1
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Opie GM, Hughes JM, Puri R. Age-related differences in how the shape of alpha and beta oscillations change during reaction time tasks. Neurobiol Aging 2024; 142:52-64. [PMID: 39153461 DOI: 10.1016/j.neurobiolaging.2024.08.001] [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: 10/24/2023] [Revised: 07/25/2024] [Accepted: 08/06/2024] [Indexed: 08/19/2024]
Abstract
While the shape of cortical oscillations is increasingly recognised to be physiologically and functionally informative, its relevance to the aging motor system has not been established. We therefore examined the shape of alpha and beta band oscillations recorded at rest, as well as during performance of simple and go/no-go reaction time tasks, in 33 young (23.3 ± 2.9 years, 27 females) and 27 older (60.0 ± 5.2 years, 23 females) adults. The shape of individual oscillatory cycles was characterised using a recently developed pipeline involving empirical mode decomposition, before being decomposed into waveform motifs using principal component analysis. This revealed four principal components that were uniquely influenced by task and/or age. These described specific dimensions of shape and tended to be modulated during the reaction phase of each task. Our results suggest that although oscillation shape is task-dependent, the nature of this effect is altered by advancing age, possibly reflecting alterations in cortical activity. These outcomes demonstrate the utility of this approach for understanding the neurophysiological effects of ageing.
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Affiliation(s)
- George M Opie
- Discipline of Physiology, School of Biomedicine, The University of Adelaide, Adelaide, Australia.
| | - James M Hughes
- School of Mechanical Engineering, The University of Adelaide, Adelaide, Australia
| | - Rohan Puri
- School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia
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2
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Lohani M, Cooper JM, McDonnell AS, Erickson GG, Simmons TG, Carriero AE, Crabtree KW, Strayer DL. Reliable but multi-dimensional cognitive demand in operating partially automated vehicles: implications for real-world automation research. Cogn Res Princ Implic 2024; 9:60. [PMID: 39256243 PMCID: PMC11387569 DOI: 10.1186/s41235-024-00591-5] [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: 12/27/2023] [Accepted: 08/23/2024] [Indexed: 09/12/2024] Open
Abstract
The reliability of cognitive demand measures in controlled laboratory settings is well-documented; however, limited research has directly established their stability under real-life and high-stakes conditions, such as operating automated technology on actual highways. Partially automated vehicles have advanced to become an everyday mode of transportation, and research on driving these advanced vehicles requires reliable tools for evaluating the cognitive demand on motorists to sustain optimal engagement in the driving process. This study examined the reliability of five cognitive demand measures, while participants operated partially automated vehicles on real roads across four occasions. Seventy-one participants (aged 18-64 years) drove on actual highways while their heart rate, heart rate variability, electroencephalogram (EEG) alpha power, and behavioral performance on the Detection Response Task were measured simultaneously. Findings revealed that EEG alpha power had excellent test-retest reliability, heart rate and its variability were good, and Detection Response Task reaction time and hit-rate had moderate reliabilities. Thus, the current study addresses concerns regarding the reliability of these measures in assessing cognitive demand in real-world automation research, as acceptable test-retest reliabilities were found across all measures for drivers across occasions. Despite the high reliability of each measure, low intercorrelations among measures were observed, and internal consistency was better when cognitive demand was estimated as a multi-factorial construct. This suggests that they tap into different aspects of cognitive demand while operating automation in real life. The findings highlight that a combination of psychophysiological and behavioral methods can reliably capture multi-faceted cognitive demand in real-world automation research.
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Affiliation(s)
- Monika Lohani
- Department of Psychology, University of Utah, 380 S 1530 E BEHS 1003, Salt Lake City, UT, 84112, USA.
| | | | - Amy S McDonnell
- Department of Psychology, University of Utah, 380 S 1530 E BEHS 1003, Salt Lake City, UT, 84112, USA
| | - Gus G Erickson
- Department of Psychology, University of Utah, 380 S 1530 E BEHS 1003, Salt Lake City, UT, 84112, USA
| | - Trent G Simmons
- Department of Psychology, University of Utah, 380 S 1530 E BEHS 1003, Salt Lake City, UT, 84112, USA
| | - Amanda E Carriero
- Department of Psychology, University of Utah, 380 S 1530 E BEHS 1003, Salt Lake City, UT, 84112, USA
| | - Kaedyn W Crabtree
- Department of Psychology, University of Utah, 380 S 1530 E BEHS 1003, Salt Lake City, UT, 84112, USA
| | - David L Strayer
- Department of Psychology, University of Utah, 380 S 1530 E BEHS 1003, Salt Lake City, UT, 84112, USA
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3
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Cho H, Adamek M, Willie JT, Brunner P. Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics. eLife 2024; 12:RP91605. [PMID: 39240267 PMCID: PMC11379461 DOI: 10.7554/elife.91605] [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] [Indexed: 09/07/2024] Open
Abstract
Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.
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Affiliation(s)
- Hohyun Cho
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, United States
- National Center for Adaptive Neurotechnologies, St. Louis, United States
| | - Markus Adamek
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, United States
- National Center for Adaptive Neurotechnologies, St. Louis, United States
| | - Jon T Willie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, United States
- National Center for Adaptive Neurotechnologies, St. Louis, United States
| | - Peter Brunner
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, United States
- National Center for Adaptive Neurotechnologies, St. Louis, United States
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4
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Nunez MD, Fernandez K, Srinivasan R, Vandekerckhove J. A tutorial on fitting joint models of M/EEG and behavior to understand cognition. Behav Res Methods 2024; 56:6020-6050. [PMID: 38409458 PMCID: PMC11335833 DOI: 10.3758/s13428-023-02331-x] [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] [Accepted: 12/21/2023] [Indexed: 02/28/2024]
Abstract
We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.
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Affiliation(s)
- Michael D Nunez
- Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.
| | - Kianté Fernandez
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Ramesh Srinivasan
- Department of Cognitive Sciences, University of California, Irvine, CA, USA
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Joachim Vandekerckhove
- Department of Cognitive Sciences, University of California, Irvine, CA, USA
- Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
- Department of Statistics, University of California, Irvine, CA, USA
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5
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Krystecka K, Stanczyk M, Magnuski M, Szelag E, Szymaszek A. Aperiodic activity differences in individuals with high and low temporal processing efficiency. Brain Res Bull 2024; 215:111010. [PMID: 38871258 DOI: 10.1016/j.brainresbull.2024.111010] [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: 01/31/2024] [Revised: 05/24/2024] [Accepted: 06/10/2024] [Indexed: 06/15/2024]
Abstract
It is known that Temporal Information Processing (TIP) underpins our cognitive functioning. Previous research has focused on the relationship between TIP efficiency and oscillatory brain activity, especially the gamma rhythm; however, non-oscillatory (aperiodic or 1/f) brain activity has often been missed. Recent studies have identified the 1/f component as being important for the functioning of the brain. Therefore, the current study aimed to verify whether TIP efficiency is associated with specific EEG resting state cortical activity patterns, including oscillatory and non-oscillatory (aperiodic) brain activities. To measure individual TIP efficiency, we used two behavioral tasks in which the participant judges the order of two sounds separated by millisecond intervals. Based on the above procedure, participants were classified into two groups with high and low TIP efficiency. Using cluster-based permutation analyses, we examined between-group differences in oscillatory and non-oscillatory (aperiodic) components across the 1-90 Hz range. The results revealed that the groups differed in the aperiodic component across the 30-80 Hz range in fronto-central topography. In other words, participants with low TIP efficiency exhibited higher levels of aperiodic activity, and thus a flatter frequency spectrum compared to those with high TIP efficiency. We conclude that participants with low TIP efficiency display higher levels of 'neural noise', which is associated with poorer quality and speed of neural processing.
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Affiliation(s)
- Klaudia Krystecka
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Magdalena Stanczyk
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Mikolaj Magnuski
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Elzbieta Szelag
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Aneta Szymaszek
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.
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6
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Peng J, Zikereya T, Shao Z, Shi K. The neuromechanical of Beta-band corticomuscular coupling within the human motor system. Front Neurosci 2024; 18:1441002. [PMID: 39211436 PMCID: PMC11358111 DOI: 10.3389/fnins.2024.1441002] [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: 05/30/2024] [Accepted: 07/26/2024] [Indexed: 09/04/2024] Open
Abstract
Beta-band activity in the sensorimotor cortex is considered a potential biomarker for evaluating motor functions. The intricate connection between the brain and muscle (corticomuscular coherence), especially in beta band, was found to be modulated by multiple motor demands. This coherence also showed abnormality in motion-related disorders. However, although there has been a substantial accumulation of experimental evidence, the neural mechanisms underlie corticomuscular coupling in beta band are not yet fully clear, and some are still a matter of controversy. In this review, we summarized the findings on the impact of Beta-band corticomuscular coherence to multiple conditions (sports, exercise training, injury recovery, human functional restoration, neurodegenerative diseases, age-related changes, cognitive functions, pain and fatigue, and clinical applications), and pointed out several future directions for the scientific questions currently unsolved. In conclusion, an in-depth study of Beta-band corticomuscular coupling not only elucidates the neural mechanisms of motor control but also offers new insights and methodologies for the diagnosis and treatment of motor rehabilitation and related disorders. Understanding these mechanisms can lead to personalized neuromodulation strategies and real-time neurofeedback systems, optimizing interventions based on individual neurophysiological profiles. This personalized approach has the potential to significantly improve therapeutic outcomes and athletic performance by addressing the unique needs of each individual.
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Affiliation(s)
| | | | | | - Kaixuan Shi
- Physical Education Department, China University of Geosciences Beijing, Beijing, China
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7
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Erickson MA, Boudewyn MA, Winsler K, Li C, Barch DM, Carter CS, Frank MJ, Gold JM, MacDonald AW, Ragland JD, Silverstein SM, Yonelinas A, Luck SJ. Dysfunctional Alpha Modulation as a Mechanism of Working Memory Impairment in Serious Mental Illness. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00210-6. [PMID: 39117276 DOI: 10.1016/j.bpsc.2024.07.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 07/18/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND People with psychosis and mood disorders experience disruptions in working memory; however, the underlying mechanism remains unknown. We focused on two potential mechanisms: first, poor attentional engagement should be associated with elevated levels of pre-stimulus alpha-band activity within the EEG, whereas impaired working memory encoding should be associated with reduced post-stimulus alpha suppression. METHODS We collected EEG data from 68 people with schizophrenia, 43 people with bipolar disorder with a history of psychosis, and 53 people with major depressive disorder, as well as 90 healthy comparison subjects (HCS), while they completed a spatial working memory task. We quantified attention lapsing, memory precision, and memory capacity from the behavioral responses, and we quantified alpha using both traditional wavelet analysis as well as a novel approach for isolating oscillatory alpha power from aperiodic elements of the EEG signal. RESULTS We found that (1) greater pre-stimulus alpha power estimated using traditional wavelet analysis predicted behavioral errors; (2) post-stimulus alpha suppression was reduced in the patient groups; and (3) reduced suppression was associated with lower likelihood of memory storage. However, we also observed that pre-stimulus alpha was larger among HCS compared to patients, and single-trial analyses showed that it was the aperiodic elements of the pre-stimulus EEG-not oscillatory alpha-that predicted behavioral errors. DISCUSSION These results suggest that working memory impairments in serious mental illness primarily reflect an impairment in the post-stimulus encoding processes rather than reduced attentional engagement prior to stimulus onset.
