting‐state functional magnetic resonance imaging (rs‐fMRI) has been extensively used to study brain aging, but the age effect on the frequency content of the rs‐fMRI signal has scarcely been examined. Moreover, the neuronal implications of such age effects and age–sex interaction remain unclear. In this study, we examined the effects of age and sex on the rs‐fMRI signal frequency using the Leipzig mind–brain–body data set. Over a frequency band of up to 0.3 Hz, we found that the rs‐fMRI fluctuation frequency is higher in the older adults, although the fluctuation amplitude is lower. The rs‐fMRI signal frequency is also higher in men than in women. Both age and sex effects on fMRI frequency vary with the frequency band examined but are not found in the frequency of physiological‐noise components. This higher rs‐fMRI frequency in older adults is not mediated by the electroencephalograph (EEG)‐frequency increase but a likely link between fMRI signal frequency and EEG entropy, which vary with age and sex. Additionally, in different rs‐fMRI frequency bands, the fMRI‐EEG amplitude ratio is higher in young adults. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs‐fMRI signal and may lead to the development of new, frequency‐based rs‐fMRI metrics. Our study demonstrates that Fourier analysis of the fMRI signal can reveal novel information about aging. Furthermore, fMRI and EEG signals reflect different aspects of age‐ and sex‐related brain differences, but the signal frequency and complexity, instead of amplitude, may hold their link.
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Affiliation(s)
- Xiaole Z Zhong
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - J Jean Chen
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
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23
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Samogin J, Rueda Delgado L, Taberna GA, Swinnen SP, Mantini D. Age-related differences of frequency-dependent functional connectivity in brain networks and their link to motor performance. Brain Connect 2022; 12:686-698. [DOI: 10.1089/brain.2021.0135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Jessica Samogin
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Laura Rueda Delgado
- Trinity College Institute of Neuroscience, 71434, Dublin, Ireland
- Cumulus Neuroscience, Ltd. , Dublin, Ireland
| | - Gaia Amaranta Taberna
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Stephan P. Swinnen
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute , Leuven, Belgium
| | - Dante Mantini
- Leuven, Belgium
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
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24
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Scheijbeler EP, van Nifterick AM, Stam CJ, Hillebrand A, Gouw AA, de Haan W. Network-level permutation entropy of resting-state MEG recordings: a novel biomarker for early-stage Alzheimer’s disease? Netw Neurosci 2021; 6:382-400. [PMID: 35733433 PMCID: PMC9208018 DOI: 10.1162/netn_a_00224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/15/2021] [Indexed: 11/24/2022] Open
Abstract
Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer’s disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5–93.3%]) slightly outperformed PE (76.9% [60.3–93.4%]) and relative theta power–based models (76.9% [60.4–93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies. Functional network disruption is a well-established finding in Alzheimer’s disease. Sensitive network-based biomarkers are however not available. We aimed to detect neuronal dysfunction at a predementia (mild cognitive impairment, MCI) stage of Alzheimer’s disease, by applying a network-level neural variability measure to magnetoencephalography data: the inverted joint permutation entropy (JPEinv). This measure integrates information on local signal variability/complexity and nonlinear coupling. We found significant differences in JPEinv between subjects with subjective cognitive decline and MCI, primarily in the theta band. The diagnostic ability of the JPEinv was reported to be similar to that of relative theta power, the most potent neurophysiological biomarker of predementia Alzheimer’s disease to date.
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Affiliation(s)
- Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
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25
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Jacob MS, Roach BJ, Sargent KS, Mathalon DH, Ford JM. Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: A combined EEG-fMRI study. Neuroimage 2021; 245:118705. [PMID: 34798229 DOI: 10.1016/j.neuroimage.2021.118705] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 10/11/2021] [Accepted: 11/01/2021] [Indexed: 11/24/2022] Open
Abstract
The hallmark of resting EEG spectra are distinct rhythms emerging from a broadband, aperiodic background. This aperiodic neural signature accounts for most of total EEG power, although its significance and relation to functional neuroanatomy remains obscure. We hypothesized that aperiodic EEG reflects a significant metabolic expenditure and therefore might be associated with the default mode network while at rest. During eyes-open, resting-state recordings of simultaneous EEG-fMRI, we find that aperiodic and periodic components of EEG power are only minimally associated with activity in the default mode network. However, a whole-brain analysis identifies increases in aperiodic power correlated with hemodynamic activity in an auditory-salience-cerebellar network, and decreases in aperiodic power are correlated with hemodynamic activity in prefrontal regions. Desynchronization in residual alpha and beta power is associated with visual and sensorimotor hemodynamic activity, respectively. These findings suggest that resting-state EEG signals acquired in an fMRI scanner reflect a balance of top-down and bottom-up stimulus processing, even in the absence of an explicit task.
