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Elhamiasl M, Sanches Braga Figueira J, Barry-Anwar R, Pestana Z, Keil A, Scott LS. The emergence of the EEG dominant rhythm across the first year of life. Cereb Cortex 2024; 34:bhad425. [PMID: 37955646 DOI: 10.1093/cercor/bhad425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 11/14/2023] Open
Abstract
The spectral composition of EEG provides important information on the function of the developing brain. For example, the frequency of the dominant rhythm, a salient features of EEG data, increases from infancy to adulthood. Changes of the dominant rhythm during infancy are yet to be fully characterized, in terms of their developmental trajectory and spectral characteristics. In this study, the development of dominant rhythm frequency was examined during a novel sustained attention task across 6-month-old (n = 39), 9-month-old (n = 30), and 12-month-old (n = 28) infants. During this task, computer-generated objects and faces floated down a computer screen for 10 s after a 5-second fixation cross. The peak frequency in the range between 5 and 9 Hz was calculated using center of gravity (CoG) and examined in response to faces and objects. Results indicated that peak frequency increased from 6 to 9 to 12 months of age in face and object conditions. We replicated the same result for the baseline. There was high reliability between the CoGs in the face, object, and baseline conditions across all channels. The developmental increase in CoG was more reliable than measures of mode frequency across different conditions. These findings suggest that CoG is a robust index of brain development across infancy.
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Affiliation(s)
- Mina Elhamiasl
- Department of Psychology, University of Florida, Gainesville, FL 32611, United States
| | | | - Ryan Barry-Anwar
- Department of Psychology, University of Florida, Gainesville, FL 32611, United States
| | - Zoe Pestana
- Department of Psychology, University of California, Davis, CA 95616, United States
| | - Andreas Keil
- Department of Psychology, University of Florida, Gainesville, FL 32611, United States
| | - Lisa S Scott
- Department of Psychology, University of Florida, Gainesville, FL 32611, United States
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Boenke LT, Zeghbib A, Spiliopoulou M, Alais D, Ohl FW. Prestimulus α/β power in temporal-order judgments: individuals differ in direction of modulation but show consistency over auditory and visual tasks. Front Comput Neurosci 2023; 17:1145267. [PMID: 37303589 PMCID: PMC10248147 DOI: 10.3389/fncom.2023.1145267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 04/04/2023] [Indexed: 06/13/2023] Open
Abstract
The processing of incoming sensory information can be differentially affected by varying levels of α-power in the electroencephalogram (EEG). A prominent hypothesis is that relatively low prestimulus α-power is associated with improved perceptual performance. However, there are studies in the literature that do not fit easily into this picture, and the reasons for this are poorly understood and rarely discussed. To evaluate the robustness of previous findings and to better understand the overall mixed results, we used a spatial TOJ task in which we presented auditory and visual stimulus pairs in random order while recording EEG. For veridical and non-veridical TOJs, we calculated the power spectral density (PSD) for 3 frequencies (5 Hz steps: 10, 15, and 20 Hz). We found on the group level: (1) Veridical auditory TOJs, relative to non-veridical, were associated with higher β-band (20 Hz) power over central electrodes. (2) Veridical visual TOJs showed higher β-band (10, 15 Hz) power over parieto-occipital electrodes (3) Electrode site interacted with TOJ condition in the β-band: For auditory TOJs, PSD over central electrodes was higher for veridical than non-veridical and over parieto-occipital electrodes was lower for veridical than non-veridical trials, while the latter pattern was reversed for visual TOJs. While our group-level result showed a clear direction of prestimulus modulation, the individual-level modulation pattern was variable and included activations opposite to the group mean. Interestingly, our results at the individual-level mirror the situation in the literature, where reports of group-level prestimulus modulation were found in either direction. Because the direction of individual activation of electrodes over auditory brain regions and parieto-occipital electrodes was always negatively correlated in the respective TOJ conditions, this activation opposite to the group mean cannot be easily dismissed as noise. The consistency of the individual-level data cautions against premature generalization of group-effects and suggests different strategies that participants initially adopted and then consistently followed. We discuss our results in light of probabilistic information processing and complex system properties, and suggest that a general description of brain activity must account for variability in modulation directions at both the group and individual levels.
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Affiliation(s)
- Lars T. Boenke
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany
- School of Psychology, University of Sydney, Sydney, NSW, Australia
| | - Abdelhafid Zeghbib
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany
- Department of Automatic Control and Systems Engineering (ACSE), University of Sheffield, Sheffield, United Kingdom
- National Institute for Physiological Sciences (NIPS), Okazaki, Japan
| | - Myra Spiliopoulou
- Research Lab Knowledge Management and Discovery, Faculty of Computer Science, Otto-von-Guericke University, Magdeburg, Germany
| | - David Alais
- School of Psychology, University of Sydney, Sydney, NSW, Australia
| | - Frank W. Ohl
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany
- Faculty of Science, Otto-von-Guericke University, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
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Chikhi S, Matton N, Sanna M, Blanchet S. Mental strategies and resting state EEG: Effect on high alpha amplitude modulation by neurofeedback in healthy young adults. Biol Psychol 2023; 178:108521. [PMID: 36801435 DOI: 10.1016/j.biopsycho.2023.108521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/30/2022] [Accepted: 02/15/2023] [Indexed: 02/19/2023]
Abstract
Neurofeedback (NFB) is a brain-computer interface which allows individuals to modulate their brain activity. Despite the self-regulatory nature of NFB, the effectiveness of strategies used during NFB training has been little investigated. In a single session of NFB training (6*3 min training blocks) with healthy young participants, we experimentally tested if providing a list of mental strategies (list group, N = 46), compared with a group receiving no strategies (no list group, N = 39), affected participants' neuromodulation ability of high alpha (10-12 Hz) amplitude. We additionally asked participants to verbally report the mental strategies used to enhance high alpha amplitude. The verbatim was then classified in pre-established categories in order to examine the effect of type of mental strategy on high alpha amplitude. First, we found that giving a list to the participants did not promote the ability to neuromodulate high alpha activity. However, our analysis of the specific strategies reported by learners during training blocks revealed that cognitive effort and recalling memories were associated with higher high alpha amplitude. Furthermore, the resting amplitude of trained high alpha frequency predicted an amplitude increase during training, a factor that may optimize inclusion in NFB protocols. The present results also corroborate the interrelation with other frequency bands during NFB training. Although these findings are based on a single NFB session, our study represents a further step towards developing effective protocols for high alpha neuromodulation by NFB.
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Affiliation(s)
- Samy Chikhi
- Université Paris Cité, Laboratoire Mémoire, Cerveau et Cognition, F-92100 Boulogne-Billancourt, France
| | - Nadine Matton
- CLLE, Université de Toulouse, CNRS (UMR 5263), Toulouse, France; ENAC, École Nationale d'Aviation Civile, Université de Toulouse, France
| | - Marie Sanna
- Université Paris Cité, Laboratoire Mémoire, Cerveau et Cognition, F-92100 Boulogne-Billancourt, France
| | - Sophie Blanchet
- Université Paris Cité, Laboratoire Mémoire, Cerveau et Cognition, F-92100 Boulogne-Billancourt, France.
