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de Toledo GRA, Reissig GN, Senko LGS, Pereira DR, da Silva AF, Souza GM. Common bean under different water availability reveals classifiable stimuli-specific signatures in plant electrome. PLANT SIGNALING & BEHAVIOR 2024; 19:2333144. [PMID: 38545860 PMCID: PMC10984121 DOI: 10.1080/15592324.2024.2333144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 01/30/2024] [Indexed: 04/04/2024]
Abstract
Plant electrophysiology has unveiled the involvement of electrical signals in the physiology and behavior of plants. Spontaneously generated bioelectric activity can be altered in response to changes in environmental conditions, suggesting that a plant's electrome may possess a distinct signature associated with various stimuli. Analyzing electrical signals, particularly the electrome, in conjunction with Machine Learning (ML) techniques has emerged as a promising approach to classify characteristic electrical signals corresponding to each stimulus. This study aimed to characterize the electrome of common bean (Phaseolus vulgaris L.) cv. BRS-Expedito, subjected to different water availabilities, seeking patterns linked to these stimuli. For this purpose, bean plants in the vegetative stage were subjected to the following treatments: (I) distilled water; (II) half-strength Hoagland's nutrient solution; (III) -2 MPa PEG solution; and (IV) -2 MPa NaCl solution. Electrical signals were recorded within a Faraday's cage using the MP36 electronic system for data acquisition. Concurrently, plant water status was assessed by monitoring leaf turgor variation. Leaf temperature was additionally measured. Various analyses were conducted on the electrical time series data, including arithmetic average of voltage variation, skewness, kurtosis, Probability Density Function (PDF), autocorrelation, Power Spectral Density (PSD), Approximate Entropy (ApEn), Fast Fourier Transform (FFT), and Multiscale Approximate Entropy (ApEn(s)). Statistical analyses were performed on leaf temperature, voltage variation, skewness, kurtosis, PDF µ exponent, autocorrelation, PSD β exponent, and approximate entropy data. Machine Learning analyses were applied to identify classifiable patterns in the electrical time series. Characterization of the electrome of BRS-Expedito beans revealed stimulus-dependent profiles, even when alterations in water availability stimuli were similar in terms of quality and intensity. Additionally, it was observed that the bean electrome exhibits high levels of complexity, which are altered by different stimuli, with more intense and aversive stimuli leading to drastic reductions in complexity levels. Notably, one of the significant findings was the 100% accuracy of Small Vector Machine in detecting salt stress using electrome data. Furthermore, the study highlighted alterations in the plant electrome under low water potential before observable leaf turgor changes. This work demonstrates the potential use of the electrome as a physiological indicator of the water status in bean plants.
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Affiliation(s)
- Gabriel R. A. de Toledo
- Laboratory of Plant Cognition and Electrophysiology, Department of Botany, Institute of Biology, Federal University of Pelotas, Pelotas, Brazil
| | - Gabriela N. Reissig
- Laboratory of Plant Cognition and Electrophysiology, Department of Botany, Institute of Biology, Federal University of Pelotas, Pelotas, Brazil
| | - Luiz G. S. Senko
- Laboratory of Plant Cognition and Electrophysiology, Department of Botany, Institute of Biology, Federal University of Pelotas, Pelotas, Brazil
| | | | - Arlan F. da Silva
- Department of Physics, Federal University of Pelotas, Pelotas, Brazil
| | - Gustavo M. Souza
- Laboratory of Plant Cognition and Electrophysiology, Department of Botany, Institute of Biology, Federal University of Pelotas, Pelotas, Brazil
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Earl RJ, Ford TC, Lum JAG, Enticott PG, Hill AT. Exploring aperiodic activity in first episode schizophrenia spectrum psychosis: A resting-state EEG analysis. Brain Res 2024; 1840:149052. [PMID: 38844199 DOI: 10.1016/j.brainres.2024.149052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/10/2024]
Abstract
Abnormalities in brain oscillatory patterns have long been observed in schizophrenia and psychotic disorders more broadly. However, far less is known about aperiodic neural activity in these disorders, which has been linked to excitation/inhibition balance and neuronal population spiking within the brain. Here, we analysed resting-state electroencephalographic (EEG) recordings from 43 first episode schizophrenia spectrum psychosis (FESSP) patients and 28 healthy controls to examine whether aperiodic activity is disrupted in FESSP. We further assessed potential associations between aperiodic activity in FESSP and clinical symptom severity using the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). We found no significant differences in either the 1/f-like aperiodic exponent or the broadband aperiodic offset between the FESSP and healthy control groups when analysing the global neural signal averaged across all EEG electrodes. Bayesian analyses further supported these non-significant findings. However, additional non-parametric cluster-based permutation analyses did identify reduced aperiodic offset in the FESSP group, relative to controls across broad central, temporal, parietal and select frontal regions. No associations were found between either exponent or offset and clinical symptom severity when examining all FESSP participants, irrespective of antipsychotic medication status. However, offset was shown to predict BPRS and SANS scores in medication naive patients. In sum, this research presents an initial analysis of aperiodic neural activity in FESSP, offering preliminary evidence of altered aperiodic offset in this disorder. This contributes to a broader understanding of disrupted neural dynamics in early psychosis.
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Affiliation(s)
- Ruby J Earl
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia
| | - Talitha C Ford
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia; Centre for Human Psychopharmacology, Faculty of Health, Arts and Design, Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Jarrad A G Lum
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia
| | - Peter G Enticott
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia
| | - Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia.
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Kopf M, Martini J, Stier C, Ethofer S, Braun C, Li Hegner Y, Focke NK, Marquetand J, Helfrich RF. Aperiodic Activity Indexes Neural Hyperexcitability in Generalized Epilepsy. eNeuro 2024; 11:ENEURO.0242-24.2024. [PMID: 39137987 PMCID: PMC11376430 DOI: 10.1523/eneuro.0242-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 07/22/2024] [Accepted: 07/25/2024] [Indexed: 08/15/2024] Open
Abstract
Generalized epilepsy (GE) encompasses a heterogeneous group of hyperexcitability disorders that clinically manifest as seizures. At the whole-brain level, distinct seizure patterns as well as interictal epileptic discharges (IEDs) reflect key signatures of hyperexcitability in magneto- and electroencephalographic (M/EEG) recordings. Moreover, it had been suggested that aperiodic activity, specifically the slope of the 1/ƒx decay function of the power spectrum, might index neural excitability. However, it remained unclear if hyperexcitability as encountered at the cellular level directly translates to putative large-scale excitability signatures, amenable to M/EEG. In order to test whether the power spectrum is altered in hyperexcitable states, we recorded resting-state MEG from male and female GE patients (n = 51; 29 females; 28.82 ± 12.18 years; mean ± SD) and age-matched healthy controls (n = 49; 22 females; 32.10 ± 12.09 years). We parametrized the power spectra using FOOOF ("fitting oscillations and one over f") to separate oscillatory from aperiodic activity to directly test whether aperiodic activity is systematically altered in GE patients. We further identified IEDs to quantify the temporal dynamics of aperiodic activity around overt epileptic activity. The results demonstrate that aperiodic activity indexes hyperexcitability in GE at the whole-brain level, especially during epochs when no IEDs were present (p = 0.0130; d = 0.52). Upon IEDs, large-scale circuits transiently shifted to a less excitable network state (p = 0.001; d = 0.68). In sum, these results uncover that MEG background activity might index hyperexcitability based on the current brain state and does not rely on the presence of epileptic waveforms.
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Affiliation(s)
- Markus Kopf
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
| | - Jan Martini
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen 72076, Germany
| | - Christina Stier
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster 48149, Germany
| | - Silke Ethofer
- Department of Neurosurgery, University Medical Center Tübingen, Tübingen 72076, Germany
| | - Christoph Braun
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
- Magnetoencephalography (MEG) Center, University of Tübingen, Tübingen 72076, Germany
- CIMeC Center for Mind/Brain Sciences, University of Trento, Rovereto 38068, Italy
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
| | - Yiwen Li Hegner
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
- Magnetoencephalography (MEG) Center, University of Tübingen, Tübingen 72076, Germany
| | - Niels K Focke
- Department of Neurology, University Medical Center Göttingen, Göttingen 37075, Germany
| | - Justus Marquetand
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
- Magnetoencephalography (MEG) Center, University of Tübingen, Tübingen 72076, Germany
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
- Department of Neurology and Epileptology, University Medical Center Tübingen, Tübingen 72076, Germany
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart 70569, Germany
| | - Randolph F Helfrich
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
- Department of Neurology and Epileptology, University Medical Center Tübingen, Tübingen 72076, Germany
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Melara RD, Root JC, Edelman JA, Estelle MC, Mohr I, Ahles TA. Effects of Breast Cancer Treatment on Neural Noise: a Longitudinal Design. Arch Clin Neuropsychol 2024:acae066. [PMID: 39197121 DOI: 10.1093/arclin/acae066] [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: 01/07/2024] [Revised: 07/08/2024] [Accepted: 08/09/2024] [Indexed: 08/30/2024] Open
Abstract
OBJECTIVE Cognitive dysfunction has been observed consistently in a subset of breast cancer survivors. Yet the precise neurophysiological origins of cancer-related cognitive decline remain unknown. The current study assessed neural noise (1/f activity in electroencephalogram [EEG]) in breast cancer survivors as a potential contributor to observed cognitive dysfunction from pre- to post-treatment. METHODS We measured EEG in a longitudinal design during performance of the paired-click task and the revised Attention Network Test (ANT-R) to investigate pre- versus post-treatment effects of neural noise in breast cancer patients (n = 20 in paired click; n = 19 in ANT-R) compared with healthy controls (n = 32 in paired click; n = 29 in ANT-R). RESULTS In both paradigms, one sensory (paired click) and one cognitive (ANT-R), we found that neural noise was significantly elevated after treatment in patients, remaining constant from pretest to posttest in controls. In the ANT-R, patients responded more slowly than controls on invalid cuing trials. Increased neural noise was associated with poorer alerting and poorer inhibitory control of attention (as measured by behavioral network scores), particularly for patients after treatment. CONCLUSIONS The current study is the first to show a deleterious effect of breast cancer and/or cancer treatment on neural noise, pointing to alterations in the relative balance of excitatory and inhibitory synaptic inputs, while also suggesting promising approaches for cognitive rehabilitation.
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Affiliation(s)
- Robert D Melara
- Department of Psychology, The City College, City University of New York, 160 Convent Avenue, NAC 7-120, New York, NY 10031, USA
| | - James C Root
- Memorial Sloan Kettering Cancer Center, Department of Psychiatry and Behavioral Science Services, 641 Lexington Avenue, 7th Floor, New York, New York 10022, USA
| | - Jay A Edelman
- Department of Biology, The City College, City University of New York, 160 Convent Avenue, MR 526, New York, NY 10031, USA
| | - Maria Camilla Estelle
- Memorial Sloan Kettering Cancer Center, Department of Psychiatry and Behavioral Science Services, 641 Lexington Avenue, 7th Floor, New York, New York 10022, USA
| | - Isabella Mohr
- Memorial Sloan Kettering Cancer Center, Department of Psychiatry and Behavioral Science Services, 641 Lexington Avenue, 7th Floor, New York, New York 10022, USA
| | - Tim A Ahles
- Memorial Sloan Kettering Cancer Center, Department of Psychiatry and Behavioral Science Services, 641 Lexington Avenue, 7th Floor, New York, New York 10022, USA
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Vandewouw MM, Sato J, Safar K, Rhodes N, Taylor MJ. The development of aperiodic and periodic resting-state power between early childhood and adulthood: New insights from optically pumped magnetometers. Dev Cogn Neurosci 2024; 69:101433. [PMID: 39126820 DOI: 10.1016/j.dcn.2024.101433] [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: 04/05/2024] [Revised: 07/04/2024] [Accepted: 08/06/2024] [Indexed: 08/12/2024] Open
Abstract
Neurophysiological signals, comprised of both periodic (e.g., oscillatory) and aperiodic (e.g., non-oscillatory) activity, undergo complex developmental changes between childhood and adulthood. With much of the existing literature primarily focused on the periodic features of brain function, our understanding of aperiodic signals is still in its infancy. Here, we are the first to examine age-related changes in periodic (peak frequency and power) and aperiodic (slope and offset) activity using optically pumped magnetometers (OPMs), a new, wearable magnetoencephalography (MEG) technology that is particularly well-suited for studying development. We examined age-related changes in these spectral features in a sample (N=65) of toddlers (1-3 years), children (4-5 years), young adults (20-26 years), and adults (27-38 years). Consistent with the extant literature, we found significant age-related decreases in the aperiodic slope and offset, and changes in peak frequency and power that were frequency-specific; we are the first to show that the effect sizes of these changes also varied across brain regions. This work not only adds to the growing body of work highlighting the advantages of using OPMs, especially for studying development, but also contributes novel information regarding the variation of neurophysiological changes with age across the brain.