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Affiliation(s)
- Molly A Erickson
- Department of Psychiatry & Behavioral Neuroscience, University of Chicago, 5841 S. Maryland Ave, Chicago, IL 60622.
| | | | - Kurt Winsler
- Center for Mind & Brain, University of California, Davis
| | - Charlotte Li
- Department of Psychiatry & Behavioral Neuroscience, University of Chicago, 5841 S. Maryland Ave, Chicago, IL 60622
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis
| | - Cameron S Carter
- Department of Psychiatry, University of California, Davis, School of Medicine
| | - Michael J Frank
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
| | - James M Gold
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine
| | | | - John D Ragland
- Department of Psychiatry, University of California, Davis, School of Medicine
| | | | | | - Steven J Luck
- Center for Mind & Brain, University of California, Davis
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8
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Maschke C, O'Byrne J, Colombo MA, Boly M, Gosseries O, Laureys S, Rosanova M, Jerbi K, Blain-Moraes S. Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity. Commun Biol 2024; 7:946. [PMID: 39103539 DOI: 10.1038/s42003-024-06613-8] [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: 02/13/2024] [Accepted: 07/22/2024] [Indexed: 08/07/2024] Open
Abstract
Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigate dynamical properties of the resting-state electroencephalogram (EEG) of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. Importantly, all participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams), enabling an experimental dissociation between unresponsiveness and unconsciousness. For each condition, we measure (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related metrics, revealing that states of unconsciousness are characterized by a distancing from both avalanche criticality and the edge of chaos. We then ask whether these same dynamical properties are predictive of the perturbational complexity index (PCI), a TMS-based measure that has shown remarkably high sensitivity in detecting consciousness independently of behavior. We successfully predict individual subjects' PCI values with considerably high accuracy from resting-state EEG dynamical properties alone. Our results establish a firm link between perturbational complexity and criticality, and provide further evidence that criticality is a necessary condition for the emergence of consciousness.
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Affiliation(s)
- Charlotte Maschke
- Montreal General Hospital, McGill University Health Centre, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
- Cognitive & Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, QC, Canada
| | - Jordan O'Byrne
- Cognitive & Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, QC, Canada
- MILA (Québec Artificial Intelligence Institute), Montréal, QC, Canada
| | | | - Melanie Boly
- Department of Neurology and Department of Psychiatry, University of Wisconsin, Madison, WI, USA
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du cerveau, CHU of Liège, Liège, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- CERVO Brain Research Centre, Laval University, Laval, QC, Canada
- Consciousness Science Institute, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Karim Jerbi
- Cognitive & Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, QC, Canada
- MILA (Québec Artificial Intelligence Institute), Montréal, QC, Canada
- Centre UNIQUE (Union Neurosciences & Intelligence Artificielle), Montréal, QC, Canada
| | - Stefanie Blain-Moraes
- Montreal General Hospital, McGill University Health Centre, Montreal, QC, Canada.
- School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada.
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9
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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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Vaz A, Wathen C, Miranda S, Thomas R, Darlington T, Jabarkheel R, Tomlinson S, Arena J, Bond K, Salwi S, Ajmera S, Bachschmid-Romano L, Gugger J, Sandsmark D, Diaz-Arrastia R, Schuster J, Ramayya AG, Cajigas I, Pesaran B, Chen HI, Petrov D. Return of intracranial beta oscillations and traveling waves with recovery from traumatic brain injury. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.19.604293. [PMID: 39091808 PMCID: PMC11291083 DOI: 10.1101/2024.07.19.604293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Traumatic brain injury (TBI) remains a pervasive clinical problem associated with significant morbidity and mortality. However, TBI remains clinically and biophysically ill-defined, and prognosis remains difficult even with the standardization of clinical guidelines and advent of multimodality monitoring. Here we leverage a unique data set from TBI patients implanted with either intracranial strip electrodes during craniotomy or quad-lumen intracranial bolts with depth electrodes as part of routine clinical practice. By extracting spectral profiles of this data, we found that the presence of narrow-band oscillatory activity in the beta band (12-30 Hz) closely corresponds with the neurological exam as quantified with the standard Glasgow Coma Scale (GCS). Further, beta oscillations were distributed over the cortical surface as traveling waves, and the evolution of these waves corresponded to recovery from coma, consistent with the putative role of waves in perception and cognitive activity. We consequently propose that beta oscillations and traveling waves are potential biomarkers of recovery from TBI. In a broader sense, our findings suggest that emergence from coma results from recovery of thalamo-cortical interactions that coordinate cortical beta rhythms.
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Affiliation(s)
- Alex Vaz
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Connor Wathen
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Stephen Miranda
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rachel Thomas
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Timothy Darlington
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rashad Jabarkheel
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Samuel Tomlinson
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John Arena
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kamila Bond
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sanjana Salwi
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sonia Ajmera
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - James Gugger
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle Sandsmark
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ramon Diaz-Arrastia
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James Schuster
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ashwin G Ramayya
- Department of Neurosurgery, Stanford University, Palo Alto, CA, 94305, USA
| | - Iahn Cajigas
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bijan Pesaran
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - H Isaac Chen
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
| | - Dmitriy Petrov
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
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11
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Jiang Z, An X, Liu S, Yin E, Yan Y, Ming D. Beyond alpha band: prestimulus local oscillation and interregional synchrony of the beta band shape the temporal perception of the audiovisual beep-flash stimulus. J Neural Eng 2024; 21:036035. [PMID: 37419108 DOI: 10.1088/1741-2552/ace551] [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: 10/12/2022] [Accepted: 07/07/2023] [Indexed: 07/09/2023]
Abstract
Objective.Multisensory integration is more likely to occur if the multimodal inputs are within a narrow temporal window called temporal binding window (TBW). Prestimulus local neural oscillations and interregional synchrony within sensory areas can modulate cross-modal integration. Previous work has examined the role of ongoing neural oscillations in audiovisual temporal integration, but there is no unified conclusion. This study aimed to explore whether local ongoing neural oscillations and interregional audiovisual synchrony modulate audiovisual temporal integration.Approach.The human participants performed a simultaneity judgment (SJ) task with the beep-flash stimuli while recording electroencephalography. We focused on two stimulus onset asynchrony (SOA) conditions where subjects report ∼50% proportion of synchronous responses in auditory- and visual-leading SOA (A50V and V50A).Main results.We found that the alpha band power is larger in synchronous response in the central-right posterior and posterior sensors in A50V and V50A conditions, respectively. The results suggested that the alpha band power reflects neuronal excitability in the auditory or visual cortex, which can modulate audiovisual temporal perception depending on the leading sense. Additionally, the SJs were modulated by the opposite phases of alpha (5-10 Hz) and low beta (14-20 Hz) bands in the A50V condition while the low beta band (14-18 Hz) in the V50A condition. One cycle of alpha or two cycles of beta oscillations matched an auditory-leading TBW of ∼86 ms, while two cycles of beta oscillations matched a visual-leading TBW of ∼105 ms. This result indicated the opposite phases in the alpha and beta bands reflect opposite cortical excitability, which modulated the audiovisual SJs. Finally, we found stronger high beta (21-28 Hz) audiovisual phase synchronization for synchronous response in the A50V condition. The phase synchrony of the beta band might be related to maintaining information flow between visual and auditory regions in a top-down manner.Significance.These results clarified whether and how the prestimulus brain state, including local neural oscillations and functional connectivity between brain regions, affects audiovisual temporal integration.
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Affiliation(s)
- Zeliang Jiang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
| | - Xingwei An
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
| | - Shuang Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
| | - Erwei Yin
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
- Defense Innovation Institute, Academy of Military Sciences (AMS), 100071 Beijing, People's Republic of China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), 300457 Tianjin, People's Republic of China
| | - Ye Yan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
- Defense Innovation Institute, Academy of Military Sciences (AMS), 100071 Beijing, People's Republic of China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), 300457 Tianjin, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
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12
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Ward C, Nasrallah K, Tran D, Sabri E, Vazquez A, Sjulson L, Castillo PE, Batista-Brito R. Developmental Disruption of Mef2c in Medial Ganglionic Eminence-Derived Cortical Inhibitory Interneurons Impairs Cellular and Circuit Function. Biol Psychiatry 2024:S0006-3223(24)01360-X. [PMID: 38848814 DOI: 10.1016/j.biopsych.2024.05.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/25/2024] [Accepted: 05/22/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND MEF2C is strongly linked to various neurodevelopmental disorders including autism, intellectual disability, schizophrenia, and attention-deficit/hyperactivity disorder. Mice that constitutively lack 1 copy of Mef2c or selectively lack both copies of Mef2c in cortical excitatory neurons display a variety of behavioral phenotypes associated with neurodevelopmental disorders. The MEF2C protein is a transcription factor necessary for cellular development and synaptic modulation of excitatory neurons. MEF2C is also expressed in a subset of cortical GABAergic (gamma-aminobutyric acidergic) inhibitory neurons, but its function in those cell types remains largely unknown. METHODS Using conditional deletions of the Mef2c gene in mice, we investigated the role of MEF2C in parvalbumin-expressing interneurons (PV-INs), the largest subpopulation of cortical GABAergic cells, at 2 developmental time points. We performed slice electrophysiology, in vivo recordings, and behavior assays to test how embryonic and late postnatal loss of MEF2C from GABAergic INs impacts their survival and maturation and alters brain function and behavior. RESULTS Loss of MEF2C from PV-INs during embryonic, but not late postnatal, development resulted in reduced PV-IN number and failure of PV-INs to molecularly and synaptically mature. In association with these deficits, early loss of MEF2C in GABAergic INs led to abnormal cortical network activity, hyperactive and stereotypic behavior, and impaired cognitive and social behavior. CONCLUSIONS MEF2C expression is critical for the development of cortical GABAergic INs, particularly PV-INs. Embryonic loss of function of MEF2C mediates dysfunction of GABAergic INs, leading to altered in vivo patterns of cortical activity and behavioral phenotypes associated with neurodevelopmental disorders.
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Affiliation(s)
- Claire Ward
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York
| | - Kaoutsar Nasrallah
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York; Department of Biological Sciences, Fordham University, Bronx, New York
| | - Duy Tran
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York
| | - Ehsan Sabri
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York
| | - Arenski Vazquez
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York
| | - Lucas Sjulson
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York; Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, New York
| | - Pablo E Castillo
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York; Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, New York
| | - Renata Batista-Brito
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York; Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, New York; Department of Genetics, Albert Einstein College of Medicine, Bronx, New York.
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13
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Pacheco LB, Feuerriegel D, Jach HK, Robinson E, Duong VN, Bode S, Smillie LD. Disentangling periodic and aperiodic resting EEG correlates of personality. Neuroimage 2024; 293:120628. [PMID: 38688430 DOI: 10.1016/j.neuroimage.2024.120628] [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: 08/17/2023] [Revised: 04/26/2024] [Accepted: 04/27/2024] [Indexed: 05/02/2024] Open
Abstract
Previous studies of resting electroencephalography (EEG) correlates of personality traits have conflated periodic and aperiodic sources of EEG signals. Because these are associated with different underlying neural dynamics, disentangling them can avoid measurement confounds and clarify findings. In a large sample (n = 300), we investigated how disentangling these activities impacts findings related to two research programs within personality neuroscience. In Study 1 we examined associations between Extraversion and two putative markers of reward sensitivity-Left Frontal Alpha asymmetry (LFA) and Frontal-Posterior Theta (FPT). In Study 2 we used machine learning to predict personality trait scores from resting EEG. In both studies, power within each EEG frequency bin was quantified as both total power and separate contributions of periodic and aperiodic activity. In Study 1, total power LFA and FPT correlated negatively with Extraversion (r ∼ -0.14), but there was no relation when LFA and FPT were derived only from periodic activity. In Study 2, all Big Five traits could be decoded from periodic power (r ∼ 0.20), and Agreeableness could also be decoded from total power and from aperiodic indices. Taken together, these results show how separation of periodic and aperiodic activity in resting EEG may clarify findings in personality neuroscience. Disentangling these signals allows for more reliable findings relating to periodic EEG markers of personality, and highlights novel aperiodic markers to be explored in future research.