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Affiliation(s)
- Michael S Jacob
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Brian J Roach
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Kaia S Sargent
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
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26
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Sadaghiani S, Brookes MJ, Baillet S. Connectomics of human electrophysiology. Neuroimage 2021; 247:118788. [PMID: 34906715 DOI: 10.1016/j.neuroimage.2021.118788] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 11/03/2021] [Accepted: 12/06/2021] [Indexed: 12/15/2022] Open
Abstract
We present both a scientific overview and conceptual positions concerning the challenges and assets of electrophysiological measurements in the search for the nature and functions of the human connectome. We discuss how the field has been inspired by findings and approaches from functional magnetic resonance imaging (fMRI) and informed by a small number of significant multimodal empirical studies, which show that the canonical networks that are commonplace in fMRI are in fact rooted in electrophysiological processes. This review is also an opportunity to produce a brief, up-to-date critical survey of current data modalities and analytical methods available for deriving both static and dynamic connectomes from electrophysiology. We review hurdles that challenge the significance and impact of current electrophysiology connectome research. We then encourage the field to take a leap of faith and embrace the wealth of electrophysiological signals, despite their apparent, disconcerting complexity. Our position is that electrophysiology connectomics is poised to inform testable mechanistic models of information integration in hierarchical brain networks, constructed from observable oscillatory and aperiodic signal components and their polyrhythmic interactions.
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Affiliation(s)
- Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana-Champaign, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign, IL, United States
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG72RD, United Kingdom
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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27
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Serban CA, Barborica A, Roceanu AM, Mindruta I, Ciurea J, Pâslaru AC, Zăgrean AM, Zăgrean L, Moldovan M. A method to assess the default EEG macrostate and its reactivity to stimulation. Clin Neurophysiol 2021; 134:50-64. [PMID: 34973517 DOI: 10.1016/j.clinph.2021.12.002] [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: 02/07/2021] [Revised: 08/23/2021] [Accepted: 12/04/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVE The default mode network (DMN) is deactivated by stimulation. We aimed to assess the DMN reactivity impairment by routine EEG recordings in stroke patients with impaired consciousness. METHODS Binocular light flashes were delivered at 1 Hz in 1-minute epochs, following a 1-minute baseline (PRE). The EEG was decomposed in a series of binary oscillatory macrostates by topographic spectral clustering. The most deactivated macrostate was labeled the default EEG macrostate (DEM). Its reactivity (DER) was quantified as the decrease in DEM occurrence probability during stimulation. A normalized DER index (DERI) was calculated as DER/PRE. The measures were compared between 14 healthy controls and 32 comatose patients under EEG monitoring following an acute stroke. RESULTS The DEM was mapped to the posterior DMN hubs. In the patients, these DEM source dipoles were 3-4 times less frequent and were associated with an increased theta activity. Even in a reduced 6-channel montage, a DER below 6.26% corresponding to a DERI below 0.25 could discriminate the patients with sensitivity and specificity well above 80%. CONCLUSION The method detected the DMN impairment in post-stroke coma patients. SIGNIFICANCE The DEM and its reactivity to stimulation could be useful to monitor the DMN function at bedside.