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Affiliation(s)
- Marlee M Vandewouw
- Department of Diagnostic & Interventional Radiology, Hospital for Sick Children, Toronto, Canada; Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Canada; Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada.
| | - Julie Sato
- Department of Diagnostic & Interventional Radiology, Hospital for Sick Children, Toronto, Canada; Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Canada
| | - Kristina Safar
- Department of Diagnostic & Interventional Radiology, Hospital for Sick Children, Toronto, Canada; Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Canada
| | - Natalie Rhodes
- Department of Diagnostic & Interventional Radiology, Hospital for Sick Children, Toronto, Canada; Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Margot J Taylor
- Department of Diagnostic & Interventional Radiology, Hospital for Sick Children, Toronto, Canada; Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, Canada; Department of Psychology, University of Toronto, Toronto, Canada
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6
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Stanyard RA, Mason D, Ellis C, Dickson H, Short R, Batalle D, Arichi T. Aperiodic and Hurst EEG exponents across early human brain development: A systematic review. Dev Cogn Neurosci 2024; 68:101402. [PMID: 38917647 PMCID: PMC11254951 DOI: 10.1016/j.dcn.2024.101402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/12/2024] [Accepted: 06/03/2024] [Indexed: 06/27/2024] Open
Abstract
In electroencephalographic (EEG) data, power-frequency slope exponents (1/f_β) can provide non-invasive markers of in vivo neural activity excitation-inhibition (E:I) balance. E:I balance may be altered in neurodevelopmental conditions; hence, understanding how 1/fβ evolves across infancy/childhood has implications for developing early assessments/interventions. This systematic review (PROSPERO-ID: CRD42023363294) explored the early maturation (0-26 yrs) of resting-state EEG 1/f measures (aperiodic [AE], power law [PLE] and Hurst [HE] exponents), including studies containing ≥1 1/f measures and ≥10 typically developing participants. Five databases (including Embase and Scopus) were searched during March 2023. Forty-two studies were identified (Nparticipants=3478). Risk of bias was assessed using the Quality Assessment with Diverse Studies tool. Narrative synthesis of HE data suggests non-stationary EEG activity occurs throughout development. Age-related trends were complex, with rapid decreases in AEs during infancy and heterogenous changes thereafter. Regionally, AE maxima shifted developmentally, potentially reflecting spatial trends in maturing brain connectivity. This work highlights the importance of further characterising the development of 1/f measures to better understand how E:I balance shapes brain and cognitive development.
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Affiliation(s)
- R A Stanyard
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
| | - D Mason
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - C Ellis
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - H Dickson
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - R Short
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - D Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - T Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, United Kingdom; Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, United Kingdom; Department of Bioengineering, Imperial College London, United Kingdom
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7
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Chong NJL, Feng L. Self-organization toward 1/f noise in deep neural networks. CHAOS (WOODBURY, N.Y.) 2024; 34:081101. [PMID: 39088349 DOI: 10.1063/5.0224138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 07/12/2024] [Indexed: 08/03/2024]
Abstract
In biological neural networks, it has been well recognized that a healthy brain exhibits 1/f noise patterns. However, in artificial neural networks that are increasingly matching or even out-performing human cognition, this phenomenon has yet to be established. In this work, we found that similar to that of their biological counterparts, 1/f noise exists in artificial neural networks when trained on time series classification tasks. Additionally, we found that the activations of the neurons are the closest to 1/f noise when the neurons are highly utilized. Conversely, if the network is too large and many neurons are underutilized, the neuron activations deviate from 1/f noise patterns toward that of white noise.
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Affiliation(s)
| | - Ling Feng
- Department of Physics, National University of Singapore, Singapore 117551
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore 13863
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Park SE, Chung J, Lee J, Kim MJB, Kim J, Jeon HJ, Kim H, Woo C, Kim H, Lee SA. Digital assessment of cognitive-affective biases related to mental health. PLOS DIGITAL HEALTH 2024; 3:e0000595. [PMID: 39208388 PMCID: PMC11361731 DOI: 10.1371/journal.pdig.0000595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 07/28/2024] [Indexed: 09/04/2024]
Abstract
With an increasing societal need for digital therapy solutions for poor mental health, we face a corresponding rise in demand for scientifically validated digital contents. In this study we aimed to lay a sound scientific foundation for the development of brain-based digital therapeutics to assess and monitor cognitive effects of social and emotional bias across diverse populations and age-ranges. First, we developed three computerized cognitive tasks using animated graphics: 1) an emotional flanker task designed to test attentional bias, 2) an emotional go-no-go task to measure bias in memory and executive function, and 3) an emotional social evaluation task to measure sensitivity to social judgments. Then, we confirmed the generalizability of our results in a wide range of samples (children (N = 50), young adults (N = 172), older adults (N = 39), online young adults (N=93), and depression patients (N = 41)) using touchscreen and online computer-based tasks, and devised a spontaneous thought generation task that was strongly associated with, and therefore could potentially serve as an alternative to, self-report scales. Using PCA, we extracted five components that represented different aspects of cognitive-affective function (emotional bias, emotional sensitivity, general accuracy, and general/social attention). Next, a gamified version of the above tasks was developed to test the feasibility of digital cognitive training over a 2-week period. A pilot training study utilizing this application showed decreases in emotional bias in the training group (that were not observed in the control group), which was correlated with a reduction in anxiety symptoms. Using a 2-channel wearable EEG system, we found that frontal alpha and gamma power were associated with both emotional bias and its reduction across the 2-week training period.
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Affiliation(s)
- Sang-Eon Park
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
| | - Jisu Chung
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
| | - Jeonghyun Lee
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
| | - Minwoo JB Kim
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Jinhee Kim
- School of Psychology, Korea University, Seoul, Republic of Korea
| | - Hong Jin Jeon
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyungsook Kim
- Hanyang Digital Healthcare Center, Hanyang University, Seoul, Republic of Korea
| | - Choongwan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Hackjin Kim
- School of Psychology, Korea University, Seoul, Republic of Korea
| | - Sang Ah Lee
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
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Kozma C, Schroeder G, Owen T, de Tisi J, McEvoy AW, Miserocchi A, Duncan J, Wang Y, Taylor PN. Identifying epileptogenic abnormality by decomposing intracranial EEG and MEG power spectra. J Neurosci Methods 2024; 408:110180. [PMID: 38795977 DOI: 10.1016/j.jneumeth.2024.110180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/08/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Accurate identification of abnormal electroencephalographic (EEG) activity is pivotal for diagnosing and treating epilepsy. Recent studies indicate that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) components can illuminate the drivers of spectral activity changes. NEW METHODS We analysed intracranial EEG (iEEG) data from 234 subjects, creating a normative map. This map was compared to a cohort of 63 patients with refractory focal epilepsy under consideration for neurosurgery. The normative map was computed using three approaches: (i) relative complete band power, (ii) relative band power with the aperiodic component removed, and (iii) the aperiodic exponent. Abnormalities were calculated for each approach in the patient cohort. We evaluated the spatial profiles, assessed their ability to localize abnormalities, and replicated the findings using magnetoencephalography (MEG). RESULTS Normative maps of relative complete band power and relative periodic band power exhibited similar spatial profiles, while the aperiodic normative map revealed higher exponent values in the temporal lobe. Abnormalities estimated through complete band power effectively distinguished between good and bad outcome patients. Combining periodic and aperiodic abnormalities enhanced performance, like the complete band power approach. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS Sparing cerebral tissue with abnormalities in both periodic and aperiodic activity may result in poor surgical outcomes. Both periodic and aperiodic components do not carry sufficient information in isolation. The relative complete band power solution proved to be the most reliable method for this purpose. Future studies could investigate how cerebral location or pathology influences periodic or aperiodic abnormalities.
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Affiliation(s)
- Csaba Kozma
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.
| | - Gabrielle Schroeder
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Tom Owen
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Andrew W McEvoy
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - John Duncan
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
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10
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Pi Y, Yan J, Pscherer C, Gao S, Mückschel M, Colzato L, Hommel B, Beste C. Interindividual aperiodic resting-state EEG activity predicts cognitive-control styles. Psychophysiology 2024; 61:e14576. [PMID: 38556626 DOI: 10.1111/psyp.14576] [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: 12/04/2023] [Revised: 03/01/2024] [Accepted: 03/20/2024] [Indexed: 04/02/2024]
Abstract
The ability to find the right balance between more persistent and more flexible cognitive-control styles is known as "metacontrol." Recent findings suggest a relevance of aperiodic EEG activity and task conditions that are likely to elicit a specific metacontrol style. Here we investigated whether individual differences in aperiodic EEG activity obtained off-task (during resting state) predict individual cognitive-control styles under task conditions that pose different demands on metacontrol. We analyzed EEG resting-state data, task-EEG, and behavioral outcomes from a sample of N = 65 healthy participants performing a Go/Nogo task. We examined aperiodic activity as indicator of "neural noise" in the EEG power spectrum, and participants were assigned to a high-noise or low-noise group according to a median split of the exponents obtained for resting state. We found that off-task aperiodic exponents predicted different cognitive-control styles in Go and Nogo conditions: Overall, aperiodic exponents were higher (i.e., noise was lower) in the low-noise group, who however showed no difference between Go and Nogo trials, whereas the high-noise group exhibited significant noise reduction in the more persistence-heavy Nogo condition. This suggests that trait-like biases determine the default cognitive-control style, which however can be overwritten or compensated for under challenging task demands. We suggest that aperiodic activity in EEG signals represents valid indicators of highly dynamic arbitration between metacontrol styles, representing the brain's capability to reorganize itself and adapt its neural activity patterns to changing environmental conditions.
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Affiliation(s)
- Yu Pi
- Department of Psychology, Shandong Normal University, Jinan, China
| | - Jimin Yan
- Department of Psychology, Shandong Normal University, Jinan, China
| | - Charlotte Pscherer
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Shudan Gao
- Department of Psychology, Shandong Normal University, Jinan, China
| | - Moritz Mückschel
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Lorenza Colzato
- Department of Psychology, Shandong Normal University, Jinan, China
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Bernhard Hommel
- Department of Psychology, Shandong Normal University, Jinan, China
| | - Christian Beste
- Department of Psychology, Shandong Normal University, Jinan, China
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
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11
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Smith MK, Grabowecky M, Suzuki S. Dynamic Formation of a Posterior-to-Anterior Peak-Alpha-Frequency Gradient Driven by Two Distinct Processes. eNeuro 2024; 11:ENEURO.0273-24.2024. [PMID: 39142821 PMCID: PMC11373881 DOI: 10.1523/eneuro.0273-24.2024] [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: 06/22/2024] [Accepted: 07/22/2024] [Indexed: 08/16/2024] Open
Abstract
Peak-alpha frequency varies across individuals and mental states, but it also forms a negative gradient from posterior to anterior regions in association with increases in cortical thickness and connectivity, reflecting a cortical hierarchy in temporal integration. Tracking the spatial standard deviation of peak-alpha frequency in scalp EEG, we observed that a posterior-to-anterior gradient dynamically formed and dissolved. Periods of high spatial standard deviation yielded robustly negative posterior-to-anterior gradients-the "gradient state"-while periods of low spatial standard deviation yielded globally converged peak-alpha frequency-the "uniform state." The state variations were characterized by a combination of slow (0.3-0.5 Hz) oscillations and random-walk-like fluctuations. They were relatively independently correlated with peak-alpha frequency variations in anterior regions and peak-alpha power variations in central regions driven by posterior regions (together accounting for ∼50% of the state variations), suggesting that two distinct mechanisms modulate the state variations: an anterior mechanism that directly adjusts peak-alpha frequencies and a posterior-central mechanism that indirectly adjusts them by influencing synchronization. The state variations likely reflect general operations as their spatiotemporal characteristics remained unchanged while participants engaged in a variety of tasks (breath focus, vigilance, working memory, mental arithmetic, and generative thinking) with their eyes closed or watched a silent nature video. The ongoing state variations may dynamically balance two global processing modes, one that facilitates greater temporal integration (and potentially also information influx) toward anterior regions in the gradient state and the other that facilitates flexible global communication (via phase locking) in the uniform state.