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Affiliation(s)
- Luiza Bonfim Pacheco
- Melbourne School of Psychological Sciences, The University of Melbourne, Victoria, Australia.
| | - Daniel Feuerriegel
- Melbourne School of Psychological Sciences, The University of Melbourne, Victoria, Australia
| | - Hayley K Jach
- Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Tübingen, Germany
| | - Elizabeth Robinson
- Melbourne School of Psychological Sciences, The University of Melbourne, Victoria, Australia; Bolton Clarke Research Institute, Melbourne, Victoria, Australia
| | - Vu Ngoc Duong
- Melbourne School of Psychological Sciences, The University of Melbourne, Victoria, Australia
| | - Stefan Bode
- Melbourne School of Psychological Sciences, The University of Melbourne, Victoria, Australia
| | - Luke D Smillie
- Melbourne School of Psychological Sciences, The University of Melbourne, Victoria, Australia
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14
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Zamm A, Loehr JD, Vesper C, Konvalinka I, Kappel SL, Heggli OA, Vuust P, Keller PE. A practical guide to EEG hyperscanning in joint action research: from motivation to implementation. Soc Cogn Affect Neurosci 2024; 19:nsae026. [PMID: 38584414 PMCID: PMC11086947 DOI: 10.1093/scan/nsae026] [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: 05/07/2023] [Revised: 12/31/2023] [Accepted: 03/15/2024] [Indexed: 04/09/2024] Open
Abstract
Developments in cognitive neuroscience have led to the emergence of hyperscanning, the simultaneous measurement of brain activity from multiple people. Hyperscanning is useful for investigating social cognition, including joint action, because of its ability to capture neural processes that occur within and between people as they coordinate actions toward a shared goal. Here, we provide a practical guide for researchers considering using hyperscanning to study joint action and seeking to avoid frequently raised concerns from hyperscanning skeptics. We focus specifically on Electroencephalography (EEG) hyperscanning, which is widely available and optimally suited for capturing fine-grained temporal dynamics of action coordination. Our guidelines cover questions that are likely to arise when planning a hyperscanning project, ranging from whether hyperscanning is appropriate for answering one's research questions to considerations for study design, dependent variable selection, data analysis and visualization. By following clear guidelines that facilitate careful consideration of the theoretical implications of research design choices and other methodological decisions, joint action researchers can mitigate interpretability issues and maximize the benefits of hyperscanning paradigms.
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Affiliation(s)
- Anna Zamm
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus 8000, Denmark
- Interacting Minds Center, Aarhus University, Aarhus 8000, Denmark
| | - Janeen D Loehr
- Department of Psychology and Health Studies, University of Saskatchewan, Saskatoon, SK S7N 5A5, Canada
| | - Cordula Vesper
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus 8000, Denmark
- Interacting Minds Center, Aarhus University, Aarhus 8000, Denmark
| | - Ivana Konvalinka
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kongens Lyngby DK-2800, Denmark
| | - Simon L Kappel
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N 8200, Denmark
| | - Ole A Heggli
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus 8000, Denmark
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus 8000, Denmark
| | - Peter E Keller
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus 8000, Denmark
- MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, New South Wales 2751, Australia
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15
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Tanaka M, Battaglia S, Giménez-Llort L, Chen C, Hepsomali P, Avenanti A, Vécsei L. Innovation at the Intersection: Emerging Translational Research in Neurology and Psychiatry. Cells 2024; 13:790. [PMID: 38786014 PMCID: PMC11120114 DOI: 10.3390/cells13100790] [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: 04/16/2024] [Accepted: 04/28/2024] [Indexed: 05/25/2024] Open
Abstract
Translational research in neurological and psychiatric diseases is a rapidly advancing field that promises to redefine our approach to these complex conditions [...].
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Affiliation(s)
- Masaru Tanaka
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Tisza Lajos krt. 113, H-6725 Szeged, Hungary;
| | - Simone Battaglia
- Center for Studies and Research in Cognitive Neuroscience, Department of Psychology “Renzo Canestrari”, Cesena Campus, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy;
- Department of Psychology, University of Turin, 10124 Turin, Italy
| | - Lydia Giménez-Llort
- Institut de Neurociències, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain;
- Department of Psychiatry & Forensic Medicine, Faculty of Medicine, Campus Bellaterra, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain
| | - Chong Chen
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi 755-8505, Japan;
| | - Piril Hepsomali
- School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6 6ET, UK;
| | - Alessio Avenanti
- Center for Studies and Research in Cognitive Neuroscience, Department of Psychology “Renzo Canestrari”, Cesena Campus, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy;
- Neuropsychology and Cognitive Neuroscience Research Center (CINPSI Neurocog), Universidad Católica del Maule, Talca 3460000, Chile
| | - László Vécsei
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Tisza Lajos krt. 113, H-6725 Szeged, Hungary;
- Department of Neurology, Albert Szent-Györgyi Medical School, University of Szeged, Semmelweis u. 6, H-6725 Szeged, Hungary
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16
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Geiger M, Hurewitz SR, Pawlowski K, Baumer NT, Wilkinson CL. Alterations in aperiodic and periodic EEG activity in young children with Down syndrome. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.01.24306729. [PMID: 38746335 PMCID: PMC11092732 DOI: 10.1101/2024.05.01.24306729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Down syndrome is the most common cause of intellectual disability, yet little is known about the neurobiological pathways leading to cognitive impairments. Electroencephalographic (EEG) measures are commonly used to study neurodevelopmental disorders, but few studies have focused on young children with DS. Here we assess resting state EEG data collected from toddlers/preschoolers with DS (n=29, age 13-48 months old) and compare their aperiodic and periodic EEG features with both age-matched (n=29) and cognitive-matched (n=58) comparison groups. DS participants exhibited significantly reduced aperiodic slope, increased periodic theta power, and decreased alpha peak amplitude. A majority of DS participants displayed a prominent peak in the theta range, whereas a theta peak was not present in age-matched participants. Overall, similar findings were also observed when comparing DS and cognitive-matched groups, suggesting that EEG differences are not explained by delayed cognitive ability.
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17
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Ward C, Nasrallah K, Tran D, Sabri E, Vazquez A, Sjulson L, Castillo PE, Batista-Brito R. Developmental disruption of Mef2c in Medial Ganglionic Eminence-derived cortical inhibitory interneurons impairs cellular and circuit function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.592084. [PMID: 38746148 PMCID: PMC11092645 DOI: 10.1101/2024.05.01.592084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
MEF2C is strongly linked to various neurodevelopmental disorders (NDDs) including autism, intellectual disability, schizophrenia, and attention-deficit/hyperactivity. Mice constitutively lacking one copy of Mef2c , or selectively lacking both copies of Mef2c in cortical excitatory neurons, display a variety of behavioral phenotypes associated with NDDs. The MEF2C protein is a transcription factor necessary for cellular development and synaptic modulation of excitatory neurons. MEF2C is also expressed in a subset of cortical GABAergic inhibitory neurons, but its function in those cell types remains largely unknown. Using conditional deletions of the Mef2c gene in mice, we investigated the role of MEF2C in Parvalbumin-expressing Interneurons (PV-INs), the largest subpopulation of cortical GABAergic cells, at two developmental timepoints. We performed slice electrophysiology, in vivo recordings, and behavior assays to test how embryonic and late postnatal loss of MEF2C from GABAergic interneurons impacts their survival and maturation, and alters brain function and behavior. We found that loss of MEF2C from PV-INs during embryonic, but not late postnatal, development resulted in reduced PV-IN number and failure of PV-INs to molecularly and synaptically mature. In association with these deficits, early loss of MEF2C in GABAergic interneurons lead to abnormal cortical network activity, hyperactive and stereotypic behavior, and impaired cognitive and social behavior. Our findings indicate that MEF2C expression is critical for the development of cortical GABAergic interneurons, particularly PV-INs. Embryonic loss of function of MEF2C mediates dysfunction of GABAergic interneurons, leading to altered in vivo patterns of cortical activity and behavioral phenotypes associated with neurodevelopmental disorders.
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18
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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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19
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Solomon EA, Wang JB, Oya H, Howard MA, Trapp NT, Uitermarkt BD, Boes AD, Keller CJ. TMS provokes target-dependent intracranial rhythms across human cortical and subcortical sites. Brain Stimul 2024; 17:698-712. [PMID: 38821396 PMCID: PMC11313454 DOI: 10.1016/j.brs.2024.05.014] [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: 11/24/2023] [Revised: 05/25/2024] [Accepted: 05/26/2024] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) is believed to alter ongoing neural activity and cause circuit-level changes in brain function. While the electrophysiological effects of TMS have been extensively studied with scalp electroencephalography (EEG), this approach generally evaluates low-frequency neural activity at the cortical surface. However, TMS can be safely used in patients with intracranial electrodes (iEEG), allowing for direct assessment of deeper and more localized oscillatory responses across the frequency spectrum. OBJECTIVE/HYPOTHESIS Our study used iEEG to understand the effects of TMS on human neural activity in the spectral domain. We asked (1) which brain regions respond to cortically-targeted TMS, and in what frequency bands, (2) whether deeper brain structures exhibit oscillatory responses, and (3) whether the neural responses to TMS reflect evoked versus induced oscillations. METHODS We recruited 17 neurosurgical patients with indwelling electrodes and recorded neural activity while patients underwent repeated trials of single-pulse TMS at either the dorsolateral prefrontal cortex (DLPFC) or parietal cortex. iEEG signals were analyzed using spectral methods to understand the oscillatory responses to TMS. RESULTS Stimulation to DLPFC drove widespread low-frequency increases (3-8 Hz) in frontolimbic cortices and high-frequency decreases (30-110 Hz) in frontotemporal areas, including the hippocampus. Stimulation to parietal cortex specifically provoked low-frequency responses in the medial temporal lobe. While most low-frequency activity was consistent with phase-locked evoked responses, anterior frontal regions exhibited induced theta oscillations following DLPFC stimulation. CONCLUSIONS By combining TMS with intracranial EEG recordings, our results suggest that TMS is an effective means to perturb oscillatory neural activity in brain-wide networks, including deeper structures not directly accessed by stimulation itself.
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Affiliation(s)
- Ethan A Solomon
- Dept. of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Palo Alto, 94305, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, 94305, CA, USA.
| | - Jeffrey B Wang
- Dept. of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Palo Alto, 94305, CA, USA; Biophysics Graduate Program, Stanford University Medical Center, Stanford, 94305, CA, USA
| | - Hiroyuki Oya
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, Iowa City, 52242, IA, USA
| | - Matthew A Howard
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, Iowa City, 52242, IA, USA
| | - Nicholas T Trapp
- Department of Neurology, Carver College of Medicine, University of Iowa, Iowa City, 52242, IA, USA; Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, 52242, IA, USA
| | - Brandt D Uitermarkt
- Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, 52242, IA, USA
| | - Aaron D Boes
- Department of Neurology, Carver College of Medicine, University of Iowa, Iowa City, 52242, IA, USA; Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, 52242, IA, USA; Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, 52242, IA, USA
| | - Corey J Keller
- Dept. of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Palo Alto, 94305, CA, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, 94305, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, 94305, CA, USA
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20
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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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Nwakamma MC, Stillman AM, Gabard-Durnam LJ, Cavanagh JF, Hillman CH, Morris TP. Slowing of Parameterized Resting-State Electroencephalography After Mild Traumatic Brain Injury. Neurotrauma Rep 2024; 5:448-461. [PMID: 38666007 PMCID: PMC11044859 DOI: 10.1089/neur.2024.0004] [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] [Indexed: 04/28/2024] Open
Abstract
Reported changes in electroencephalography (EEG)-derived spectral power after mild traumatic brain injury (mTBI) remains inconsistent across existing literature. However, this may be a result of previous analyses depending solely on observing spectral power within traditional canonical frequency bands rather than accounting for the aperiodic activity within the collected neural signal. Therefore, the aim of this study was to test for differences in rhythmic and arrhythmic time series across the brain, and in the cognitively relevant frontoparietal (FP) network, and observe whether those differences were associated with cognitive recovery post-mTBI. Resting-state electroencephalography (rs-EEG) was collected from 88 participants (56 mTBI and 32 age- and sex-matched healthy controls) within 14 days of injury for the mTBI participants. A battery of executive function (EF) tests was collected at the first session with follow-up metrics collected approximately 2 and 4 months after the initial visit. After spectral parameterization, a significant between-group difference in aperiodic-adjusted alpha center peak frequency within the FP network was observed, where a slowing of alpha peak frequency was found in the mTBI group in comparison to the healthy controls. This slowing of week 2 (collected within 2 weeks of injury) aperiodic-adjusted alpha center peak frequency within the FP network was associated with increased EF over time (evaluated using executive composite scores) post-mTBI. These findings suggest alpha center peak frequency within the FP network as a candidate prognostic marker of EF recovery and may inform clinical rehabilitative methods post-mTBI.