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Chowdhury NS, Skippen P, Si E, Chiang AKI, Millard SK, Furman AJ, Chen S, Schabrun SM, Seminowicz DA. The reliability of two prospective cortical biomarkers for pain: EEG peak alpha frequency and TMS corticomotor excitability. J Neurosci Methods 2023; 385:109766. [PMID: 36495945 PMCID: PMC9848447 DOI: 10.1016/j.jneumeth.2022.109766] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/10/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Many pain biomarkers fail to move from discovery to clinical application, attributed to poor reliability and an inability to accurately classify at-risk individuals. Preliminary evidence has shown that high pain sensitivity is associated with slow peak alpha frequency (PAF), and depression of corticomotor excitability (CME), potentially due to impairments in ascending sensory and descending motor pathway signalling respectively NEW METHOD: The present study evaluated the reliability of PAF and CME responses during sustained pain. Specifically, we determined whether, over several days of pain, a) PAF remains stable and b) individuals show two stable and distinct CME responses: facilitation and depression. Participants were given an injection of nerve growth factor (NGF) into the right masseter muscle on Day 0 and Day 2, inducing sustained pain. Electroencephalography (EEG) to assess PAF and transcranial magnetic stimulation (TMS) to assess CME were recorded on Day 0, Day 2 and Day 5. RESULTS Using a weighted peak estimate, PAF reliability (n = 75) was in the excellent range even without standard pre-processing and ∼2 min recording length. Using a single peak estimate, PAF reliability was in the moderate-good range. For CME (n = 74), 80% of participants showed facilitation or depression of CME beyond an optimal cut-off point, with the stability of these changes in the good range. COMPARISON WITH EXISTING METHODS No study has assessed the reliability of PAF or feasibility of classifying individuals as facilitators/depressors, in response to sustained pain. PAF was reliable even in the presence of pain. The use of a weighted peak estimate for PAF is recommended, as excellent test-retest reliability can be obtained even when using minimal pre-processing and ∼2 min recording. We also showed that 80% of individuals exhibit either facilitation or depression of CME, with these changes being stable across sessions. CONCLUSIONS Our study provides support for the reliability of PAF and CME as prospective cortical biomarkers. As such, our paper adds important methodological advances to the rapidly growing field of pain biomarkers.
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Affiliation(s)
- Nahian S Chowdhury
- Center for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia; University of New South Wales, Sydney, New South Wales, Australia.
| | - Patrick Skippen
- Center for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia
| | - Emily Si
- Center for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia
| | - Alan K I Chiang
- Center for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia; University of New South Wales, Sydney, New South Wales, Australia
| | - Samantha K Millard
- Center for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia; University of New South Wales, Sydney, New South Wales, Australia
| | - Andrew J Furman
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, USA; Center to Advance Chronic Pain Research, University of Maryland Baltimore, USA
| | - Shuo Chen
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, USA; Center to Advance Chronic Pain Research, University of Maryland Baltimore, USA
| | - Siobhan M Schabrun
- Center for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia; School of Physical Therapy, University of Western Ontario, London, Canada
| | - David A Seminowicz
- Center for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia; Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, USA; Center to Advance Chronic Pain Research, University of Maryland Baltimore, USA; Department of Medical Biophysics, University of Western Ontario, London, Canada
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Cannard C, Wahbeh H, Delorme A. Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System. Front Hum Neurosci 2021; 15:745135. [PMID: 35002651 PMCID: PMC8740323 DOI: 10.3389/fnhum.2021.745135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/15/2021] [Indexed: 12/02/2022] Open
Abstract
Electroencephalography (EEG) alpha asymmetry is thought to reflect crucial brain processes underlying executive control, motivation, and affect. It has been widely used in psychopathology and, more recently, in novel neuromodulation studies. However, inconsistencies remain in the field due to the lack of consensus in methodological approaches employed and the recurrent use of small samples. Wearable technologies ease the collection of large and diversified EEG datasets that better reflect the general population, allow longitudinal monitoring of individuals, and facilitate real-world experience sampling. We tested the feasibility of using a low-cost wearable headset to collect a relatively large EEG database (N = 230, 22-80 years old, 64.3% female), and an open-source automatic method to preprocess it. We then examined associations between well-being levels and the alpha center of gravity (CoG) as well as trait EEG asymmetries, in the frontal and temporoparietal (TP) areas. Robust linear regression models did not reveal an association between well-being and alpha (8-13 Hz) asymmetry in the frontal regions, nor with the CoG. However, well-being was associated with alpha asymmetry in the TP areas (i.e., corresponding to relatively less left than right TP cortical activity as well-being levels increased). This effect was driven by oscillatory activity in lower alpha frequencies (8-10.5 Hz), reinforcing the importance of dissociating sub-components of the alpha band when investigating alpha asymmetries. Age was correlated with both well-being and alpha asymmetry scores, but gender was not. Finally, EEG asymmetries in the other frequency bands were not associated with well-being, supporting the specific role of alpha asymmetries with the brain mechanisms underlying well-being levels. Interpretations, limitations, and recommendations for future studies are discussed. This paper presents novel methodological, experimental, and theoretical findings that help advance human neurophysiological monitoring techniques using wearable neurotechnologies and increase the feasibility of their implementation into real-world applications.
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Affiliation(s)
- Cédric Cannard
- Centre de Recherche Cerveau et Cognition (CerCo), Centre National de la Recherche Scientifique (CNRS), Paul Sabatier University, Toulouse, France
- Institute of Noetic Sciences (IONS), Petaluma, CA, United States
| | - Helané Wahbeh
- Institute of Noetic Sciences (IONS), Petaluma, CA, United States
| | - Arnaud Delorme
- Centre de Recherche Cerveau et Cognition (CerCo), Centre National de la Recherche Scientifique (CNRS), Paul Sabatier University, Toulouse, France
- Institute of Noetic Sciences (IONS), Petaluma, CA, United States
- Swartz Center for Computational Neuroscience (SCCN), Institute of Neural Computation (INC), University of California, San Diego, San Diego, CA, United States
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6
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Olejarczyk E, Sobieszek A. Commentary: Is So-Called "Split Alpha" in EEG Spectral Analysis a Result of Methodological and Interpretation Errors? Front Neurosci 2021; 15:726912. [PMID: 34630016 PMCID: PMC8497753 DOI: 10.3389/fnins.2021.726912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 08/20/2021] [Indexed: 11/16/2022] Open
Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
| | - Aleksander Sobieszek
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
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7
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Nguyen G, Postnova S. Progress in modelling of brain dynamics during anaesthesia and the role of sleep-wake circuitry. Biochem Pharmacol 2021; 191:114388. [DOI: 10.1016/j.bcp.2020.114388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/16/2020] [Accepted: 12/17/2020] [Indexed: 12/28/2022]
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Donoghue T, Schaworonkow N, Voytek B. Methodological considerations for studying neural oscillations. Eur J Neurosci 2021; 55:3502-3527. [PMID: 34268825 DOI: 10.1111/ejn.15361] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/25/2021] [Accepted: 06/16/2021] [Indexed: 12/29/2022]
Abstract
Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modelling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology and mechanisms of cognition. However, despite their prevalence, there are many concerns-new and old-about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analysing neural oscillations. The considerations are to (1) verify the presence of oscillations, as they may be absent; (2) validate oscillation band definitions, to address variable peak frequencies; (3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for (4) temporal variability and (5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; (6) separate spatially overlapping rhythms, which may interfere with each other; and (7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.