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Affiliation(s)
- Max Kailler Smith
- Department of Psychology, Northwestern University, Evanston, Illinois 60208
| | - Marcia Grabowecky
- Department of Psychology and Interdepartmental Neuroscience, Northwestern University, Evanston, Illinois 60208
| | - Satoru Suzuki
- Department of Psychology and Interdepartmental Neuroscience, Northwestern University, Evanston, Illinois 60208
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12
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Kramer MA, Chu CJ. A General, Noise-Driven Mechanism for the 1/f-Like Behavior of Neural Field Spectra. Neural Comput 2024; 36:1643-1668. [PMID: 39028955 DOI: 10.1162/neco_a_01682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/25/2024] [Indexed: 07/21/2024]
Abstract
Consistent observations across recording modalities, experiments, and neural systems find neural field spectra with 1/f-like scaling, eliciting many alternative theories to explain this universal phenomenon. We show that a general dynamical system with stochastic drive and minimal assumptions generates 1/f-like spectra consistent with the range of values observed in vivo without requiring a specific biological mechanism or collective critical behavior.
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Affiliation(s)
- Mark A Kramer
- Department of Mathematics and Statistics, and Center for Systems Neuroscience, Boston University, Boston, MA 02214, U.S.A.
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, U.S.A.
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13
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Guisande N, Montani F. Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in EEG/iEEG data. Front Comput Neurosci 2024; 18:1342985. [PMID: 39081659 PMCID: PMC11287776 DOI: 10.3389/fncom.2024.1342985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 06/21/2024] [Indexed: 08/02/2024] Open
Abstract
Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.
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Affiliation(s)
| | - Fernando Montani
- Instituto de Física de La Plata (IFLP), Consejo Nacional de Investigaciones Científicas y Técnicas – Universidad Nacional de La Plata (CONICET-UNLP), La Plata, Buenos Aires, Argentina
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14
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Northoff G, Zilio F, Zhang J. Beyond task response-Pre-stimulus activity modulates contents of consciousness. Phys Life Rev 2024; 49:19-37. [PMID: 38492473 DOI: 10.1016/j.plrev.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 03/03/2024] [Indexed: 03/18/2024]
Abstract
The current discussion on the neural correlates of the contents of consciousness (NCCc) focuses mainly on the post-stimulus period of task-related activity. This neglects the substantial impact of the spontaneous or ongoing activity of the brain as manifest in pre-stimulus activity. Does the interaction of pre- and post-stimulus activity shape the contents of consciousness? Addressing this gap in our knowledge, we review and converge two recent lines of findings, that is, pre-stimulus alpha power and pre- and post-stimulus alpha trial-to-trial variability (TTV). The data show that pre-stimulus alpha power modulates post-stimulus activity including specifically the subjective features of conscious contents like confidence and vividness. At the same time, alpha pre-stimulus variability shapes post-stimulus TTV reduction including the associated contents of consciousness. We propose that non-additive rather than merely additive interaction of the internal pre-stimulus activity with the external stimulus in the alpha band is key for contents to become conscious. This is mediated by mechanisms on different levels including neurophysiological, neurocomputational, neurodynamic, neuropsychological and neurophenomenal levels. Overall, considering the interplay of pre-stimulus intrinsic and post-stimulus extrinsic activity across wider timescales, not just evoked responses in the post-stimulus period, is critical for identifying neural correlates of consciousness. This is well in line with both processing and especially the Temporo-spatial theory of consciousness (TTC).
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Affiliation(s)
- Georg Northoff
- University of Ottawa, Institute of Mental Health Research at the Royal Ottawa Hospital, Ottawa, Canada.
| | - Federico Zilio
- Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Padua, Italy
| | - Jianfeng Zhang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China.
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15
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Schiller B, Sperl MFJ, Kleinert T, Nash K, Gianotti LRR. EEG Microstates in Social and Affective Neuroscience. Brain Topogr 2024; 37:479-495. [PMID: 37523005 PMCID: PMC11199304 DOI: 10.1007/s10548-023-00987-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/05/2023] [Indexed: 08/01/2023]
Abstract
Social interactions require both the rapid processing of multifaceted socio-affective signals (e.g., eye gaze, facial expressions, gestures) and their integration with evaluations, social knowledge, and expectations. Researchers interested in understanding complex social cognition and behavior face a "black box" problem: What are the underlying mental processes rapidly occurring between perception and action and why are there such vast individual differences? In this review, we promote electroencephalography (EEG) microstates as a powerful tool for both examining socio-affective states (e.g., processing whether someone is in need in a given situation) and identifying the sources of heterogeneity in socio-affective traits (e.g., general willingness to help others). EEG microstates are identified by analyzing scalp field maps (i.e., the distribution of the electrical field on the scalp) over time. This data-driven, reference-independent approach allows for identifying, timing, sequencing, and quantifying the activation of large-scale brain networks relevant to our socio-affective mind. In light of these benefits, EEG microstates should become an indispensable part of the methodological toolkit of laboratories working in the field of social and affective neuroscience.
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Affiliation(s)
- Bastian Schiller
- Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Freiburg, Germany.
- Freiburg Brain Imaging Center, University Medical Center, Albert-Ludwigs-University of Freiburg, Freiburg, Germany.
| | - Matthias F J Sperl
- Department of Clinical Psychology and Psychotherapy, University of Giessen, Giessen, Germany
- Center for Mind, Brain and Behavior, Universities of Marburg and Giessen (Research Campus Central Hessen), Marburg, Germany
| | - Tobias Kleinert
- Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Freiburg, Germany
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Kyle Nash
- Department of Psychology, University of Alberta, Edmonton, Canada.
| | - Lorena R R Gianotti
- Department of Social Neuroscience and Social Psychology, Institute of Psychology, University of Bern, Bern, Switzerland.
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16
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Arutiunian V, Arcara G, Buyanova I, Fedorov M, Davydova E, Pereverzeva D, Sorokin A, Tyushkevich S, Mamokhina U, Danilina K, Dragoy O. Abnormalities in both stimulus-induced and baseline MEG alpha oscillations in the auditory cortex of children with Autism Spectrum Disorder. Brain Struct Funct 2024; 229:1225-1242. [PMID: 38683212 DOI: 10.1007/s00429-024-02802-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 04/22/2024] [Indexed: 05/01/2024]
Abstract
The neurobiology of Autism Spectrum Disorder (ASD) is hypothetically related to the imbalance between neural excitation (E) and inhibition (I). Different studies have revealed that alpha-band (8-12 Hz) activity in magneto- and electroencephalography (MEG and EEG) may reflect E and I processes and, thus, can be of particular interest in ASD research. Previous findings indicated alterations in event-related and baseline alpha activity in different cortical systems in individuals with ASD, and these abnormalities were associated with core and co-occurring conditions of ASD. However, the knowledge on auditory alpha oscillations in this population is limited. This MEG study investigated stimulus-induced (Event-Related Desynchronization, ERD) and baseline alpha-band activity (both periodic and aperiodic) in the auditory cortex and also the relationships between these neural activities and behavioral measures of children with ASD. Ninety amplitude-modulated tones were presented to two groups of children: 20 children with ASD (5 girls, Mage = 10.03, SD = 1.7) and 20 typically developing controls (9 girls, Mage = 9.11, SD = 1.3). Children with ASD had a bilateral reduction of alpha-band ERD, reduced baseline aperiodic-adjusted alpha power, and flattened aperiodic exponent in comparison to TD children. Moreover, lower raw baseline alpha power and aperiodic offset in the language-dominant left auditory cortex were associated with better language skills of children with ASD measured in formal assessment. The findings highlighted the alterations of E / I balance metrics in response to basic auditory stimuli in children with ASD and also provided evidence for the contribution of low-level processing to language difficulties in ASD.
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Affiliation(s)
- Vardan Arutiunian
- Center for Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, Seattle, WA, 98101, United States of America.
| | | | - Irina Buyanova
- Center for Language and Brain, HSE University, Moscow, Russia
- University of Otago, Dunedin, New Zealand
| | - Makar Fedorov
- Center for Language and Brain, HSE University, Nizhny Novgorod, Russia
| | - Elizaveta Davydova
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
- Chair of Differential Psychology and Psychophysiology, Moscow State University of Psychology and Education, Moscow, Russia
| | - Darya Pereverzeva
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Alexander Sorokin
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
- Haskins Laboratories, New Haven, CT, United States of America
| | - Svetlana Tyushkevich
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Uliana Mamokhina
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Kamilla Danilina
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
- Scientific Research and Practical Center of Pediatric Psychoneurology, Moscow, Russia
| | - Olga Dragoy
- Center for Language and Brain, HSE University, Moscow, Russia
- Institute of Linguistics, Russian Academy of Sciences, Moscow, Russia
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17
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Chis-Ciure R, Melloni L, Northoff G. A measure centrality index for systematic empirical comparison of consciousness theories. Neurosci Biobehav Rev 2024; 161:105670. [PMID: 38615851 DOI: 10.1016/j.neubiorev.2024.105670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 03/15/2024] [Accepted: 04/08/2024] [Indexed: 04/16/2024]
Abstract
Consciousness science is marred by disparate constructs and methodologies, making it challenging to systematically compare theories. This foundational crisis casts doubts on the scientific character of the field itself. Addressing it, we propose a framework for systematically comparing consciousness theories by introducing a novel inter-theory classification interface, the Measure Centrality Index (MCI). Recognizing its gradient distribution, the MCI assesses the degree of importance a specific empirical measure has for a given consciousness theory. We apply the MCI to probe how the empirical measures of the Global Neuronal Workspace Theory (GNW), Integrated Information Theory (IIT), and Temporospatial Theory of Consciousness (TTC) would fare within the context of the other two. We demonstrate that direct comparison of IIT, GNW, and TTC is meaningful and valid for some measures like Lempel-Ziv Complexity (LZC), Autocorrelation Window (ACW), and possibly Mutual Information (MI). In contrast, it is problematic for others like the anatomical and physiological neural correlates of consciousness (NCC) due to their MCI-based differential weightings within the structure of the theories. In sum, we introduce and provide proof-of-principle of a novel systematic method for direct inter-theory empirical comparisons, thereby addressing isolated evolution of theories and confirmatory bias issues in the state-of-the-art neuroscience of consciousness.
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Affiliation(s)
- Robert Chis-Ciure
- New York University (NYU), New York, USA; International Center for Neuroscience and Ethics (CINET), Tatiana Foundation, Madrid, Spain; Wolfram Physics Project, USA.
| | - Lucia Melloni
- Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Georg Northoff
- University of Ottawa, Institute of Mental Health Research at the Royal Ottawa Hospital, Ottawa, Canada
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18
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De Ridder D, Siddiqi MA, Dauwels J, Serdijn WA, Strydis C. NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed? Neuromodulation 2024; 27:711-729. [PMID: 38639704 DOI: 10.1016/j.neurom.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/05/2023] [Accepted: 01/10/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVES Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. MATERIALS AND METHODS A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. RESULTS Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. CONCLUSION The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.