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Affiliation(s)
- Mark C. Nwakamma
- Department of Physical Therapy Human Movement Sciences, Northeastern University, Boston, Massachusetts, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, Massachusetts, USA
| | - Alexandra M. Stillman
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Laurel J. Gabard-Durnam
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, Massachusetts, USA
| | - James F. Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Charles H. Hillman
- Department of Physical Therapy Human Movement Sciences, Northeastern University, Boston, Massachusetts, USA
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, Massachusetts, USA
| | - Timothy P. Morris
- Department of Physical Therapy Human Movement Sciences, Northeastern University, Boston, Massachusetts, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, Massachusetts, USA
- Department of Applied Psychology, Northeastern University, Boston, Massachusetts, USA
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22
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Boudewyn MA, Erickson MA, Winsler K, Barch DM, Carter CS, Frank MJ, Gold JM, MacDonald AW, Ragland JD, Silverstein SM, Yonelinas AP, Luck SJ. Assessing Trial-by-Trial Electrophysiological and Behavioral Markers of Attentional Control and Sensory Precision in Psychotic and Mood Disorders. Schizophr Bull 2024:sbae038. [PMID: 38616053 DOI: 10.1093/schbul/sbae038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
BACKGROUND AND HYPOTHESIS The current study investigated the extent to which changes in attentional control contribute to performance on a visual perceptual discrimination task, on a trial-by-trial basis in a transdiagnostic clinical sample. STUDY DESIGN Participants with schizophrenia (SZ; N = 58), bipolar disorder (N = 42), major depression disorder (N = 51), and psychiatrically healthy controls (N = 92) completed a visual perception task in which stimuli appeared briefly. The design allowed us to estimate the lapse rate and the precision of perceptual representations of the stimuli. Electroencephalograms (EEG) were recorded to examine pre-stimulus activity in the alpha band (8-13 Hz), overall and in relation to behavior performance on the task. STUDY RESULTS We found that the attention lapse rate was elevated in the SZ group compared with all other groups. We also observed group differences in pre-stimulus alpha activity, with control participants showing the highest levels of pre-stimulus alpha when averaging across trials. However, trial-by-trial analyses showed within-participant fluctuations in pre-stimulus alpha activity significantly predicted the likelihood of making an error, in all groups. Interestingly, our analysis demonstrated that aperiodic contributions to the EEG signal (which affect power estimates across frequency bands) serve as a significant predictor of behavior as well. CONCLUSIONS These results confirm the elevated attention lapse rate that has been observed in SZ, validate pre-stimulus EEG markers of attentional control and their use as a predictor of behavior on a trial-by-trial basis, and suggest that aperiodic contributions to the EEG signal are an important target for further research in this area, in addition to alpha-band activity.
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Affiliation(s)
- Megan A Boudewyn
- Department of Psychology, University of California, Santa Cruz, California, USA
| | - Molly A Erickson
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, Illinois, USA
| | - Kurt Winsler
- Department of Psychology, University of California, Davis, California, USA
| | - Deanna M Barch
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Cameron S Carter
- Department of Psychiatry and Human Behavior, University of California, Irvine, California, USA
| | - Michael J Frank
- Department of Cognitive, Linguistics and Psychological Sciences, Brown University, Providence, Rhode Island, USA
| | - James M Gold
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Angus W MacDonald
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - J Daniel Ragland
- Department of Psychology, University of California, Davis, California, USA
| | - Steven M Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, USA
| | - Andrew P Yonelinas
- Department of Psychology, University of California, Davis, California, USA
| | - Steven J Luck
- Department of Psychology, University of California, Davis, California, USA
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23
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Myrov V, Siebenhühner F, Juvonen JJ, Arnulfo G, Palva S, Palva JM. Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture. Commun Biol 2024; 7:405. [PMID: 38570628 PMCID: PMC10991572 DOI: 10.1038/s42003-024-06083-y] [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: 09/05/2023] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for the direct quantification of rhythmicity. We applied pACF to human intracerebral stereoelectroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.
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Affiliation(s)
- Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki, Finland
| | - Joonas J Juvonen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, Genoa, Italy
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - J Matias Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
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24
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Gaižauskaitė R, Gladutytė L, Zelionkaitė I, Čėsnaitė E, Busch NA, Grikšienė R. The search for the relationship between female hormonal status, alpha oscillations, and aperiodic features of resting state EEG. Int J Psychophysiol 2024; 198:112312. [PMID: 38336163 DOI: 10.1016/j.ijpsycho.2024.112312] [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: 11/07/2023] [Revised: 01/12/2024] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
Fluctuations in sex steroid levels during the menstrual cycle and the use of hormonal contraceptives have been linked to changes in cognitive function and emotions in females. Such variations may be mediated by overall brain activity and excitability. We aimed to investigate the impact of female hormonal status on resting state EEG (rsEEG) parameters, including periodic (individual alpha frequency, alpha power) and aperiodic (1/f slope) features. rsEEG was recorded in healthy females (mean age 26.4 ± 4.6 years), who were naturally cycling in the early follicular (n = 33) or mid-luteal phases (n = 35), or who used either oral contraceptives (n = 35) or hormonal intrauterine devices (n = 28). Salivary concentrations of estradiol, progesterone, and testosterone were measured. Contrary to previous findings, this study did not reveal significant differences in rsEEG parameters between groups or significant relationships with hormonal levels. Age emerged as a covariate negatively related to the median 1/f slope. Based on these findings, we found no significant evidence to suggest that the periodic (alpha power and peak frequency) or aperiodic activity patterns in the brain during the resting state differ between the groups of females under investigation.
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Affiliation(s)
- Rimantė Gaižauskaitė
- Department of Neurobiology and Biophysics, Vilnius University, Saulėtekio ave. 7, 10257 Vilnius, Lithuania.
| | - Lina Gladutytė
- Department of Neurobiology and Biophysics, Vilnius University, Saulėtekio ave. 7, 10257 Vilnius, Lithuania
| | - Ingrida Zelionkaitė
- Department of Neurobiology and Biophysics, Vilnius University, Saulėtekio ave. 7, 10257 Vilnius, Lithuania
| | - Elena Čėsnaitė
- Institute of Psychology, University of Münster, Fliednerstr. 21, 48149 Münster, Germany
| | - Niko A Busch
- Institute of Psychology, University of Münster, Fliednerstr. 21, 48149 Münster, Germany
| | - Ramunė Grikšienė
- Department of Neurobiology and Biophysics, Vilnius University, Saulėtekio ave. 7, 10257 Vilnius, Lithuania
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25
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Hernandez A, Sania A, Bowers ME, Leach SC, McSweeney M, Yoder L, Fifer W, Elliott AJ, Shuffrey L, Rauh V, Him DA, Fox NA, Morales S. Examining the impact of prenatal maternal internalizing symptoms and socioeconomic status on children's frontal alpha asymmetry and psychopathology. Dev Psychobiol 2024; 66:e22476. [PMID: 38433442 DOI: 10.1002/dev.22476] [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: 08/18/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 03/05/2024]
Abstract
Prenatal maternal internalizing psychopathology (depression and anxiety) and socioeconomic status (SES) have been independently associated with higher risk for internalizing and externalizing problems in children. However, the pathways behind these associations are not well understood. Numerous studies have linked greater right frontal alpha asymmetry to internalizing problems; however, findings have been mixed. Several studies have also linked maternal internalizing psychopathology to children's frontal alpha asymmetry. Additionally, emerging studies have linked SES to children's frontal alpha asymmetry. To date, only a limited number of studies have examined these associations within a longitudinal design, and the majority have utilized relatively small samples. The current preregistered study utilizes data from a large prospective study of young children (N = 415; Meanage = 7.27 years; Rangeage = 5-11 years) to examine the association between prenatal maternal internalizing symptoms, children's frontal alpha asymmetry, and behavior problems. Prenatal maternal internalizing symptoms did not predict children's frontal alpha asymmetry, and there was no association between frontal alpha asymmetry and behavior problems. However, mothers' internalizing symptoms during pregnancy predicted children's internalizing and externalizing outcomes. Non-preregistered analyses showed that lower prenatal maternal SES predicted greater child right frontal alpha asymmetry and internalizing problems. Additional non-preregistered analyses did not find evidence for frontal alpha asymmetry as a moderator of the relation between prenatal maternal internalizing psychopathology and SES to children's behavior problems. Future research should examine the impact of SES on children's frontal alpha asymmetry in high-risk samples.
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Affiliation(s)
- Alexis Hernandez
- Department of Psychology, University of Southern California, Los Angeles, California, USA
| | - Ayesha Sania
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
| | - Maureen E Bowers
- Department of Human Development and Quantitative Methodology, University of Maryland - College Park, College Park, Maryland, USA
| | - Stephanie C Leach
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, Iowa, USA
| | - Marco McSweeney
- Department of Human Development and Quantitative Methodology, University of Maryland - College Park, College Park, Maryland, USA
| | - Lydia Yoder
- Department of Human Development and Quantitative Methodology, University of Maryland - College Park, College Park, Maryland, USA
| | - William Fifer
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
| | - Amy J Elliott
- Avera Research Institute, Sioux Falls, South Dakota, USA
| | - Lauren Shuffrey
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
| | - Virginia Rauh
- Heilbrunn Department of Population and Family Health, Columbia University, New York, New York, USA
| | | | - Nathan A Fox
- Department of Human Development and Quantitative Methodology, University of Maryland - College Park, College Park, Maryland, USA
| | - Santiago Morales
- Department of Psychology, University of Southern California, Los Angeles, California, USA
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26
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Cho H, Adamek M, Willie JT, Brunner P. Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.04.560843. [PMID: 38562725 PMCID: PMC10983872 DOI: 10.1101/2023.10.04.560843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence and frequency of neural oscillations are determined by identifying peaks over 1/f noise within the power spectrum. However, this approach solely operates within the frequency domain and thus cannot adequately distinguish between the fundamental frequency of a non-sinusoidal oscillation and its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing the false-positive detection rate - a confounding factor in the analysis of neural oscillations. To overcome these limitations, we define the fundamental criteria that characterize a neural oscillation and introduce the Cyclic Homogeneous Oscillation (CHO) detection method that implements these criteria based on an auto-correlation approach that determines the oscillation's periodicity and fundamental frequency. We evaluated CHO by verifying its performance on simulated sinusoidal and non-sinusoidal oscillatory bursts convolved with 1/f noise. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. Specifically, we determined the sensitivity and specificity of CHO as a function of signal-to-noise ratio (SNR). We further assessed CHO by testing it on electrocorticographic (ECoG, 8 subjects) and electroencephalographic (EEG, 7 subjects) signals recorded during the pre-stimulus period of an auditory reaction time task and on electrocorticographic signals (6 SEEG subjects and 6 ECoG subjects) collected during resting state. In the reaction time task, the CHO method detected auditory alpha and pre-motor beta oscillations in ECoG signals and occipital alpha and pre-motor beta oscillations in EEG signals. Moreover, CHO determined the fundamental frequency of hippocampal oscillations in the human hippocampus during the resting state (6 SEEG subjects). In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.