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Affiliation(s)
- Thomas Donoghue
- Department of Cognitive Science, University of California, San Diego, San Diego, California, USA
| | - Natalie Schaworonkow
- Department of Cognitive Science, University of California, San Diego, San Diego, California, USA
| | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego, San Diego, California, USA.,Neurosciences Graduate Program, University of California, San Diego, San Diego, California, USA.,Halıcıoğlu Data Science Institute, University of California, San Diego, San Diego, California, USA.,Kavli Institute for Brain and Mind, University of California, San Diego, San Diego, California, USA
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9
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El-Zghir RK, Gabay NC, Robinson PA. Modal-Polar Representation of Evoked Response Potentials in Multiple Arousal States. Front Hum Neurosci 2021; 15:642479. [PMID: 34163339 PMCID: PMC8215109 DOI: 10.3389/fnhum.2021.642479] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 05/10/2021] [Indexed: 11/13/2022] Open
Abstract
An expansion of the corticothalamic transfer function into eigenmodes and resonant poles is used to derive a simple formula for evoked response potentials (ERPs) in various states of arousal. The transfer function corresponds to the cortical response to an external stimulus, which encodes all the information and properties of the linear system. This approach links experimental observations of resonances and characteristic timescales in brain activity with physically based neural field theory (NFT). The present work greatly simplifies the formula of the analytical ERP, and separates its spatial part (eigenmodes) from the temporal part (poles). Within this framework, calculations involve contour integrations that yield an explicit expression for ERPs. The dominant global mode is considered explicitly in more detail to study how the ERP varies with time in this mode and to illustrate the method. For each arousal state in sleep and wake, the resonances of the system are determined and it is found that five poles are sufficient to study the main dynamics of the system in waking eyes-open and eyes-closed states. Similarly, it is shown that six poles suffice to reproduce ERPs in rapid-eye movement sleep, sleep state 1, and sleep state 2 states, whereas just four poles suffice to reproduce the dynamics in slow wave sleep. Thus, six poles are sufficient to preserve the main global ERP dynamics of the system for all states of arousal. These six poles correspond to the dominant resonances of the system at slow-wave, alpha, and beta frequencies. These results provide the basis for simplified analytic treatment of brain dynamics and link observations more closely to theory.
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Affiliation(s)
- Rawan K El-Zghir
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Natasha C Gabay
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Peter A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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Peterson SM, Steine-Hanson Z, Davis N, Rao RPN, Brunton BW. Generalized neural decoders for transfer learning across participants and recording modalities. J Neural Eng 2021; 18. [PMID: 33418552 DOI: 10.1088/1741-2552/abda0b] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/08/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants. APPROACH We introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant. MAIN RESULTS HTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet's trained weights and demonstrate its ability to extract physiologically-relevant features. SIGNIFICANCE By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.
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Affiliation(s)
- Steven M Peterson
- Biology, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Zoe Steine-Hanson
- Computer Science and Engineering, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Nathan Davis
- Computer Science and Engineering, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Rajesh P N Rao
- Computer Science and Engineering, University of Washington, 185 E Stevens Way NE, Seattle, Washington, 98195, UNITED STATES
| | - Bingni W Brunton
- Biology, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
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López ME, Turrero A, Cuesta P, Rodríguez-Rojo IC, Barabash A, Marcos A, Maestú F, Fernández A. A multivariate model of time to conversion from mild cognitive impairment to Alzheimer's disease. GeroScience 2020; 42:1715-1732. [PMID: 32886293 PMCID: PMC7732920 DOI: 10.1007/s11357-020-00260-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 08/24/2020] [Indexed: 11/26/2022] Open
Abstract
The present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological, and neuroanatomical factors may predict differences in time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). To this end, a sample of 121 MCIs was followed up during a 5-year period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the "progressive" MCI group (n = 46; mean time to progression 17 ± 9.73 months) and (ii) the "stable" MCI group (n = 75; mean time of follow-up 31.37 ± 14.58 months). Kaplan-Meier survival analyses were applied to explore each variable's relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model to estimate differences in time to progression. The final model included three variables (in order of relevance): left parahippocampal volume (corrected by intracranial volume, LP_ ICV), delayed recall (DR), and left inferior occipital lobe individual alpha peak frequency (LIOL_IAPF). Those MCIs with LP_ICV volume, DR score, and LIOL_IAPF value lower than the defined cutoff had 6 times, 5.5 times, and 3 times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were "unfavorable" (i.e., values below the cutoff), 100% of cases progressed to AD at the end of follow-up. Our results highlighted the relevance of neurophysiological markers as predictors of conversion (LIOL_IAPF) and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.
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Affiliation(s)
- María Eugenia López
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain.
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain.
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain.
| | - Agustín Turrero
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Statistics and Operational Research, Complutense University of Madrid, Madrid, Spain
| | - Pablo Cuesta
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
| | - Inmaculada Concepción Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
- Psychology Faculty, Centro Universitario Villanueva, Madrid, Spain
- Physiotherapy and Nursing Faculty, University of Castilla-La Mancha, Toledo, Spain
| | - Ana Barabash
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Laboratory of Psychoneuroendocrinology and Genetics, San Carlos University Hospital, Madrid, Spain
| | - Alberto Marcos
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Neurology Department, San Carlos University Hospital, Madrid, Spain
| | - Fernando Maestú
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Legal Medicine, Psychiatry and Pathology, Complutense University of Madrid, Madrid, Spain
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12
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Zalewska E. Is So Called "Split Alpha" in EEG Spectral Analysis a Result of Methodological and Interpretation Errors? Front Neurosci 2020; 14:608453. [PMID: 33324157 PMCID: PMC7726354 DOI: 10.3389/fnins.2020.608453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 10/20/2020] [Indexed: 11/17/2022] Open
Abstract
This paper attempts to explain some methodological issues regarding EEG signal analysis which might lead to misinterpretation and therefore to unsubstantiated conclusions. The so called “split-alpha,” a “new phenomenon” in EEG spectral analysis described lately in few papers is such a case. We have shown that spectrum feature presented as a “split alpha” can be the result of applying improper means of analysis of the spectrum of the EEG signal that did not take into account the significant properties of the applied Fast Fourier Transform (FFT) method. Analysis of the shortcomings of the FFT method applied to EEG signal such as limited duration of analyzed signal, dependence of frequency resolution on time window duration, influence of window duration and shape, overlapping and spectral leakage was performed. Our analyses of EEG data as well as simulations indicate that double alpha spectra called as “split alpha” can appear, as spurious peaks, for short signal window when the EEG signal being studied shows multiple frequencies and frequency bands. These peaks have no relation to any frequencies of the signal and are an effect of spectrum leakage. Our paper is intended to explain the reasons underlying a spectrum pattern called as a “split alpha” and give some practical indications for using spectral analysis of EEG signal that might be useful for readers and allow to avoid EEG spectrum misinterpretation in further studies and publications as well as in clinical practice.
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Affiliation(s)
- Ewa Zalewska
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
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13
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Cohen MX. A data-driven method to identify frequency boundaries in multichannel electrophysiology data. J Neurosci Methods 2021; 347:108949. [PMID: 33031865 DOI: 10.1016/j.jneumeth.2020.108949] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/04/2020] [Accepted: 09/15/2020] [Indexed: 11/21/2022]
Abstract
BACKGROUND Electrophysiological recordings of the brain often exhibit neural oscillations, defined as narrowband bumps that deviate from the background power spectrum. These narrowband dynamics are grouped into frequency ranges, and the study of how activities in these ranges are related to cognition and disease is a major part of the neuroscience corpus. Frequency ranges are nearly always defined according to integer boundaries, such as 4-8 Hz for the theta band and 8-12 Hz for the alpha band. NEW METHOD A data-driven multivariate method is presented to identify empirical frequency boundaries based on clustering of spatiotemporal similarities across a range of frequencies. The method, termed gedBounds, identifies patterns in covariance matrices that maximally separate narrowband from broadband activity, and then identifies clusters in the correlation matrix of those spatial patterns over all frequencies, using the dbscan clustering algorithm. Those clusters are empirically derived frequency bands, from which boundaries can be extracted. RESULTS gedBounds recovers ground truth results in simulated data with high accuracy. The method was tested on EEG resting-state data from Parkinson's patients and control, and several features of the frequency components differed between patients and controls. COMPARISON WITH EXISTING METHODS The proposed method offers higher precision in defining subject-specific frequency boundaries compared to the current standard approach. CONCLUSIONS gedBounds can increase the precision and feature extraction of spectral dynamics in electrophysiology data.