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Affiliation(s)
- Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
| | - Muhammad Ali Siddiqi
- Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan; Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
| | - Justin Dauwels
- Microelectronics Department, Delft University of Technology, Delft, The Netherlands
| | - Wouter A Serdijn
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Section Bioelectronics, Delft University of Technology, Delft, The Netherlands
| | - Christos Strydis
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
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19
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Tan E, Troller-Renfree SV, Morales S, Buzzell GA, McSweeney M, Antúnez M, Fox NA. Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research. Dev Cogn Neurosci 2024; 67:101404. [PMID: 38852382 PMCID: PMC11214181 DOI: 10.1016/j.dcn.2024.101404] [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: 11/02/2023] [Revised: 05/28/2024] [Accepted: 06/06/2024] [Indexed: 06/11/2024] Open
Abstract
The theta band is one of the most prominent frequency bands in the electroencephalography (EEG) power spectrum and presents an interesting paradox: while elevated theta power during resting state is linked to lower cognitive abilities in children and adolescents, increased theta power during cognitive tasks is associated with higher cognitive performance. Why does theta power, measured during resting state versus cognitive tasks, show differential correlations with cognitive functioning? This review provides an integrated account of the functional correlates of theta across different contexts. We first present evidence that higher theta power during resting state is correlated with lower executive functioning, attentional abilities, language skills, and IQ. Next, we review research showing that theta power increases during memory, attention, and cognitive control, and that higher theta power during these processes is correlated with better performance. Finally, we discuss potential explanations for the differential correlations between resting/task-related theta and cognitive functioning, and offer suggestions for future research in this area.
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Affiliation(s)
- Enda Tan
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD 20740, USA; Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20740, USA.
| | | | - Santiago Morales
- Department of Psychology, University of Southern California, CA 90007, USA
| | - George A Buzzell
- Department of Psychology, Florida International University, FL 33199, USA
| | - Marco McSweeney
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD 20740, USA
| | - Martín Antúnez
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD 20740, USA
| | - Nathan A Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD 20740, USA; Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20740, USA
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20
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Van Schependom J, Baetens K, Nagels G, Olmi S, Beste C. Neurophysiological avenues to better conceptualizing adaptive cognition. Commun Biol 2024; 7:626. [PMID: 38789522 PMCID: PMC11126671 DOI: 10.1038/s42003-024-06331-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
We delve into the human brain's remarkable capacity for adaptability and sustained cognitive functioning, phenomena traditionally encompassed as executive functions or cognitive control. The neural underpinnings that enable the seamless navigation between transient thoughts without detracting from overarching goals form the core of our article. We discuss the concept of "metacontrol," which builds upon conventional cognitive control theories by proposing a dynamic balancing of processes depending on situational demands. We critically discuss the role of oscillatory processes in electrophysiological activity at different scales and the importance of desynchronization and partial phase synchronization in supporting adaptive behavior including neural noise accounts, transient dynamics, phase-based measures (coordination dynamics) and neural mass modelling. The cognitive processes focused and neurophysiological avenues outlined are integral to understanding diverse psychiatric disorders thereby contributing to a more nuanced comprehension of cognitive control and its neural bases in both health and disease.
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Affiliation(s)
- Jeroen Van Schependom
- AIMS Lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium
| | - Kris Baetens
- Brain, Body and Cognition, Vrije Universiteit Brussel, Brussels, Belgium
| | - Guy Nagels
- AIMS Lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- UZ Brussel, Department of Neurology, Brussels, Belgium
- St Edmund Hall, University of Oxford, Oxford, United Kingdom
| | - Simona Olmi
- CNR-Consiglio Nazionale delle Ricerche - Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany.
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21
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Dziego CA, Bornkessel-Schlesewsky I, Schlesewsky M, Sinha R, Immink MA, Cross ZR. Augmenting complex and dynamic performance through mindfulness-based cognitive training: An evaluation of training adherence, trait mindfulness, personality and resting-state EEG. PLoS One 2024; 19:e0292501. [PMID: 38768220 PMCID: PMC11104625 DOI: 10.1371/journal.pone.0292501] [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] [Received: 09/21/2023] [Accepted: 05/03/2024] [Indexed: 05/22/2024] Open
Abstract
Human performance applications of mindfulness-based training have demonstrated its utility in enhancing cognitive functioning. Previous studies have illustrated how these interventions can improve performance on traditional cognitive tests, however, little investigation has explored the extent to which mindfulness-based training can optimise performance in more dynamic and complex contexts. Further, from a neuroscientific perspective, the underlying mechanisms responsible for performance enhancements remain largely undescribed. With this in mind, the following study aimed to investigate how a short-term mindfulness intervention (one week) augments performance on a dynamic and complex task (target motion analyst task; TMA) in young, healthy adults (n = 40, age range = 18-38). Linear mixed effect modelling revealed that increased adherence to the web-based mindfulness-based training regime (ranging from 0-21 sessions) was associated with improved performance in the second testing session of the TMA task, controlling for baseline performance. Analyses of resting-state electroencephalographic (EEG) metrics demonstrated no change across testing sessions. Investigations of additional individual factors demonstrated that enhancements associated with training adherence remained relatively consistent across varying levels of participants' resting-state EEG metrics, personality measures (i.e., trait mindfulness, neuroticism, conscientiousness), self-reported enjoyment and timing of intervention adherence. Our results thus indicate that mindfulness-based cognitive training leads to performance enhancements in distantly related tasks, irrespective of several individual differences. We also revealed nuances in the magnitude of cognitive enhancements contingent on the timing of adherence, regardless of total volume of training. Overall, our findings suggest that mindfulness-based training could be used in a myriad of settings to elicit transferable performance enhancements.
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Affiliation(s)
- Chloe A. Dziego
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
| | - Ina Bornkessel-Schlesewsky
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
| | - Matthias Schlesewsky
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
| | - Ruchi Sinha
- Centre for Workplace Excellence, University of South Australia, Adelaide, South Australia
| | - Maarten A. Immink
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
- Sport, Health, Activity, Performance and Exercise (SHAPE) Research Centre, Flinders University, Adelaide, Australia
| | - Zachariah R. Cross
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
- Department of Medical Social Sciences, Northwestern Feinberg School of Medicine, Chicago, Illinois, United States of America
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22
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Manley J, Lu S, Barber K, Demas J, Kim H, Meyer D, Traub FM, Vaziri A. Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number. Neuron 2024; 112:1694-1709.e5. [PMID: 38452763 PMCID: PMC11098699 DOI: 10.1016/j.neuron.2024.02.011] [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: 11/23/2022] [Revised: 05/18/2023] [Accepted: 02/14/2024] [Indexed: 03/09/2024]
Abstract
The brain's remarkable properties arise from the collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, can such low-dimensional representations truly explain the vast range of brain activity, and if not, what is the appropriate resolution and scale of recording to capture them? Imaging neural activity at cellular resolution and near-simultaneously across the mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number in populations up to 1 million neurons. Although half of the neural variance is contained within sixteen dimensions correlated with behavior, our discovered scaling of dimensionality corresponds to an ever-increasing number of neuronal ensembles without immediate behavioral or sensory correlates. The activity patterns underlying these higher dimensions are fine grained and cortex wide, highlighting that large-scale, cellular-resolution recording is required to uncover the full substrates of neuronal computations.
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Affiliation(s)
- Jason Manley
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA; The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
| | - Sihao Lu
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Kevin Barber
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Jeffrey Demas
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA; The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
| | - Hyewon Kim
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - David Meyer
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Francisca Martínez Traub
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Alipasha Vaziri
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA; The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA.
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23
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Ni S, Harris B, Gong P. Distributed and dynamical communication: a mechanism for flexible cortico-cortical interactions and its functional roles in visual attention. Commun Biol 2024; 7:550. [PMID: 38719883 PMCID: PMC11078951 DOI: 10.1038/s42003-024-06228-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 04/22/2024] [Indexed: 05/12/2024] Open
Abstract
Perceptual and cognitive processing relies on flexible communication among cortical areas; however, the underlying neural mechanism remains unclear. Here we report a mechanism based on the realistic spatiotemporal dynamics of propagating wave patterns in neural population activity. Using a biophysically plausible, multiarea spiking neural circuit model, we demonstrate that these wave patterns, characterized by their rich and complex dynamics, can account for a wide variety of empirically observed neural processes. The coordinated interactions of these wave patterns give rise to distributed and dynamic communication (DDC) that enables flexible and rapid routing of neural activity across cortical areas. We elucidate how DDC unifies the previously proposed oscillation synchronization-based and subspace-based views of interareal communication, offering experimentally testable predictions that we validate through the analysis of Allen Institute Neuropixels data. Furthermore, we demonstrate that DDC can be effectively modulated during attention tasks through the interplay of neuromodulators and cortical feedback loops. This modulation process explains many neural effects of attention, underscoring the fundamental functional role of DDC in cognition.
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Affiliation(s)
- Shencong Ni
- School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Brendan Harris
- School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW, Australia.
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24
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Jia S, Liu D, Song W, Beste C, Colzato L, Hommel B. Tracing conflict-induced cognitive-control adjustments over time using aperiodic EEG activity. Cereb Cortex 2024; 34:bhae185. [PMID: 38771238 DOI: 10.1093/cercor/bhae185] [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: 01/25/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/22/2024] Open
Abstract
Cognitive-control theories assume that the experience of response conflict can trigger control adjustments. However, while some approaches focus on adjustments that impact the selection of the present response (in trial N), other approaches focus on adjustments in the next upcoming trial (N + 1). We aimed to trace control adjustments over time by quantifying cortical noise by means of the fitting oscillations and one over f algorithm, a measure of aperiodic activity. As predicted, conflict trials increased the aperiodic exponent in a large sample of 171 healthy adults, thus indicating noise reduction. While this adjustment was visible in trial N already, it did not affect response selection before the next trial. This suggests that control adjustments do not affect ongoing response-selection processes but prepare the system for tighter control in the next trial. We interpret the findings in terms of a conflict-induced switch from metacontrol flexibility to metacontrol persistence, accompanied or even implemented by a reduction of cortical noise.
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Affiliation(s)
- Shiwei Jia
- School of Psychology, Shandong Normal University, No. 88 East Wenhua Road, Jinan, 250014 Shandong Province, China
| | - Dandan Liu
- School of Psychology, Shandong Normal University, No. 88 East Wenhua Road, Jinan, 250014 Shandong Province, China
| | - Wenqi Song
- School of Psychology, Shandong Normal University, No. 88 East Wenhua Road, Jinan, 250014 Shandong Province, China
| | - Christian Beste
- School of Psychology, Shandong Normal University, No. 88 East Wenhua Road, Jinan, 250014 Shandong Province, China
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universitaet Dresden, Schubertstrasse 42, 01309 Dresden, Germany
| | - Lorenza Colzato
- School of Psychology, Shandong Normal University, No. 88 East Wenhua Road, Jinan, 250014 Shandong Province, China
| | - Bernhard Hommel
- School of Psychology, Shandong Normal University, No. 88 East Wenhua Road, Jinan, 250014 Shandong Province, China
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25
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Vohryzek J, Cabral J, Timmermann C, Atasoy S, Roseman L, Nutt DJ, Carhart-Harris RL, Deco G, Kringelbach ML. The flattening of spacetime hierarchy of the N,N-dimethyltryptamine brain state is characterized by harmonic decomposition of spacetime (HADES) framework. Natl Sci Rev 2024; 11:nwae124. [PMID: 38778818 PMCID: PMC11110867 DOI: 10.1093/nsr/nwae124] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 02/15/2024] [Accepted: 03/11/2024] [Indexed: 05/25/2024] Open
Abstract
The human brain is a complex system, whose activity exhibits flexible and continuous reorganization across space and time. The decomposition of whole-brain recordings into harmonic modes has revealed a repertoire of gradient-like activity patterns associated with distinct brain functions. However, the way these activity patterns are expressed over time with their changes in various brain states remains unclear. Here, we investigate healthy participants taking the serotonergic psychedelic N,N-dimethyltryptamine (DMT) with the Harmonic Decomposition of Spacetime (HADES) framework that can characterize how different harmonic modes defined in space are expressed over time. HADES demonstrates significant decreases in contributions across most low-frequency harmonic modes in the DMT-induced brain state. When normalizing the contributions by condition (DMT and non-DMT), we detect a decrease specifically in the second functional harmonic, which represents the uni- to transmodal functional hierarchy of the brain, supporting the leading hypothesis that functional hierarchy is changed in psychedelics. Moreover, HADES' dynamic spacetime measures of fractional occupancy, life time and latent space provide a precise description of the significant changes of the spacetime hierarchical organization of brain activity in the psychedelic state.