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Affiliation(s)
- Hohyun Cho
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
- National Center for Adaptive Neurotechnologies, St. Louis, MO, USA
| | - Markus Adamek
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
- National Center for Adaptive Neurotechnologies, St. Louis, MO, USA
| | - Jon T. Willie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
- National Center for Adaptive Neurotechnologies, St. Louis, MO, USA
| | - Peter Brunner
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
- National Center for Adaptive Neurotechnologies, St. Louis, MO, USA
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27
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Pöpplau JA, Schwarze T, Dorofeikova M, Pochinok I, Günther A, Marquardt A, Hanganu-Opatz IL. Reorganization of adolescent prefrontal cortex circuitry is required for mouse cognitive maturation. Neuron 2024; 112:421-440.e7. [PMID: 37979584 PMCID: PMC10855252 DOI: 10.1016/j.neuron.2023.10.024] [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: 01/11/2023] [Revised: 08/31/2023] [Accepted: 10/19/2023] [Indexed: 11/20/2023]
Abstract
Most cognitive functions involving the prefrontal cortex emerge during late development. Increasing evidence links this delayed maturation to the protracted timeline of prefrontal development, which likely does not reach full maturity before the end of adolescence. However, the underlying mechanisms that drive the emergence and fine-tuning of cognitive abilities during adolescence, caused by circuit wiring, are still unknown. Here, we continuously monitored prefrontal activity throughout the postnatal development of mice and showed that an initial activity increase was interrupted by an extensive microglia-mediated breakdown of activity, followed by the rewiring of circuit elements to achieve adult-like patterns and synchrony. Interfering with these processes during adolescence, but not adulthood, led to a long-lasting microglia-induced disruption of prefrontal activity and neuronal morphology and decreased cognitive abilities. These results identified a nonlinear reorganization of prefrontal circuits during adolescence and revealed its importance for adult network function and cognitive processing.
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Affiliation(s)
- Jastyn A Pöpplau
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Timo Schwarze
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mariia Dorofeikova
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Irina Pochinok
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Anne Günther
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Annette Marquardt
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ileana L Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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28
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Bigoni C, Pagnamenta S, Cadic-Melchior A, Bevilacqua M, Harquel S, Raffin E, Hummel FC. MEP and TEP features variability: is it just the brain-state? J Neural Eng 2024; 21:016011. [PMID: 38211341 DOI: 10.1088/1741-2552/ad1dc2] [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: 08/29/2023] [Accepted: 01/11/2024] [Indexed: 01/13/2024]
Abstract
Objective.The literature investigating the effects of alpha oscillations on corticospinal excitability is divergent. We believe inconsistency in the findings may arise, among others, from the electroencephalography (EEG) processing for brain-state determination. Here, we provide further insights in the effects of the brain-state on cortical and corticospinal excitability and quantify the impact of different EEG processing.Approach.Corticospinal excitability was measured using motor evoked potential (MEP) peak-to-peak amplitudes elicited with transcranial magnetic stimulation (TMS); cortical responses were studied through TMS-evoked potentials' TEPs features. A TMS-EEG-electromyography (EMG) dataset of 18 young healthy subjects who received 180 single-pulse (SP) and 180 paired pulses (PP) to determine short-intracortical inhibition (SICI) was investigated. To study the effect of different EEG processing, we compared the brain-state estimation deriving from three published methods. The influence of presence of neural oscillations was also investigated. To evaluate the effect of the brain-state on MEP and TEP features variability, we defined the brain-state based on specific EEG phase and power combinations, only in trials where neural oscillations were present. The relationship between TEPs and MEPs was further evaluated.Main results.The presence of neural oscillations resulted in more consistent results regardless of the EEG processing approach. Nonetheless, the latter still critically affected the outcomes, making conclusive claims complex. With our approach, the MEP amplitude was positively modulated by the alpha power and phase, with stronger responses during the trough phase and high power. Power and phase also affected TEP features. Importantly, similar effects were observed in both TMS conditions.Significance.These findings support the view that the brain state of alpha oscillations is associated with the variability observed in cortical and corticospinal responses to TMS, with a tight correlation between the two. The results further highlight the importance of closed-loop stimulation approaches while underlining that care is needed in designing experiments and choosing the analytical approaches, which should be based on knowledge from offline studies to control for the heterogeneity originating from different EEG processing strategies.
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Affiliation(s)
- Claudia Bigoni
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Sion 1951, Switzerland
| | - Sara Pagnamenta
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
| | - Andéol Cadic-Melchior
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Sion 1951, Switzerland
| | - Michele Bevilacqua
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Sion 1951, Switzerland
| | - Sylvain Harquel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Sion 1951, Switzerland
| | - Estelle Raffin
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Sion 1951, Switzerland
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Sion 1951, Switzerland
- Clinical Neuroscience, University of Geneva Medical School, 1202 Geneva, Switzerland
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29
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Papadopoulos S, Szul MJ, Congedo M, Bonaiuto JJ, Mattout J. Beta bursts question the ruling power for brain-computer interfaces. J Neural Eng 2024; 21:016010. [PMID: 38167234 DOI: 10.1088/1741-2552/ad19ea] [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: 09/15/2023] [Accepted: 01/02/2024] [Indexed: 01/05/2024]
Abstract
Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.
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Affiliation(s)
- Sotirios Papadopoulos
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS, UMR5292, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Maciej J Szul
- University Lyon 1, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Marco Congedo
- GIPSA-lab, University Grenoble Alpes, CNRS, Grenoble-INP, Grenoble, France
| | - James J Bonaiuto
- University Lyon 1, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Jérémie Mattout
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS, UMR5292, Lyon, France
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30
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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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Schonhaut DR, Rao AM, Ramayya AG, Solomon EA, Herweg NA, Fried I, Kahana MJ. MTL neurons phase-lock to human hippocampal theta. eLife 2024; 13:e85753. [PMID: 38193826 PMCID: PMC10948143 DOI: 10.7554/elife.85753] [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: 12/22/2022] [Accepted: 01/08/2024] [Indexed: 01/10/2024] Open
Abstract
Memory formation depends on neural activity across a network of regions, including the hippocampus and broader medial temporal lobe (MTL). Interactions between these regions have been studied indirectly using functional MRI, but the bases for interregional communication at a cellular level remain poorly understood. Here, we evaluate the hypothesis that oscillatory currents in the hippocampus synchronize the firing of neurons both within and outside the hippocampus. We recorded extracellular spikes from 1854 single- and multi-units simultaneously with hippocampal local field potentials (LFPs) in 28 neurosurgical patients who completed virtual navigation experiments. A majority of hippocampal neurons phase-locked to oscillations in the slow (2-4 Hz) or fast (6-10 Hz) theta bands, with a significant subset exhibiting nested slow theta × beta frequency (13-20 Hz) phase-locking. Outside of the hippocampus, phase-locking to hippocampal oscillations occurred only at theta frequencies and primarily among neurons in the entorhinal cortex and amygdala. Moreover, extrahippocampal neurons phase-locked to hippocampal theta even when theta did not appear locally. These results indicate that spike-time synchronization with hippocampal theta is a defining feature of neuronal activity in the hippocampus and structurally connected MTL regions. Theta phase-locking could mediate flexible communication with the hippocampus to influence the content and quality of memories.
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Affiliation(s)
- Daniel R Schonhaut
- Department of Neuroscience, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Aditya M Rao
- Department of Psychology, University of PennsylvaniaPhiladelphiaUnited States
| | - Ashwin G Ramayya
- Department of Neurosurgery, University of PennsylvaniaPhiladelphiaUnited States
| | - Ethan A Solomon
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Nora A Herweg
- Department of Psychology, University of PennsylvaniaPhiladelphiaUnited States
| | - Itzhak Fried
- Department of Neurosurgery, Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los AngelesLos AngelesUnited States
- Faculty of Medicine, Tel-Aviv UniversityTel-AvivIsrael
| | - Michael J Kahana
- Department of Psychology, University of PennsylvaniaPhiladelphiaUnited States
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Singh S, Becker S, Trappenberg T, Nunes A. Granule cells perform frequency-dependent pattern separation in a computational model of the dentate gyrus. Hippocampus 2024; 34:14-28. [PMID: 37950569 DOI: 10.1002/hipo.23585] [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: 05/14/2023] [Revised: 09/28/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023]
Abstract
Mnemonic discrimination (MD) may be dependent on oscillatory perforant path input frequencies to the hippocampus in a "U"-shaped fashion, where some studies show that slow and fast input frequencies support MD, while other studies show that intermediate frequencies disrupt MD. We hypothesize that pattern separation (PS) underlies frequency-dependent MD performance. We aim to study, in a computational model of the hippocampal dentate gyrus (DG), the network and cellular mechanisms governing this putative "U"-shaped PS relationship. We implemented a biophysical model of the DG that produces the hypothesized "U"-shaped input frequency-PS relationship, and its associated oscillatory electrophysiological signatures. We subsequently evaluated the network's PS ability using an adapted spatiotemporal task. We undertook systematic lesion studies to identify the network-level mechanisms driving the "U"-shaped input frequency-PS relationship. A minimal circuit of a single granule cell (GC) stimulated with oscillatory inputs was also used to study potential cellular-level mechanisms. Lesioning synapses onto GCs did not impact the "U"-shaped input frequency-PS relationship. Furthermore, GC inhibition limits PS performance for fast frequency inputs, while enhancing PS for slow frequency inputs. GC interspike interval was found to be input frequency dependent in a "U"-shaped fashion, paralleling frequency-dependent PS observed at the network level. Additionally, GCs showed an attenuated firing response for fast frequency inputs. We conclude that independent of network-level inhibition, GCs may intrinsically be capable of producing a "U"-shaped input frequency-PS relationship. GCs may preferentially decorrelate slow and fast inputs via spike timing reorganization and high frequency filtering.
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Affiliation(s)
- Selena Singh
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Suzanna Becker
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Thomas Trappenberg
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Abraham Nunes
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
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Hill AT, Bailey NW, Zomorrodi R, Hadas I, Kirkovski M, Das S, Lum JAG, Enticott PG. EEG microstates in early-to-middle childhood show associations with age, biological sex, and alpha power. Hum Brain Mapp 2023; 44:6484-6498. [PMID: 37873867 PMCID: PMC10681660 DOI: 10.1002/hbm.26525] [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] [Received: 05/16/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/25/2023] Open
Abstract
Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large-scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early-to-middle childhood (4-12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A-E) across both eyes-closed and eyes-open resting-state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f-like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes-open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large-scale functional brain networks during early-to-middle childhood, extending understanding of neural dynamics across this important period for brain development.