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14
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Franciotti R, Pilotto A, Moretti DV, Falasca NW, Arnaldi D, Taylor JP, Nobili F, Kramberger M, Ptacek SG, Padovani A, Aarlsand D, Onofrj M, Bonanni L. Anterior EEG slowing in dementia with Lewy bodies: a multicenter European cohort study. Neurobiol Aging 2020; 93:55-60. [PMID: 32450445 DOI: 10.1016/j.neurobiolaging.2020.04.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 04/19/2020] [Accepted: 04/21/2020] [Indexed: 02/08/2023]
Abstract
Electroencephalography (EEG) slowing with prealpha dominant frequency (DF) in posterior derivations is a biomarker for dementia with Lewy bodies (DLB) diagnosis, in contrast with Alzheimer's disease (AD). However, an intrasubject re-evaluation of the original data, which contributed to the identification of EEG DLB biomarker, showed that DF was slower in anterior than posterior derivations. We suppose this anterior-posterior gradient of DF slowing could arise in DLB from a thalamocortical dysrhythmia, differently involving the anterior and posterior cortical areas, and correlating with cognitive impairment (Mini-Mental State Examination). EEG was recorded in 144 DLB, 116 AD, and 65 controls from 7 Centers of the European DLB Consortium. Spectra were divided into delta, theta, prealpha, alpha frequency bands. In DLB, mean DF was prealpha both anteriorly and posteriorly, but lower anteriorly (p < 0.001). In 14% of DLB, DF was prealpha anteriorly, whereas alpha posteriorly. In AD and controls, DF was constantly alpha. EEG slowing in DLB correlated with cognitive impairment. Thalamocortical dysrhythmia gives rise to prealpha rhythm with an anterior-posterior gradient and correlates with impaired cognition.
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Affiliation(s)
- Raffaella Franciotti
- Department of Neuroscience, Imaging and Clinical Science, and Aging Research Centre, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Andrea Pilotto
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Davide V Moretti
- Alzheimer's Epidemiology and Rehabilitation in Alzheimer's Disease Operative Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Nicola Walter Falasca
- Department of Neuroscience, Imaging and Clinical Science, and Aging Research Centre, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Dario Arnaldi
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - John-Paul Taylor
- Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK
| | - Flavio Nobili
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Milica Kramberger
- Department of Neurology, University Medical Centre, Ljubljana, Slovenia
| | - Sara Garcia Ptacek
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, and Memory Clinic Department of Geriatric Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Alessandro Padovani
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | | | - Marco Onofrj
- Department of Neuroscience, Imaging and Clinical Science, and Aging Research Centre, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Laura Bonanni
- Department of Neuroscience, Imaging and Clinical Science, and Aging Research Centre, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.
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15
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Liley DTJ, Muthukumaraswamy SD. Evidence that alpha blocking is due to increases in system-level oscillatory damping not neuronal population desynchronisation. Neuroimage 2019; 208:116408. [PMID: 31790751 DOI: 10.1016/j.neuroimage.2019.116408] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 11/14/2019] [Accepted: 11/26/2019] [Indexed: 11/19/2022] Open
Abstract
The attenuation of the alpha rhythm following eyes-opening (alpha blocking) is among the most robust features of the human electroencephalogram with the prevailing view being that it is caused by changes in neuronal population synchrony. To further study the basis for this phenomenon we use theoretically motivated fixed-order Auto-Regressive Moving-Average (ARMA) time series modelling to study the oscillatory dynamics of spontaneous alpha-band electroencephalographic activity in eyes-open and eyes-closed conditions and its modulation by the NMDA antagonist ketamine. We find that the reduction in alpha-band power between eyes-closed and eyes-open states is explicable in terms of an increase in the damping of stochastically perturbed alpha-band relaxation oscillatory activity. These changes in damping are putatively modified by the antagonism of NMDA-mediated glutamatergic neurotransmission but are not directly driven by changes in input to cortex nor by reductions in the phase synchronisation of populations of near identical oscillators. These results not only provide a direct challenge to the dominant view of the role that thalamus and neuronal population de-/synchronisation have in the genesis and modulation of alpha electro-/magnetoencephalographic activity but also suggest potentially important physiological determinants underlying its dynamical control and regulation.
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Affiliation(s)
- David T J Liley
- Department of Medicine, The University of Melbourne, Parkville, VIC, 3010, Australia; Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia.
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16
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Benda M, Volosyak I. Peak Detection with Online Electroencephalography (EEG) Artifact Removal for Brain-Computer Interface (BCI) Purposes. Brain Sci 2019; 9:E347. [PMID: 31795398 PMCID: PMC6955994 DOI: 10.3390/brainsci9120347] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 11/19/2019] [Accepted: 11/25/2019] [Indexed: 11/16/2022] Open
Abstract
Brain-computer interfaces (BCIs) measure brain activity and translate it to control computer programs or external devices. However, the activity generated by the BCI makes measurements for objective fatigue evaluation very difficult, and the situation is further complicated due to different movement artefacts. The BCI performance could be increased if an online method existed to measure the fatigue objectively and accurately. While BCI-users are moving, a novel automatic online artefact removal technique is used to filter out these movement artefacts. The effects of this filter on BCI performance and mainly on peak frequency detection during BCI use were investigated in this paper. A successful peak alpha frequency measurement can lead to more accurately determining objective user fatigue. Fifteen subjects performed various imaginary and actual movements in separate tasks, while fourteen electroencephalography (EEG) electrodes were used. Afterwards, a steady-state visual evoked potential (SSVEP)-based BCI speller was used, and the users were instructed to perform various movements. An offline curve fitting method was used for alpha peak detection to assess the effect of the artefact filtering. Peak detection was improved by the filter, by finding 10.91% and 9.68% more alpha peaks during simple EEG recordings and BCI use, respectively. As expected, BCI performance deteriorated from movements, and also from artefact removal. Average information transfer rates (ITRs) were 20.27 bit/min, 16.96 bit/min, and 14.14 bit/min for the (1) movement-free, (2) the moving and unfiltered, and (3) the moving and filtered scenarios, respectively.