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Affiliation(s)
- Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, Department of Psychiatry, University of Oxford, Oxford OX3 9BX, UK
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
- Center for Music in the Brain, Aarhus University, Aarhus 8000, Denmark
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08005, Spain
| | - Joana Cabral
- Centre for Eudaimonia and Human Flourishing, Linacre College, Department of Psychiatry, University of Oxford, Oxford OX3 9BX, UK
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga 4710-057, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães 4710-057, Portugal
| | - Christopher Timmermann
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London SW7 2AZ, UK
| | - Selen Atasoy
- Centre for Eudaimonia and Human Flourishing, Linacre College, Department of Psychiatry, University of Oxford, Oxford OX3 9BX, UK
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
| | - Leor Roseman
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London SW7 2AZ, UK
| | - David J Nutt
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London SW7 2AZ, UK
| | - Robin L Carhart-Harris
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London SW7 2AZ, UK
- Departments of Neurology and Psychiatry, University of California San Francisco, San Francisco 94143, USA
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08005, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, Department of Psychiatry, University of Oxford, Oxford OX3 9BX, UK
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
- Center for Music in the Brain, Aarhus University, Aarhus 8000, Denmark
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26
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Hu S, Zhang Z, Zhang X, Wu X, Valdes-Sosa PA. [Formula: see text]-[Formula: see text]: A Nonparametric Model for Neural Power Spectra Decomposition. IEEE J Biomed Health Inform 2024; 28:2624-2635. [PMID: 38335090 DOI: 10.1109/jbhi.2024.3364499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, ξ- π was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. ξ- π was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that ξ- π improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that ξ- π produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that ξ- π approached the clinical findings in peak discovery. Overall, ξ- π offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. ξ- π is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.
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27
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S A A, C S, P D, G A, Maniyan Lathikakumari A, V Thomas S, N Menon R. Analysis of EEG microstates as biomarkers in neuropsychological processes - Review. Comput Biol Med 2024; 173:108266. [PMID: 38531248 DOI: 10.1016/j.compbiomed.2024.108266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 02/08/2024] [Accepted: 03/06/2024] [Indexed: 03/28/2024]
Abstract
Microstate analysis is a spatiotemporal method where instantaneous scalp potential topography represents the current state of the brain. The temporal evolution of these scalp topographies gives an understanding of quasi-stable periods of long-range coherence between distant electrodes, reflecting functional coordination within large-scale cortical networks. It has been proven potential in identification and characterization of neurophysiological indicators associated with neuropsychiatric conditions. Changes in microstates connected to symptoms and cognitive impairments of neuropsychiatric conditions. It is useful in the study of cognitive processes and disorders related to memory. Researchers may probe into the relationships between microstates and other cognitive processes, such as memory retrieval and encoding. This is a tool for clinicians to enhance the precision of diagnosis and inform possibilities for treatment by acquiring information regarding individual diversity in microstates could lead to tailored medical methods. Customizing treatment according to a patient's microstate patterns could improve the efficacy of treatment. The papers selected for the review span a broad-spectrum including memory related disorders, psychiatry and neurological disorders. A section in the review article has been dedicated to source localization of EEG microstates. The selection of review papers shed light on the importance and huge potential of application of EEG microstate analysis in various neuropsychological processes. The review concludes with the need for standardization of microstate analysis. It suggests the incorporation of widely accepted machine learning techniques for increasing the accuracy, reliability and acceptability of microstate analysis as reliable biomarkers for neurological conditions in the future.
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Affiliation(s)
- Asha S A
- Health Technology Group, Centre for Development of Advanced Computing (CDAC), Thiruvananthapuram, Kerala, India.
| | - Sudalaimani C
- Health Technology Group, Centre for Development of Advanced Computing (CDAC), Thiruvananthapuram, Kerala, India.
| | - Devanand P
- Health Technology Group, Centre for Development of Advanced Computing (CDAC), Thiruvananthapuram, Kerala, India.
| | - Alexander G
- Health Technology Group, Centre for Development of Advanced Computing (CDAC), Thiruvananthapuram, Kerala, India.
| | - Arya Maniyan Lathikakumari
- R Madhavan Nayar Centre for Comprehensive Epilepsy Care, Sree Chitra Tirunal Institute of Medical Sciences & Technology (SCTIMST), Thiruvananthapuram, Kerala, India.
| | - Sanjeev V Thomas
- R Madhavan Nayar Centre for Comprehensive Epilepsy Care, Sree Chitra Tirunal Institute of Medical Sciences & Technology (SCTIMST), Thiruvananthapuram, Kerala, India.
| | - Ramshekhar N Menon
- R Madhavan Nayar Centre for Comprehensive Epilepsy Care, Sree Chitra Tirunal Institute of Medical Sciences & Technology (SCTIMST), Thiruvananthapuram, Kerala, India.
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28
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Charlebois CM, Anderson DN, Smith EH, Davis TS, Newman BJ, Peters AY, Arain AM, Dorval AD, Rolston JD, Butson CR. Circadian changes in aperiodic activity are correlated with seizure reduction in patients with mesial temporal lobe epilepsy treated with responsive neurostimulation. Epilepsia 2024; 65:1360-1373. [PMID: 38517356 PMCID: PMC11138949 DOI: 10.1111/epi.17938] [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: 07/28/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVES Responsive neurostimulation (RNS) is an established therapy for drug-resistant epilepsy that delivers direct electrical brain stimulation in response to detected epileptiform activity. However, despite an overall reduction in seizure frequency, clinical outcomes are variable, and few patients become seizure-free. The aim of this retrospective study was to evaluate aperiodic electrophysiological activity, associated with excitation/inhibition balance, as a novel electrographic biomarker of seizure reduction to aid early prognostication of the clinical response to RNS. METHODS We identified patients with intractable mesial temporal lobe epilepsy who were implanted with the RNS System between 2015 and 2021 at the University of Utah. We parameterized the neural power spectra from intracranial RNS System recordings during the first 3 months following implantation into aperiodic and periodic components. We then correlated circadian changes in aperiodic and periodic parameters of baseline neural recordings with seizure reduction at the most recent follow-up. RESULTS Seizure reduction was correlated significantly with a patient's average change in the day/night aperiodic exponent (r = .50, p = .016, n = 23 patients) and oscillatory alpha power (r = .45, p = .042, n = 23 patients) across patients for baseline neural recordings. The aperiodic exponent reached its maximum during nighttime hours (12 a.m. to 6 a.m.) for most responders (i.e., patients with at least a 50% reduction in seizures). SIGNIFICANCE These findings suggest that circadian modulation of baseline broadband activity is a biomarker of response to RNS early during therapy. This marker has the potential to identify patients who are likely to respond to mesial temporal RNS. Furthermore, we propose that less day/night modulation of the aperiodic exponent may be related to dysfunction in excitation/inhibition balance and its interconnected role in epilepsy, sleep, and memory.
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Affiliation(s)
- Chantel M. Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
- Department of Pharmacology & Toxicology, University of Utah, Salt Lake City, Utah, USA
- School of Biomedical Engineering, University of Sydney, Darlington, NSW, Australia
| | - Elliot H. Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Tyler S. Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Blake J. Newman
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Angela Y. Peters
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Amir M. Arain
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Alan D. Dorval
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - John D. Rolston
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Department of Neurosurgery, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Christopher R. Butson
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, Utah, USA
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida, USA
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29
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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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30
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Williams JG, Harrison WJ, Beale HA, Mattingley JB, Harris AM. Effects of neural oscillation power and phase on discrimination performance in a visual tilt illusion. Curr Biol 2024; 34:1801-1809.e4. [PMID: 38569544 DOI: 10.1016/j.cub.2024.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/25/2024] [Accepted: 03/12/2024] [Indexed: 04/05/2024]
Abstract
Neural oscillations reflect fluctuations in the relative excitation/inhibition of neural systems1,2,3,4,5 and are theorized to play a critical role in canonical neural computations6,7,8,9 and cognitive processes.10,11,12,13,14 These theories have been supported by findings that detection of visual stimuli fluctuates with the phase of oscillations prior to stimulus onset.15,16,17,18,19,20,21,22,23 However, null results have emerged in studies seeking to demonstrate these effects in visual discrimination tasks,24,25,26,27 raising questions about the generalizability of these phenomena to wider neural processes. Recently, we suggested that methodological limitations may mask effects of phase in higher-level sensory processing.28 To test the generality of phasic influences on perception requires a task that involves stimulus discrimination while also depending on early sensory processing. Here, we examined the influence of oscillation phase on the visual tilt illusion, in which a center grating has its perceived orientation biased away from the orientation of a surround grating29 due to lateral inhibitory interactions in early visual processing.30,31,32 We presented center gratings at participants' subjective vertical angle and had participants report whether the grating appeared tilted clockwise or counterclockwise from vertical on each trial while measuring their brain activity with electroencephalography (EEG). In addition to effects of alpha power and aperiodic slope, we observed robust associations between orientation perception and alpha and theta phase, consistent with fluctuating illusion magnitude across the oscillatory cycle. These results confirm that oscillation phase affects the complex processing involved in stimulus discrimination, consistent with its purported role in canonical computations that underpin cognition.
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Affiliation(s)
- Jessica G Williams
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, St Lucia, Brisbane, QLD 4072, Australia; School of Psychology, The University of Queensland, McElwain Building, Campbell Road, St Lucia, Brisbane, QLD 4072, Australia
| | - William J Harrison
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, St Lucia, Brisbane, QLD 4072, Australia; School of Psychology, The University of Queensland, McElwain Building, Campbell Road, St Lucia, Brisbane, QLD 4072, Australia; School of Health, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD 4556, Australia
| | - Henry A Beale
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, St Lucia, Brisbane, QLD 4072, Australia
| | - Jason B Mattingley
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, St Lucia, Brisbane, QLD 4072, Australia; School of Psychology, The University of Queensland, McElwain Building, Campbell Road, St Lucia, Brisbane, QLD 4072, Australia; Canadian Institute for Advanced Research (CIFAR), MaRS Centre, West Tower, 661 University Ave., Suite 505, Toronto, ON M5G 1M1, Canada
| | - Anthony M Harris
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, St Lucia, Brisbane, QLD 4072, Australia.