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Affiliation(s)
- Aron T. Hill
- Cognitive Neuroscience Unit, School of PsychologyDeakin UniversityGeelongAustralia
- Department of Psychiatry, Central Clinical SchoolMonash UniversityMelbourneAustralia
| | - Neil W. Bailey
- Monarch Research InstituteMonarch Mental Health GroupSydneyAustralia
- School of Medicine and PsychologyThe Australian National UniversityCanberraAustralia
| | - Reza Zomorrodi
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental HealthUniversity of TorontoTorontoCanada
| | - Itay Hadas
- Department of Psychiatry, School of MedicineUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Melissa Kirkovski
- Cognitive Neuroscience Unit, School of PsychologyDeakin UniversityGeelongAustralia
- Institute for Health and SportVictoria UniversityMelbourneAustralia
| | - Sushmit Das
- Azrieli Adult Neurodevelopmental CentreCentre for Addiction and Mental HealthTorontoCanada
| | - Jarrad A. G. Lum
- Cognitive Neuroscience Unit, School of PsychologyDeakin UniversityGeelongAustralia
| | - Peter G. Enticott
- Cognitive Neuroscience Unit, School of PsychologyDeakin UniversityGeelongAustralia
- Department of Psychiatry, Central Clinical SchoolMonash UniversityMelbourneAustralia
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Faiman I, Sparks R, Winston JS, Brunnhuber F, Ciulini N, Young AH, Shotbolt P. Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders. Brain Commun 2023; 5:fcad330. [PMID: 38107505 PMCID: PMC10724050 DOI: 10.1093/braincomms/fcad330] [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: 05/09/2023] [Revised: 09/25/2023] [Accepted: 11/29/2023] [Indexed: 12/19/2023] Open
Abstract
Differentiating between epilepsy and psychogenic non-epileptic seizures presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment and long diagnostic delays. Quantitative markers extracted from resting-state EEG may reveal subtle neurophysiological differences that are diagnostically relevant. Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and psychogenic non-epileptic seizures. Clinical EEG data were collected for 179 quasi-consecutive patients (age > 18) with a suspected diagnosis of epilepsy or psychogenic non-epileptic seizures who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with psychogenic non-epileptic seizures, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers. The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e. theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and psychogenic non-epileptic seizures (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45-60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges. Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with psychogenic non-epileptic seizures. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.
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Affiliation(s)
- Irene Faiman
- Department of Psychological Medicine, King’s College London Institute of Psychiatry Psychology and Neuroscience, London SE5 8AB, UK
| | - Rachel Sparks
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Joel S Winston
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AB, UK
- Department of Clinical Neurophysiology, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Franz Brunnhuber
- Department of Clinical Neurophysiology, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Naima Ciulini
- Department of Clinical Neurophysiology, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Allan H Young
- Department of Psychological Medicine, King’s College London Institute of Psychiatry Psychology and Neuroscience, London SE5 8AB, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Beckenham, Kent BR3 3BX, UK
| | - Paul Shotbolt
- Department of Psychological Medicine, King’s College London Institute of Psychiatry Psychology and Neuroscience, London SE5 8AB, UK
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Wei Y, Wang X, Luo R, Mai X, Li S, Meng J. Decoding movement frequencies and limbs based on steady-state movement-related rhythms from noninvasive EEG. J Neural Eng 2023; 20:066019. [PMID: 37816342 DOI: 10.1088/1741-2552/ad01de] [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] [Accepted: 10/10/2023] [Indexed: 10/12/2023]
Abstract
Objective.Decoding different types of movements noninvasively from electroencephalography (EEG) is an essential topic in neural engineering, especially in brain-computer interface. Although the widely used sensorimotor rhythm (SMR) is efficient in limb decoding, it lacks efficacy in decoding movement frequencies. Accumulating evidence supports the notion that the movement frequency is encoded in the steady-state movement-related rhythm (SSMRR). Our study has two primary objectives: firstly, to investigate the spatial-spectral representation of SSMRR in EEG during voluntary movements; secondly, to assess whether movement frequencies and limbs can be effectively decoded based on SSMRR.Approach.To comprehensively examine the representation of SSMRR, we investigated the frequency characteristics and spatial patterns associated with various rhythmic finger movements. Coherence analysis was performed between the sensor or source domain EEG and finger movements recorded by data gloves. A fusion model based on spectral SNR features and filter-bank common spatial pattern features was utilized to decode movement frequencies and limbs.Main results.At the group-level, sensor domain, and source domain coherence maps demonstrated that the accurate movement frequency (f0) and its first harmonic (f1) were encoded in the contralateral motor cortex. For the four-class classification, including two movement frequencies for both hands, the decoding accuracies for externally paced and internally paced movements were 73.14 ± 15.86% and 66.30 ± 17.26% (averaged across ten subjects, chance levels at 31.05% and 30.96%). Notably, the average results of five subjects with the highest decoding accuracies reached 87.21 ± 7.44% and 80.44 ± 7.99%.Significance.Our results verified the EEG representation of SSMRR and proved that the movement frequency and limb could be effectively decoded based on spatial-spectral features extracted from SSMRR. We suggest that SSMRR can serve as a complement to SMR to expand the range of decodable movement types and the approaches of limb decoding.
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Affiliation(s)
- Yuxuan Wei
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xu Wang
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Ruijie Luo
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Ximing Mai
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Songwei Li
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Jianjun Meng
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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36
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Rubinov M. Circular and unified analysis in network neuroscience. eLife 2023; 12:e79559. [PMID: 38014843 PMCID: PMC10684154 DOI: 10.7554/elife.79559] [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: 04/18/2022] [Accepted: 10/18/2023] [Indexed: 11/29/2023] Open
Abstract
Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.
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Affiliation(s)
- Mika Rubinov
- Departments of Biomedical Engineering, Computer Science, and Psychology, Vanderbilt UniversityNashvilleUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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37
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Smith SE, Kosik EL, van Engen Q, Kohn J, Hill AT, Zomorrodi R, Blumberger DM, Daskalakis ZJ, Hadas I, Voytek B. Magnetic seizure therapy and electroconvulsive therapy increase aperiodic activity. Transl Psychiatry 2023; 13:347. [PMID: 37968260 PMCID: PMC10651875 DOI: 10.1038/s41398-023-02631-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/09/2023] [Accepted: 10/13/2023] [Indexed: 11/17/2023] Open
Abstract
Major depressive disorder (MDD) is a leading cause of disability worldwide. One of the most efficacious treatments for treatment-resistant MDD is electroconvulsive therapy (ECT). Recently, magnetic seizure therapy (MST) was developed as an alternative to ECT due to its more favorable side effect profile. While these approaches have been very successful clinically, the neural mechanisms underlying their therapeutic effects are unknown. For example, clinical "slowing" of the electroencephalogram beginning in the postictal state and extending days to weeks post-treatment has been observed in both treatment modalities. However, a recent longitudinal study of a small cohort of ECT patients revealed that, rather than delta oscillations, clinical slowing was better explained by increases in aperiodic activity, an emerging EEG signal linked to neural inhibition. Here we investigate the role of aperiodic activity in a cohort of patients who received ECT and a cohort of patients who received MST treatment. We find that aperiodic neural activity increases significantly in patients receiving either ECT or MST. Although not directly related to clinical efficacy in this dataset, increased aperiodic activity is linked to greater amounts of neural inhibition, which is suggestive of a potential shared neural mechanism of action across ECT and MST.
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Affiliation(s)
- Sydney E Smith
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.
| | - Eena L Kosik
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Quirine van Engen
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Jordan Kohn
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
- Department of Psychiatry, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Reza Zomorrodi
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Daniel M Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Itay Hadas
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Bradley Voytek
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
- Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, USA
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38
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Boudewyn MA, Erickson MA, Winsler K, Ragland JD, Yonelinas A, Frank M, Silverstein SM, Gold J, MacDonald AW, Carter CS, Barch DM, Luck SJ. Managing EEG studies: How to prepare and what to do once data collection has begun. Psychophysiology 2023; 60:e14365. [PMID: 37314113 PMCID: PMC11276027 DOI: 10.1111/psyp.14365] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 05/08/2023] [Accepted: 05/27/2023] [Indexed: 06/15/2023]
Abstract
In this paper, we provide guidance for the organization and implementation of EEG studies. This work was inspired by our experience conducting a large-scale, multi-site study, but many elements could be applied to any EEG project. Section 1 focuses on study activities that take place before data collection begins. Topics covered include: establishing and training study teams, considerations for task design and piloting, setting up equipment and software, development of formal protocol documents, and planning communication strategy with all study team members. Section 2 focuses on what to do once data collection has already begun. Topics covered include: (1) how to effectively monitor and maintain EEG data quality, (2) how to ensure consistent implementation of experimental protocols, and (3) how to develop rigorous preprocessing procedures that are feasible for use in a large-scale study. Links to resources are also provided, including sample protocols, sample equipment and software tracking forms, sample code, and tutorial videos (to access resources, please visit: https://osf.io/wdrj3/).
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Affiliation(s)
- Megan A. Boudewyn
- Department of Psychology, University of California Santa Cruz, Santa Cruz, California, USA
| | - Molly A. Erickson
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, Illinois, USA
| | - Kurt Winsler
- Department of Psychology, University of California, Davis, California, USA
| | - John Daniel Ragland
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, California, USA
| | - Andrew Yonelinas
- Department of Psychology, University of California, Davis, California, USA
| | - Michael Frank
- Department of Cognitive, Linguistics and Psychological Sciences, Brown University, Providence, Rhode Island, USA
| | - Steven M. Silverstein
- Department of Psychiatry, Neuroscience and Opthamology, University of Rochester, Rochester Medical Center, Rochester, New York, USA
| | - Jim Gold
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Angus W. MacDonald
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Cameron S. Carter
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, California, USA
| | - Deanna M. Barch
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Steven J. Luck
- Department of Psychology, University of California, Davis, California, USA
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39
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Smith SE, Kosik EL, van Engen Q, Kohn J, Hill AT, Zomorrodi R, Blumberger DM, Daskalakis ZJ, Hadas I, Voytek B. Magnetic seizure therapy and electroconvulsive therapy increase aperiodic activity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.11.23284450. [PMID: 36711765 PMCID: PMC9882553 DOI: 10.1101/2023.01.11.23284450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Major depressive disorder (MDD) is a leading cause of disability worldwide. One of the most efficacious treatments for treatment-resistant MDD is electroconvulsive therapy (ECT). Recently, magnetic seizure therapy (MST) was developed as an alternative to ECT due to its more favorable side effect profile. While these approaches have been very successful clinically, the neural mechanisms underlying their therapeutic effects are unknown. For example, clinical "slowing" of the electroencephalogram beginning in the postictal state and extending days to weeks post-treatment has been observed in both treatment modalities. However, a recent longitudinal study of a small cohort of ECT patients revealed that, rather than delta oscillations, clinical slowing was better explained by increases in aperiodic activity, an emerging EEG signal linked to neural inhibition. Here we investigate the role of aperiodic activity in a cohort of patients who received ECT and a cohort of patients who received MST treatment. We find that aperiodic neural activity increases significantly in patients receiving either ECT or MST. Although not directly related to clinical efficacy in this dataset, increased aperiodic activity is linked to greater amounts of neural inhibition, which is suggestive of a potential shared neural mechanism of action across ECT and MST.