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Affiliation(s)
| | - Ivan Volosyak
- Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany
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17
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Furman AJ, Thapa T, Summers SJ, Cavaleri R, Fogarty JS, Steiner GZ, Schabrun SM, Seminowicz DA. Cerebral peak alpha frequency reflects average pain severity in a human model of sustained, musculoskeletal pain. J Neurophysiol 2019; 122:1784-1793. [PMID: 31389754 PMCID: PMC6843105 DOI: 10.1152/jn.00279.2019] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 08/07/2019] [Accepted: 08/07/2019] [Indexed: 11/22/2022] Open
Abstract
Heightened pain sensitivity, the amount of pain experienced in response to a noxious event, is a known risk factor for development of chronic pain. We have previously reported that pain-free, sensorimotor peak alpha frequency (PAF) is a reliable biomarker of pain sensitivity for thermal, prolonged pains lasting tens of minutes. To test whether PAF can provide information about pain sensitivity occurring over clinically relevant timescales (i.e., weeks), EEG was recorded before and while participants experienced a long-lasting pain model, repeated intramuscular injection of nerve growth factor (NGF), that produces progressively developing muscle pain for up to 21 days. We demonstrate that pain-free, sensorimotor PAF is negatively correlated with NGF pain sensitivity; increasingly slower PAF is associated with increasingly greater pain sensitivity. Furthermore, PAF remained stable following NGF injection, indicating that the presence of NGF pain for multiple weeks is not sufficient to induce the PAF slowing reported in chronic pain. In total, our results demonstrate that slower pain-free, sensorimotor PAF is associated with heightened sensitivity to a long-lasting musculoskeletal pain and also suggest that the apparent slowing of PAF in chronic pain may reflect predisease pain sensitivity.NEW & NOTEWORTHY Pain sensitivity, the intensity of pain experienced after injury, has been identified as an important risk factor in the development of chronic pain. Biomarkers of pain sensitivity have the potential to ease chronic pain burdens by preventing disease emergence. In the current study, we demonstrate that the speed of pain-free, sensorimotor peak alpha frequency recorded during resting-state EEG predicts pain sensitivity to a clinically-relevant, human model of prolonged pain that persists for weeks.
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Affiliation(s)
- Andrew J Furman
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, Maryland
- Center to Advance Chronic Pain Research, University of Maryland Baltimore, Baltimore, Maryland
- Program in Neuroscience, University of Maryland School of Medicine, Baltimore, Maryland
| | - Tribikram Thapa
- School of Science and Health, Western Sydney University, Penrith, New South Wales, Australia
| | - Simon J Summers
- School of Science and Health, Western Sydney University, Penrith, New South Wales, Australia
| | - Rocco Cavaleri
- School of Science and Health, Western Sydney University, Penrith, New South Wales, Australia
| | - Jack S Fogarty
- NICM Health Research Institute, Western Sydney University, Penrith, New South Wales, Australia
| | - Genevieve Z Steiner
- NICM Health Research Institute, Western Sydney University, Penrith, New South Wales, Australia
- Translational Health Research Institute, Western Sydney University, Penrith, New South Wales, Australia
| | - Siobhan M Schabrun
- Neuroscience Research Australia (NeuRA), Sydney, New South Wales, Australia
| | - David A Seminowicz
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, Maryland
- Center to Advance Chronic Pain Research, University of Maryland Baltimore, Baltimore, Maryland
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18
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Corcoran AW, Alday PM, Schlesewsky M, Bornkessel-Schlesewsky I. Toward a reliable, automated method of individual alpha frequency (IAF) quantification. Psychophysiology 2018; 55:e13064. [PMID: 29357113 DOI: 10.1111/psyp.13064] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 11/22/2017] [Accepted: 12/19/2017] [Indexed: 11/29/2022]
Abstract
Individual alpha frequency (IAF) is a promising electrophysiological marker of interindividual differences in cognitive function. IAF has been linked with trait-like differences in information processing and general intelligence, and provides an empirical basis for the definition of individualized frequency bands. Despite its widespread application, however, there is little consensus on the optimal method for estimating IAF, and many common approaches are prone to bias and inconsistency. Here, we describe an automated strategy for deriving two of the most prevalent IAF estimators in the literature: peak alpha frequency (PAF) and center of gravity (CoG). These indices are calculated from resting-state power spectra that have been smoothed using a Savitzky-Golay filter (SGF). We evaluate the performance characteristics of this analysis procedure in both empirical and simulated EEG data sets. Applying the SGF technique to resting-state data from n = 63 healthy adults furnished 61 PAF and 62 CoG estimates. The statistical properties of these estimates were consistent with previous reports. Simulation analyses revealed that the SGF routine was able to reliably extract target alpha components, even under relatively noisy spectral conditions. The routine consistently outperformed a simpler method of automated peak detection that did not involve spectral smoothing. The SGF technique is fast, open source, and available in two popular programming languages (MATLAB, Python), and thus can easily be integrated within the most popular M/EEG toolsets (EEGLAB, FieldTrip, MNE-Python). As such, it affords a convenient tool for improving the reliability and replicability of future IAF-related research.
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Affiliation(s)
- Andrew W Corcoran
- Cognition and Philosophy Laboratory, Monash University, Melbourne, Victoria, Australia.,Centre for Cognitive and Systems Neuroscience, University of South Australia, Adelaide, South Australia, Australia
| | - Phillip M Alday
- Centre for Cognitive and Systems Neuroscience, University of South Australia, Adelaide, South Australia, Australia.,Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Matthias Schlesewsky
- Centre for Cognitive and Systems Neuroscience, University of South Australia, Adelaide, South Australia, Australia
| | - Ina Bornkessel-Schlesewsky
- Centre for Cognitive and Systems Neuroscience, University of South Australia, Adelaide, South Australia, Australia
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19
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Olejarczyk E, Bogucki P, Sobieszek A. The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation. Front Neurosci 2017; 11:506. [PMID: 28955192 PMCID: PMC5601034 DOI: 10.3389/fnins.2017.00506] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Accepted: 08/28/2017] [Indexed: 11/13/2022] Open
Abstract
Electroencephalographic (EEG) patterns were analyzed in a group of ambulatory patients who ranged in age and sex using spectral analysis as well as Directed Transfer Function, a method used to evaluate functional brain connectivity. We tested the impact of window size and choice of reference electrode on the identification of two or more peaks with close frequencies in the spectral power distribution, so called "split alpha." Together with the connectivity analysis, examination of spatiotemporal maps showing the distribution of amplitudes of EEG patterns allowed for better explanation of the mechanisms underlying the generation of split alpha peaks. It was demonstrated that the split alpha spectrum can be generated by two or more independent and interconnected alpha wave generators located in different regions of the cerebral cortex, but not necessarily in the occipital cortex. We also demonstrated the importance of appropriate reference electrode choice during signal recording. In addition, results obtained using the original data were compared with results obtained using re-referenced data, using average reference electrode and reference electrode standardization techniques.
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Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of SciencesWarsaw, Poland
| | - Piotr Bogucki
- Department of Neurology and Epileptology, Medical Center for Postgraduate EducationWarsaw, Poland
| | - Aleksander Sobieszek
- Department of Neurology and Epileptology, Medical Center for Postgraduate EducationWarsaw, Poland
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20
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Abstract
In recent years, more and more surgeries under general anesthesia have been performed with the assistance of electroencephalogram (EEG) monitors. An increase in anesthetic concentration leads to characteristic changes in the power spectra of the EEG. Although tracking the anesthetic-induced changes in EEG rhythms can be employed to estimate the depth of anesthesia, their precise underlying mechanisms are still unknown. A prominent feature in the EEG of some patients is the emergence of a strong power peak in the β-frequency band, which moves to the α-frequency band while increasing the anesthetic concentration. This feature is called the beta-buzz. In the present study, we use a thalamo-cortical neural population feedback model to reproduce observed characteristic features in frontal EEG power obtained experimentally during propofol general anesthesia, such as this beta-buzz. First, we find that the spectral power peak in the α- and δ-frequency ranges depend on the decay rate constant of excitatory and inhibitory synapses, but the anesthetic action on synapses does not explain the beta-buzz. Moreover, considering the action of propofol on the transmission delay between cortex and thalamus, the model reveals that the beta-buzz may result from a prolongation of the transmission delay by increasing propofol concentration. A corresponding relationship between transmission delay and anesthetic blood concentration is derived. Finally, an analytical stability study demonstrates that increasing propofol concentration moves the systems resting state towards its stability threshold.