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Affiliation(s)
- Cosmin-Andrei Serban
- Physics Department, University of Bucharest, Romania; Termobit Prod SRL, Bucharest, Romania; FHC Inc, Bowdoin, ME, USA.
| | - Andrei Barborica
- Physics Department, University of Bucharest, Romania; Termobit Prod SRL, Bucharest, Romania; FHC Inc, Bowdoin, ME, USA.
| | | | - Ioana Mindruta
- Neurology Department, University Emergency Hospital, Bucharest, Romania.
| | - Jan Ciurea
- Department of Neurosurgery, Bagdasar-Arseni Emergency Hospital, Bucharest, Romania.
| | - Alexandru C Pâslaru
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Ana-Maria Zăgrean
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Leon Zăgrean
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Mihai Moldovan
- Termobit Prod SRL, Bucharest, Romania; Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; Neuroscience, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark.
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28
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Tsvetanov KA, Henson RNA, Jones PS, Mutsaerts H, Fuhrmann D, Tyler LK, Rowe JB. The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors. Psychophysiology 2021; 58:e13714. [PMID: 33210312 PMCID: PMC8244027 DOI: 10.1111/psyp.13714] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 07/27/2020] [Accepted: 09/28/2020] [Indexed: 12/18/2022]
Abstract
Accurate identification of brain function is necessary to understand neurocognitive aging, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is, therefore, essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable, and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health (CVH) and cerebrovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and cerebrovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and cerebrovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and cerebrovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age differences in RSFA, after controlling for CVH. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and cerebrovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterize neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.
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Affiliation(s)
- Kamen A. Tsvetanov
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - Richard N. A. Henson
- Medical Research Council Cognition and Brain Sciences UnitCambridgeUK
- Department of PsychiatryUniversity of CambridgeCambridgeUK
| | - P. Simon Jones
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - Henk Mutsaerts
- Department of Radiology and Nuclear MedicineAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Delia Fuhrmann
- Medical Research Council Cognition and Brain Sciences UnitCambridgeUK
| | - Lorraine K. Tyler
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - Cam‐CAN
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- Medical Research Council Cognition and Brain Sciences UnitCambridgeUK
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29
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Riha C, Güntensperger D, Oschwald J, Kleinjung T, Meyer M. Application of Latent Growth Curve modeling to predict individual trajectories during neurofeedback treatment for tinnitus. PROGRESS IN BRAIN RESEARCH 2021; 263:109-136. [PMID: 34243885 DOI: 10.1016/bs.pbr.2021.04.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Tinnitus is a heterogeneous phenomenon indexed by various EEG oscillatory profiles. Applying neurofeedback (NFB) with the aim of changing these oscillatory patterns not only provides help for those who suffer from the phantom percept, but a promising foundation from which to probe influential factors. The reliable attribution of influential factors that potentially predict oscillatory changes during the course of NFB training may lead to the identification of subgroups of individuals that are more or less responsive to NFB training. The present study investigated oscillatory trajectories of delta (3-4Hz) and individual alpha (8.5-12Hz) during 15 NFB training sessions, based on a Latent Growth Curve framework. First, we found the desired enhancement of alpha, while delta was stable throughout the NFB training. Individual differences in tinnitus-specific variables and general-, as well as health-related quality of life predictors were largely unrelated to oscillatory change prior to and across the training. Only the predictors age and sex at baseline were clearly related to slow-wave delta, particularly so for older female individuals who showed higher delta power values from the start. Second, we confirmed a hierarchical cross-frequency association between the two frequency bands; however, in opposing directions to those anticipated in tinnitus. The establishment of individually tailored NFB protocols would boost this therapy's effectiveness in the treatment of tinnitus. In our analysis, we propose a conceptual groundwork toward this goal of developing more targeted treatment.