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31
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Steina A, Sure S, Butz M, Vesper J, Schnitzler A, Hirschmann J. Mapping Subcortico-Cortical Coupling-A Comparison of Thalamic and Subthalamic Oscillations. Mov Disord 2024; 39:684-693. [PMID: 38380765 DOI: 10.1002/mds.29730] [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: 06/27/2023] [Revised: 11/29/2023] [Accepted: 01/08/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND The ventral intermediate nucleus of the thalamus (VIM) is an effective target for deep brain stimulation in tremor patients. Despite its therapeutic importance, its oscillatory coupling to cortical areas has rarely been investigated in humans. OBJECTIVES The objective of this study was to identify the cortical areas coupled to the VIM in patients with essential tremor. METHODS We combined resting-state magnetoencephalography with local field potential recordings from the VIM of 19 essential tremor patients. Whole-brain maps of VIM-cortex coherence in several frequency bands were constructed using beamforming and compared with corresponding maps of subthalamic nucleus (STN) coherence based on data from 19 patients with Parkinson's disease. In addition, we computed spectral Granger causality. RESULTS The topographies of VIM-cortex and STN-cortex coherence were very similar overall but differed quantitatively. Both nuclei were coupled to the ipsilateral sensorimotor cortex in the high-beta band; to the sensorimotor cortex, brainstem, and cerebellum in the low-beta band; and to the temporal cortex, brainstem, and cerebellum in the alpha band. High-beta coherence to sensorimotor cortex was stronger for the STN (P = 0.014), whereas low-beta coherence to the brainstem was stronger for the VIM (P = 0.017). Although the STN was driven by cortical activity in the high-beta band, the VIM led the sensorimotor cortex in the alpha band. CONCLUSIONS Thalamo-cortical coupling is spatially and spectrally organized. The overall similar topographies of VIM-cortex and STN-cortex coherence suggest that functional connections are not necessarily unique to one subcortical structure but might reflect larger frequency-specific networks involving VIM and STN to a different degree. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Alexandra Steina
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Sarah Sure
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Markus Butz
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Jan Vesper
- Department of Functional Neurosurgery and Stereotaxy, Neurosurgical Clinic, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Jan Hirschmann
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
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32
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McKeon SD, Perica MI, Parr AC, Calabro FJ, Foran W, Hetherington H, Moon CH, Luna B. Aperiodic EEG and 7T MRSI evidence for maturation of E/I balance supporting the development of working memory through adolescence. Dev Cogn Neurosci 2024; 66:101373. [PMID: 38574406 PMCID: PMC11000172 DOI: 10.1016/j.dcn.2024.101373] [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: 02/12/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/06/2024] Open
Abstract
Adolescence has been hypothesized to be a critical period for the development of human association cortex and higher-order cognition. A defining feature of critical period development is a shift in the excitation: inhibition (E/I) balance of neural circuitry, however how changes in E/I may enhance cortical circuit function to support maturational improvements in cognitive capacities is not known. Harnessing ultra-high field 7 T MR spectroscopy and EEG in a large, longitudinal cohort of youth (N = 164, ages 10-32 years old, 347 neuroimaging sessions), we delineate biologically specific associations between age-related changes in excitatory glutamate and inhibitory GABA neurotransmitters and EEG-derived measures of aperiodic neural activity reflective of E/I balance in prefrontal association cortex. Specifically, we find that developmental increases in E/I balance reflected in glutamate:GABA balance are linked to changes in E/I balance assessed by the suppression of prefrontal aperiodic activity, which in turn facilitates robust improvements in working memory. These findings indicate a role for E/I-engendered changes in prefrontal signaling mechanisms in the maturation of cognitive maintenance. More broadly, this multi-modal imaging study provides evidence that human association cortex undergoes physiological changes consistent with critical period plasticity during adolescence.
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Affiliation(s)
- Shane D McKeon
- Department of Bioengineering, University of Pittsburgh, PA, USA; The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA.
| | - Maria I Perica
- The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA; Department of Psychology, University of Pittsburgh, PA, USA
| | - Ashley C Parr
- The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, PA, USA
| | - Finnegan J Calabro
- Department of Bioengineering, University of Pittsburgh, PA, USA; The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, PA, USA
| | - Will Foran
- Department of Psychiatry, University of Pittsburgh, PA, USA
| | - Hoby Hetherington
- Resonance Research Incorporated, Billerica, MA, USA; Department of Radiology, University of Missouri, Columbia, MO, USA
| | - Chan-Hong Moon
- Department of Radiology, University of Pittsburgh, PA, USA
| | - Beatriz Luna
- The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, PA, USA.
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Riddle J, Schooler JW. Hierarchical consciousness: the Nested Observer Windows model. Neurosci Conscious 2024; 2024:niae010. [PMID: 38504828 PMCID: PMC10949963 DOI: 10.1093/nc/niae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/31/2024] [Accepted: 02/26/2024] [Indexed: 03/21/2024] Open
Abstract
Foremost in our experience is the intuition that we possess a unified conscious experience. However, many observations run counter to this intuition: we experience paralyzing indecision when faced with two appealing behavioral choices, we simultaneously hold contradictory beliefs, and the content of our thought is often characterized by an internal debate. Here, we propose the Nested Observer Windows (NOW) Model, a framework for hierarchical consciousness wherein information processed across many spatiotemporal scales of the brain feeds into subjective experience. The model likens the mind to a hierarchy of nested mosaic tiles-where an image is composed of mosaic tiles, and each of these tiles is itself an image composed of mosaic tiles. Unitary consciousness exists at the apex of this nested hierarchy where perceptual constructs become fully integrated and complex behaviors are initiated via abstract commands. We define an observer window as a spatially and temporally constrained system within which information is integrated, e.g. in functional brain regions and neurons. Three principles from the signal analysis of electrical activity describe the nested hierarchy and generate testable predictions. First, nested observer windows disseminate information across spatiotemporal scales with cross-frequency coupling. Second, observer windows are characterized by a high degree of internal synchrony (with zero phase lag). Third, observer windows at the same spatiotemporal level share information with each other through coherence (with non-zero phase lag). The theoretical framework of the NOW Model accounts for a wide range of subjective experiences and a novel approach for integrating prominent theories of consciousness.
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Affiliation(s)
- Justin Riddle
- Department of Psychology, Florida State University, 1107 W Call St, Tallahassee, FL 32304, USA
| | - Jonathan W Schooler
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, Psychological & Brain Sciences, Santa Barbara, CA 93106, USA
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Liao J, Wang J, Zhan CA, Yang F. Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection. Phys Eng Sci Med 2024; 47:31-47. [PMID: 37747646 DOI: 10.1007/s13246-023-01340-6] [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: 01/05/2023] [Accepted: 09/14/2023] [Indexed: 09/26/2023]
Abstract
Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms. We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score. Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity. EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.
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Affiliation(s)
- Jiahui Liao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Chang'an A Zhan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
| | - Feng Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
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35
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Bódizs R, Schneider B, Ujma PP, Horváth CG, Dresler M, Rosenblum Y. Fundamentals of sleep regulation: Model and benchmark values for fractal and oscillatory neurodynamics. Prog Neurobiol 2024; 234:102589. [PMID: 38458483 DOI: 10.1016/j.pneurobio.2024.102589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 01/26/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024]
Abstract
Homeostatic, circadian and ultradian mechanisms play crucial roles in the regulation of sleep. Evidence suggests that ratios of low-to-high frequency power in the electroencephalogram (EEG) spectrum indicate the instantaneous level of sleep pressure, influenced by factors such as individual sleep-wake history, current sleep stage, age-related differences and brain topography characteristics. These effects are well captured and reflected in the spectral exponent, a composite measure of the constant low-to-high frequency ratio in the periodogram, which is scale-free and exhibits lower interindividual variability compared to slow wave activity, potentially serving as a suitable standardization and reference measure. Here we propose an index of sleep homeostasis based on the spectral exponent, reflecting the level of membrane hyperpolarization and/or network bistability in the central nervous system in humans. In addition, we advance the idea that the U-shaped overnight deceleration of oscillatory slow and fast sleep spindle frequencies marks the biological night, providing somnologists with an EEG-index of circadian sleep regulation. Evidence supporting this assertion comes from studies based on sleep replacement, forced desynchrony protocols and high-resolution analyses of sleep spindles. Finally, ultradian sleep regulatory mechanisms are indicated by the recurrent, abrupt shifts in dominant oscillatory frequencies, with spindle ranges signifying non-rapid eye movement and non-spindle oscillations - rapid eye movement phases of the sleep cycles. Reconsidering the indicators of fundamental sleep regulatory processes in the framework of the new Fractal and Oscillatory Adjustment Model (FOAM) offers an appealing opportunity to bridge the gap between the two-process model of sleep regulation and clinical somnology.
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Affiliation(s)
- Róbert Bódizs
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.
| | - Bence Schneider
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
| | - Péter P Ujma
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
| | - Csenge G Horváth
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
| | - Martin Dresler
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
| | - Yevgenia Rosenblum
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
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Höhn C, Hahn MA, Lendner JD, Hoedlmoser K. Spectral Slope and Lempel-Ziv Complexity as Robust Markers of Brain States during Sleep and Wakefulness. eNeuro 2024; 11:ENEURO.0259-23.2024. [PMID: 38471778 PMCID: PMC10978822 DOI: 10.1523/eneuro.0259-23.2024] [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: 07/14/2023] [Revised: 01/22/2024] [Accepted: 02/09/2024] [Indexed: 03/14/2024] Open
Abstract
Nonoscillatory measures of brain activity such as the spectral slope and Lempel-Ziv complexity are affected by many neurological disorders and modulated by sleep. A multitude of frequency ranges, particularly a broadband (encompassing the full spectrum) and a narrowband approach, have been used especially for estimating the spectral slope. However, the effects of choosing different frequency ranges have not yet been explored in detail. Here, we evaluated the impact of sleep stage and task engagement (resting, attention, and memory) on slope and complexity in a narrowband (30-45 Hz) and broadband (1-45 Hz) frequency range in 28 healthy male human subjects (21.54 ± 1.90 years) using a within-subject design over 2 weeks with three recording nights and days per subject. We strived to determine how different brain states and frequency ranges affect slope and complexity and how the two measures perform in comparison. In the broadband range, the slope steepened, and complexity decreased continuously from wakefulness to N3 sleep. REM sleep, however, was best discriminated by the narrowband slope. Importantly, slope and complexity also differed between tasks during wakefulness. While narrowband complexity decreased with task engagement, the slope flattened in both frequency ranges. Interestingly, only the narrowband slope was positively correlated with task performance. Our results show that slope and complexity are sensitive indices of brain state variations during wakefulness and sleep. However, the spectral slope yields more information and could be used for a greater variety of research questions than Lempel-Ziv complexity, especially when a narrowband frequency range is used.
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Affiliation(s)
- Christopher Höhn
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, 5020 Salzburg, Austria
| | - Michael A Hahn
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany
| | - Janna D Lendner
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany
- Department of Anesthesiology and Intensive Care Medicine, University Medical Center Tübingen, 72076 Tübingen, Germany
| | - Kerstin Hoedlmoser
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, 5020 Salzburg, Austria
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Fink L, Simola J, Tavano A, Lange E, Wallot S, Laeng B. From pre-processing to advanced dynamic modeling of pupil data. Behav Res Methods 2024; 56:1376-1412. [PMID: 37351785 PMCID: PMC10991010 DOI: 10.3758/s13428-023-02098-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/20/2023] [Indexed: 06/24/2023]
Abstract
The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants' pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest.
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Affiliation(s)
- Lauren Fink
- Department of Music, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, 60322, Frankfurt am Main, Germany.
- Department of Psychology, Neuroscience & Behavior, McMaster University, 1280 Main St. West, Hamilton, Ontario, L8S 4L8, Canada.
| | - Jaana Simola
- Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland
- Department of Education, University of Helsinki, Helsinki, Finland
| | - Alessandro Tavano
- Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Elke Lange
- Department of Music, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, 60322, Frankfurt am Main, Germany
| | - Sebastian Wallot
- Department of Literature, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
- Institute for Sustainability Education and Psychologyy, Leuphana University, Lüneburg, Germany
| | - Bruno Laeng
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary studies in Rhythm, Time, and Motion, University of Oslo, Oslo, Norway
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38
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Schreiner JE, Kessler U, Oedegaard KJ, Mardal KA, Oltedal L. Exploring New Electroencephalogram Parameters in Electroconvulsive Therapy. J ECT 2024; 40:20-30. [PMID: 37310067 DOI: 10.1097/yct.0000000000000930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVE This pilot study aims to evaluate a novel metric based on the power spectrum of the EEG recordings from ECT-induced seizures-its association to volume changes in the hippocampus after ECT and improvement in depression rating scores. METHODS Depressed patients treated with ECT underwent brain magnetic resonance imaging before and after treatment and the EEG from each seizure was recorded (N = 29). Hippocampal volume changes and EEG parameters were recorded in addition to clinician-rated and self-reported measures of depressive symptoms. The slope of the power law in the power spectral density of the EEG was calculated. Multivariate linear models relating seizure parameters to volume change or clinical outcome were systematically and successively simplified. The best models were selected according to Akaike information criterion. RESULTS The slope of the power law was steeper in the right than the left hemisphere (P < 0.001). Electroencephalogram measures were included in the best models of volume change for both hippocampi as well as in the models explaining clinical outcome ( P = 0.014, P = 0.004). CONCLUSIONS In this pilot study, novel EEG measures were explored and contributed in models explaining the variation in volume change in the hippocampus and in clinical outcome after ECT.