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Affiliation(s)
- Meysam Hashemi
- INRIA Grand Est - Nancy, Team NEUROSYS, Villers-lès-Nancy, France
- CNRS, Loria, UMR nō 7503, Vandoeuvre-lès-Nancy, France
- Université de Lorraine, Loria, UMR nō 7503, Vandoeuvre-lès-Nancy, France
- Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
- * E-mail:
| | - Axel Hutt
- German Meteorology Service, Offenbach am Main, Germany
- Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom
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21
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López-Sanz D, Bruña R, Garcés P, Camara C, Serrano N, Rodríguez-Rojo IC, Delgado ML, Montenegro M, López-Higes R, Yus M, Maestú F. Alpha band disruption in the AD-continuum starts in the Subjective Cognitive Decline stage: a MEG study. Sci Rep 2016; 6:37685. [PMID: 27883082 PMCID: PMC5121589 DOI: 10.1038/srep37685] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 11/01/2016] [Indexed: 11/09/2022] Open
Abstract
The consideration of Subjective Cognitive Decline (SCD) as a preclinical stage of AD remains still a matter of debate. Alpha band alterations represent one of the most significant changes in the electrophysiological profile of AD. In particular, AD patients exhibit reduced alpha relative power and frequency. We used alpha band activity measured with MEG to study whether SCD and MCI elders present these electrophysiological changes characteristic of AD, and to determine the evolution of the observed alterations across AD spectrum. The total sample consisted of 131 participants: 39 elders without SCD, 41 elders with SCD and 51 MCI patients. All of them underwent MEG and MRI scans and neuropsychological assessment. SCD and MCI patients exhibited a similar reduction in alpha band activity compared with the no SCD group. However, only MCI patients showed a slowing in their alpha peak frequency compared with both SCD and no SCD. These changes in alpha band were related to worse cognition. Our results suggest that AD-related alterations may start in the SCD stage, with a reduction in alpha relative power. It is later, in the MCI stage, where the slowing of the spectral profile takes place, giving rise to objective deficits in cognitive functioning.
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Affiliation(s)
- D López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - R Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - P Garcés
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - C Camara
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - N Serrano
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - I C Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - M L Delgado
- Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - M Montenegro
- Memory Decline Prevention Center Madrid Salud, Ayuntamiento de Madrid, Spain
| | - R López-Higes
- Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - M Yus
- Radiology Department, San Carlos University Hospital, Madrid, Spain
| | - F Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
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22
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Hashemi M, Hutt A, Sleigh J. How the cortico-thalamic feedback affects the EEG power spectrum over frontal and occipital regions during propofol-induced sedation. J Comput Neurosci 2015; 39:155-79. [PMID: 26256583 DOI: 10.1007/s10827-015-0569-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Revised: 07/05/2015] [Accepted: 07/13/2015] [Indexed: 12/16/2022]
Abstract
Increasing concentrations of the anaesthetic agent propofol initially induces sedation before achieving full general anaesthesia. During this state of anaesthesia, the observed specific changes in electroencephalographic (EEG) rhythms comprise increased activity in the δ- (0.5-4 Hz) and α- (8-13 Hz) frequency bands over the frontal region, but increased δ- and decreased α-activity over the occipital region. It is known that the cortex, the thalamus, and the thalamo-cortical feedback loop contribute to some degree to the propofol-induced changes in the EEG power spectrum. However the precise role of each structure to the dynamics of the EEG is unknown. In this paper we apply a thalamo-cortical neuronal population model to reproduce the power spectrum changes in EEG during propofol-induced anaesthesia sedation. The model reproduces the power spectrum features observed experimentally both in frontal and occipital electrodes. Moreover, a detailed analysis of the model indicates the importance of multiple resting states in brain activity. The work suggests that the α-activity originates from the cortico-thalamic relay interaction, whereas the emergence of δ-activity results from the full cortico-reticular-relay-cortical feedback loop with a prominent enforced thalamic reticular-relay interaction. This model suggests an important role for synaptic GABAergic receptors at relay neurons and, more generally, for the thalamus in the generation of both the δ- and the α- EEG patterns that are seen during propofol anaesthesia sedation.
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23
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Saggar M, Zanesco AP, King BG, Bridwell DA, MacLean KA, Aichele SR, Jacobs TL, Wallace BA, Saron CD, Miikkulainen R. Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training. Neuroimage 2015; 114:88-104. [PMID: 25862265 DOI: 10.1016/j.neuroimage.2015.03.073] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 12/10/2014] [Accepted: 03/27/2015] [Indexed: 12/18/2022] Open
Abstract
Meditation training has been shown to enhance attention and improve emotion regulation. However, the brain processes associated with such training are poorly understood and a computational modeling framework is lacking. Modeling approaches that can realistically simulate neurophysiological data while conforming to basic anatomical and physiological constraints can provide a unique opportunity to generate concrete and testable hypotheses about the mechanisms supporting complex cognitive tasks such as meditation. Here we applied the mean-field computational modeling approach using the scalp-recorded electroencephalogram (EEG) collected at three assessment points from meditating participants during two separate 3-month-long shamatha meditation retreats. We modeled cortical, corticothalamic, and intrathalamic interactions to generate a simulation of EEG signals recorded across the scalp. We also present two novel extensions to the mean-field approach that allow for: (a) non-parametric analysis of changes in model parameter values across all channels and assessments; and (b) examination of variation in modeled thalamic reticular nucleus (TRN) connectivity over the retreat period. After successfully fitting whole-brain EEG data across three assessment points within each retreat, two model parameters were found to replicably change across both meditation retreats. First, after training, we observed an increased temporal delay between modeled cortical and thalamic cells. This increase provides a putative neural mechanism for a previously observed reduction in individual alpha frequency in these same participants. Second, we found decreased inhibitory connection strength between the TRN and secondary relay nuclei (SRN) of the modeled thalamus after training. This reduction in inhibitory strength was found to be associated with increased dynamical stability of the model. Altogether, this paper presents the first computational approach, taking core aspects of physiology and anatomy into account, to formally model brain processes associated with intensive meditation training. The observed changes in model parameters inform theoretical accounts of attention training through meditation, and may motivate future study on the use of meditation in a variety of clinical populations.
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Affiliation(s)
- Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Department of Computer Science, University of Texas at Austin, TX, USA.
| | - Anthony P Zanesco
- Department of Psychology, University of California, Davis, CA, USA; Center for Mind and Brain, University of California, Davis, CA, USA
| | - Brandon G King
- Department of Psychology, University of California, Davis, CA, USA; Center for Mind and Brain, University of California, Davis, CA, USA
| | | | - Katherine A MacLean
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Stephen R Aichele
- Department of Psychology, University of California, Davis, CA, USA; Center for Mind and Brain, University of California, Davis, CA, USA
| | - Tonya L Jacobs
- Center for Mind and Brain, University of California, Davis, CA, USA
| | - B Alan Wallace
- Santa Barbara Institute for Consciousness Studies, Santa Barbara, CA, USA
| | - Clifford D Saron
- Center for Mind and Brain, University of California, Davis, CA, USA; The M.I.N.D. Institute, University of California, Davis, Sacramento, CA, USA
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Veth CPM, Arns M, Drinkenburg W, Talloen W, Peeters PJ, Gordon E, Buitelaar JK. Association between COMT Val158Met genotype and EEG alpha peak frequency tested in two independent cohorts. Psychiatry Res 2014; 219:221-4. [PMID: 24889847 DOI: 10.1016/j.psychres.2014.05.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Revised: 04/23/2014] [Accepted: 05/06/2014] [Indexed: 11/18/2022]
Abstract
This study could not confirm the association between the Catechol-O-Methyltransferase Val158Met polymorphism (COMT) and electroencephalographic (EEG) alpha peak frequency (APF) in two independent cohorts of 187 (96 depressed and 91 healthy participants) and 413 healthy participants. If COMT and APF play a role in depression or antidepressant treatment response, they do not have a shared pathway. We emphasize the importance of publishing null-findings for obtaining more accurate overall estimates of genetic effects.