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Affiliation(s)
- Constanze Riha
- Chair of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland; Research Priority Program "ESIT-European School of Interdisciplinary Tinnitus Research", Zurich, Switzerland
| | - Dominik Güntensperger
- Chair of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Jessica Oschwald
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland
| | - Tobias Kleinjung
- Department of Otorhinolaryngology, University Hospital Zurich, Zurich, Switzerland
| | - Martin Meyer
- Chair of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
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30
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Hinault T, Mijalkov M, Pereira JB, Volpe G, Bakke A, Courtney SM. Age-related differences in network structure and dynamic synchrony of cognitive control. Neuroimage 2021; 236:118070. [PMID: 33887473 DOI: 10.1016/j.neuroimage.2021.118070] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 12/18/2022] Open
Abstract
Cognitive trajectories vary greatly across older individuals, and the neural mechanisms underlying these differences remain poorly understood. Here, we investigate the cognitive variability in older adults by linking the influence of white matter microstructure on the task-related organization of fast and effective communications between brain regions. Using diffusion tensor imaging and electroencephalography, we show that individual differences in white matter network organization are associated with network clustering and efficiency in the alpha and high-gamma bands, and that functional network dynamics partly explain individual differences in cognitive control performance in older adults. We show that older individuals with high versus low structural network clustering differ in task-related network dynamics and cognitive performance. These findings were corroborated by investigating magnetoencephalography networks in an independent dataset. This multimodal (fMRI and biological markers) brain connectivity framework of individual differences provides a holistic account of how differences in white matter microstructure underlie age-related variability in dynamic network organization and cognitive performance.
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Affiliation(s)
- T Hinault
- U1077 Inserm-Ephe-unicaen, Caen 14032, France; Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, United States.
| | - M Mijalkov
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm 17177, Sweden
| | - J B Pereira
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm 17177, Sweden; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmo 47700, Sweden
| | - Giovanni Volpe
- Department of Physics, Goteborg University, Goteborg 41296, Sweden
| | - A Bakke
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States; F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD 21287, United States
| | - S M Courtney
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, United States; F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD 21287, United States; Department of Neuroscience, Johns Hopkins University, MD 21287, United States
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31
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Escrichs A, Biarnes C, Garre-Olmo J, Fernández-Real JM, Ramos R, Pamplona R, Brugada R, Serena J, Ramió-Torrentà L, Coll-De-Tuero G, Gallart L, Barretina J, Vilanova JC, Mayneris-Perxachs J, Essig M, Figley CR, Pedraza S, Puig J, Deco G. Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club. Cereb Cortex 2021; 31:2466-2481. [PMID: 33350451 DOI: 10.1093/cercor/bhaa367] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/29/2020] [Accepted: 10/29/2020] [Indexed: 12/19/2022] Open
Abstract
Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50-64 years versus older group (n = 310); age range, 65-91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.
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Affiliation(s)
- Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Carles Biarnes
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Josep Garre-Olmo
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Institut d'Assistència Sanitària, Salt (Girona), Spain
| | - José Manuel Fernández-Real
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Diabetes, Endocrinology and Nutrition, IDIBGI, Hospital Universitari de Girona Dr Josep Trueta, and CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Girona, Spain
| | - Rafel Ramos
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Vascular Health Research Group of Girona (ISV-Girona), Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Girona, Spain.,Primary Care Services, Catalan Institute of Health (ICS), Girona, Spain
| | - Reinald Pamplona
- Department of Experimental Medicine, Faculty of Medicine, University of Lleida-IRBLleida, Lleida, Spain
| | - Ramon Brugada
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Cardiovascular Genetics Center, IDIBGI, CIBER-CV, Girona, Spain
| | - Joaquin Serena
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Neurology, Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Lluís Ramió-Torrentà
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Neurology, Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Gabriel Coll-De-Tuero
- Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Vascular Health Research Group of Girona (ISV-Girona), Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Girona, Spain.,CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Luís Gallart
- Biobanc, Girona Biomedical Research Institute (IDIBGI), Girona, Spain
| | - Jordi Barretina
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Joan C Vilanova
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Jordi Mayneris-Perxachs