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Affiliation(s)
- Sydney E. Smith
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Eena L. Kosik
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Quirine van Engen
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Jordan Kohn
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Aron T. Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
- Department of Psychiatry, Central Clinical School, Monash University, Melbourne, Australia
| | - Reza Zomorrodi
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
| | - Daniel M. Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
| | - Zafiris J. Daskalakis
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Itay Hadas
- Department of Psychiatry, Central Clinical School, Monash University, Melbourne, Australia
| | - Bradley Voytek
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
- Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, USA
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40
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Metzger BA, Kalva P, Mocchi MM, Cui B, Adkinson JA, Wang Z, Mathura R, Kanja K, Gavvala J, Krishnan V, Lin L, Maheshwari A, Shofty B, Magnotti JF, Willie JT, Sheth SA, Bijanki KR. Intracranial stimulation and EEG feature analysis reveal affective salience network specialization. Brain 2023; 146:4366-4377. [PMID: 37293814 PMCID: PMC10545499 DOI: 10.1093/brain/awad200] [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: 09/13/2022] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 06/10/2023] Open
Abstract
Emotion is represented in limbic and prefrontal brain areas, herein termed the affective salience network (ASN). Within the ASN, there are substantial unknowns about how valence and emotional intensity are processed-specifically, which nodes are associated with affective bias (a phenomenon in which participants interpret emotions in a manner consistent with their own mood). A recently developed feature detection approach ('specparam') was used to select dominant spectral features from human intracranial electrophysiological data, revealing affective specialization within specific nodes of the ASN. Spectral analysis of dominant features at the channel level suggests that dorsal anterior cingulate (dACC), anterior insula and ventral-medial prefrontal cortex (vmPFC) are sensitive to valence and intensity, while the amygdala is primarily sensitive to intensity. Akaike information criterion model comparisons corroborated the spectral analysis findings, suggesting all four nodes are more sensitive to intensity compared to valence. The data also revealed that activity in dACC and vmPFC were predictive of the extent of affective bias in the ratings of facial expressions-a proxy measure of instantaneous mood. To examine causality of the dACC in affective experience, 130 Hz continuous stimulation was applied to dACC while patients viewed and rated emotional faces. Faces were rated significantly happier during stimulation, even after accounting for differences in baseline ratings. Together the data suggest a causal role for dACC during the processing of external affective stimuli.
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Affiliation(s)
- Brian A Metzger
- Department of Psychology, Swarthmore College, Swarthmore, PA 19081, USA
| | - Prathik Kalva
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Madaline M Mocchi
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Brian Cui
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Joshua A Adkinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhengjia Wang
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raissa Mathura
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kourtney Kanja
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jay Gavvala
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Vaishnav Krishnan
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lu Lin
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Atul Maheshwari
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ben Shofty
- Department of Neurosurgery, University of Utah Health, Salt Lake City, UT 84132, USA
| | - John F Magnotti
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jon T Willie
- Department of Neurosurgery, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
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41
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Tan E, Tang A, Debnath R, Humphreys KL, Zeanah CH, Nelson CA, Fox NA. Resting brain activity in early childhood predicts IQ at 18 years. Dev Cogn Neurosci 2023; 63:101287. [PMID: 37531865 PMCID: PMC10407667 DOI: 10.1016/j.dcn.2023.101287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/09/2023] [Accepted: 07/28/2023] [Indexed: 08/04/2023] Open
Abstract
Resting brain activity has been widely used as an index of brain development in neuroscience and clinical research. However, it remains unclear whether early differences in resting brain activity have meaningful implications for predicting long-term cognitive outcomes. Using data from the Bucharest Early Intervention Project (Zeanah et al., 2003), we examined the impact of institutional rearing and the consequences of early foster care intervention on 18-year IQ. We found that higher resting theta electroencephalogram (EEG) power, reflecting atypical neurodevelopment, across three assessments from 22 to 42 months predicted lower full-scale IQ at 18 years, providing the first evidence that brain activity in early childhood predicts cognitive outcomes into adulthood. In addition, both institutional rearing and later (vs. earlier) foster care intervention predicted higher resting theta power in early childhood, which in turn predicted lower IQ at 18 years. These findings demonstrate that experientially-induced changes in brain activity early in life have profound impact on long-term cognitive development, highlighting the importance of early intervention for promoting healthy development among children living in disadvantaged environments.
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Affiliation(s)
- Enda Tan
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park 20740, USA; Neuroscience and Cognitive Science Program, University of Maryland, College Park 20740, USA.
| | - Alva Tang
- Department of Psychology, University of Texas at Dallas, Richardson 75080, USA.
| | - Ranjan Debnath
- Leibniz Institute for Neurobiology, Magdeburg 39118, Germany.
| | - Kathryn L Humphreys
- Department of Psychology and Human Development, Vanderbilt University, Nashville 37203, USA.
| | - Charles H Zeanah
- Department of Psychiatry and Behavioral Sciences, Tulane University, New Orleans 70118, USA.
| | - Charles A Nelson
- Boston Children's Hospital of Harvard Medical School, Boston 02115, USA; Harvard Graduate School of Education, Harvard University, Cambridge 02138, USA.
| | - Nathan A Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park 20740, USA; Neuroscience and Cognitive Science Program, University of Maryland, College Park 20740, USA.
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42
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Kumar WS, Ray S. Healthy ageing and cognitive impairment alter EEG functional connectivity in distinct frequency bands. Eur J Neurosci 2023; 58:3432-3449. [PMID: 37559505 DOI: 10.1111/ejn.16114] [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: 05/11/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023]
Abstract
Functional connectivity (FC) indicates the interdependencies between brain signals recorded from spatially distinct locations in different frequency bands, which is modulated by cognitive tasks and is known to change with ageing and cognitive disorders. Recently, the power of narrow-band gamma oscillations induced by visual gratings have been shown to reduce with both healthy ageing and in subjects with mild cognitive impairment (MCI). However, the impact of ageing/MCI on stimulus-induced gamma FC has not been well studied. We recorded electroencephalogram (EEG) from a large cohort (N = 229) of elderly subjects (>49 years) while they viewed large cartesian gratings to induce gamma oscillations and studied changes in alpha and gamma FC with healthy ageing (N = 218) and MCI (N = 11). Surprisingly, we found distinct differences across age and MCI groups in power and FC. With healthy ageing, alpha power did not change but FC decreased significantly. MCI reduced gamma but not alpha FC significantly compared with age and gender matched controls, even when power was matched between the two groups. Overall, our results suggest distinct effects of ageing and disease on EEG power and FC, suggesting different mechanisms underlying ageing and cognitive disorders.
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Affiliation(s)
| | - Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bengaluru, India
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43
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Comeaux P, Clark K, Noudoost B. A recruitment through coherence theory of working memory. Prog Neurobiol 2023; 228:102491. [PMID: 37393039 PMCID: PMC10530428 DOI: 10.1016/j.pneurobio.2023.102491] [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: 02/06/2023] [Revised: 06/14/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
Abstract
The interactions between prefrontal cortex and other areas during working memory have been studied for decades. Here we outline a conceptual framework describing interactions between these areas during working memory, and review evidence for key elements of this model. We specifically suggest that a top-down signal sent from prefrontal to sensory areas drives oscillations in these areas. Spike timing within sensory areas becomes locked to these working-memory-driven oscillations, and the phase of spiking conveys information about the representation available within these areas. Downstream areas receiving these phase-locked spikes from sensory areas can recover this information via a combination of coherent oscillations and gating of input efficacy based on the phase of their local oscillations. Although the conceptual framework is based on prefrontal interactions with sensory areas during working memory, we also discuss the broader implications of this framework for flexible communication between brain areas in general.
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Affiliation(s)
- Phillip Comeaux
- Dept. of Biomedical Engineering, University of Utah, 36 S. Wasatch Drive, Salt Lake City, UT 84112, USA; Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
| | - Kelsey Clark
- Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
| | - Behrad Noudoost
- Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA.
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44
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Sysoeva O, Maximenko V, Kuc A, Voinova V, Martynova O, Hramov A. Abnormal spectral and scale-free properties of resting-state EEG in girls with Rett syndrome. Sci Rep 2023; 13:12932. [PMID: 37558701 PMCID: PMC10412611 DOI: 10.1038/s41598-023-39398-7] [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: 11/14/2022] [Accepted: 07/25/2023] [Indexed: 08/11/2023] Open
Abstract
Spontaneous EEG contains important information about neuronal network properties that is valuable for understanding different neurological and psychiatric conditions. Rett syndrome (RTT) is a rare neurodevelopmental disorder, caused by mutation in the MECP2 gene. RTT is characterized by severe motor impairments that prevent adequate assessment of cognitive functions. Here we probe EEG parameters obtained in no visual input condition from a 28-channels system in 23 patients with Rett Syndrome and 38 their typically developing peers aged 3-17 years old. Confirming previous results, RTT showed a fronto-central theta power (4-6.25 Hz) increase that correlates with a progression of the disease. Alpha power (6.75-11.75 Hz) across multiple regions was, on the contrary, decreased in RTT, also corresponding to general background slowing reported previously. Among novel results we found an increase in gamma power (31-39.5 Hz) across frontal, central and temporal electrodes, suggesting elevated excitation/inhibition ratio. Long-range temporal correlation measured by detrended fluctuation analysis within 6-13 Hz was also increased, pointing to a more predictable oscillation pattern in RTT. Overall measured EEG parameters allow to differentiate groups with high accuracy, ROC AUC value of 0.92 ± 0.08, indicating clinical relevance.
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Affiliation(s)
- Olga Sysoeva
- Center for Cognitive Sciences, Sirius University of Science and Technology, Sochi, Russia, 354340.
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Butlerova St. 5a, Moscow, Russia, 117485.
| | - Vladimir Maximenko
- Artificial Intelligence and Neurotechnology Lab, Privolzhsky Research Medical University, Nizhny Novgorod, Russia, 603950
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, A. Nevskogo Str., Kaliningrad, Russia, 236016
| | - Alexander Kuc
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, A. Nevskogo Str., Kaliningrad, Russia, 236016
| | - Victoria Voinova
- Veltischev Research and Clinical Institute for Pediatrics of the Pirogov, Russian National Research Medical University, Ministry of Health of Russian Federation, Moscow, Russia, 125412
- Mental Health Research Center, Moscow, Russia, 117152
| | - Olga Martynova
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Butlerova St. 5a, Moscow, Russia, 117485
| | - Alexander Hramov
- Artificial Intelligence and Neurotechnology Lab, Privolzhsky Research Medical University, Nizhny Novgorod, Russia, 603950
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, A. Nevskogo Str., Kaliningrad, Russia, 236016
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45
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Masaracchia L, Fredes F, Woolrich MW, Vidaurre D. Dissecting unsupervised learning through hidden Markov modeling in electrophysiological data. J Neurophysiol 2023; 130:364-379. [PMID: 37403598 PMCID: PMC10625837 DOI: 10.1152/jn.00054.2023] [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: 02/01/2023] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 07/06/2023] Open
Abstract
Unsupervised, data-driven methods are commonly used in neuroscience to automatically decompose data into interpretable patterns. These patterns differ from one another depending on the assumptions of the models. How these assumptions affect specific data decompositions in practice, however, is often unclear, which hinders model applicability and interpretability. For instance, the hidden Markov model (HMM) automatically detects characteristic, recurring activity patterns (so-called states) from time series data. States are defined by a certain probability distribution, whose state-specific parameters are estimated from the data. But what specific features, from all of those that the data contain, do the states capture? That depends on the choice of probability distribution and on other model hyperparameters. Using both synthetic and real data, we aim to better characterize the behavior of two HMM types that can be applied to electrophysiological data. Specifically, we study which differences in data features (such as frequency, amplitude, or signal-to-noise ratio) are more salient to the models and therefore more likely to drive the state decomposition. Overall, we aim at providing guidance for the appropriate use of this type of analysis on one- or two-channel neural electrophysiological data and an informed interpretation of its results given the characteristics of the data and the purpose of the analysis.NEW & NOTEWORTHY Compared with classical supervised methods, unsupervised methods of analysis have the advantage to be freer of subjective biases. However, it is not always clear what aspects of the data these methods are most sensitive to, which complicates interpretation. Focusing on the hidden Markov model, commonly used to describe electrophysiological data, we explore in detail the nature of its estimates through simulations and real data examples, providing important insights about what to expect from these models.