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Affiliation(s)
- Cornelis P M Veth
- Radboud University Medical Center, Department of Psychiatry, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Cognitive Neuroscience, PO Box 9101, 6500 HB Nijmegen, The Netherlands.
| | - Martijn Arns
- Research Institute Brainclinics, Bijleveldsingel 34, 6524 AD Nijmegen, The Netherlands; Utrecht University, Department of Experimental Psychology, Utrecht, The Netherlands
| | - Wilhelmus Drinkenburg
- Janssen Research and Development, Pharmaceutical Companies of Johnson & Johnson, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Willem Talloen
- Janssen Research and Development, Pharmaceutical Companies of Johnson & Johnson, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Pieter J Peeters
- Janssen Research and Development, Pharmaceutical Companies of Johnson & Johnson, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Evian Gordon
- Brain Resource Limited, Level 12, 235 Jones St. Ultimo, NSW 2007, Australia
| | - Jan K Buitelaar
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Cognitive Neuroscience, PO Box 9101, 6500 HB Nijmegen, The Netherlands
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25
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Abeysuriya RG, Rennie CJ, Robinson PA, Kim JW. Experimental observation of a theoretically predicted nonlinear sleep spindle harmonic in human EEG. Clin Neurophysiol 2014; 125:2016-23. [PMID: 24583091 DOI: 10.1016/j.clinph.2014.01.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2013] [Revised: 01/23/2014] [Accepted: 01/24/2014] [Indexed: 10/25/2022]
Abstract
OBJECTIVE To investigate the properties of a sleep spindle harmonic oscillation previously predicted by a theoretical neural field model of the brain. METHODS Spindle oscillations were extracted from EEG data from nine subjects using an automated algorithm. The power and frequency of the spindle oscillation and the harmonic oscillation were compared across subjects. The bicoherence of the EEG was calculated to identify nonlinear coupling. RESULTS All subjects displayed a spindle harmonic at almost exactly twice the frequency of the spindle. The power of the harmonic scaled nonlinearly with that of the spindle peak, consistent with model predictions. Bicoherence was observed at the spindle frequency, confirming the nonlinear origin of the harmonic oscillation. CONCLUSIONS The properties of the sleep spindle harmonic were consistent with the theoretical modeling of the sleep spindle harmonic as a nonlinear phenomenon. SIGNIFICANCE Most models of sleep spindle generation are unable to produce a spindle harmonic oscillation, so the observation and theoretical explanation of the harmonic is a significant step in understanding the mechanisms of sleep spindle generation. Unlike seizures, sleep spindles produce nonlinear effects that can be observed in healthy controls, and unlike the alpha oscillation, there is no linearly generated harmonic that can obscure nonlinear effects. This makes the spindle harmonic a good candidate for future investigation of nonlinearity in the brain.
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Affiliation(s)
- R G Abeysuriya
- School of Physics, University of Sydney, New South Wales 2006, Australia; Brain Dynamics Center, Sydney Medical School - Western, University of Sydney, Westmead, New South Wales 2145, Australia; Center for Integrated Research and Understanding of Sleep, 431 Glebe Point Rd, Glebe, New South Wales 2037, Australia.
| | - C J Rennie
- School of Physics, University of Sydney, New South Wales 2006, Australia; Brain Dynamics Center, Sydney Medical School - Western, University of Sydney, Westmead, New South Wales 2145, Australia
| | - P A Robinson
- School of Physics, University of Sydney, New South Wales 2006, Australia; Brain Dynamics Center, Sydney Medical School - Western, University of Sydney, Westmead, New South Wales 2145, Australia; Center for Integrated Research and Understanding of Sleep, 431 Glebe Point Rd, Glebe, New South Wales 2037, Australia
| | - J W Kim
- School of Physics, University of Sydney, New South Wales 2006, Australia; Brain Dynamics Center, Sydney Medical School - Western, University of Sydney, Westmead, New South Wales 2145, Australia; Center for Integrated Research and Understanding of Sleep, 431 Glebe Point Rd, Glebe, New South Wales 2037, Australia
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Garcés P, Vicente R, Wibral M, Pineda-Pardo JÁ, López ME, Aurtenetxe S, Marcos A, de Andrés ME, Yus M, Sancho M, Maestú F, Fernández A. Brain-wide slowing of spontaneous alpha rhythms in mild cognitive impairment. Front Aging Neurosci 2013; 5:100. [PMID: 24409145 PMCID: PMC3873508 DOI: 10.3389/fnagi.2013.00100] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Accepted: 12/15/2013] [Indexed: 11/13/2022] Open
Abstract
The neurophysiological changes associated with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) include an increase in low frequency activity, as measured with electroencephalography or magnetoencephalography (MEG). A relevant property of spectral measures is the alpha peak, which corresponds to the dominant alpha rhythm. Here we studied the spatial distribution of MEG resting state alpha peak frequency and amplitude values in a sample of 27 MCI patients and 24 age-matched healthy controls. Power spectra were reconstructed in source space with linearly constrained minimum variance beamformer. Then, 88 Regions of Interest (ROIs) were defined and an alpha peak per ROI and subject was identified. Statistical analyses were performed at every ROI, accounting for age, sex and educational level. Peak frequency was significantly decreased (p < 0.05) in MCIs in many posterior ROIs. The average peak frequency over all ROIs was 9.68 ± 0.71 Hz for controls and 9.05 ± 0.90 Hz for MCIs and the average normalized amplitude was (2.57 ± 0.59)·10−2 for controls and (2.70 ± 0.49)·10−2 for MCIs. Age and gender were also found to play a role in the alpha peak, since its frequency was higher in females than in males in posterior ROIs and correlated negatively with age in frontal ROIs. Furthermore, we examined the dependence of peak parameters with hippocampal volume, which is a commonly used marker of early structural AD-related damage. Peak frequency was positively correlated with hippocampal volume in many posterior ROIs. Overall, these findings indicate a pathological alpha slowing in MCI.