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Diabetes, Endocrinology and Nutrition, IDIBGI, Hospital Universitari de Girona Dr Josep Trueta, and CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Girona, Spain
| | - Marco Essig
- Department of Radiology, University of Manitoba, Winnipeg, Canada
| | - Chase R Figley
- Department of Radiology, University of Manitoba, Winnipeg, Canada
| | - Salvador Pedraza
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Josep Puig
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.,Institucio Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Catalonia, Spain.,Department of Neuropsychology, Max Planck Institute for human Cognitive and Brain Sciences, Leipzig, Germany.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia
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32
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Angulo-Ruiz BY, Muñoz V, Rodríguez-Martínez EI, Gómez CM. Absolute and relative variability changes of the resting state brain rhythms from childhood and adolescence to young adulthood. Neurosci Lett 2021; 749:135747. [PMID: 33610662 DOI: 10.1016/j.neulet.2021.135747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/26/2021] [Accepted: 02/14/2021] [Indexed: 10/22/2022]
Abstract
The present report aimed to analyze the possible relationship of spontaneous EEG power variability across epochs in individual subjects (absolute and relative) with age. For this purpose, the resting state EEG of a sample of 258 healthy subjects (6-29 years old) in open and closed eyes experimental conditions were recorded. The power spectral density (PSD) was calculated from 0.5-45 Hz. Three electrodes with the highest PSD in each band were selected, and linear and inverse regression of the mean, standard deviation (SD), and coefficient of variation CV of the PSD vs age were computed. The results showed that the EEG absolute variability (SD) decreases with age, and in contrast, the relative variability (CV) increased, except for high frequencies in which it remains stable during maturation. We conclude that the variability in the EEG PSD when is not influenced by the mean PSD tends to increase from childhood and adolescence to young adulthood. Present results complement the extensive literature on changes of EEG power in different brain rhythms with the changes in EEG power variability during maturation.
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Affiliation(s)
- Brenda Y Angulo-Ruiz
- University of Sevilla, Experimental Psychology Department, Human Psychobiology Lab., Sevilla, Spain.
| | - Vanesa Muñoz
- University of Sevilla, Experimental Psychology Department, Human Psychobiology Lab., Sevilla, Spain.
| | | | - Carlos M Gómez
- University of Sevilla, Experimental Psychology Department, Human Psychobiology Lab., Sevilla, Spain.
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Tsvetanov KA, Henson RNA, Rowe JB. Separating vascular and neuronal effects of age on fMRI BOLD signals. Philos Trans R Soc Lond B Biol Sci 2021; 376:20190631. [PMID: 33190597 PMCID: PMC7741031 DOI: 10.1098/rstb.2019.0631] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2020] [Indexed: 12/14/2022] Open
Abstract
Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood oxygenation level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. Despite over 10 000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular mechanisms in the brain do not simply control blood flow to support the metabolic needs of neurons, but form complex neurovascular interactions that influence neuronal function in health and disease. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.
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Affiliation(s)
- Kamen A. Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
| | - Richard N. A. Henson
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SP, UK
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - James B. Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
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Zhao R, Su Q, Chen Z, Sun H, Liang M, Xue Y. Neural Correlates of Cognitive Dysfunctions in Cervical Spondylotic Myelopathy Patients: A Resting-State fMRI Study. Front Neurol 2020; 11:596795. [PMID: 33424749 PMCID: PMC7785814 DOI: 10.3389/fneur.2020.596795] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/19/2020] [Indexed: 12/13/2022] Open
Abstract
Cervical spondylotic myelopathy (CSM) is a common disease of the elderly that is characterized by gait instability, sensorimotor deficits, etc. Recurrent symptoms including memory loss, poor attention, etc. have also been reported in recent studies. However, these have been rarely investigated in CSM patients. To investigate the cognitive deficits and their correlation with brain functional alterations, we conducted resting-state fMRI (rs-fMRI) signal variability. This is a novel indicator in the neuroimaging field for assessing the regional neural activity in CSM patients. Further, to explore the network changes in patients, functional connectivity (FC) and graph theory analyses were performed. Compared with the controls, the signal variabilities were significantly lower in the widespread brain regions especially at the default mode network (DMN), visual network, and somatosensory network. The altered inferior parietal lobule signal variability positively correlated with the cognitive function level. Moreover, the FC and the global efficiency of DMN increased in patients with CSM and positively correlated with the cognitive function level. According to the study results, (1) the cervical spondylotic myelopathy patients exhibited regional neural impairments, which correlated with the severity of cognitive deficits in the DMN brain regions, and (2) the increased FC and global efficiency of DMN can compensate for the regional impairment.