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Affiliation(s)
- Laura Masaracchia
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Felipe Fredes
- Center for Proteins in Memory, Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Mark W Woolrich
- Psychiatry Department, Oxford Centre for Human Brain Activity, Oxford University, Oxford, United Kingdom
| | - Diego Vidaurre
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Psychiatry Department, Oxford Centre for Human Brain Activity, Oxford University, Oxford, United Kingdom
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46
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Pepper JL, Nuttall HE. Age-Related Changes to Multisensory Integration and Audiovisual Speech Perception. Brain Sci 2023; 13:1126. [PMID: 37626483 PMCID: PMC10452685 DOI: 10.3390/brainsci13081126] [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: 05/26/2023] [Revised: 07/20/2023] [Accepted: 07/22/2023] [Indexed: 08/27/2023] Open
Abstract
Multisensory integration is essential for the quick and accurate perception of our environment, particularly in everyday tasks like speech perception. Research has highlighted the importance of investigating bottom-up and top-down contributions to multisensory integration and how these change as a function of ageing. Specifically, perceptual factors like the temporal binding window and cognitive factors like attention and inhibition appear to be fundamental in the integration of visual and auditory information-integration that may become less efficient as we age. These factors have been linked to brain areas like the superior temporal sulcus, with neural oscillations in the alpha-band frequency also being implicated in multisensory processing. Age-related changes in multisensory integration may have significant consequences for the well-being of our increasingly ageing population, affecting their ability to communicate with others and safely move through their environment; it is crucial that the evidence surrounding this subject continues to be carefully investigated. This review will discuss research into age-related changes in the perceptual and cognitive mechanisms of multisensory integration and the impact that these changes have on speech perception and fall risk. The role of oscillatory alpha activity is of particular interest, as it may be key in the modulation of multisensory integration.
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Affiliation(s)
| | - Helen E. Nuttall
- Department of Psychology, Lancaster University, Bailrigg LA1 4YF, UK;
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47
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Cho S, Choi JH. A guide towards optimal detection of transient oscillatory bursts with unknown parameters. J Neural Eng 2023; 20:046007. [PMID: 37339619 DOI: 10.1088/1741-2552/acdffd] [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: 09/26/2022] [Accepted: 06/20/2023] [Indexed: 06/22/2023]
Abstract
Objectives. Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a neural signature that bridges dynamic neural states to cognition and behaviors. Following this insight, our study aimed to (1) compare the efficacy of common burst detection algorithms under varying signal-to-noise ratios and event durations using synthetic signals and (2) establish a strategic guideline for selecting the optimal algorithm for real datasets with undefined properties.Approach.We tested the robustness of burst detection algorithms using a simulation dataset comprising bursts of multiple frequencies. To systematically assess their performance, we used a metric called 'detection confidence', quantifying classification accuracy and temporal precision in a balanced manner. Given that burst properties in empirical data are often unknown in advance, we then proposed a selection rule to identify an optimal algorithm for a given dataset and validated its application on local field potentials of basolateral amygdala recorded from male mice (n=8) exposed to a natural threat.Main Results.Our simulation-based evaluation demonstrated that burst detection is contingent upon event duration, whereas accurately pinpointing burst onsets is more susceptible to noise level. For real data, the algorithm chosen based on the selection rule exhibited superior detection and temporal accuracy, although its statistical significance differed across frequency bands. Notably, the algorithm chosen by human visual screening differed from the one recommended by the rule, implying a potential misalignment between human priors and mathematical assumptions of the algorithms.Significance.Therefore, our findings underscore that the precise detection of transient bursts is fundamentally influenced by the chosen algorithm. The proposed algorithm-selection rule suggests a potentially viable solution, while also emphasizing the inherent limitations originating from algorithmic design and volatile performances across datasets. Consequently, this study cautions against relying solely on heuristic-based approaches, advocating for a careful algorithm selection in burst detection studies.
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Affiliation(s)
- SungJun Cho
- Center for Neuroscience, Korea Institute of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 02792, Republic of Korea
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, United Kingdom
| | - Jee Hyun Choi
- Center for Neuroscience, Korea Institute of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 02792, Republic of Korea
- Department of Neural Sciences, University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea
- Department of Physics and Center for Theoretical Physics, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
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48
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Favaro J, Colombo MA, Mikulan E, Sartori S, Nosadini M, Pelizza MF, Rosanova M, Sarasso S, Massimini M, Toldo I. The maturation of aperiodic EEG activity across development reveals a progressive differentiation of wakefulness from sleep. Neuroimage 2023:120264. [PMID: 37399931 DOI: 10.1016/j.neuroimage.2023.120264] [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: 03/07/2023] [Revised: 06/04/2023] [Accepted: 07/01/2023] [Indexed: 07/05/2023] Open
Abstract
During development, the brain undergoes radical structural and functional changes following a posterior-to-anterior gradient, associated with profound changes of cortical electrical activity during both wakefulness and sleep. However, a systematic assessment of the developmental effects on aperiodic EEG activity maturation across vigilance states is lacking, particularly regarding its topographical aspects. Here, in a population of 160 healthy infants, children and teenagers (from 2 to 17 years, 10 subjects for each year), we investigated the development of aperiodic EEG activity in wakefulness and sleep. Specifically, we parameterized the shape of the aperiodic background of the EEG Power Spectral Density (PSD) by means of the spectral exponent and offset; the exponent reflects the rate of exponential decay of power over increasing frequencies and the offset reflects an estimate of the y-intercept of the PSD. We found that sleep and development caused the EEG-PSD to rotate over opposite directions: during wakefulness the PSD showed a flatter decay and reduced offset over development, while during sleep it showed a steeper decay and a higher offset as sleep becomes deeper. During deep sleep (N2, N3) only the spectral offset decreased over age, indexing a broad-band voltage reduction. As a result, the difference between values in deep sleep and those in both light sleep (N1) and wakefulness increased with age, suggesting a progressive differentiation of wakefulness from sleep EEG activity, most prominent over the frontal regions, the latest to complete maturation. Notably, the broad-band spectral exponent values during deep sleep stages were entirely separated from wakefulness values, consistently across developmental ages and in line with previous findings in adults. Concerning topographical development, the location showing the steepest PSD decay and largest offset shifted from posterior to anterior regions with age. This shift, particularly evident during deep sleep, paralleled the migration of sleep slow wave activity and was consistent with neuroanatomical and cognitive development. Overall, aperiodic EEG activity distinguishes wakefulness from sleep regardless of age; while, during development, it reveals a postero-anterior topographical maturation and a progressive differentiation of wakefulness from sleep. Our study could help to interpret changes due to pathological conditions and may elucidate the neurophysiological processes underlying the development of wakefulness and sleep.
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Affiliation(s)
- Jacopo Favaro
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children Health, University of Padua, 35128, Padua, Italy.
| | - Michele A Colombo
- Department of Clinical and Biomedical Sciences "L. Sacco", University of Milan, 20157, Milan, Italy.
| | - Ezequiel Mikulan
- Department of Clinical and Biomedical Sciences "L. Sacco", University of Milan, 20157, Milan, Italy
| | - Stefano Sartori
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children Health, University of Padua, 35128, Padua, Italy; Neuroimmunology Group, Pediatric Research Institute "Città della Speranza", 35127, Padua, Italy; Department of Neuroscience, University of Padua, 35121, Padua, Italy
| | - Margherita Nosadini
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children Health, University of Padua, 35128, Padua, Italy; Neuroimmunology Group, Pediatric Research Institute "Città della Speranza", 35127, Padua, Italy
| | - Maria Federica Pelizza
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children Health, University of Padua, 35128, Padua, Italy
| | - Mario Rosanova
- Department of Clinical and Biomedical Sciences "L. Sacco", University of Milan, 20157, Milan, Italy
| | - Simone Sarasso
- Department of Clinical and Biomedical Sciences "L. Sacco", University of Milan, 20157, Milan, Italy
| | - Marcello Massimini
- Department of Clinical and Biomedical Sciences "L. Sacco", University of Milan, 20157, Milan, Italy; IRCCS, Fondazione Don Carlo Gnocchi Onlus, 20148, Milan, Italy.
| | - Irene Toldo
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children Health, University of Padua, 35128, Padua, Italy
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49
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Brandes-Aitken A, Pini N, Weatherhand M, Brito NH. Maternal hair cortisol predicts periodic and aperiodic infant frontal EEG activity longitudinally across infancy. Dev Psychobiol 2023; 65:e22393. [PMID: 37338255 PMCID: PMC10316429 DOI: 10.1002/dev.22393] [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: 08/04/2022] [Revised: 12/23/2022] [Accepted: 01/29/2023] [Indexed: 06/21/2023]
Abstract
Maternal stress is known to be an important factor in shaping child development, yet the complex pattern of associations between stress and infant brain development remains understudied. To better understand the nuanced relations between maternal stress and infant neurodevelopment, research investigating longitudinal relations between maternal chronic physiological stress and infant brain function is warranted. In this study, we leveraged longitudinal data to disentangle between- from within-person associations of maternal hair cortisol and frontal electroencephalography (EEG) power at three time points across infancy at 3, 9, and 15 months. We analyzed both aperiodic power spectral density (PSD) slope and traditional periodic frequency band activity. On the within-person level, maternal hair cortisol was associated with a flattening of frontal PSD slope and an increase in relative frontal beta. However, on the between-person level, higher maternal hair cortisol was associated with steeper frontal PSD slope, increased relative frontal theta, and decreased relative frontal beta. The within-person findings may reflect an adaptive neural response to relative shifts in maternal stress levels, while the between-person results demonstrate the potentially detrimental effects of chronically elevated maternal stress. This analysis offers a novel, quantitative insight into the relations between maternal physiological stress and infant cortical function.
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Affiliation(s)
| | - Nicolò Pini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, USA
| | | | - Natalie H. Brito
- Department of Applied Psychology, New York University, New York, NY, USA
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50
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Seifpour S, Šatka A. Tensor Decomposition Analysis of Longitudinal EEG Signals Reveals Differential Oscillatory Dynamics in Eyes-Closed and Eyes-Open Motor Imagery BCI: A Case Report. Brain Sci 2023; 13:1013. [PMID: 37508946 PMCID: PMC10377314 DOI: 10.3390/brainsci13071013] [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: 05/10/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Functional dissociation of brain neural activity induced by opening or closing the eyes has been well established. However, how the temporal dynamics of the underlying neuronal modulations differ between these eye conditions during movement-related behaviours is less known. Using a robotic-assisted motor imagery brain-computer interface (MI BCI), we measured neural activity over the motor regions with electroencephalography (EEG) in a stroke survivor during his longitudinal rehabilitation training. We investigated lateralized oscillatory sensorimotor rhythm modulations while the patient imagined moving his hemiplegic hand with closed and open eyes to control an external robotic splint. In order to precisely identify the main profiles of neural activation affected by MI with eyes-open (MIEO) and eyes-closed (MIEC), a data-driven approach based on parallel factor analysis (PARAFAC) tensor decomposition was employed. Using the proposed framework, a set of narrow-band, subject-specific sensorimotor rhythms was identified; each of them had its own spatial and time signature. When MIEC trials were compared with MIEO trials, three key narrow-band rhythms whose peak frequencies centred at ∼8.0 Hz, ∼11.5 Hz, and ∼15.5 Hz, were identified with differently modulated oscillatory dynamics during movement preparation, initiation, and completion time frames. Furthermore, we observed that lower and higher sensorimotor oscillations represent different functional mechanisms within the MI paradigm, reinforcing the hypothesis that rhythmic activity in the human sensorimotor system is dissociated. Leveraging PARAFAC, this study achieves remarkable precision in estimating latent sensorimotor neural substrates, aiding the investigation of the specific functional mechanisms involved in the MI process.
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Affiliation(s)
- Saman Seifpour
- RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
- Institute of Measurement Science, Slovak Academy of Sciences, Dubravska cesta 9, 84104 Bratislava, Slovakia
| | - Alexander Šatka
- Institute of Measurement Science, Slovak Academy of Sciences, Dubravska cesta 9, 84104 Bratislava, Slovakia
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