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Wang Y, Liu W, Wang Y, Ouyang G, Guo Y. Long-term HD-tDCS modulates dynamic changes of brain activity on patients with disorders of consciousness: A resting-state EEG study. Comput Biol Med 2024; 170:108084. [PMID: 38295471 DOI: 10.1016/j.compbiomed.2024.108084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/10/2024] [Accepted: 01/27/2024] [Indexed: 02/02/2024]
Abstract
OBJECTIVE High-definition transcranial direct current stimulation (HD-tDCS) has been an effective neurostimulation method in the treatment of disorders of consciousness (DOC). However, the effects and mechanism of HD-tDCS are still unclear. METHODS This study recruited 8 DOC patients and applied 20-min sessions of 2 mA HD-tDCS (central anode electrode at Pz) for 14 consecutive days. We record DOC patients' EEG data and Coma Recovery Scale-Revised (CRS-R) values at four time point: baseline (T0), after 1 day's and 7,14 days' parietal HD-tDCS treatment (T1, T2, T3). Power spectral density (PSD), relative power (RP), spectral entropy and spectral exponent were calculated to evaluate the EEG dynamic changes of DOC patients during long-term parietal HD-tDCS. At last, we calculated the correlation between changes of EEG features and changes of CRS-R values. RESULT After 1 day's parietal HD-tDCS, DOC patients' CRS-R value had not changed (8.25 ± 1.91). HD-tDCS improved DOC patients' CRS-R value at T2 (9.75 ± 1.91, p < 0.05) and at T3 (11.38 ± 2.77, p < 0.05), compared with that at T0 (8.25 ± 1.91). As the treatment time increased, the EEG PSD decayed more slowly. Specifically, the delta frequency band RP decreased, while the alpha, beta, and gamma frequency bands RP increased. EEG oscillation characteristics changed but not significant at T1 (p > 0.05), and showed significant changes at T2 and T3 (p < 0.05). The spectral entropy continuously increased and the spectral exponent continuously decreased from T0 to T3. Specifically, the spectral entropy and spectral exponent of the parietal and occipital regions were significantly higher at T2 and T3 than that at T0 (p < 0.05). In addition, The changes in EEG features of the parietal and occipital lobes were correlated with changes in CRS-R value, especially between T2 and T0. CONCLUSION Long-term parietal HD-tDCS can improve the consciousness level and brain activity in DOC patients. Resting-state EEG can evaluate the dynamic changes of brain activity in DOC patients during HD-tDCS. EEG oscillation and non-oscillatory activity might be used to explain the mechanism of HD-tDCS on DOC patients.
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Affiliation(s)
- Yong Wang
- Zhuhai UM Science & Technology Research Institute, Zhuhai, China
| | - Wanqing Liu
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yingying Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, Normal University, Beijing, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, Normal University, Beijing, China.
| | - Yongkun Guo
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China.
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40
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Brake N, Duc F, Rokos A, Arseneau F, Shahiri S, Khadra A, Plourde G. A neurophysiological basis for aperiodic EEG and the background spectral trend. Nat Commun 2024; 15:1514. [PMID: 38374047 PMCID: PMC10876973 DOI: 10.1038/s41467-024-45922-8] [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: 09/06/2023] [Accepted: 02/06/2024] [Indexed: 02/21/2024] Open
Abstract
Electroencephalograms (EEGs) display a mixture of rhythmic and broadband fluctuations, the latter manifesting as an apparent 1/f spectral trend. While network oscillations are known to generate rhythmic EEG, the neural basis of broadband EEG remains unexplained. Here, we use biophysical modelling to show that aperiodic neural activity can generate detectable scalp potentials and shape broadband EEG features, but that these aperiodic signals do not significantly perturb brain rhythm quantification. Further model analysis demonstrated that rhythmic EEG signals are profoundly corrupted by shifts in synapse properties. To examine this scenario, we recorded EEGs of human subjects being administered propofol, a general anesthetic and GABA receptor agonist. Drug administration caused broadband EEG changes that quantitatively matched propofol's known effects on GABA receptors. We used our model to correct for these confounding broadband changes, which revealed that delta power, uniquely, increased within seconds of individuals losing consciousness. Altogether, this work details how EEG signals are shaped by neurophysiological factors other than brain rhythms and elucidates how these signals can undermine traditional EEG interpretation.
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Affiliation(s)
- Niklas Brake
- Quantiative Life Sciences PhD Program, McGill University, Montreal, Canada
- Department of Physiology, McGill University, Montreal, Canada
| | - Flavie Duc
- Department of Anesthesia, McGill University, Montreal, Canada
| | - Alexander Rokos
- Department of Anesthesia, McGill University, Montreal, Canada
| | | | - Shiva Shahiri
- School of Nursing, McGill University, Montreal, Canada
| | - Anmar Khadra
- Department of Physiology, McGill University, Montreal, Canada.
| | - Gilles Plourde
- Department of Anesthesia, McGill University, Montreal, Canada.
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Peng C, Wang Z, Sun Y, Mo Y, Hu K, Li Q, Hou X, Zhu Z, He X, Xue S, Zhang S. Subthalamic nucleus dynamics track microlesion effect in Parkinson's disease. Front Cell Dev Biol 2024; 12:1370287. [PMID: 38434618 PMCID: PMC10906266 DOI: 10.3389/fcell.2024.1370287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 02/06/2024] [Indexed: 03/05/2024] Open
Abstract
Parkinson's Disease (PD) is characterized by the temporary alleviation of motor symptoms following electrode implantation (or nucleus destruction), known as the microlesion effect (MLE). Electrophysiological studies have explored different PD stages, but understanding electrophysiological characteristics during the MLE period remains unclear. The objective was to examine the characteristics of local field potential (LFP) signals in the subthalamic nucleus (STN) during the hyperacute period following implantation (within 2 days) and 1 month post-implantation. 15 patients diagnosed with PD were enrolled in this observational study, with seven simultaneous recordings of bilateral STN-LFP signals using wireless sensing technology from an implantable pulse generator. Recordings were made in both on and off medication states over 1 month after implantation. We used a method to parameterize the neuronal power spectrum to separate periodic oscillatory and aperiodic components effectively. Our results showed that beta power exhibited a significant increase in the off medication state 1 month after implantation, compared to the postoperative hyperacute period. Notably, this elevation was effectively attenuated by levodopa administration. Furthermore, both the exponents and offsets displayed a decrease at 1 month postoperatively when compared to the hyperacute postoperative period. Remarkably, levodopa medication exerted a modulatory effect on these aperiodic parameters, restoring them back to levels observed during the hyperacute period. Our findings suggest that both periodic and aperiodic components partially capture distinct electrophysiological characteristics during the MLE. It is crucial to adequately evaluate such discrepancies when exploring the mechanisms of MLE and optimizing adaptive stimulus protocols.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Sha Xue
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Shizhong Zhang
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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42
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Karimi-Rouzbahani H. Evidence for Multiscale Multiplexed Representation of Visual Features in EEG. Neural Comput 2024; 36:412-436. [PMID: 38363657 DOI: 10.1162/neco_a_01649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 12/01/2023] [Indexed: 02/18/2024]
Abstract
Distinct neural processes such as sensory and memory processes are often encoded over distinct timescales of neural activations. Animal studies have shown that this multiscale coding strategy is also implemented for individual components of a single process, such as individual features of a multifeature stimulus in sensory coding. However, the generalizability of this encoding strategy to the human brain has remained unclear. We asked if individual features of visual stimuli were encoded over distinct timescales. We applied a multiscale time-resolved decoding method to electroencephalography (EEG) collected from human subjects presented with grating visual stimuli to estimate the timescale of individual stimulus features. We observed that the orientation and color of the stimuli were encoded in shorter timescales, whereas spatial frequency and the contrast of the same stimuli were encoded in longer timescales. The stimulus features appeared in temporally overlapping windows along the trial supporting a multiplexed coding strategy. These results provide evidence for a multiplexed, multiscale coding strategy in the human visual system.
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Affiliation(s)
- Hamid Karimi-Rouzbahani
- Neurosciences Centre, Mater Hospital, Brisbane 4101, Australia
- Queensland Brain Institute, University of Queensland, Brisbane 4067, Australia
- Mater Research Institute, University of Queensland, Brisbane 4101, Australia
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43
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Wang Z, Liu A, Yu J, Wang P, Bi Y, Xue S, Zhang J, Guo H, Zhang W. The effect of aperiodic components in distinguishing Alzheimer's disease from frontotemporal dementia. GeroScience 2024; 46:751-768. [PMID: 38110590 PMCID: PMC10828513 DOI: 10.1007/s11357-023-01041-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 12/07/2023] [Indexed: 12/20/2023] Open
Abstract
Distinguishing between Alzheimer's disease (AD) and frontotemporal dementia (FTD) presents a clinical challenge. Inexpensive and accessible techniques such as electroencephalography (EEG) are increasingly being used to address this challenge. In particular, the potential relevance between aperiodic components of EEG activity and these disorders has gained interest as our understanding evolves. This study aims to determine the differences in aperiodic activity between AD and FTD and evaluate its potential for distinguishing between the two disorders. A total of 88 participants, including 36 patients with AD, 23 patients with FTD, and 29 healthy controls (CN) underwent cognitive assessment and scalp EEG acquisition. Neuronal power spectra were parameterized to decompose the EEG spectrum, enabling comparison of group differences in different components. A support vector machine was employed to assess the impact of aperiodic parameters on the differential diagnosis. Compared with the CN group, both the AD and FTD groups showed varying degrees of increased alpha power (both periodic and raw power) and theta alpha power ratio. At the channel level, theta power (both periodic and raw power) in the frontal regions was higher in the AD group compared to the FTD group, and aperiodic parameters (both exponents and offsets) in the frontal, temporal, central, and parietal regions were higher in the AD group than in the FTD group. Importantly, the inclusion of aperiodic parameters led to improved performance in distinguishing between the two disorders. These findings highlight the significance of aperiodic components in discriminating dementia-related diseases.
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Affiliation(s)
- Zhuyong Wang
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Anyang Liu
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Jianshen Yu
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Pengfei Wang
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Yuewei Bi
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Sha Xue
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-Sen University, No. 135, Xingang Xi Road, Guangzhou, People's Republic of China.
| | - Hongbo Guo
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China.
| | - Wangming Zhang
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China.
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44
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Czoch A, Kaposzta Z, Mukli P, Stylianou O, Eke A, Racz FS. Resting-state fractal brain connectivity is associated with impaired cognitive performance in healthy aging. GeroScience 2024; 46:473-489. [PMID: 37458934 PMCID: PMC10828136 DOI: 10.1007/s11357-023-00836-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 05/20/2023] [Indexed: 01/31/2024] Open
Abstract
Aging affects cognitive functions even in the absence of ongoing pathologies. The neurophysiological basis of age-related cognitive decline (CD), however, is not completely understood. Alterations in both functional brain connectivity and in the fractal scaling of neuronal dynamics have been linked to aging and cognitive performance. Recently, fractal connectivity (FrC) has been proposed - combining the two concepts - for capturing long-term interactions among brain regions. FrC was shown to be influenced by increased mental workload; however, no prior studies investigated how resting-state FrC relates to cognitive performance and plausible CD in healthy aging. We recruited 19 healthy elderly (HE) and 24 young control (YC) participants, who underwent resting-state electroencephalography (EEG) measurements and comprehensive cognitive evaluation using 7 tests of the Cambridge Neurophysiological Test Automated Battery. FrC networks were reconstructed from EEG data using the recently introduced multiple-resampling cross-spectral analysis (MRCSA). Elderly individuals could be characterized with increased response latency and reduced performance in 4-4 tasks, respectively, with both reaction time and accuracy being affected in two tasks. Auto- and cross-spectral exponents - characterizing regional fractal dynamics and FrC, respectively, - were found reduced in HE when compared to YC over most of the cortex. Additionally, fractal scaling of frontoparietal connections expressed an inverse relationship with task performance in visual memory and sustained attention domains in elderly, but not in young individuals. Our results confirm that the fractal nature of brain connectivity - as captured by MRCSA - is affected in healthy aging. Furthermore, FrC appears as a sensitive neurophysiological marker of age-related CD.