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Affiliation(s)
- Rui Zhao
- Department of Orthopedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Qian Su
- Department of Molecular Imaging and Nuclear Medicine, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zhao Chen
- Department of Orthopedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Haoran Sun
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Yuan Xue
- Department of Orthopedics Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Spine and Spinal Cord, Tianjin Medical University General Hospital, Tianjin, China
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Stoica T, Depue B. Shared Characteristics of Intrinsic Connectivity Networks Underlying Interoceptive Awareness and Empathy. Front Hum Neurosci 2020; 14:571070. [PMID: 33364926 PMCID: PMC7751325 DOI: 10.3389/fnhum.2020.571070] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 11/09/2020] [Indexed: 01/09/2023] Open
Abstract
Awareness of internal bodily sensations (interoceptive awareness; IA) and its connection to complex socioemotional abilities like empathy has been postulated, yet the functional neural circuitry they share remains poorly understood. The present fMRI study employs independent component analysis (ICA) to investigate which empathy facet (Cognitive or Affective) shares resting-state functional connectivity (rsFC) and/or BOLD variability (rsBOLD) with IA. Healthy participants viewed an abstract nonsocial movie demonstrated to evoke strong rsFC in brain networks resembling rest (InScapes), and resultant rsFC and rsBOLD data were correlated with self-reported empathy and IA questionnaires. We demonstrate a bidirectional behavioral and neurobiological relationship between empathy and IA, depending on the type of empathy interrogated: Affective empathy and IA share both rsFC and rsBOLD, while Cognitive empathy and IA only share rsBOLD. Specifically, increased rsFC in the right inferior frontal operculum (rIFO) of a larger attention network was associated with increased vicarious experience but decreased awareness of inner body sensations. Furthermore, increased rsBOLD between brain regions of an interoceptive network was related to increased sensitivity to internal sensations along with decreased Affective empathy. Finally, increased rsBOLD between brain regions subserving a mentalizing network related to not only an improved ability to take someone's perspective, but also a better sense of mind-body interconnectedness. Overall, these findings suggest that the awareness of one's own internal body changes (IA) is related to the socioemotional ability of feeling and understanding another's emotional state (empathy) and critically, that this relationship is reflected in the brain's resting state neuroarchitecture. Methodologically, this work highlights the importance of utilizing rsBOLD as a complementary window alongside rsFC to better understand neurological phenomena. Our results may be beneficial in aiding diagnosis in clinical populations such as autism spectrum disorder (ASD), where participants may be unable to complete tasks or questionnaires due to the severity of their symptoms.
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Affiliation(s)
- Teodora Stoica
- Department of Psychological and Brain Sciences, University of Louisville, Louisville, KY, United States
| | - Brendan Depue
- Department of Psychological and Brain Sciences, University of Louisville, Louisville, KY, United States
- Department of Anatomical Sciences and Neurobiology, University of Louisville, Louisville, KY, United States
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Engemann DA, Kozynets O, Sabbagh D, Lemaître G, Varoquaux G, Liem F, Gramfort A. Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers. eLife 2020; 9:e54055. [PMID: 32423528 PMCID: PMC7308092 DOI: 10.7554/elife.54055] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 05/09/2020] [Indexed: 12/14/2022] Open
Abstract
Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as a surrogate biomarker in 674 subjects from the Cam-CAN dataset. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by cortical power spectra between 8 and 30 Hz. Finally, we demonstrate that the model preserves benefits of stacking when some data is missing. The proposed framework, hence, enables multimodal learning for a wide range of biomarkers from diverse types of brain signals.
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Affiliation(s)
- Denis A Engemann
- Université Paris-Saclay, Inria, CEAPalaiseauFrance
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | | | - David Sabbagh
- Université Paris-Saclay, Inria, CEAPalaiseauFrance
- Inserm, UMRS-942, Paris Diderot UniversityParisFrance
- Department of Anaesthesiology and Critical Care, Lariboisière Hospital, Assistance Publique Hôpitaux de ParisParisFrance
| | | | | | - Franziskus Liem
- University Research Priority Program Dynamics of Healthy Aging, University of ZürichZürichSwitzerland
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