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Affiliation(s)
- Pilar Garcés
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain ; Department of Applied Physics III, Faculty of Physics, Complutense University of Madrid Madrid, Spain
| | - Raul Vicente
- MEG Unit, Brain Imaging Center, Goethe University Frankfurt, Germany ; Max-Planck Institute for Brain Research Frankfurt, Germany ; Institute of Computer Science, Faculty of Mathematics and Computer Science, University of Tartu Tartu, Estonia
| | - Michael Wibral
- MEG Unit, Brain Imaging Center, Goethe University Frankfurt, Germany
| | - Jose Ángel Pineda-Pardo
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain
| | - Maria Eugenia López
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Madrid, Spain
| | - Sara Aurtenetxe
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Madrid, Spain
| | - Alberto Marcos
- Neurology Department, San Carlos Clinical Hospital Madrid, Spain
| | | | - Miguel Yus
- Radiology Department, San Carlos Clinical Hospital Madrid, Spain
| | - Miguel Sancho
- Department of Applied Physics III, Faculty of Physics, Complutense University of Madrid Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Madrid, Spain
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain ; Department of Psychiatry and Medical Psychology, Faculty of Medicine, Complutense University of Madrid Madrid, Spain
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van Albada SJ, Robinson PA. Relationships between Electroencephalographic Spectral Peaks Across Frequency Bands. Front Hum Neurosci 2013; 7:56. [PMID: 23483663 PMCID: PMC3586764 DOI: 10.3389/fnhum.2013.00056] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2012] [Accepted: 02/11/2013] [Indexed: 11/18/2022] Open
Abstract
The degree to which electroencephalographic spectral peaks are independent, and the relationships between their frequencies have been debated. A novel fitting method was used to determine peak parameters in the range 2-35 Hz from a large sample of eyes-closed spectra, and their interrelationships were investigated. Findings were compared with a mean-field model of thalamocortical activity, which predicts near-harmonic relationships between peaks. The subject set consisted of 1424 healthy subjects from the Brain Resource International Database. Peaks in the theta range occurred on average near half the alpha peak frequency, while peaks in the beta range tended to occur near twice and three times the alpha peak frequency on an individual-subject basis. Moreover, for the majority of subjects, alpha peak frequencies were significantly positively correlated with frequencies of peaks in the theta and low and high beta ranges. Such a harmonic progression agrees semiquantitatively with theoretical predictions from the mean-field model. These findings indicate a common or analogous source for different rhythms, and help to define appropriate individual frequency bands for peak identification.
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Affiliation(s)
- S. J. van Albada
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and Jülich-Aachen Research AllianceJülich, Germany
- School of Physics, The University of SydneySydney, NSW, Australia
- Brain Dynamics Center, Sydney Medical School – Western, University of SydneySydney, NSW, Australia
| | - P. A. Robinson
- School of Physics, The University of SydneySydney, NSW, Australia
- Brain Dynamics Center, Sydney Medical School – Western, University of SydneySydney, NSW, Australia
- Center for Integrated Research and Understanding of SleepGlebe, NSW, Australia
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Chiang A, Rennie C, Robinson P, van Albada S, Kerr C. Age trends and sex differences of alpha rhythms including split alpha peaks. Clin Neurophysiol 2011; 122:1505-17. [DOI: 10.1016/j.clinph.2011.01.040] [Citation(s) in RCA: 110] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2010] [Revised: 01/11/2011] [Accepted: 01/18/2011] [Indexed: 11/17/2022]
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Lodder SS, van Putten MJ. Automated EEG analysis: Characterizing the posterior dominant rhythm. J Neurosci Methods 2011; 200:86-93. [DOI: 10.1016/j.jneumeth.2011.06.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2011] [Revised: 06/13/2011] [Accepted: 06/14/2011] [Indexed: 11/30/2022]
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Abstract
Human infants rapidly develop their auditory perceptual abilities and acquire culture-specific knowledge in speech and music in the second 6 months of life. In the adult brain, neural rhythm around 10 Hz in the temporal lobes is thought to reflect sound analysis and subsequent cognitive processes such as memory and attention. To study when and how such rhythm emerges in infancy, we examined electroencephalogram (EEG) recordings in infants 4 and 12 months of age during sound stimulation and silence. In the 4-month-olds, the amplitudes of narrowly tuned 4-Hz brain rhythm, recorded from bilateral temporal electrodes, were modulated by sound stimuli. In the 12-month-olds, the sound-induced modulation occurred at faster 6-Hz rhythm at temporofrontal locations. The brain rhythms in the older infants consisted of more complex components, as even evident in individual data. These findings suggest that auditory-specific rhythmic neural activity, which is already established before 6 months of age, involves more speed-efficient long-range neural networks by the age of 12 months when long-term memory for native phoneme representation and for musical rhythmic features is formed. We suggest that maturation of distinct rhythmic components occurs in parallel, and that sensory-specific functions bound to particular thalamo-cortical networks are transferred to newly developed higher-order networks step by step until adult hierarchical neural oscillatory mechanisms are achieved across the whole brain.
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Affiliation(s)
- Takako Fujioka
- Department of Psychology, Neuroscience & Behaviour, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
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Kerr C, Rennie C, Robinson P. Model-based analysis and quantification of age trends in auditory evoked potentials. Clin Neurophysiol 2011; 122:134-47. [DOI: 10.1016/j.clinph.2010.05.030] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2009] [Revised: 04/07/2010] [Accepted: 05/15/2010] [Indexed: 11/24/2022]
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Shackman AJ, McMenamin BW, Maxwell JS, Greischar LL, Davidson RJ. Identifying robust and sensitive frequency bands for interrogating neural oscillations. Neuroimage 2010; 51:1319-33. [PMID: 20304076 DOI: 10.1016/j.neuroimage.2010.03.037] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2009] [Revised: 03/07/2010] [Accepted: 03/11/2010] [Indexed: 11/26/2022] Open
Abstract
Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic ("resting" or "spontaneous") electroencephalogram (EEG) into five bands: delta (1-5Hz), alpha-low (6-9Hz), alpha-high (10-11Hz), beta (12-19Hz), and gamma (>21Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)-based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time-frequency (event-related spectral perturbation, event-related synchronization/desynchronization) domains.
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Affiliation(s)
- Alexander J Shackman
- Wisconsin Psychiatric Institute and Clinics, Departments of Psychology and Psychiatry, University of Wisconsin-Madison, WI 53706, USA.
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van Albada SJ, Kerr CC, Chiang AKI, Rennie CJ, Robinson PA. Neurophysiological changes with age probed by inverse modeling of EEG spectra. Clin Neurophysiol 2009; 121:21-38. [PMID: 19854102 DOI: 10.1016/j.clinph.2009.09.021] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2009] [Revised: 08/19/2009] [Accepted: 09/22/2009] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To investigate age-associated changes in physiologically-based EEG spectral parameters in the healthy population. METHODS Eyes-closed EEG spectra of 1498 healthy subjects aged 6-86 years were fitted to a mean-field model of thalamocortical dynamics in a cross-sectional study. Parameters were synaptodendritic rates, cortical wave decay rates, connection strengths (gains), axonal delays for thalamocortical loops, and power normalizations. Age trends were approximated using smooth asymptotically linear functions with a single turning point. We also considered sex differences and relationships between model parameters and traditional quantitative EEG measures. RESULTS The cross-sectional data suggest that changes tend to be most rapid in childhood, generally leveling off at age 15-20 years. Most gains decrease in magnitude with age, as does power normalization. Axonal and dendritic delays decrease in childhood and then increase. Axonal delays and gains show small but significant sex differences. CONCLUSIONS Mean-field brain modeling allows interpretation of age-associated EEG trends in terms of physiological processes, including the growth and regression of white matter, influencing axonal delays, and the establishment and pruning of synaptic connections, influencing gains. SIGNIFICANCE This study demonstrates the feasibility of inverse modeling of EEG spectra as a noninvasive method for investigating large-scale corticothalamic dynamics, and provides a basis for future comparisons.
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Affiliation(s)
- S J van Albada
- School of Physics, The University of Sydney, NSW 2006, Australia.
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GORDON EVIAN, BARNETT KYLIEJ, COOPER NICHOLASJ, TRAN NGOC, WILLIAMS LEANNEM. AN "INTEGRATIVE NEUROSCIENCE" PLATFORM: APPLICATION TO PROFILES OF NEGATIVITY AND POSITIVITY BIAS. J Integr Neurosci 2008. [DOI: 10.1142/s0219635208001927] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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