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Affiliation(s)
- Akos Czoch
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Zalan Kaposzta
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- International Training Program in Geroscience, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
| | - Orestis Stylianou
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- Berlin Institute of Health at Charité, University Hospital Berlin, Berlin, Germany
- Department of Neurology With Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Frigyes Samuel Racz
- Department of Physiology, Semmelweis University, Budapest, Hungary.
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
- Mulva Clinic for the Neurosciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
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45
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Boncompte G, Freedman I, Qu J, Turco I, Khawaja ZQ, Cortinez I, Pedemonte JC, Akeju O. Cognitive function mediates the relationship between age and anaesthesia-induced oscillatory-specific alpha power. Brain Commun 2024; 6:fcae023. [PMID: 38370449 PMCID: PMC10873139 DOI: 10.1093/braincomms/fcae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/22/2023] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
Cognitive decline is common among older individuals, and although the underlying brain mechanisms are not entirely understood, researchers have suggested using EEG frontal alpha activity during general anaesthesia as a potential biomarker for cognitive decline. This is because frontal alpha activity associated with GABAergic general anaesthetics has been linked to cognitive function. However, oscillatory-specific alpha power has also been linked with chronological age. We hypothesize that cognitive function mediates the association between chronological age and (oscillatory-specific) alpha power. We analysed data from 380 participants (aged over 60) with baseline screening assessments and intraoperative EEG. We utilized the telephonic Montreal Cognitive Assessment to assess cognitive function. We computed total band power, oscillatory-specific alpha power, and aperiodics to measure anaesthesia-induced alpha activity. To test our mediation hypotheses, we employed structural equation modelling. Pairwise correlations between age, cognitive function and alpha activity were significant. Cognitive function mediated the association between age and classical alpha power [age → cognitive function → classical alpha; β = -0.0168 (95% confidence interval: -0.0313 to -0.00521); P = 0.0016] as well as the association between age and oscillatory-specific alpha power [age → cognitive function → oscillatory-specific alpha power; β = -0.00711 (95% confidence interval: -0.0154 to -0.000842); P = 0.028]. However, cognitive function did not mediate the association between age and aperiodic activity (1/f slope, P = 0.43; offset, P = 0.0996). This study is expected to provide valuable insights for anaesthesiologists, enabling them to make informed inferences about a patient's age and cognitive function from an analysis of anaesthetic-induced EEG signals in the operating room. To ensure generalizability, further studies across different populations are needed.
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Affiliation(s)
- Gonzalo Boncompte
- Division of Anesthesiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
- Neurodynamics of Cognition Lab, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - Isaac Freedman
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Jason Qu
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Isabella Turco
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Zain Q Khawaja
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Ignacio Cortinez
- Division of Anesthesiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - Juan C Pedemonte
- Division of Anesthesiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
- Programa de Farmacología y Toxicología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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46
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Pauls KAM, Salmela E, Korsun O, Kujala J, Salmelin R, Renvall H. Human Sensorimotor Beta Event Characteristics and Aperiodic Signal Are Highly Heritable. J Neurosci 2024; 44:e0265232023. [PMID: 37973377 PMCID: PMC10860623 DOI: 10.1523/jneurosci.0265-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 10/24/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023] Open
Abstract
Individuals' phenotypes, including the brain's structure and function, are largely determined by genes and their interplay. The resting brain generates salient rhythmic patterns that can be characterized noninvasively using functional neuroimaging such as magnetoencephalography (MEG). One of these rhythms, the somatomotor (rolandic) beta rhythm, shows intermittent high amplitude "events" that predict behavior across tasks and species. Beta rhythm is altered in neurological disease. The aperiodic (1/f) signal present in electrophysiological recordings is also modulated by some neurological conditions and aging. Both sensorimotor beta and aperiodic signal could thus serve as biomarkers of sensorimotor function. Knowledge about the extent to which these brain functional measures are heritable could shed light on the mechanisms underlying their generation. We investigated the heritability and variability of human spontaneous sensorimotor beta rhythm events and aperiodic activity in 210 healthy male and female adult siblings' spontaneous MEG activity. The most heritable trait was the aperiodic 1/f signal, with a heritability of 0.87 in the right hemisphere. Time-resolved beta event amplitude parameters were also highly heritable, whereas the heritabilities for overall beta power, peak frequency, and measures of event duration remained nonsignificant. Human sensorimotor neural activity can thus be dissected into different components with variable heritability. We postulate that these differences partially reflect different underlying signal-generating mechanisms. The 1/f signal and beta event amplitude measures may depend more on fixed, anatomical parameters, whereas beta event duration and its modulation reflect dynamic characteristics, guiding their use as potential disease biomarkers.
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Affiliation(s)
- K Amande M Pauls
- Department of Neurology, Helsinki University Hospital, and Department of Clinical Neurosciences, University of Helsinki, 00029 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland
| | - Elina Salmela
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland
- Department of Biology, University of Turku, 20014 Turku, Finland
| | - Olesia Korsun
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
| | - Jan Kujala
- Department of Psychology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Riitta Salmelin
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
| | - Hanna Renvall
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
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47
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Chung H, Wilkinson CL, Job Said A, Tager-Flusberg H, Nelson CA. Evaluating early EEG correlates of restricted and repetitive behaviors for toddlers with or without autism. RESEARCH SQUARE 2024:rs.3.rs-3871138. [PMID: 38313269 PMCID: PMC10836096 DOI: 10.21203/rs.3.rs-3871138/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Background Restricted and repetitive behaviors (RRB) are among the primary characteristics of autism spectrum disorder (ASD). Despite the potential impact on later developmental outcomes, our understanding of the neural underpinnings of RRBs is limited. Alterations in EEG alpha activity have been observed in ASD and implicated in RRBs, however, developmental changes within the alpha band requires careful methodological considerations when studying its role in brain-behavior relationships during infancy and early childhood. Novel approaches now enable the parameterization of the power spectrum into periodic and aperiodic components. This study aimed to characterize the neural correlates of RRBs in infancy by (1) comparing infant resting-state measures (periodic alpha and aperiodic activity) between infants who develop ASD, elevated likelihood infants without ASD, and low likelihood infants without ASD, and (2) evaluate whether these infant EEG measures are associated with frequency of RRBs measured at 24 months. Methods Baseline non-task related EEG data were collected from 12-to-14-month-old infants with and without elevated likelihood of autism (N=160), and periodic alpha activity (periodic alpha power, individual peak alpha frequency and amplitude), and aperiodic activity measures (aperiodic exponent) were calculated. Parent-reported RRBs were obtained at 24 months using the Repetitive Behavior Scale-Revised questionnaire. Group differences in EEG measures were evaluated using ANCOVA, and multiple linear regressions were conducted to assess relationships between EEG and RRB measures. Results No group-level differences in infant EEG measures were observed. Marginal effects analysis of linear regressions revealed significant associations within the ASD group, such that higher periodic alpha power, lower peak alpha frequency, and lower aperiodic exponent, were associated with elevated RRBs at 24 months. No significant associations were observed for non-ASD outcome groups. Limitations The sample size for ASD (N=19) was modest for examining brain-behavior relations. Larger sample sizes are needed to increase statistical power. Conclusion For infants with later ASD diagnoses, measures of alpha and aperiodic activity measured at 1-year of age were associated with later manifestation of RRBs at 2-years. Longitudinal studies are needed to elucidate whether the early trajectory of these EEG measures and their dynamic relations in development influence manifestations of RRBs in ASD.
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48
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Wang ZJ, Lee HC, Chuang CH, Hsiao FC, Lee SH, Hsu AL, Wu CW. Traces of EEG-fMRI coupling reveals neurovascular dynamics on sleep inertia. Sci Rep 2024; 14:1537. [PMID: 38233587 PMCID: PMC10794702 DOI: 10.1038/s41598-024-51694-4] [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: 07/13/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024] Open
Abstract
Upon emergence from sleep, individuals experience temporary hypo-vigilance and grogginess known as sleep inertia. During the transient period of vigilance recovery from prior nocturnal sleep, the neurovascular coupling (NVC) may not be static and constant as assumed by previous neuroimaging studies. Stemming from this viewpoint of sleep inertia, this study aims to probe the NVC changes as awakening time prolongs using simultaneous EEG-fMRI. The time-lagged coupling between EEG features of vigilance and BOLD-fMRI signals, in selected regions of interest, was calculated with one pre-sleep and three consecutive post-awakening resting-state measures. We found marginal changes in EEG theta/beta ratio and spectral slope across post-awakening sessions, demonstrating alterations of vigilance during sleep inertia. Time-varying EEG-fMRI coupling as awakening prolonged was evidenced by the changing time lags of the peak correlation between EEG alpha-vigilance and fMRI-thalamus, as well as EEG spectral slope and fMRI-anterior cingulate cortex. This study provides the first evidence of potential dynamicity of NVC occurred in sleep inertia and opens new avenues for non-invasive neuroimaging investigations into the neurophysiological mechanisms underlying brain state transitions.
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Affiliation(s)
- Zhitong John Wang
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, 5 Floor, 301, Yuantong Rd., Zhonghe Dist, New Taipei, 235040, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Research Center of Sleep Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Chun-Hsiang Chuang
- Research Center for Education and Mind Sciences, College of Education, National Tsing Hua University, Hsinchu, Taiwan
| | - Fan-Chi Hsiao
- Department of Counseling, Clinical and Industrial/Organizational Psychology, Ming Chuan University, Taoyuan, Taiwan
| | - Shwu-Hua Lee
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, 259, Wenhua 1St Rd., Guishan Dist., Taoyuan, 33302, Taiwan
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ai-Ling Hsu
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, 259, Wenhua 1St Rd., Guishan Dist., Taoyuan, 33302, Taiwan.
- Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan.
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, 5 Floor, 301, Yuantong Rd., Zhonghe Dist, New Taipei, 235040, Taiwan.
- Research Center of Sleep Medicine, Taipei Medical University Hospital, Taipei, Taiwan.
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49
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McKeown DJ, Finley AJ, Kelley NJ, Cavanagh JF, Keage HAD, Baumann O, Schinazi VR, Moustafa AA, Angus DJ. Test-retest reliability of spectral parameterization by 1/f characterization using SpecParam. Cereb Cortex 2024; 34:bhad482. [PMID: 38100367 PMCID: PMC10793580 DOI: 10.1093/cercor/bhad482] [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: 09/20/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
SpecParam (formally known as FOOOF) allows for the refined measurements of electroencephalography periodic and aperiodic activity, and potentially provides a non-invasive measurement of excitation: inhibition balance. However, little is known about the psychometric properties of this technique. This is integral for understanding the usefulness of SpecParam as a tool to determine differences in measurements of cognitive function, and electroencephalography activity. We used intraclass correlation coefficients to examine the test-retest reliability of parameterized activity across three sessions (90 minutes apart and 30 days later) in 49 healthy young adults at rest with eyes open, eyes closed, and during three eyes closed cognitive tasks including subtraction (Math), music recall (Music), and episodic memory (Memory). Intraclass correlation coefficients were good for the aperiodic exponent and offset (intraclass correlation coefficients > 0.70) and parameterized periodic activity (intraclass correlation coefficients > 0.66 for alpha and beta power, central frequency, and bandwidth) across conditions. Across all three sessions, SpecParam performed poorly in eyes open (40% of participants had poor fits over non-central sites) and had poor test-retest reliability for parameterized periodic activity. SpecParam mostly provides reliable metrics of individual differences in parameterized neural activity. More work is needed to understand the suitability of eyes open resting data for parameterization using SpecParam.
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Affiliation(s)
- Daniel J McKeown
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Anna J Finley
- Institute on Aging, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Nicholas J Kelley
- School of Psychology, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - James F Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, NM 87106, United States
| | - Hannah A D Keage
- School of Psychology, University of South Australia, Adelaide, SA 5001, Australia
| | - Oliver Baumann
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Victor R Schinazi
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Ahmed A Moustafa
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Douglas J Angus
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
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50
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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] [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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